The value of intraoperative ultrasonography during the resection of relapsed irradiated malignant gliomas in the brain.

PubMed

Mursch, Kay; Scholz, Martin; Brück, Wolfgang; Behnke-Mursch, Julianne

2017-01-01

The aim of this study was to investigate whether intraoperative ultrasonography (IOUS) helped the surgeon navigate towards the tumor as seen in preoperative magnetic resonance imaging and whether IOUS was able to distinguish between tumor margins and the surrounding tissue. Twenty-five patients suffering from high-grade gliomas who were previously treated by surgery and radiotherapy were included. Intraoperatively, two histopathologic samples were obtained a sample of unequivocal tumor tissue (according to anatomical landmarks and the surgeon's visual and tactile impressions) and a small tissue sample obtained using a navigated needle when the surgeon decided to stop the resection. This specimen was considered to be a boundary specimen, where no tumor tissue was apparent. The decision to take the second sample was not influenced by IOUS. The effect of IOUS was analyzed semi-quantitatively. All 25 samples of unequivocal tumor tissue were histopathologically classified as tumor tissue and were hyperechoic on IOUS. Of the boundary specimens, eight were hypoechoic. Only one harbored tumor tissue (P=0.150). Seventeen boundaries were moderately hyperechoic, and these samples contained all possible histological results (i.e., tumor, infiltration, or no tumor). During surgery performed on relapsed, irradiated, high-grade gliomas, IOUS provided a reliable method of navigating towards the core of the tumor. At borders, it did not reliably distinguish between remnants or tumor-free tissue, but hypoechoic areas seldom contained tumor tissue.

Identification of beta-lactam antibiotics in tissue samples containing unknown microbial inhibitors.

PubMed

Moats, W A; Romanowski, R D; Medina, M B

1998-01-01

Antibiotic residues in animal tissues can be detected by various screening tests based on microbial inhibition. In the 7-plate assay used by the U.S. Department of Agriculture's Food Safety and Inspection Service (FSIS), penicillinase is incorporated into all but one plate to distinguish beta-lactam antibiotics from other types. However, beta-lactams such as cloxacillin and the cephalosporins are resistant to degradation by penicillinase. They may not be identified as beta-lactams by this procedure, and thus, they may be identified as unidentified microbial inhibitors (UMIs). However, these penicillinase-resistant compounds can be degraded by other beta-lactamases. The present study describes an improved screening protocol to identify beta-lactam antibiotics classified as UMIs. A multiresidue liquid chromatographic procedure based on a method for determining beta-lactams in milk was also used to identify and quantitate residues. The 2 methods were tested with 24 tissue FSIS samples classified as containing UMIs. Of these, 3 contained penicillin G, including one at a violative level, and 5 contained a metabolite of ceftiofur. The others were negative for beta-lactam antibiotics.

A novel multi-tissue RNA diagnostic of healthy ageing relates to cognitive health status.

PubMed

Sood, Sanjana; Gallagher, Iain J; Lunnon, Katie; Rullman, Eric; Keohane, Aoife; Crossland, Hannah; Phillips, Bethan E; Cederholm, Tommy; Jensen, Thomas; van Loon, Luc J C; Lannfelt, Lars; Kraus, William E; Atherton, Philip J; Howard, Robert; Gustafsson, Thomas; Hodges, Angela; Timmons, James A

2015-09-07

Diagnostics of the human ageing process may help predict future healthcare needs or guide preventative measures for tackling diseases of older age. We take a transcriptomics approach to build the first reproducible multi-tissue RNA expression signature by gene-chip profiling tissue from sedentary normal subjects who reached 65 years of age in good health. One hundred and fifty probe-sets form an accurate classifier of young versus older muscle tissue and this healthy ageing RNA classifier performed consistently in independent cohorts of human muscle, skin and brain tissue (n = 594, AUC = 0.83-0.96) and thus represents a biomarker for biological age. Using the Uppsala Longitudinal Study of Adult Men birth-cohort (n = 108) we demonstrate that the RNA classifier is insensitive to confounding lifestyle biomarkers, while greater gene score at age 70 years is independently associated with better renal function at age 82 years and longevity. The gene score is 'up-regulated' in healthy human hippocampus with age, and when applied to blood RNA profiles from two large independent age-matched dementia case-control data sets (n = 717) the healthy controls have significantly greater gene scores than those with cognitive impairment. Alone, or when combined with our previously described prototype Alzheimer disease (AD) RNA 'disease signature', the healthy ageing RNA classifier is diagnostic for AD. We identify a novel and statistically robust multi-tissue RNA signature of human healthy ageing that can act as a diagnostic of future health, using only a peripheral blood sample. This RNA signature has great potential to assist research aimed at finding treatments for and/or management of AD and other ageing-related conditions.

Statistical analysis and machine learning algorithms for optical biopsy

NASA Astrophysics Data System (ADS)

Wu, Binlin; Liu, Cheng-hui; Boydston-White, Susie; Beckman, Hugh; Sriramoju, Vidyasagar; Sordillo, Laura; Zhang, Chunyuan; Zhang, Lin; Shi, Lingyan; Smith, Jason; Bailin, Jacob; Alfano, Robert R.

2018-02-01

Analyzing spectral or imaging data collected with various optical biopsy methods is often times difficult due to the complexity of the biological basis. Robust methods that can utilize the spectral or imaging data and detect the characteristic spectral or spatial signatures for different types of tissue is challenging but highly desired. In this study, we used various machine learning algorithms to analyze a spectral dataset acquired from human skin normal and cancerous tissue samples using resonance Raman spectroscopy with 532nm excitation. The algorithms including principal component analysis, nonnegative matrix factorization, and autoencoder artificial neural network are used to reduce dimension of the dataset and detect features. A support vector machine with a linear kernel is used to classify the normal tissue and cancerous tissue samples. The efficacies of the methods are compared.

Automated classification of optical coherence tomography images of human atrial tissue

NASA Astrophysics Data System (ADS)

Gan, Yu; Tsay, David; Amir, Syed B.; Marboe, Charles C.; Hendon, Christine P.

2016-10-01

Tissue composition of the atria plays a critical role in the pathology of cardiovascular disease, tissue remodeling, and arrhythmogenic substrates. Optical coherence tomography (OCT) has the ability to capture the tissue composition information of the human atria. In this study, we developed a region-based automated method to classify tissue compositions within human atria samples within OCT images. We segmented regional information without prior information about the tissue architecture and subsequently extracted features within each segmented region. A relevance vector machine model was used to perform automated classification. Segmentation of human atrial ex vivo datasets was correlated with trichrome histology and our classification algorithm had an average accuracy of 80.41% for identifying adipose, myocardium, fibrotic myocardium, and collagen tissue compositions.

Transferring genomics to the clinic: distinguishing Burkitt and diffuse large B cell lymphomas.

PubMed

Sha, Chulin; Barrans, Sharon; Care, Matthew A; Cunningham, David; Tooze, Reuben M; Jack, Andrew; Westhead, David R

2015-01-01

Classifiers based on molecular criteria such as gene expression signatures have been developed to distinguish Burkitt lymphoma and diffuse large B cell lymphoma, which help to explore the intermediate cases where traditional diagnosis is difficult. Transfer of these research classifiers into a clinical setting is challenging because there are competing classifiers in the literature based on different methodology and gene sets with no clear best choice; classifiers based on one expression measurement platform may not transfer effectively to another; and, classifiers developed using fresh frozen samples may not work effectively with the commonly used and more convenient formalin fixed paraffin-embedded samples used in routine diagnosis. Here we thoroughly compared two published high profile classifiers developed on data from different Affymetrix array platforms and fresh-frozen tissue, examining their transferability and concordance. Based on this analysis, a new Burkitt and diffuse large B cell lymphoma classifier (BDC) was developed and employed on Illumina DASL data from our own paraffin-embedded samples, allowing comparison with the diagnosis made in a central haematopathology laboratory and evaluation of clinical relevance. We show that both previous classifiers can be recapitulated using very much smaller gene sets than originally employed, and that the classification result is closely dependent on the Burkitt lymphoma criteria applied in the training set. The BDC classification on our data exhibits high agreement (~95 %) with the original diagnosis. A simple outcome comparison in the patients presenting intermediate features on conventional criteria suggests that the cases classified as Burkitt lymphoma by BDC have worse response to standard diffuse large B cell lymphoma treatment than those classified as diffuse large B cell lymphoma. In this study, we comprehensively investigate two previous Burkitt lymphoma molecular classifiers, and implement a new gene expression classifier, BDC, that works effectively on paraffin-embedded samples and provides useful information for treatment decisions. The classifier is available as a free software package under the GNU public licence within the R statistical software environment through the link http://www.bioinformatics.leeds.ac.uk/labpages/softwares/ or on github https://github.com/Sharlene/BDC.

Multi-class biological tissue classification based on a multi-classifier: Preliminary study of an automatic output power control for ultrasonic surgical units.

PubMed

Youn, Su Hyun; Sim, Taeyong; Choi, Ahnryul; Song, Jinsung; Shin, Ki Young; Lee, Il Kwon; Heo, Hyun Mu; Lee, Daeweon; Mun, Joung Hwan

2015-06-01

Ultrasonic surgical units (USUs) have the advantage of minimizing tissue damage during surgeries that require tissue dissection by reducing problems such as coagulation and unwanted carbonization, but the disadvantage of requiring manual adjustment of power output according to the target tissue. In order to overcome this limitation, it is necessary to determine the properties of in vivo tissues automatically. We propose a multi-classifier that can accurately classify tissues based on the unique impedance of each tissue. For this purpose, a multi-classifier was built based on single classifiers with high classification rates, and the classification accuracy of the proposed model was compared with that of single classifiers for various electrode types (Type-I: 6 mm invasive; Type-II: 3 mm invasive; Type-III: surface). The sensitivity and positive predictive value (PPV) of the multi-classifier by cross checks were determined. According to the 10-fold cross validation results, the classification accuracy of the proposed model was significantly higher (p<0.05 or <0.01) than that of existing single classifiers for all electrode types. In particular, the classification accuracy of the proposed model was highest when the 3mm invasive electrode (Type-II) was used (sensitivity=97.33-100.00%; PPV=96.71-100.00%). The results of this study are an important contribution to achieving automatic optimal output power adjustment of USUs according to the properties of individual tissues. Copyright © 2015 Elsevier Ltd. All rights reserved.

Assessment of tissue-specific cortisol activity with regard to degeneration of the suspensory ligaments in horses with pituitary pars intermedia dysfunction.

PubMed

Hofberger, Sina C; Gauff, Felicia; Thaller, Denise; Morgan, Ruth; Keen, John A; Licka, Theresia F

2018-02-01

OBJECTIVE To identify signs of tissue-specific cortisol activity in samples of suspensory ligament (SL) and neck skin tissue from horses with and without pituitary pars intermedia dysfunction (PPID). SAMPLE Suspensory ligament and neck skin tissue samples obtained from 26 euthanized horses with and without PPID. PROCEDURES Tissue samples were collected from 12 horses with and 14 horses without PPID (controls). Two control horses had received treatment with dexamethasone; data from those horses were not used in statistical analyses. The other 12 control horses were classified as old horses (≥ 14 years old) and young horses (≤ 9 years old). Standard histologic staining, staining for proteoglycan accumulation, and immunostaining of SL and neck skin tissue sections for glucocorticoid receptors, insulin, 11β hydroxysteroid dehydrogenase type 1, and 11β hydroxysteroid dehydrogenase type 2 were performed. Findings for horses with PPID were compared with findings for young and old horses without PPID. RESULTS Compared with findings for old and young control horses, there were significantly more cells stained for glucocorticoid receptors in SL samples and for 11 β hydroxysteroid dehydrogenase type 1 in SL and skin tissue samples from horses with PPID. Insulin could not be detected in any of the SL or skin tissue samples. Horses with PPID had evidence of SL degeneration with significantly increased proteoglycan accumulation. Neck skin tissue was found to be significantly thinner in PPID-affected horses than in young control horses. CONCLUSIONS AND CLINICAL RELEVANCE Results suggested that tissue-specific dysregulation of cortisol metabolism may contribute to the SL degeneration associated with PPID in horses.

Advancements in automated tissue segmentation pipeline for contrast-enhanced CT scans of adult and pediatric patients

NASA Astrophysics Data System (ADS)

Somasundaram, Elanchezhian; Kaufman, Robert; Brady, Samuel

2017-03-01

The development of a random forests machine learning technique is presented for fully-automated neck, chest, abdomen, and pelvis tissue segmentation of CT images using Trainable WEKA (Waikato Environment for Knowledge Analysis) Segmentation (TWS) plugin of FIJI (ImageJ, NIH). The use of a single classifier model to segment six tissue classes (lung, fat, muscle, solid organ, blood/contrast agent, bone) in the CT images is studied. An automated unbiased scheme to sample pixels from the training images and generate a balanced training dataset over the seven classes is also developed. Two independent training datasets are generated from a pool of 4 adult (>55 kg) and 3 pediatric patients (<=55 kg) with 7 manually contoured slices for each patient. Classifier training investigated 28 image filters comprising a total of 272 features. Highly correlated and insignificant features are eliminated using Correlated Feature Subset (CFS) selection with Best First Search (BFS) algorithms in WEKA. The 2 training models (from the 2 training datasets) had 74 and 71 input training features, respectively. The study also investigated the effect of varying the number of trees (25, 50, 100, and 200) in the random forest algorithm. The performance of the 2 classifier models are evaluated on inter-patient intra-slice, intrapatient inter-slice and inter-patient inter-slice test datasets. The Dice similarity coefficients (DSC) and confusion matrices are used to understand the performance of the classifiers across the tissue segments. The effect of number of features in the training input on the performance of the classifiers for tissue classes with less than optimal DSC values is also studied. The average DSC values for the two training models on the inter-patient intra-slice test data are: 0.98, 0.89, 0.87, 0.79, 0.68, and 0.84, for lung, fat, muscle, solid organ, blood/contrast agent, and bone, respectively. The study demonstrated that a robust segmentation accuracy for lung, muscle and fat tissue classes. For solid-organ, blood/contrast and bone, the performance of the segmentation pipeline improved significantly by using the advanced capabilities of WEKA. However, further improvements are needed to reduce the noise in the segmentation.

Analysis of Lung Tissue Using Ion Beams

NASA Astrophysics Data System (ADS)

Alvarez, J. L.; Barrera, R.; Miranda, J.

2002-08-01

In this work a comparative study is presented of the contents of metals in lung tissue from healthy patients and with lung cancer, by means of two analytical techniques: Particle Induced X-ray Emission (PIXE) and Rutherford Backscattering Spectrometry (RBS). The samples of cancerous tissue were taken from 26 autopsies made to individuals died in the National Institute of Respiratory Disease (INER), 22 of cancer and 4 of other non-cancer biopsies. When analyzing the entirety of the samples, in the cancerous tissues, there were increments in the concentrations of S (4%), K (635%), Co (85%) and Cu (13%). Likewise, there were deficiencies in the concentrations of Cl (59%), Ca (6%), Fe (26%) and Zn (7%). Only in the cancerous tissues there were appearances of P, Ca, Ti, V, Cr, Mn, Ni, Br and Sr. The tissue samples were classified according to cancer types (adenocarcinomas, epidermoides and of small cell carcinoma), personal habits (smokers and alcoholic), genetic predisposition and residence place. There was a remarkable decrease in the concentration of Ca and a marked increment in the Cu in the epidermoide tissue samples with regard to those of adenocarcinoma or of small cells cancer. Also, decrements were detected in K and increments of Fe, Co and Cu in the sample belonging to people that resided in Mexico City with regard to those that resided in the State of Mexico.

Spatial cluster analysis of nanoscopically mapped serotonin receptors for classification of fixed brain tissue

NASA Astrophysics Data System (ADS)

Sams, Michael; Silye, Rene; Göhring, Janett; Muresan, Leila; Schilcher, Kurt; Jacak, Jaroslaw

2014-01-01

We present a cluster spatial analysis method using nanoscopic dSTORM images to determine changes in protein cluster distributions within brain tissue. Such methods are suitable to investigate human brain tissue and will help to achieve a deeper understanding of brain disease along with aiding drug development. Human brain tissue samples are usually treated postmortem via standard fixation protocols, which are established in clinical laboratories. Therefore, our localization microscopy-based method was adapted to characterize protein density and protein cluster localization in samples fixed using different protocols followed by common fluorescent immunohistochemistry techniques. The localization microscopy allows nanoscopic mapping of serotonin 5-HT1A receptor groups within a two-dimensional image of a brain tissue slice. These nanoscopically mapped proteins can be confined to clusters by applying the proposed statistical spatial analysis. Selected features of such clusters were subsequently used to characterize and classify the tissue. Samples were obtained from different types of patients, fixed with different preparation methods, and finally stored in a human tissue bank. To verify the proposed method, samples of a cryopreserved healthy brain have been compared with epitope-retrieved and paraffin-fixed tissues. Furthermore, samples of healthy brain tissues were compared with data obtained from patients suffering from mental illnesses (e.g., major depressive disorder). Our work demonstrates the applicability of localization microscopy and image analysis methods for comparison and classification of human brain tissues at a nanoscopic level. Furthermore, the presented workflow marks a unique technological advance in the characterization of protein distributions in brain tissue sections.

Active learning based segmentation of Crohns disease from abdominal MRI.

PubMed

Mahapatra, Dwarikanath; Vos, Franciscus M; Buhmann, Joachim M

2016-05-01

This paper proposes a novel active learning (AL) framework, and combines it with semi supervised learning (SSL) for segmenting Crohns disease (CD) tissues from abdominal magnetic resonance (MR) images. Robust fully supervised learning (FSL) based classifiers require lots of labeled data of different disease severities. Obtaining such data is time consuming and requires considerable expertise. SSL methods use a few labeled samples, and leverage the information from many unlabeled samples to train an accurate classifier. AL queries labels of most informative samples and maximizes gain from the labeling effort. Our primary contribution is in designing a query strategy that combines novel context information with classification uncertainty and feature similarity. Combining SSL and AL gives a robust segmentation method that: (1) optimally uses few labeled samples and many unlabeled samples; and (2) requires lower training time. Experimental results show our method achieves higher segmentation accuracy than FSL methods with fewer samples and reduced training effort. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

The pathology of lumbosacral lipomas: macroscopic and microscopic disparity have implications for embryogenesis and mode of clinical deterioration.

PubMed

Jones, Victoria; Wykes, Victoria; Cohen, Nicki; Thompson, Dominic; Jacques, Tom S

2018-06-01

Lumbosacral lipomas (LSL) are congenital disorders of the terminal spinal cord region that have the potential to cause significant spinal cord dysfunction in children. They are of unknown embryogenesis with variable clinical presentation and natural history. It is unclear whether the spinal cord dysfunction reflects a primary developmental dysplasia or whether it occurs secondarily to mechanical traction (spinal cord tethering) with growth. While different anatomical subtypes are recognised and classified according to radiological criteria, these subtypes correlate poorly with clinical prognosis. We have undertaken an analysis of surgical specimens in order to describe the spectrum of histological changes that occur and have correlated the histology with the anatomical type of LSL to determine if there are distinct histological subtypes. The histopathology was reviewed of 64 patients who had undergone surgical resection of LSL. The presence of additional tissues and cell types were recorded. LSLs were classified from pre-operative magnetic resonance imaging (MRI) scans according to Chapman classification. Ninety-five per cent of the specimens consisted predominantly of mature adipocytes with all containing thickened bands of connective tissue and peripheral nerve fibres, 91% of samples contained ectatic blood vessels with thickened walls, while 22% contained central nervous system (CNS) glial tissue. Additional tissue was identified of both mesodermal and neuroectodermal origin. Our analysis highlights the heterogeneity of tissue types within all samples, not reflected in the nomenclature. The diversity of tissue types, consistent across all subtypes, challenges currently held notions regarding the embryogenesis of LSLs and the assumption that clinical deterioration is due simply to tethering. © 2018 The Authors. Histopathology Published by John Wiley & Sons Ltd.

Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks

NASA Astrophysics Data System (ADS)

Halicek, Martin; Little, James V.; Wang, Xu; Patel, Mihir; Griffith, Christopher C.; El-Deiry, Mark W.; Chen, Amy Y.; Fei, Baowei

2018-02-01

Successful outcomes of surgical cancer resection necessitate negative, cancer-free surgical margins. Currently, tissue samples are sent to pathology for diagnostic confirmation. Hyperspectral imaging (HSI) is an emerging, non-contact optical imaging technique. A reliable optical method could serve to diagnose and biopsy specimens in real-time. Using convolutional neural networks (CNNs) as a tissue classifier, we developed a method to use HSI to perform an optical biopsy of ex-vivo surgical specimens, collected from 21 patients undergoing surgical cancer resection. Training and testing on samples from different patients, the CNN can distinguish squamous cell carcinoma (SCCa) from normal aerodigestive tract tissues with an area under the curve (AUC) of 0.82, 81% accuracy, 81% sensitivity, and 80% specificity. Additionally, normal oral tissues can be sub-classified into epithelium, muscle, and glandular mucosa using a decision tree method, with an average AUC of 0.94, 90% accuracy, 93% sensitivity, and 89% specificity. After separately training on thyroid tissue, the CNN differentiates between thyroid carcinoma and normal thyroid with an AUC of 0.95, 92% accuracy, 92% sensitivity, and 92% specificity. Moreover, the CNN can discriminate medullary thyroid carcinoma from benign multi-nodular goiter (MNG) with an AUC of 0.93, 87% accuracy, 88% sensitivity, and 85% specificity. Classical-type papillary thyroid carcinoma is differentiated from benign MNG with an AUC of 0.91, 86% accuracy, 86% sensitivity, and 86% specificity. Our preliminary results demonstrate that an HSI-based optical biopsy method using CNNs can provide multi-category diagnostic information for normal head-and-neck tissue, SCCa, and thyroid carcinomas. More patient data are needed in order to fully investigate the proposed technique to establish reliability and generalizability of the work.

Multiclass cancer diagnosis using tumor gene expression signatures

DOE PAGES

Ramaswamy, S.; Tamayo, P.; Rifkin, R.; ...

2001-12-11

The optimal treatment of patients with cancer depends on establishing accurate diagnoses by using a complex combination of clinical and histopathological data. In some instances, this task is difficult or impossible because of atypical clinical presentation or histopathology. To determine whether the diagnosis of multiple common adult malignancies could be achieved purely by molecular classification, we subjected 218 tumor samples, spanning 14 common tumor types, and 90 normal tissue samples to oligonucleotide microarray gene expression analysis. The expression levels of 16,063 genes and expressed sequence tags were used to evaluate the accuracy of a multiclass classifier based on a supportmore » vector machine algorithm. Overall classification accuracy was 78%, far exceeding the accuracy of random classification (9%). Poorly differentiated cancers resulted in low-confidence predictions and could not be accurately classified according to their tissue of origin, indicating that they are molecularly distinct entities with dramatically different gene expression patterns compared with their well differentiated counterparts. Taken together, these results demonstrate the feasibility of accurate, multiclass molecular cancer classification and suggest a strategy for future clinical implementation of molecular cancer diagnostics.« less

Computer-aided Prognosis of Neuroblastoma on Whole-slide Images: Classification of Stromal Development

PubMed Central

Sertel, O.; Kong, J.; Shimada, H.; Catalyurek, U.V.; Saltz, J.H.; Gurcan, M.N.

2009-01-01

We are developing a computer-aided prognosis system for neuroblastoma (NB), a cancer of the nervous system and one of the most malignant tumors affecting children. Histopathological examination is an important stage for further treatment planning in routine clinical diagnosis of NB. According to the International Neuroblastoma Pathology Classification (the Shimada system), NB patients are classified into favorable and unfavorable histology based on the tissue morphology. In this study, we propose an image analysis system that operates on digitized H&E stained whole-slide NB tissue samples and classifies each slide as either stroma-rich or stroma-poor based on the degree of Schwannian stromal development. Our statistical framework performs the classification based on texture features extracted using co-occurrence statistics and local binary patterns. Due to the high resolution of digitized whole-slide images, we propose a multi-resolution approach that mimics the evaluation of a pathologist such that the image analysis starts from the lowest resolution and switches to higher resolutions when necessary. We employ an offine feature selection step, which determines the most discriminative features at each resolution level during the training step. A modified k-nearest neighbor classifier is used to determine the confidence level of the classification to make the decision at a particular resolution level. The proposed approach was independently tested on 43 whole-slide samples and provided an overall classification accuracy of 88.4%. PMID:20161324

Towards exaggerated emphysema stereotypes

NASA Astrophysics Data System (ADS)

Chen, C.; Sørensen, L.; Lauze, F.; Igel, C.; Loog, M.; Feragen, A.; de Bruijne, M.; Nielsen, M.

2012-03-01

Classification is widely used in the context of medical image analysis and in order to illustrate the mechanism of a classifier, we introduce the notion of an exaggerated image stereotype based on training data and trained classifier. The stereotype of some image class of interest should emphasize/exaggerate the characteristic patterns in an image class and visualize the information the employed classifier relies on. This is useful for gaining insight into the classification and serves for comparison with the biological models of disease. In this work, we build exaggerated image stereotypes by optimizing an objective function which consists of a discriminative term based on the classification accuracy, and a generative term based on the class distributions. A gradient descent method based on iterated conditional modes (ICM) is employed for optimization. We use this idea with Fisher's linear discriminant rule and assume a multivariate normal distribution for samples within a class. The proposed framework is applied to computed tomography (CT) images of lung tissue with emphysema. The synthesized stereotypes illustrate the exaggerated patterns of lung tissue with emphysema, which is underpinned by three different quantitative evaluation methods.

Nondestructive tissue analysis for ex vivo and in vivo cancer diagnosis using a handheld mass spectrometry system

PubMed Central

Zhang, Jialing; Rector, John; Lin, John Q.; Young, Jonathan H.; Sans, Marta; Katta, Nitesh; Giese, Noah; Yu, Wendong; Nagi, Chandandeep; Suliburk, James; Liu, Jinsong; Bensussan, Alena; DeHoog, Rachel J.; Garza, Kyana Y.; Ludolph, Benjamin; Sorace, Anna G.; Syed, Anum; Zahedivash, Aydin; Milner, Thomas E.; Eberlin, Livia S.

2018-01-01

Conventional methods for histopathologic tissue diagnosis are labor- and time-intensive and can delay decision-making during diagnostic and therapeutic procedures. We report the development of an automated and biocompatible handheld mass spectrometry device for rapid and nondestructive diagnosis of human cancer tissues. The device, named MasSpec Pen, enables controlled and automated delivery of a discrete water droplet to a tissue surface for efficient extraction of biomolecules. We used the MasSpec Pen for ex vivo molecular analysis of 20 human cancer thin tissue sections and 253 human patient tissue samples including normal and cancerous tissues from breast, lung, thyroid, and ovary. The mass spectra obtained presented rich molecular profiles characterized by a variety of potential cancer biomarkers identified as metabolites, lipids, and proteins. Statistical classifiers built from the histologically validated molecular database allowed cancer prediction with high sensitivity (96.4%), specificity (96.2%), and overall accuracy (96.3%), as well as prediction of benign and malignant thyroid tumors and different histologic subtypes of lung cancer. Notably, our classifier allowed accurate diagnosis of cancer in marginal tumor regions presenting mixed histologic composition. Last, we demonstrate that the MasSpec Pen is suited for in vivo cancer diagnosis during surgery performed in tumor-bearing mouse models, without causing any observable tissue harm or stress to the animal. Our results provide evidence that the MasSpec Pen could potentially be used as a clinical and intraoperative technology for ex vivo and in vivo cancer diagnosis. PMID:28878011

Transcript levels of ten-eleven translocation type 1–3 in cervical cancer and non-cancerous cervical tissues

PubMed Central

Bronowicka-Kłys, Dorota Ewa; Roszak, Andrzej; Pawlik, Piotr; Sajdak, Stefan; Sowińska, Anna; Jagodziński, Paweł Piotr

2017-01-01

Decreased expression of ten-eleven translocation (TET1, TET2 and TET3) proteins has been reported in various types of cancer. However, the expression levels of TET proteins in cervical cancer (CC) remain to be elucidated. The present study determined the levels of TET1, TET2 and TET3 transcripts in cancerous (n=80) and non-cancerous cervical tissues (n=41). The results revealed a significant reduction in TET1 transcripts (P=0.0000001) in cervical tissue samples from patients with primary CC compared with samples from control patients. Significantly decreased TET1 transcript levels, as compared to non-cancerous cervical tissues, were also observed in tissue samples with the following characteristics: Stage I (P=0.016), II (P<0.0001), III (P=0.00007) and grade of differentiation G1 (P=0.026), G2 (P=0.00006), G3 (P=0.0007) and Gx (P=0.0004) and squamous histological type (P<0.00001). TET1 transcript levels were significantly lower in patients aged 45–60 years (P=0.0002) and patients age >60 years (P=0.003), as compared with non-cancerous cervical tissues. TET2 transcript levels were lower in cervical cancer tissues classified as stage II (P=0.043) and TET3 transcript levels were lower in stage III samples (P=0.010), tissue samples with a grade of differentiation of G3 (P=0.025) and tissue with squamous type histology (P=0.047), all compared with non-cancerous cervical tissues. The present study demonstrated a significantly reduced level of TET1 transcripts in cancerous cervical tissues, as compared with non-cancerous tissues. Furthermore, decreased TET1-3 transcript levels were identified when patients with CC were stratified by clinicopathological variables, as compared with non-cancerous cervical tissues. PMID:28521490

Epithelial ovarian carcinoma diagnosis by desorption electrospray ionization mass spectrometry imaging

PubMed Central

Dória, Maria Luisa; McKenzie, James S.; Mroz, Anna; Phelps, David L.; Speller, Abigail; Rosini, Francesca; Strittmatter, Nicole; Golf, Ottmar; Veselkov, Kirill; Brown, Robert; Ghaem-Maghami, Sadaf; Takats, Zoltan

2016-01-01

Ovarian cancer is highly prevalent among European women, and is the leading cause of gynaecological cancer death. Current histopathological diagnoses of tumour severity are based on interpretation of, for example, immunohistochemical staining. Desorption electrospray mass spectrometry imaging (DESI-MSI) generates spatially resolved metabolic profiles of tissues and supports an objective investigation of tumour biology. In this study, various ovarian tissue types were analysed by DESI-MSI and co-registered with their corresponding haematoxylin and eosin (H&E) stained images. The mass spectral data reveal tissue type-dependent lipid profiles which are consistent across the n = 110 samples (n = 107 patients) used in this study. Multivariate statistical methods were used to classify samples and identify molecular features discriminating between tissue types. Three main groups of samples (epithelial ovarian carcinoma, borderline ovarian tumours, normal ovarian stroma) were compared as were the carcinoma histotypes (serous, endometrioid, clear cell). Classification rates >84% were achieved for all analyses, and variables differing statistically between groups were determined and putatively identified. The changes noted in various lipid types help to provide a context in terms of tumour biochemistry. The classification of unseen samples demonstrates the capability of DESI-MSI to characterise ovarian samples and to overcome existing limitations in classical histopathology. PMID:27976698

Automated classification of immunostaining patterns in breast tissue from the human protein atlas.

PubMed

Swamidoss, Issac Niwas; Kårsnäs, Andreas; Uhlmann, Virginie; Ponnusamy, Palanisamy; Kampf, Caroline; Simonsson, Martin; Wählby, Carolina; Strand, Robin

2013-01-01

The Human Protein Atlas (HPA) is an effort to map the location of all human proteins (http://www.proteinatlas.org/). It contains a large number of histological images of sections from human tissue. Tissue micro arrays (TMA) are imaged by a slide scanning microscope, and each image represents a thin slice of a tissue core with a dark brown antibody specific stain and a blue counter stain. When generating antibodies for protein profiling of the human proteome, an important step in the quality control is to compare staining patterns of different antibodies directed towards the same protein. This comparison is an ultimate control that the antibody recognizes the right protein. In this paper, we propose and evaluate different approaches for classifying sub-cellular antibody staining patterns in breast tissue samples. The proposed methods include the computation of various features including gray level co-occurrence matrix (GLCM) features, complex wavelet co-occurrence matrix (CWCM) features, and weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM)-inspired features. The extracted features are used into two different multivariate classifiers (support vector machine (SVM) and linear discriminant analysis (LDA) classifier). Before extracting features, we use color deconvolution to separate different tissue components, such as the brownly stained positive regions and the blue cellular regions, in the immuno-stained TMA images of breast tissue. We present classification results based on combinations of feature measurements. The proposed complex wavelet features and the WND-CHARM features have accuracy similar to that of a human expert. Both human experts and the proposed automated methods have difficulties discriminating between nuclear and cytoplasmic staining patterns. This is to a large extent due to mixed staining of nucleus and cytoplasm. Methods for quantification of staining patterns in histopathology have many applications, ranging from antibody quality control to tumor grading.

Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets.

PubMed

Rouillard, Andrew D; Hurle, Mark R; Agarwal, Pankaj

2018-05-01

Target selection is the first and pivotal step in drug discovery. An incorrect choice may not manifest itself for many years after hundreds of millions of research dollars have been spent. We collected a set of 332 targets that succeeded or failed in phase III clinical trials, and explored whether Omic features describing the target genes could predict clinical success. We obtained features from the recently published comprehensive resource: Harmonizome. Nineteen features appeared to be significantly correlated with phase III clinical trial outcomes, but only 4 passed validation schemes that used bootstrapping or modified permutation tests to assess feature robustness and generalizability while accounting for target class selection bias. We also used classifiers to perform multivariate feature selection and found that classifiers with a single feature performed as well in cross-validation as classifiers with more features (AUROC = 0.57 and AUPR = 0.81). The two predominantly selected features were mean mRNA expression across tissues and standard deviation of expression across tissues, where successful targets tended to have lower mean expression and higher expression variance than failed targets. This finding supports the conventional wisdom that it is favorable for a target to be present in the tissue(s) affected by a disease and absent from other tissues. Overall, our results suggest that it is feasible to construct a model integrating interpretable target features to inform target selection. We anticipate deeper insights and better models in the future, as researchers can reuse the data we have provided to improve methods for handling sample biases and learn more informative features. Code, documentation, and data for this study have been deposited on GitHub at https://github.com/arouillard/omic-features-successful-targets.

Multi-spectral brain tissue segmentation using automatically trained k-Nearest-Neighbor classification.

PubMed

Vrooman, Henri A; Cocosco, Chris A; van der Lijn, Fedde; Stokking, Rik; Ikram, M Arfan; Vernooij, Meike W; Breteler, Monique M B; Niessen, Wiro J

2007-08-01

Conventional k-Nearest-Neighbor (kNN) classification, which has been successfully applied to classify brain tissue in MR data, requires training on manually labeled subjects. This manual labeling is a laborious and time-consuming procedure. In this work, a new fully automated brain tissue classification procedure is presented, in which kNN training is automated. This is achieved by non-rigidly registering the MR data with a tissue probability atlas to automatically select training samples, followed by a post-processing step to keep the most reliable samples. The accuracy of the new method was compared to rigid registration-based training and to conventional kNN-based segmentation using training on manually labeled subjects for segmenting gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) in 12 data sets. Furthermore, for all classification methods, the performance was assessed when varying the free parameters. Finally, the robustness of the fully automated procedure was evaluated on 59 subjects. The automated training method using non-rigid registration with a tissue probability atlas was significantly more accurate than rigid registration. For both automated training using non-rigid registration and for the manually trained kNN classifier, the difference with the manual labeling by observers was not significantly larger than inter-observer variability for all tissue types. From the robustness study, it was clear that, given an appropriate brain atlas and optimal parameters, our new fully automated, non-rigid registration-based method gives accurate and robust segmentation results. A similarity index was used for comparison with manually trained kNN. The similarity indices were 0.93, 0.92 and 0.92, for CSF, GM and WM, respectively. It can be concluded that our fully automated method using non-rigid registration may replace manual segmentation, and thus that automated brain tissue segmentation without laborious manual training is feasible.

Comparison of mechanical behavior between implant-simulated bone tissue and implant-jaw bone tissue interfaces based on Pull Out testing

NASA Astrophysics Data System (ADS)

Lopez, C.; Muñoz, J. C.; Pinillos, J. C.

2013-11-01

The main purpose of this research was to achieve a better understanding of the relationship within the mechanical properties of human cadaver jaw bone with kind D2 density regarding a substitute polymer to simulate bone tissue, proposed by the ASTM, to evaluate orthopedic implants. However, despite the existence of several densities of foams and his mechanical characterization has been classified into different degrees of tissue densities to simulate cancellous and cortical bone, the value of the densities are different contrasted with the densities of bone tissue, making difficult to establish direct relationship about mechanical behavior between the polymer and the bone material, and therefore no clear criteria known for choosing the polymeric foam which describes the mechanical behavior of tissue for a specific or particular study. To understand such behavior from bone tissue regarding the polymer samples, on this research was a dental implant inserted into the samples, and subjected to destructive Pull Out test according to ASTM F543The Pull Out strength was compared between implant-jawbone and implant-rigid polyurethane foam interfaces. Thus, the test pieces with mechanical behavior similar to bone tissue, enabling an approximation to choose degree appropriate of polymer to replace the bone tissue in future trials biomechanical.

Orientational tomography of optical axes directions distributions of multilayer biological tissues birefringent polycrystalline networks

NASA Astrophysics Data System (ADS)

Zabolotna, Natalia I.; Dovhaliuk, Rostyslav Y.

2013-09-01

We present a novel measurement method of optic axes orientation distribution which uses a relatively simple measurement setup. The principal difference of our method from other well-known methods lies in direct approach for measuring the orientation of optical axis of polycrystalline networks biological crystals. Our test polarimetry setup consists of HeNe laser, quarter wave plate, two linear polarizers and a CCD camera. We also propose a methodology for processing of measured optic axes orientation distribution which consists of evaluation of statistical, correlational and spectral moments. Such processing of obtained data can be used to classify particular tissue sample as "healthy" or "pathological". For our experiment we use thin layers of histological section of normal and muscular dystrophy tissue sections. It is shown that the difference between mentioned moments` values of normal and pathological samples can be quite noticeable with relative difference up to 6.26.

Gametes or organs? How should we legally classify ovaries used for transplantation in the USA?

PubMed Central

Campo-Engelstein, Lisa

2011-01-01

Ovarian tissue transplantation is an experimental procedure that can be used to treat both infertility and premature menopause. Working within the current legal framework in the USA, I examine whether ovarian tissue should be legally treated like gametes or organs in the case of ovarian tissue transplantation between two women. One option is to base classification upon its intended use: ovarian tissue used to treat infertility would be classified like gametes, and ovarian tissue used to treat premature menopause would be classified like organs. In the end, however, I argue that this approach will not work because it engenders too many legal, cultural and logistical concerns and that, at least for the near future, we should treat ovarian tissue like gametes. PMID:21245477

Spectroscopic optical coherence tomography for ex vivo brain tumor analysis

NASA Astrophysics Data System (ADS)

Lenz, Marcel; Krug, Robin; Dillmann, Christopher; Gerling, Alexandra; Gerhardt, Nils C.; Welp, Hubert; Schmieder, Kirsten; Hofmann, Martin R.

2017-02-01

For neurosurgeries precise tumor resection is essential for the subsequent recovery of the patients since nearby healthy tissue that may be harmed has a huge impact on the life quality after the surgery. However, so far no satisfying methodology has been established to assist the surgeon during surgery to distinguish between healthy and tumor tissue. Optical Coherence Tomography (OCT) potentially enables non-contact in vivo image acquisition at penetration depths of 1-2 mm with a resolution of approximately 1-15 μm. To analyze the potential of OCT for distinction between brain tumors and healthy tissue, we used a commercially available Thorlabs Callisto system to measure healthy tissue and meningioma samples ex vivo. All samples were measured with the OCT system and three dimensional datasets were generated. Afterwards they were sent to the pathology for staining with hematoxylin and eosin and then investigated with a bright field microscope to verify the tissue type. This is the actual gold standard for ex vivo analysis. The images taken by the OCT system exhibit variations in the structure for different tissue types, but these variations may not be objectively evaluated from raw OCT images. Since an automated distinction between tumor and healthy tissue would be highly desirable to guide the surgeon, we applied Spectroscopic Optical Coherence Tomography to further enhance the differences between the tissue types. Pattern recognition and machine learning algorithms were applied to classify the derived spectroscopic information. Finally, the classification results are analyzed in comparison to the histological analysis of the samples.

Rapid intra-operative diagnosis of kidney cancer by attenuated total reflection infrared spectroscopy of tissue smears.

PubMed

Pucetaite, Milda; Velicka, Martynas; Urboniene, Vidita; Ceponkus, Justinas; Bandzeviciute, Rimante; Jankevicius, Feliksas; Zelvys, Arunas; Sablinskas, Valdas; Steiner, Gerald

2018-05-01

Herein, a technique to analyze air-dried kidney tissue impression smears by means of attenuated total reflection infrared (ATR-IR) spectroscopy is presented. Spectral tumor markers-absorption bands of glycogen-are identified in the ATR-IR spectra of the kidney tissue smear samples. Thin kidney tissue cryo-sections currently used for IR spectroscopic analysis lack such spectral markers as the sample preparation causes irreversible molecular changes in the tissue. In particular, freeze-thaw cycle results in degradation of the glycogen and reduction or complete dissolution of its content. Supervised spectral classification was applied to the recorded spectra of the smears and the test spectra were classified with a high accuracy of 92% for normal tissue and 94% for tumor tissue, respectively. For further development, we propose that combination of the method with optical fiber ATR probes could potentially be used for rapid real-time intra-operative tissue analysis without interfering with either the established protocols of pathological examination or the ordinary workflow of operating surgeon. Such approach could ensure easier transition of the method to clinical applications where it may complement the results of gold standard histopathology examination and aid in more precise resection of kidney tumors. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Quantitative Evaluation of Atherosclerotic Plaque Using Ultrasound Tissue Characterization.

NASA Astrophysics Data System (ADS)

Yigiter, Ersin

Evaluation of therapeutic methods directed toward interrupting and/or delaying atherogenesis is impeded by the lack of a reliable, non-invasive means for monitoring progression or regression of disease. The ability to characterize the predominant component of plaque may be very valuable in the study of this disease's natural history. The earlier the lesion, the more likely is lipid to be the predominant component. Progression of plaque is usually by way of overgrowth of fibrous tissues around the fatty pool. Calcification is usually a feature of the older or complicated lesion. To explore the feasibility of using ultrasound to characterize plaque we have conducted measurements of the acoustical properties of various atherosclerotic lesions found in freshly excised samples of human abdominal aorta. Our objective has been to determine whether or not the acoustical properties of plaque correlate with the type and/or chemical composition of plaque and, if so, to define a measurement scheme which could be done in-vivo and non-invasively. Our current data base consists of individual tissue samples from some 200 different aortas. Since each aorta yields between 10 to 30 tissue samples for study, we have data on some 4,468 different lesions or samples. Measurements of the acoustical properties of plaque were found to correlate well with the chemical composition of plaque. In short, measurements of impedance and attenuation seem sufficient to classify plaque as to type and to composition. Based on the in-vitro studies, the parameter of attenuation was selected as a means of classifying the plaque. For these measurements, an intravascular ultrasound scanner was modified according to our specifications. Signal processing algorithms were developed which would analyze the complex ultrasound waveforms and estimate tissue properties such as attenuation. Various methods were tried to estimate the attenuation from the pulse-echo backscattered signal. Best results were obtained by comparing averaged power spectra in small time windows at different depths for a series of A-lines. Comparisons between consequent averaged spectra at different depths provided the magnitude and frequency dependence of attenuation. Non-invasive characterization of the physical state of the tissue with quantitative ultrasound holds great promise for the extension of the diagnostic power of conventional B-mode imaging.

Texture analysis of pulmonary parenchyma in normal and emphysematous lung

NASA Astrophysics Data System (ADS)

Uppaluri, Renuka; Mitsa, Theophano; Hoffman, Eric A.; McLennan, Geoffrey; Sonka, Milan

1996-04-01

Tissue characterization using texture analysis is gaining increasing importance in medical imaging. We present a completely automated method for discriminating between normal and emphysematous regions from CT images. This method involves extracting seventeen features which are based on statistical, hybrid and fractal texture models. The best subset of features is derived from the training set using the divergence technique. A minimum distance classifier is used to classify the samples into one of the two classes--normal and emphysema. Sensitivity and specificity and accuracy values achieved were 80% or greater in most cases proving that texture analysis holds great promise in identifying emphysema.

Evaluation of a multi-fibre needle Raman probe for tissue analysis

NASA Astrophysics Data System (ADS)

Fullwood, Leanne M.; Iping Petterson, Ingeborg E.; Dudgeon, Alexander P.; Lloyd, Gavin R.; Kendall, Catherine; Hall, Charlie; Day, John C. C.; Stone, Nick

2016-03-01

Raman spectroscopy is a rapid technique for the identification of cancers. Its coupling with a hypodermic needle provides a minimally invasive instrument with the potential to aid real time assessment of suspicious lesions in vivo and guide surgery. A fibre optic Raman needle probe was utilised in this study to evaluate the classification ability of the instrument as a diagnostic tool together with multivariate analysis, through measurements of tissues from different animal species as well as various different porcine tissue types. Cross validation was performed and preliminary classification accuracies were calculated as 100% for the identification of tissue type and 97.5% for the identification of animal species. A lymph node sample was also measured using the needle probe to assess the use of the technique for human tissue and hence its efficiency as a clinical instrument. This needle probe has been demonstrated to have the capabilities to classify tissue samples based on their biochemical components. The Raman needle probe also has the potential to act as a diagnostic and surgical tool to delineate cancerous from non-cancerous cells in real time, thus assisting complete removal of a tumour.

Pulsed terahertz imaging of breast cancer in freshly excised murine tumors

NASA Astrophysics Data System (ADS)

Bowman, Tyler; Chavez, Tanny; Khan, Kamrul; Wu, Jingxian; Chakraborty, Avishek; Rajaram, Narasimhan; Bailey, Keith; El-Shenawee, Magda

2018-02-01

This paper investigates terahertz (THz) imaging and classification of freshly excised murine xenograft breast cancer tumors. These tumors are grown via injection of E0771 breast adenocarcinoma cells into the flank of mice maintained on high-fat diet. Within 1 h of excision, the tumor and adjacent tissues are imaged using a pulsed THz system in the reflection mode. The THz images are classified using a statistical Bayesian mixture model with unsupervised and supervised approaches. Correlation with digitized pathology images is conducted using classification images assigned by a modal class decision rule. The corresponding receiver operating characteristic curves are obtained based on the classification results. A total of 13 tumor samples obtained from 9 tumors are investigated. The results show good correlation of THz images with pathology results in all samples of cancer and fat tissues. For tumor samples of cancer, fat, and muscle tissues, THz images show reasonable correlation with pathology where the primary challenge lies in the overlapping dielectric properties of cancer and muscle tissues. The use of a supervised regression approach shows improvement in the classification images although not consistently in all tissue regions. Advancing THz imaging of breast tumors from mice and the development of accurate statistical models will ultimately progress the technique for the assessment of human breast tumor margins.

Transforming growth factor beta-1 expression in macrophages of human chronic periapical diseases.

PubMed

Liang, Z-Z; Li, J; Huang, S-G

2017-03-30

The objective of this study was to observe the distribution of macrophages (MPs) expressing transforming growth factor beta-1 (TGF-β1) in tissue samples from patients with different human chronic periapical diseases. In this study, samples were collected from 75 volunteers, who were divided into three groups according to classified standards, namely, healthy control (N = 25), periapical granuloma (N = 25), and periapical cyst (N = 25). The samples were fixed in 10% buffered formalin for more than 48 h, dehydrated, embedded, and stained with hematoxylin and eosin for histopathology. Double immunofluorescence was conducted to analyze the expression of TGF-β-CD14 double-positive MPs in periapical tissues. The number of double-positive cells (cells/mm 2 ) were significantly higher in the chronic periapical disease tissues (P < 0.01) compared to that in the control tissue; in addition, the density of TGF-β1-CD14 double positive cells was significantly higher in the periapical cyst group than in the periapical granuloma group (P < 0.01). The number of TGF-β1 expressing macrophages varied with human chronic periapical diseases. The TGF-β1-CD14 double-positive cells might play an important role in the pathology of human chronic periapical diseases.

Pathology of tissue loss (white syndrome) in Acropora sp. corals from the Central Pacific.

PubMed

Work, Thierry M; Aeby, Greta S

2011-06-01

We performed histological examination of 69 samples of Acropora sp. manifesting different types of tissue loss (Acropora White Syndrome-AWS) from Hawaii, Johnston Atoll and American Samoa between 2002 and 2006. Gross lesions of tissue loss were observed and classified as diffuse acute, diffuse subacute, and focal to multifocal acute to subacute. Corals with acute tissue loss manifested microscopic evidence of necrosis sometimes associated with ciliates, helminths, fungi, algae, sponges, or cyanobacteria whereas those with subacute tissue loss manifested mainly wound repair. Gross lesions of AWS have multiple different changes at the microscopic level some of which involve various microorganisms and metazoa. Elucidating this disease will require, among other things, monitoring lesions over time to determine the pathogenesis of AWS and the potential role of tissue-associated microorganisms in the genesis of tissue loss. Attempts to experimentally induce AWS should include microscopic examination of tissues to ensure that potentially causative microorganisms associated with gross lesion are not overlooked. Published by Elsevier Inc.

Pathology of tissue loss (white syndrome) in Acropora sp. corals from the Central Pacific

USGS Publications Warehouse

Work, Thierry M.; Aeby, Greta S.

2011-01-01

We performed histological examination of 69 samples of Acropora sp. manifesting different types of tissue loss (Acropora White Syndrome-AWS) from Hawaii, Johnston Atoll and American Samoa between 2002 and 2006. Gross lesions of tissue loss were observed and classified as diffuse acute, diffuse subacute, and focal to multifocal acute to subacute. Corals with acute tissue loss manifested microscopic evidence of necrosis sometimes associated with ciliates, helminths, fungi, algae, sponges, or cyanobacteria whereas those with subacute tissue loss manifested mainly wound repair. Gross lesions of AWS have multiple different changes at the microscopic level some of which involve various microorganisms and metazoa. Elucidating this disease will require, among other things, monitoring lesions over time to determine the pathogenesis of AWS and the potential role of tissue-associated microorganisms in the genesis of tissue loss. Attempts to experimentally induce AWS should include microscopic examination of tissues to ensure that potentially causative microorganisms associated with gross lesion are not overlooked.

Deep multi-spectral ensemble learning for electronic cleansing in dual-energy CT colonography

NASA Astrophysics Data System (ADS)

Tachibana, Rie; Näppi, Janne J.; Hironaka, Toru; Kim, Se Hyung; Yoshida, Hiroyuki

2017-03-01

We developed a novel electronic cleansing (EC) method for dual-energy CT colonography (DE-CTC) based on an ensemble deep convolution neural network (DCNN) and multi-spectral multi-slice image patches. In the method, an ensemble DCNN is used to classify each voxel of a DE-CTC image volume into five classes: luminal air, soft tissue, tagged fecal materials, and partial-volume boundaries between air and tagging and those between soft tissue and tagging. Each DCNN acts as a voxel classifier, where an input image patch centered at the voxel is generated as input to the DCNNs. An image patch has three channels that are mapped from a region-of-interest containing the image plane of the voxel and the two adjacent image planes. Six different types of spectral input image datasets were derived using two dual-energy CT images, two virtual monochromatic images, and two material images. An ensemble DCNN was constructed by use of a meta-classifier that combines the output of multiple DCNNs, each of which was trained with a different type of multi-spectral image patches. The electronically cleansed CTC images were calculated by removal of regions classified as other than soft tissue, followed by a colon surface reconstruction. For pilot evaluation, 359 volumes of interest (VOIs) representing sources of subtraction artifacts observed in current EC schemes were sampled from 30 clinical CTC cases. Preliminary results showed that the ensemble DCNN can yield high accuracy in labeling of the VOIs, indicating that deep learning of multi-spectral EC with multi-slice imaging could accurately remove residual fecal materials from CTC images without generating major EC artifacts.

Diagnosis of Lung Cancer in Small Biopsies and Cytology

PubMed Central

Travis, William D.; Brambilla, Elisabeth; Noguchi, Masayuki; Nicholson, Andrew G.; Geisinger, Kim; Yatabe, Yasushi; Ishikawa, Yuichi; Wistuba, Ignacio; Flieder, Douglas B.; Franklin, Wilbur; Gazdar, Adi; Hasleton, Philip S.; Henderson, Douglas W.; Kerr, Keith M.; Petersen, Iver; Roggli, Victor; Thunnissen, Erik; Tsao, Ming

2015-01-01

The new International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society lung adenocarcinoma classification provides, for the first time, standardized terminology for lung cancer diagnosis in small biopsies and cytology; this was not primarily addressed by previous World Health Organization classifications. Until recently there have been no therapeutic implications to further classification of NSCLC, so little attention has been given to the distinction of adenocarcinoma and squamous cell carcinoma in small tissue samples. This situation has changed dramatically in recent years with the discovery of several therapeutic options that are available only to patients with adenocarcinoma or NSCLC, not otherwise specified, rather than squamous cell carcinoma. This includes recommendation for use of special stains as an aid to diagnosis, particularly in the setting of poorly differentiated tumors that do not show clear differentiation by routine light microscopy. A limited diagnostic workup is recommended to preserve as much tissue for molecular testing as possible. Most tumors can be classified using a single adenocarcinoma marker (eg, thyroid transcription factor 1 or mucin) and a single squamous marker (eg, p40 or p63). Carcinomas lacking clear differentiation by morphology and special stains are classified as NSCLC, not otherwise specified. Not otherwise specified carcinomas that stain with adenocarcinoma markers are classified as NSCLC, favor adenocarcinoma, and tumors that stain only with squamous markers are classified as NSCLC, favor squamous cell carcinoma. The need for every institution to develop a multidisciplinary tissue management strategy to obtain these small specimens and process them, not only for diagnosis but also for molecular testing and evaluation of markers of resistance to therapy, is emphasized. PMID:22970842

Robust classification of contact orientation between tissue and an integrated spectroscopy and radiofrequency ablation catheter

NASA Astrophysics Data System (ADS)

Zaryab, Mohammad; Singh-Moon, Rajinder P.; Hendon, Christine P.

2017-02-01

Using light-based catheters for radiofrequency ablation (RFA) therapies grants the ability to accurately derive tissue properties such as lesion depth and overtreatment from spectroscopic information. However, this information is heavily reliant on contact quality with the treatment area and the orientation of the catheter. Thus to improve assessments of tissue properties, this work utilizes Bayesian modelling to classify whether the catheter is indeed in proper contact with the tissue. Initially in-laboratory experiments were conducted with ten fresh swine hearts submerged in blood. A total of 1555 unique near infrared spectra were collected from a spectrometer using a light-based catheter and manually tagged as "full perpendicular contact," "angled contact," and "no contact," between the catheter and heart tissue. Three features were prominent in all spectra for distinguishing purposes: area underneath the spectra, an intensity "valley" between 730 nm and 800 nm, along with the slope between 850 nm and 1150 nm. A classifier featuring bootstrapping, adaboost, and k-means techniques was thus created and achieved a 96.05% accuracy in classifying full contact, 98.33% accuracy in classifying angled contact, and 100% accuracy in classifying no contact.

Micro and bulk analysis of prostate tissues classified as hyperplasia

NASA Astrophysics Data System (ADS)

Kwiatek, W. M.; Banaś, A.; Banaś, K.; Cinque, G.; Dyduch, G.; Falkenberg, G.; Kisiel, A.; Marcelli, A.; Podgórczyk, M.

2007-07-01

BPH (Benign Prostatic Hyperplasia) is the most common benign neoplasm (non cancerous enlargement of the prostate gland), whose prevalence increases with age. The gland, when increased in size, exerts pressure on the urethra, causing obstruction to urine flow. The latter may result in severe urinary tract and kidney conditions. In this work prostate samples from patients diagnosed with BPH were analyzed using synchrotron radiation. Micro-analysis of the hyperplastic samples was carried out on the L-beam line at HASYLAB, DESY (Germany), while bulk analysis on selected samples was performed at the DRX2 beamline at LNF, Frascati (Italy). Microanalysis with a mono-energetic beam 15 μm in diameter confirmed that concentrations of certain elements, such as S, Mn, Cu, Fe and Zn, are good indicators of pathological disorders in prostate tissue that may be considered effective tracers of developing compliant. The concentrations of Mn, Cu, Fe and Zn are higher in hyperplastic tissues, as compared to normal ones, while for sulphur the opposite is observed. Additionally, Fe and S K-edge XANES (X-ray Absorption Near Edge Structure) spectroscopy experiments were carried out in order to determine the chemical speciation of these elements in our samples.

Supervised novelty detection in brain tissue classification with an application to white matter hyperintensities

NASA Astrophysics Data System (ADS)

Kuijf, Hugo J.; Moeskops, Pim; de Vos, Bob D.; Bouvy, Willem H.; de Bresser, Jeroen; Biessels, Geert Jan; Viergever, Max A.; Vincken, Koen L.

2016-03-01

Novelty detection is concerned with identifying test data that differs from the training data of a classifier. In the case of brain MR images, pathology or imaging artefacts are examples of untrained data. In this proof-of-principle study, we measure the behaviour of a classifier during the classification of trained labels (i.e. normal brain tissue). Next, we devise a measure that distinguishes normal classifier behaviour from abnormal behavior that occurs in the case of a novelty. This will be evaluated by training a kNN classifier on normal brain tissue, applying it to images with an untrained pathology (white matter hyperintensities (WMH)), and determine if our measure is able to identify abnormal classifier behaviour at WMH locations. For our kNN classifier, behaviour is modelled as the mean, median, or q1 distance to the k nearest points. Healthy tissue was trained on 15 images; classifier behaviour was trained/tested on 5 images with leave-one-out cross-validation. For each trained class, we measure the distribution of mean/median/q1 distances to the k nearest point. Next, for each test voxel, we compute its Z-score with respect to the measured distribution of its predicted label. We consider a Z-score >=4 abnormal behaviour of the classifier, having a probability due to chance of 0.000032. Our measure identified >90% of WMH volume and also highlighted other non-trained findings. The latter being predominantly vessels, cerebral falx, brain mask errors, choroid plexus. This measure is generalizable to other classifiers and might help in detecting unexpected findings or novelties by measuring classifier behaviour.

Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning

NASA Astrophysics Data System (ADS)

Nguyen, Tan H.; Sridharan, Shamira; Macias, Virgilia; Kajdacsy-Balla, Andre; Melamed, Jonathan; Do, Minh N.; Popescu, Gabriel

2017-03-01

We present an approach for automatic diagnosis of tissue biopsies. Our methodology consists of a quantitative phase imaging tissue scanner and machine learning algorithms to process these data. We illustrate the performance by automatic Gleason grading of prostate specimens. The imaging system operates on the principle of interferometry and, as a result, reports on the nanoscale architecture of the unlabeled specimen. We use these data to train a random forest classifier to learn textural behaviors of prostate samples and classify each pixel in the image into different classes. Automatic diagnosis results were computed from the segmented regions. By combining morphological features with quantitative information from the glands and stroma, logistic regression was used to discriminate regions with Gleason grade 3 versus grade 4 cancer in prostatectomy tissue. The overall accuracy of this classification derived from a receiver operating curve was 82%, which is in the range of human error when interobserver variability is considered. We anticipate that our approach will provide a clinically objective and quantitative metric for Gleason grading, allowing us to corroborate results across instruments and laboratories and feed the computer algorithms for improved accuracy.

Accounting for tissue heterogeneity in infrared spectroscopic imaging for accurate diagnosis of thyroid carcinoma subtypes.

PubMed

Martinez-Marin, David; Sreedhar, Hari; Varma, Vishal K; Eloy, Catarina; Sobrinho-Simões, Manuel; Kajdacsy-Balla, André; Walsh, Michael J

2017-07-01

Fourier transform infrared (FT-IR) microscopy was used to image tissue samples from twenty patients diagnosed with thyroid carcinoma. The spectral data were then used to differentiate between follicular thyroid carcinoma and follicular variant of papillary thyroid carcinoma using principle component analysis coupled with linear discriminant analysis and a Naïve Bayesian classifier operating on a set of computed spectral metrics. Classification of patients' disease type was accomplished by using average spectra from a wide region containing follicular cells, colloid, and fibrosis; however, classification of disease state at the pixel level was only possible when the extracted spectra were limited to follicular epithelial cells in the samples, excluding the relatively uninformative areas of fibrosis. The results demonstrate the potential of FT-IR microscopy as a tool to assist in the difficult diagnosis of these subtypes of thyroid cancer, and also highlights the importance of selectively and separately analyzing spectral information from different features of a tissue of interest.

Classification of microscopic images of breast tissue

NASA Astrophysics Data System (ADS)

Ballerini, Lucia; Franzen, Lennart

2004-05-01

Breast cancer is the most common form of cancer among women. The diagnosis is usually performed by the pathologist, that subjectively evaluates tissue samples. The aim of our research is to develop techniques for the automatic classification of cancerous tissue, by analyzing histological samples of intact tissue taken with a biopsy. In our study, we considered 200 images presenting four different conditions: normal tissue, fibroadenosis, ductal cancer and lobular cancer. Methods to extract features have been investigated and described. One method is based on granulometries, which are size-shape descriptors widely used in mathematical morphology. Applications of granulometries lead to distribution functions whose moments are used as features. A second method is based on fractal geometry, that seems very suitable to quantify biological structures. The fractal dimension of binary images has been computed using the euclidean distance mapping. Image classification has then been performed using the extracted features as input of a back-propagation neural network. A new method that combines genetic algorithms and morphological filters has been also investigated. In this case, the classification is based on a correlation measure. Very encouraging results have been obtained with pilot experiments using a small subset of images as training set. Experimental results indicate the effectiveness of the proposed methods. Cancerous tissue was correctly classified in 92.5% of the cases.

Multimodal fiber-probe spectroscopy as a clinical tool for diagnosing and classifying biological tissues

NASA Astrophysics Data System (ADS)

Cicchi, Riccardo; Anand, Suresh; Fantechi, Riccardo; Giordano, Flavio; Gacci, Mauro; Conti, Valerio; Nesi, Gabriella; Buccoliero, Anna Maria; Carini, Marco; Guerrini, Renzo; Pavone, Francesco Saverio

2017-07-01

An optical fiber probe for multimodal spectroscopy was designed, developed and used for tissue diagnostics. The probe, based on a fiber bundle with optical fibers of various size and properties, allows performing spectroscopic measurements with different techniques, including fluorescence, Raman, and diffuse reflectance, using the same probe. Two visible laser diodes were used for fluorescence spectroscopy, a laser diode emitting in the NIR was used for Raman spectroscopy, and a fiber-coupled halogen lamp for diffuse reflectance. The developed probe was successfully employed for diagnostic purposes on various tissues, including brain and bladder. In particular, the device allowed discriminating healthy tissue from both tumor and dysplastic tissue as well as to perform tumor grading. The diagnostic capabilities of the method, determined using a cross-validation method with a leave-one-out approach, demonstrated high sensitivity and specificity for all the examined samples, as well as a good agreement with histopathological examination performed on the same samples. The obtained results demonstrated that the multimodal approach is crucial for improving diagnostic capabilities with respect to what can be obtained from individual techniques. The experimental setup presented here can improve diagnostic capabilities on a broad range of tissues and has the potential of being used clinically for guiding surgical resection in the near future.

Micro-anatomical quantitative optical imaging: toward automated assessment of breast tissues.

PubMed

Dobbs, Jessica L; Mueller, Jenna L; Krishnamurthy, Savitri; Shin, Dongsuk; Kuerer, Henry; Yang, Wei; Ramanujam, Nirmala; Richards-Kortum, Rebecca

2015-08-20

Pathologists currently diagnose breast lesions through histologic assessment, which requires fixation and tissue preparation. The diagnostic criteria used to classify breast lesions are qualitative and subjective, and inter-observer discordance has been shown to be a significant challenge in the diagnosis of selected breast lesions, particularly for borderline proliferative lesions. Thus, there is an opportunity to develop tools to rapidly visualize and quantitatively interpret breast tissue morphology for a variety of clinical applications. Toward this end, we acquired images of freshly excised breast tissue specimens from a total of 34 patients using confocal fluorescence microscopy and proflavine as a topical stain. We developed computerized algorithms to segment and quantify nuclear and ductal parameters that characterize breast architectural features. A total of 33 parameters were evaluated and used as input to develop a decision tree model to classify benign and malignant breast tissue. Benign features were classified in tissue specimens acquired from 30 patients and malignant features were classified in specimens from 22 patients. The decision tree model that achieved the highest accuracy for distinguishing between benign and malignant breast features used the following parameters: standard deviation of inter-nuclear distance and number of duct lumens. The model achieved 81 % sensitivity and 93 % specificity, corresponding to an area under the curve of 0.93 and an overall accuracy of 90 %. The model classified IDC and DCIS with 92 % and 96 % accuracy, respectively. The cross-validated model achieved 75 % sensitivity and 93 % specificity and an overall accuracy of 88 %. These results suggest that proflavine staining and confocal fluorescence microscopy combined with image analysis strategies to segment morphological features could potentially be used to quantitatively diagnose freshly obtained breast tissue at the point of care without the need for tissue preparation.

Quantitative diagnosis of tongue cancer from histological images in an animal model

NASA Astrophysics Data System (ADS)

Lu, Guolan; Qin, Xulei; Wang, Dongsheng; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Chen, Zhuo G.; Fei, Baowei

2016-03-01

We developed a chemically-induced oral cancer animal model and a computer aided method for tongue cancer diagnosis. The animal model allows us to monitor the progress of the lesions over time. Tongue tissue dissected from mice was sent for histological processing. Representative areas of hematoxylin and eosin stained tissue from tongue sections were captured for classifying tumor and non-tumor tissue. The image set used in this paper consisted of 214 color images (114 tumor and 100 normal tissue samples). A total of 738 color, texture, morphometry and topology features were extracted from the histological images. The combination of image features from epithelium tissue and its constituent nuclei and cytoplasm has been demonstrated to improve the classification results. With ten iteration nested cross validation, the method achieved an average sensitivity of 96.5% and a specificity of 99% for tongue cancer detection. The next step of this research is to apply this approach to human tissue for computer aided diagnosis of tongue cancer.

TH-AB-209-12: Tissue Equivalent Phantom with Excised Human Tissue for Assessing Clinical Capabilities of Coherent Scatter Imaging Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Albanese, K; Morris, R; Spencer, J

Purpose: Previously we reported the development of anthropomorphic tissue-equivalent scatter phantoms of the human breast. Here we present the first results from the scatter imaging of the tissue equivalent breast phantoms for breast cancer diagnosis. Methods: A breast phantom was designed to assess the capability of coded aperture coherent x-ray scatter imaging to classify different types of breast tissue (adipose, fibroglandular, tumor). The phantom geometry was obtained from a prone breast geometry scanned on a dedicated breast CT system. The phantom was 3D printed using the segmented DICOM breast CT data. The 3D breast phantom was filled with lard (asmore » a surrogate for adipose tissue) and scanned in different geometries alongside excised human breast tissues (obtained from lumpectomy and mastectomy procedures). The raw data were reconstructed using a model-based reconstruction algorithm and yielded the location and form factor (i.e., momentum transfer (q) spectrum) of the materials that were imaged. The measured material form factors were then compared to the ground truth measurements acquired by x-ray diffraction (XRD) imaging. Results: Our scatter imaging system was able to define the location and composition of the various materials and tissues within the phantom. Cancerous breast tissue was detected and classified through automated spectral matching and an 86% correlation threshold. The total scan time for the sample was approximately 10 minutes and approaches workflow times for clinical use in intra-operative or other diagnostic tasks. Conclusion: This work demonstrates the first results from an anthropomorphic tissue equivalent scatter phantom to characterize a coherent scatter imaging system. The functionality of the system shows promise in applications such as intra-operative margin detection or virtual biopsy in the diagnosis of breast cancer. Future work includes using additional patient-derived tissues (e.g., human fat), and modeling additional organs (e.g., lung).« less

Diffuse reflectance spectroscopy as a tool for real-time tissue assessment during colorectal cancer surgery

NASA Astrophysics Data System (ADS)

Baltussen, Elisabeth J. M.; Snaebjornsson, Petur; de Koning, Susan G. Brouwer; Sterenborg, Henricus J. C. M.; Aalbers, Arend G. J.; Kok, Niels; Beets, Geerard L.; Hendriks, Benno H. W.; Kuhlmann, Koert F. D.; Ruers, Theo J. M.

2017-10-01

Colorectal surgery is the standard treatment for patients with colorectal cancer. To overcome two of the main challenges, the circumferential resection margin and postoperative complications, real-time tissue assessment could be of great benefit during surgery. In this ex vivo study, diffuse reflectance spectroscopy (DRS) was used to differentiate tumor tissue from healthy surrounding tissues in patients with colorectal neoplasia. DRS spectra were obtained from tumor tissue, healthy colon, or rectal wall and fat tissue, for every patient. Data were randomly divided into training (80%) and test (20%) sets. After spectral band selection, the spectra were classified using a quadratic classifier and a linear support vector machine. Of the 38 included patients, 36 had colorectal cancer and 2 had an adenoma. When the classifiers were applied to the test set, colorectal cancer could be discriminated from healthy tissue with an overall accuracy of 0.95 (±0.03). This study demonstrates the possibility to separate colorectal cancer from healthy surrounding tissue by applying DRS. High classification accuracies were obtained both in homogeneous and inhomogeneous tissues. This is a fundamental step toward the development of a tool for real-time in vivo tissue assessment during colorectal surgery.

Near infrared diffuse reflection and laser-induced fluorescence spectroscopy for myocardial tissue characterisation

NASA Astrophysics Data System (ADS)

Nilsson, A. M. K.; Heinrich, D.; Olajos, J.; Andersson-Engels, S.

1997-10-01

In order to evaluate the potential of cardiovascular tissue characterisation using near-infrared (NIR) spectroscopy, spectra in a previously unexplored wavelength region 0.8-2.3 μm were recorded from various pig heart tissue samples in vitro: normal myocardium (with and without endo/epicardium), aorta, fatty and fibrous heart tissue. The spectra were analysed with principal component analysis (PCA), revealing several spectroscopically characteristic features enabling tissue classification. Several of the identified spectral features could be attributed to specific tissue constituents by comparing the tissue signals with spectra obtained from water, elastin, collagen and cholesterol as well as with published data. The results obtained with the NIR spectroscopy technique in terms of its potential to classify different tissue types were compared with those from laser-induced fluorescence (LIF) using 337 nm excitation. LIF and NIR spectroscopy can in combination with PCA be used to discriminate between all previously mentioned tissue groups, apart from fatty versus fibrous tissue (LIF) and aorta versus fibrous tissue (NIR), respectively. The NIR analysis was improved by focusing the PCA to the wavelength segment 2.0-2.3 μm, resulting in successful spectral characterisation of all cardiovascular tissue groups.

Breast cancer risk assessment and diagnosis model using fuzzy support vector machine based expert system

NASA Astrophysics Data System (ADS)

Dheeba, J.; Jaya, T.; Singh, N. Albert

2017-09-01

Classification of cancerous masses is a challenging task in many computerised detection systems. Cancerous masses are difficult to detect because these masses are obscured and subtle in mammograms. This paper investigates an intelligent classifier - fuzzy support vector machine (FSVM) applied to classify the tissues containing masses on mammograms for breast cancer diagnosis. The algorithm utilises texture features extracted using Laws texture energy measures and a FSVM to classify the suspicious masses. The new FSVM treats every feature as both normal and abnormal samples, but with different membership. By this way, the new FSVM have more generalisation ability to classify the masses in mammograms. The classifier analysed 219 clinical mammograms collected from breast cancer screening laboratory. The tests made on the real clinical mammograms shows that the proposed detection system has better discriminating power than the conventional support vector machine. With the best combination of FSVM and Laws texture features, the area under the Receiver operating characteristic curve reached .95, which corresponds to a sensitivity of 93.27% with a specificity of 87.17%. The results suggest that detecting masses using FSVM contribute to computer-aided detection of breast cancer and as a decision support system for radiologists.

Automated tissue classification of intracardiac optical coherence tomography images (Conference Presentation)

NASA Astrophysics Data System (ADS)

Gan, Yu; Tsay, David; Amir, Syed B.; Marboe, Charles C.; Hendon, Christine P.

2016-03-01

Remodeling of the myocardium is associated with increased risk of arrhythmia and heart failure. Our objective is to automatically identify regions of fibrotic myocardium, dense collagen, and adipose tissue, which can serve as a way to guide radiofrequency ablation therapy or endomyocardial biopsies. Using computer vision and machine learning, we present an automated algorithm to classify tissue compositions from cardiac optical coherence tomography (OCT) images. Three dimensional OCT volumes were obtained from 15 human hearts ex vivo within 48 hours of donor death (source, NDRI). We first segmented B-scans using a graph searching method. We estimated the boundary of each region by minimizing a cost function, which consisted of intensity, gradient, and contour smoothness. Then, features, including texture analysis, optical properties, and statistics of high moments, were extracted. We used a statistical model, relevance vector machine, and trained this model with abovementioned features to classify tissue compositions. To validate our method, we applied our algorithm to 77 volumes. The datasets for validation were manually segmented and classified by two investigators who were blind to our algorithm results and identified the tissues based on trichrome histology and pathology. The difference between automated and manual segmentation was 51.78 +/- 50.96 μm. Experiments showed that the attenuation coefficients of dense collagen were significantly different from other tissue types (P < 0.05, ANOVA). Importantly, myocardial fibrosis tissues were different from normal myocardium in entropy and kurtosis. The tissue types were classified with an accuracy of 84%. The results show good agreements with histology.

First-time detection of Mycobacterium bovis in livestock tissues and milk in the West Bank, Palestinian Territories.

PubMed

Ereqat, Suheir; Nasereddin, Abedelmajeed; Levine, Hagai; Azmi, Kifaya; Al-Jawabreh, Amer; Greenblatt, Charles L; Abdeen, Ziad; Bar-Gal, Gila Kahila

2013-01-01

Bovine tuberculosis, bTB, is classified by the WHO as one of the seven neglected zoonontic diseases that cause animal health problems and has high potential to infect humans. In the West Bank, bTB was not studied among animals and the prevalence of human tuberculosis caused by M. bovis is unknown. Therefore, the aim of this study was to estimate the prevalence of bTB among cattle and goats and identify the molecular characteristics of bTB in our area. A total of 208 tissue samples, representing 104 animals, and 150 raw milk samples, obtained from cows and goats were examined for the presence of mycobacteria. The tissue samples were collected during routine meat inspection from the Jericho abattoir. DNA was extracted from all samples, milk and tissue biopsies (n = 358), and screened for presence of TB DNA by amplifying a 123-bp segment of the insertion sequence IS6110. Eight out of 254 animals (3.1%) were found to be TB positive based on the IS6110-PCR. Identification of M. bovis among the positive TB samples was carried out via real time PCR followed by high resolution melt curve analysis, targeting the A/G transition along the oxyR gene. Spoligotyping analysis revealed a new genotype of M. bovis that was revealed from one tissue sample. Detection of M. bovis in tissue and milk of livestock suggests that apparently healthy cattle and goats are a potential source of infection of bTB and may pose a risk to public health. Hence, appropriate measures including meat inspection at abattoirs in the region are required together with promotion of a health campaign emphasizing the importance of drinking pasteurized milk. In addition, further studies are essential at the farm level to determine the exact prevalence of bTB in goats and cattle herds in the West Bank and Israel.

First-Time Detection of Mycobacterium bovis in Livestock Tissues and Milk in the West Bank, Palestinian Territories

PubMed Central

Ereqat, Suheir; Nasereddin, Abedelmajeed; Levine, Hagai; Azmi, Kifaya; Al-Jawabreh, Amer; Greenblatt, Charles L.; Abdeen, Ziad; Bar-Gal, Gila Kahila

2013-01-01

Background Bovine tuberculosis, bTB, is classified by the WHO as one of the seven neglected zoonontic diseases that cause animal health problems and has high potential to infect humans. In the West Bank, bTB was not studied among animals and the prevalence of human tuberculosis caused by M. bovis is unknown. Therefore, the aim of this study was to estimate the prevalence of bTB among cattle and goats and identify the molecular characteristics of bTB in our area. Methodology/principal findings A total of 208 tissue samples, representing 104 animals, and 150 raw milk samples, obtained from cows and goats were examined for the presence of mycobacteria. The tissue samples were collected during routine meat inspection from the Jericho abattoir. DNA was extracted from all samples, milk and tissue biopsies (n = 358), and screened for presence of TB DNA by amplifying a 123-bp segment of the insertion sequence IS6110. Eight out of 254 animals (3.1%) were found to be TB positive based on the IS6110-PCR. Identification of M. bovis among the positive TB samples was carried out via real time PCR followed by high resolution melt curve analysis, targeting the A/G transition along the oxyR gene. Spoligotyping analysis revealed a new genotype of M. bovis that was revealed from one tissue sample. Significance Detection of M. bovis in tissue and milk of livestock suggests that apparently healthy cattle and goats are a potential source of infection of bTB and may pose a risk to public health. Hence, appropriate measures including meat inspection at abattoirs in the region are required together with promotion of a health campaign emphasizing the importance of drinking pasteurized milk. In addition, further studies are essential at the farm level to determine the exact prevalence of bTB in goats and cattle herds in the West Bank and Israel. PMID:24069475

Deep learning for tissue microarray image-based outcome prediction in patients with colorectal cancer

NASA Astrophysics Data System (ADS)

Bychkov, Dmitrii; Turkki, Riku; Haglund, Caj; Linder, Nina; Lundin, Johan

2016-03-01

Recent advances in computer vision enable increasingly accurate automated pattern classification. In the current study we evaluate whether a convolutional neural network (CNN) can be trained to predict disease outcome in patients with colorectal cancer based on images of tumor tissue microarray samples. We compare the prognostic accuracy of CNN features extracted from the whole, unsegmented tissue microarray spot image, with that of CNN features extracted from the epithelial and non-epithelial compartments, respectively. The prognostic accuracy of visually assessed histologic grade is used as a reference. The image data set consists of digitized hematoxylin-eosin (H and E) stained tissue microarray samples obtained from 180 patients with colorectal cancer. The patient samples represent a variety of histological grades, have data available on a series of clinicopathological variables including long-term outcome and ground truth annotations performed by experts. The CNN features extracted from images of the epithelial tissue compartment significantly predicted outcome (hazard ratio (HR) 2.08; CI95% 1.04-4.16; area under the curve (AUC) 0.66) in a test set of 60 patients, as compared to the CNN features extracted from unsegmented images (HR 1.67; CI95% 0.84-3.31, AUC 0.57) and visually assessed histologic grade (HR 1.96; CI95% 0.99-3.88, AUC 0.61). As a conclusion, a deep-learning classifier can be trained to predict outcome of colorectal cancer based on images of H and E stained tissue microarray samples and the CNN features extracted from the epithelial compartment only resulted in a prognostic discrimination comparable to that of visually determined histologic grade.

Evaluation of a New Ensemble Learning Framework for Mass Classification in Mammograms.

PubMed

Rahmani Seryasat, Omid; Haddadnia, Javad

2018-06-01

Mammography is the most common screening method for diagnosis of breast cancer. In this study, a computer-aided system for diagnosis of benignity and malignity of the masses was implemented in mammogram images. In the computer aided diagnosis system, we first reduce the noise in the mammograms using an effective noise removal technique. After the noise removal, the mass in the region of interest must be segmented and this segmentation is done using a deformable model. After the mass segmentation, a number of features are extracted from it. These features include: features of the mass shape and border, tissue properties, and the fractal dimension. After extracting a large number of features, a proper subset must be chosen from among them. In this study, we make use of a new method on the basis of a genetic algorithm for selection of a proper set of features. After determining the proper features, a classifier is trained. To classify the samples, a new architecture for combination of the classifiers is proposed. In this architecture, easy and difficult samples are identified and trained using different classifiers. Finally, the proposed mass diagnosis system was also tested on mini-Mammographic Image Analysis Society and digital database for screening mammography databases. The obtained results indicate that the proposed system can compete with the state-of-the-art methods in terms of accuracy. Copyright © 2017 Elsevier Inc. All rights reserved.

Analytical performance of the ThyroSeq v3 genomic classifier for cancer diagnosis in thyroid nodules.

PubMed

Nikiforova, Marina N; Mercurio, Stephanie; Wald, Abigail I; Barbi de Moura, Michelle; Callenberg, Keith; Santana-Santos, Lucas; Gooding, William E; Yip, Linwah; Ferris, Robert L; Nikiforov, Yuri E

2018-04-15

Molecular tests have clinical utility for thyroid nodules with indeterminate fine-needle aspiration (FNA) cytology, although their performance requires further improvement. This study evaluated the analytical performance of the newly created ThyroSeq v3 test. ThyroSeq v3 is a DNA- and RNA-based next-generation sequencing assay that analyzes 112 genes for a variety of genetic alterations, including point mutations, insertions/deletions, gene fusions, copy number alterations, and abnormal gene expression, and it uses a genomic classifier (GC) to separate malignant lesions from benign lesions. It was validated in 238 tissue samples and 175 FNA samples with known surgical follow-up. Analytical performance studies were conducted. In the training tissue set of samples, ThyroSeq GC detected more than 100 genetic alterations, including BRAF, RAS, TERT, and DICER1 mutations, NTRK1/3, BRAF, and RET fusions, 22q loss, and gene expression alterations. GC cutoffs were established to distinguish cancer from benign nodules with 93.9% sensitivity, 89.4% specificity, and 92.1% accuracy. This correctly classified most papillary, follicular, and Hurthle cell lesions, medullary thyroid carcinomas, and parathyroid lesions. In the FNA validation set, the GC sensitivity was 98.0%, the specificity was 81.8%, and the accuracy was 90.9%. Analytical accuracy studies demonstrated a minimal required nucleic acid input of 2.5 ng, a 12% minimal acceptable tumor content, and reproducible test results under variable stress conditions. The ThyroSeq v3 GC analyzes 5 different classes of molecular alterations and provides high accuracy for detecting all common types of thyroid cancer and parathyroid lesions. The analytical sensitivity, specificity, and robustness of the test have been successfully validated and indicate its suitability for clinical use. Cancer 2018;124:1682-90. © 2018 American Cancer Society. © 2018 American Cancer Society.

A Novel Method to Detect Early Colorectal Cancer Based on Chromosome Copy Number Variation in Plasma.

PubMed

Xu, Jun-Feng; Kang, Qian; Ma, Xing-Yong; Pan, Yuan-Ming; Yang, Lang; Jin, Peng; Wang, Xin; Li, Chen-Guang; Chen, Xiao-Chen; Wu, Chao; Jiao, Shao-Zhuo; Sheng, Jian-Qiu

2018-01-01

Colonoscopy screening has been accepted broadly to evaluate the risk and incidence of colorectal cancer (CRC) during health examination in outpatients. However, the intrusiveness, complexity and discomfort of colonoscopy may limit its application and the compliance of patients. Thus, more reliable and convenient diagnostic methods are necessary for CRC screening. Genome instability, especially copy-number variation (CNV), is a hallmark of cancer and has been proved to have potential in clinical application. We determined the diagnostic potential of chromosomal CNV at the arm level by whole-genome sequencing of CRC plasma samples (n = 32) and healthy controls (n = 38). Arm level CNV was determined and the consistence of arm-level CNV between plasma and tissue was further analyzed. Two methods including regular z score and trained Support Vector Machine (SVM) classifier were applied for detection of colorectal cancer. In plasma samples of CRC patients, the most frequent deletions were detected on chromosomes 6, 8p, 14q and 1p, and the most frequent amplifications occurred on chromosome 19, 5, 2, 9p and 20p. These arm-level alterations detected in plasma were also observed in tumor tissues. We showed that the specificity of regular z score analysis for the detection of colorectal cancer was 86.8% (33/38), whereas its sensitivity was only 56.3% (18/32). Applying a trained SVM classifier (n = 40 in trained group) as the standard to detect colorectal cancer relevance ratio in the test samples (n = 30), a sensitivity of 91.7% (11/12) and a specificity 88.9% (16/18) were finally reached. Furthermore, all five early CRC patients in stages I and II were successfully detected. Trained SVM classifier based on arm-level CNVs can be used as a promising method to screen early-stage CRC. © 2018 The Author(s). Published by S. Karger AG, Basel.

Biological classification with RNA-Seq data: Can alternatively spliced transcript expression enhance machine learning classifier?

PubMed

Johnson, Nathan T; Dhroso, Andi; Hughes, Katelyn J; Korkin, Dmitry

2018-06-25

The extent to which the genes are expressed in the cell can be simplistically defined as a function of one or more factors of the environment, lifestyle, and genetics. RNA sequencing (RNA-Seq) is becoming a prevalent approach to quantify gene expression, and is expected to gain better insights to a number of biological and biomedical questions, compared to the DNA microarrays. Most importantly, RNA-Seq allows to quantify expression at the gene and alternative splicing isoform levels. However, leveraging the RNA-Seq data requires development of new data mining and analytics methods. Supervised machine learning methods are commonly used approaches for biological data analysis, and have recently gained attention for their applications to the RNA-Seq data. In this work, we assess the utility of supervised learning methods trained on RNA-Seq data for a diverse range of biological classification tasks. We hypothesize that the isoform-level expression data is more informative for biological classification tasks than the gene-level expression data. Our large-scale assessment is done through utilizing multiple datasets, organisms, lab groups, and RNA-Seq analysis pipelines. Overall, we performed and assessed 61 biological classification problems that leverage three independent RNA-Seq datasets and include over 2,000 samples that come from multiple organisms, lab groups, and RNA-Seq analyses. These 61 problems include predictions of the tissue type, sex, or age of the sample, healthy or cancerous phenotypes and, the pathological tumor stage for the samples from the cancerous tissue. For each classification problem, the performance of three normalization techniques and six machine learning classifiers was explored. We find that for every single classification problem, the isoform-based classifiers outperform or are comparable with gene expression based methods. The top-performing supervised learning techniques reached a near perfect classification accuracy, demonstrating the utility of supervised learning for RNA-Seq based data analysis. Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Secretory Leukocyte Protease Inhibitor Expression and High-Risk HPV Infection in Anal Lesions of HIV-Positive Patients.

PubMed

Nicol, Alcina F; Brunette, Laurie L; Nuovo, Gerard J; Grinsztejn, Beatriz; Friedman, Ruth K; Veloso, Valdiléa G; Cunha, Cynthia B; Coutinho, José R; Vianna-Andrade, Cecilia; Oliveira, Nathalia S; Woodham, Andrew W; DA Silva, Diane M; Kast, W Martin

2016-09-01

The aim of this study was to evaluate secretory leukocyte protease inhibitor (SLPI) expression in anal biopsies from HIV-positive (HIV+) individuals, and compare that to anal intraepithelial neoplasia (AIN) diagnoses and human papillomavirus (HPV) status. This is a cross-sectional study of a cohort of 54 HIV+ (31 males and 23 females) from an AIDS clinic in Rio de Janeiro, Brazil. The study material consisted of anorectal tissue biopsies obtained from HIV+ subjects, which were used to construct tissue microarray paraffin blocks for immunohistochemical analysis of SLPI expression. Biopsies were evaluated by an expert pathologist and classified as low-grade AIN1, high-grade AIN2/3, or normal squamous epithelium. In addition, DNA from the biopsies was extracted and analyzed for the presence of low- or high-risk HPV DNA. Histologically, normal squamous epithelium from the anorectal region showed strong positive SLPI staining in 17/20 (85%) samples. In comparison, 9/17 (53%) dysplastic squamous epithelial samples from AIN1 patients showed strong SLPI staining, and only 5/17 (29%) samples from AIN2/3 patients exhibited strong SPLI staining, which both were significantly fewer than those from normal tissue (P = 0.005). Furthermore, there was a significantly higher proportion of samples in which oncogenic high-risk HPV genotypes were detected in low SLPI-expressing tissues than that in tissues with high SLPI expression (P = 0.040). Taken together these results suggest that low SLPI expression is associated with high-risk HPV infections in the development of AIN.

Studies on the Presence of Mycotoxins in Biological Samples: An Overview

PubMed Central

Escrivá, Laura; Font, Guillermina; Manyes, Lara

2017-01-01

Mycotoxins are fungal secondary metabolites with bioaccumulation levels leading to their carry-over into animal fluids, organs, and tissues. As a consequence, mycotoxin determination in biological samples from humans and animals has been reported worldwide. Since most mycotoxins show toxic effects at low concentrations and considering the extremely low levels present in biological samples, the application of reliable detection methods is required. This review summarizes the information regarding the studies involving mycotoxin determination in biological samples over the last 10 years. Relevant data on extraction methodology, detection techniques, sample size, limits of detection, and quantitation are presented herein. Briefly, liquid-liquid extraction followed by LC-MS/MS determination was the most common technique. The most analyzed mycotoxin was ochratoxin A, followed by zearalenone and deoxynivalenol—including their metabolites, enniatins, fumonisins, aflatoxins, T-2 and HT-2 toxins. Moreover, the studies were classified by their purpose, mainly focused on the development of analytical methodologies, mycotoxin biomonitoring, and exposure assessment. The study of tissue distribution, bioaccumulation, carry-over, persistence and transference of mycotoxins, as well as, toxicokinetics and ADME (absorption, distribution, metabolism and excretion) were other proposed goals for biological sample analysis. Finally, an overview of risk assessment was discussed. PMID:28820481

Sensitivity and specificity of univariate MRI analysis of experimentally degraded cartilage

PubMed Central

Lin, Ping-Chang; Reiter, David A.; Spencer, Richard G.

2010-01-01

MRI is increasingly used to evaluate cartilage in tissue constructs, explants, and animal and patient studies. However, while mean values of MR parameters, including T1, T2, magnetization transfer rate km, apparent diffusion coefficient ADC, and the dGEMRIC-derived fixed charge density, correlate with tissue status, the ability to classify tissue according to these parameters has not been explored. Therefore, the sensitivity and specificity with which each of these parameters was able to distinguish between normal and trypsin- degraded, and between normal and collagenase-degraded, cartilage explants were determined. Initial analysis was performed using a training set to determine simple group means to which parameters obtained from a validation set were compared. T1 and ADC showed the greatest ability to discriminate between normal and degraded cartilage. Further analysis with k-means clustering, which eliminates the need for a priori identification of sample status, generally performed comparably. Use of fuzzy c-means (FCM) clustering to define centroids likewise did not result in improvement in discrimination. Finally, a FCM clustering approach in which validation samples were assigned in a probabilistic fashion to control and degraded groups was implemented, reflecting the range of tissue characteristics seen with cartilage degradation. PMID:19705467

Histology of periapical lesions obtained during apical surgery.

PubMed

Schulz, Malte; von Arx, Thomas; Altermatt, Hans Jörg; Bosshardt, Dieter

2009-05-01

The aim of this was to evaluate the histology of periapical lesions in teeth treated with periapical surgery. After root-end resection, the root tip was removed together with the periapical pathological tissue. Histologic sectioning was performed on calcified specimens embedded in methylmethacrylate (MMA) and on demineralized specimens embedded in LR White (Fluka, Buchs, Switzerland). The samples were evaluated with light and transmission electron microscopy (TEM). The histologic findings were classified into periapical abscesses, granulomas, or cystic lesions (true or pocket cysts). The final material comprised 70% granulomas, 23% cysts and 5% abscesses, 1% scar tissues, and 1% keratocysts. Six of 125 samples could not be used. The cystic lesions could not be subdivided into pocket or true cysts. All cysts had an epithelium-lined cavity, two of them with cilia-lined epithelium. These results show the high incidence of periapical granulomas among periapical lesions obtained during apical surgery. Periapical abscesses were a rare occasion. The histologic findings from samples obtained during apical surgery may differ from findings obtained by teeth extractions. A determination between pocket and true apical cysts is hardly possible when collecting samples by apical surgery.

Expression of plakophilin 3 in diffuse malignant pleural mesothelioma.

PubMed

Mašić, Silvija; Brčić, Luka; Krušlin, Božo; Šepac, Ana; Pigac, Biserka; Stančić-Rokotov, Dinko; Jakopović, Marko; Seiwerth, Sven

2018-05-03

Diffuse malignant pleural mesothelioma (DMPM) is the most common primary malignant pleural neoplasm still posing major diagnostic, prognostic and therapeutic challenges. Plakophilins are structural proteins considered to be important for cell stability and adhesion in both tumor and normal tissues. Plakophilin 3 is a protein present in desmosomes of stratified and simple epithelia of normal tissues with presence in malignant cells of various tumors where it participates in the process of tumorigenesis. The aim of this study was to investigate the expression of plakophilin 3 protein in DMPM, but also to study its prognostic significance and relation to histologically accessible parameters of aggressive growth. Archival samples of tissue with established diagnosis of DMPM and samples of normal pleural tissue were used. Tumor samples were classified into three histological types of DMPM (epithelioid, sarcomatoid and biphasic). Additional subclassification of epithelioid mesotheliomas into nine patterns based on the prevalent histological component of the tumor was then performed. After immunohistochemical staining, cytoplasmic and membrane immunopositivity of tumor cells was assesed by scoring the intensity of the staining from 0 (no staining) to 4 (very strong staining). Prognostic value and expression of plakophilin 3 with consideration to histologically estimated aggression in tumor growth were then statistically analyzed using non- parametric tests. The results demonstrated higher level of plakophilin 3 expression in tumor samples with histologically more aggressive tumor growth, but no significant prognostic value. According to our study, plakophilin 3 appears to be involved in tumor invasion in malignant mesothelioma.

Clinical use of fungal PCR from deep tissue samples in the diagnosis of invasive fungal diseases: a retrospective observational study.

PubMed

Ala-Houhala, M; Koukila-Kähkölä, P; Antikainen, J; Valve, J; Kirveskari, J; Anttila, V-J

2018-03-01

To assess the clinical use of panfungal PCR for diagnosis of invasive fungal diseases (IFDs). We focused on the deep tissue samples. We first described the design of panfungal PCR, which is in clinical use at Helsinki University Hospital. Next we retrospectively evaluated the results of 307 fungal PCR tests performed from 2013 to 2015. Samples were taken from normally sterile tissues and fluids. The patient population was nonselected. We classified the likelihood of IFD according to the criteria of the European Organization for Research and Treatment of Cancer/Invasive Fungal Infections Cooperative Group and the National Institute of Allergy and Infectious Diseases Mycoses Study Group (EORTC/MSG), comparing the fungal PCR results to the likelihood of IFD along with culture and microscopy results. There were 48 positive (16%) and 259 negative (84%) PCR results. The sensitivity and specificity of PCR for diagnosing IFDs were 60.5% and 91.7%, respectively, while the negative predictive value and positive predictive value were 93.4% and 54.2%, respectively. The concordance between the PCR and the culture results was 86% and 87% between PCR and microscopy, respectively. Of the 48 patients with positive PCR results, 23 had a proven or probable IFD. Fungal PCR can be useful for diagnosing IFDs in deep tissue samples. It is beneficial to combine fungal PCR with culture and microscopy. Copyright © 2017 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

Spectral imaging perspective on cytomics.

PubMed

Levenson, Richard M

2006-07-01

Cytomics involves the analysis of cellular morphology and molecular phenotypes, with reference to tissue architecture and to additional metadata. To this end, a variety of imaging and nonimaging technologies need to be integrated. Spectral imaging is proposed as a tool that can simplify and enrich the extraction of morphological and molecular information. Simple-to-use instrumentation is available that mounts on standard microscopes and can generate spectral image datasets with excellent spatial and spectral resolution; these can be exploited by sophisticated analysis tools. This report focuses on brightfield microscopy-based approaches. Cytological and histological samples were stained using nonspecific standard stains (Giemsa; hematoxylin and eosin (H&E)) or immunohistochemical (IHC) techniques employing three chromogens plus a hematoxylin counterstain. The samples were imaged using the Nuance system, a commercially available, liquid-crystal tunable-filter-based multispectral imaging platform. The resulting data sets were analyzed using spectral unmixing algorithms and/or learn-by-example classification tools. Spectral unmixing of Giemsa-stained guinea-pig blood films readily classified the major blood elements. Machine-learning classifiers were also successful at the same task, as well in distinguishing normal from malignant regions in a colon-cancer example, and in delineating regions of inflammation in an H&E-stained kidney sample. In an example of a multiplexed ICH sample, brown, red, and blue chromogens were isolated into separate images without crosstalk or interference from the (also blue) hematoxylin counterstain. Cytomics requires both accurate architectural segmentation as well as multiplexed molecular imaging to associate molecular phenotypes with relevant cellular and tissue compartments. Multispectral imaging can assist in both these tasks, and conveys new utility to brightfield-based microscopy approaches. Copyright 2006 International Society for Analytical Cytology.

A naive Bayes algorithm for tissue origin diagnosis (TOD-Bayes) of synchronous multifocal tumors in the hepatobiliary and pancreatic system.

PubMed

Jiang, Weiqin; Shen, Yifei; Ding, Yongfeng; Ye, Chuyu; Zheng, Yi; Zhao, Peng; Liu, Lulu; Tong, Zhou; Zhou, Linfu; Sun, Shuo; Zhang, Xingchen; Teng, Lisong; Timko, Michael P; Fan, Longjiang; Fang, Weijia

2018-01-15

Synchronous multifocal tumors are common in the hepatobiliary and pancreatic system but because of similarities in their histological features, oncologists have difficulty in identifying their precise tissue clonal origin through routine histopathological methods. To address this problem and assist in more precise diagnosis, we developed a computational approach for tissue origin diagnosis based on naive Bayes algorithm (TOD-Bayes) using ubiquitous RNA-Seq data. Massive tissue-specific RNA-Seq data sets were first obtained from The Cancer Genome Atlas (TCGA) and ∼1,000 feature genes were used to train and validate the TOD-Bayes algorithm. The accuracy of the model was >95% based on tenfold cross validation by the data from TCGA. A total of 18 clinical cancer samples (including six negative controls) with definitive tissue origin were subsequently used for external validation and 17 of the 18 samples were classified correctly in our study (94.4%). Furthermore, we included as cases studies seven tumor samples, taken from two individuals who suffered from synchronous multifocal tumors across tissues, where the efforts to make a definitive primary cancer diagnosis by traditional diagnostic methods had failed. Using our TOD-Bayes analysis, the two clinical test cases were successfully diagnosed as pancreatic cancer (PC) and cholangiocarcinoma (CC), respectively, in agreement with their clinical outcomes. Based on our findings, we believe that the TOD-Bayes algorithm is a powerful novel methodology to accurately identify the tissue origin of synchronous multifocal tumors of unknown primary cancers using RNA-Seq data and an important step toward more precision-based medicine in cancer diagnosis and treatment. © 2017 UICC.

Gastric Cancer-Specific Protein Profile Identified Using Endoscopic Biopsy Samples via MALDI Mass Spectrometry

PubMed Central

Kim, Hark Kyun; Reyzer, Michelle L.; Choi, Il Ju; Kim, Chan Gyoo; Kim, Hee Sung; Oshima, Akira; Chertov, Oleg; Colantonio, Simona; Fisher, Robert J.; Allen, Jamie L.; Caprioli, Richard M.; Green, Jeffrey E.

2012-01-01

To date, proteomic analyses on gastrointestinal cancer tissue samples have been performed using surgical specimens only, which are obtained after a diagnosis is made. To determine if a proteomic signature obtained from endoscopic biopsy samples could be found to assist with diagnosis, frozen endoscopic biopsy samples collected from 63 gastric cancer patients and 43 healthy volunteers were analyzed using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry. A statistical classification model was developed to distinguish tumor from normal tissues using half the samples and validated with the other half. A protein profile was discovered consisting of 73 signals that could classify 32 cancer and 22 normal samples in the validation set with high predictive values (positive and negative predictive values for cancer, 96.8% and 91.3%; sensitivity, 93.8%; specificity, 95.5%). Signals overexpressed in tumors were identified as α-defensin-1, α-defensin-2, calgranulin A, and calgranulin B. A protein profile was also found to distinguish pathologic stage Ia (pT1N0M0) samples (n = 10) from more advanced stage (Ib or higher) tumors (n = 48). Thus, protein profiles obtained from endoscopic biopsy samples may be useful in assisting with the diagnosis of gastric cancer and, possibly, in identifying early stage disease. PMID:20557134

Quantitative profiling of CpG island methylation in human stool for colorectal cancer detection.

PubMed

Elliott, Giles O; Johnson, Ian T; Scarll, Jane; Dainty, Jack; Williams, Elizabeth A; Garg, D; Coupe, Amanda; Bradburn, David M; Mathers, John C; Belshaw, Nigel J

2013-01-01

The aims of this study were to investigate the use of quantitative CGI methylation data from stool DNA to classify colon cancer patients and to relate stool CGI methylation levels to those found in corresponding tissue samples. We applied a quantitative methylation-specific PCR assay to determine CGI methylation levels of six genes, previously shown to be aberrantly methylated during colorectal carcinogenesis. Assays were performed on DNA from biopsies of "normal" mucosa and stool samples from 57 patients classified as disease-free, adenoma, or cancer by endoscopy, and in tumour tissue from cancer patients. Additionally, CGI methylation was analysed in stool DNA from an asymptomatic population of individuals covering a broad age range (mean = 47 ± 24 years) CGI methylation levels in stool DNA were significantly higher than in DNA from macroscopically normal mucosa, and a significant correlation between stool and mucosa was observed for ESR1 only. Multivariate statistical analyses using the methylation levels of each CGI in stool DNA as a continuous variable revealed a highly significant (p = 0.003) classification of cancer vs. non-cancer (adenoma + disease-free) patients (sensitivity = 65 %, specificity = 81 %). CGI methylation profiling of stool DNA successfully identified patients with cancer despite the methylation status of CGIs in stool DNA not generally reflecting those in DNA from the colonic mucosa.

Segmentation of HER2 protein overexpression in immunohistochemically stained breast cancer images using Support Vector Machines

NASA Astrophysics Data System (ADS)

Pezoa, Raquel; Salinas, Luis; Torres, Claudio; Härtel, Steffen; Maureira-Fredes, Cristián; Arce, Paola

2016-10-01

Breast cancer is one of the most common cancers in women worldwide. Patient therapy is widely supported by analysis of immunohistochemically (IHC) stained tissue sections. In particular, the analysis of HER2 overexpression by immunohistochemistry helps to determine when patients are suitable to HER2-targeted treatment. Computational HER2 overexpression analysis is still an open problem and a challenging task principally because of the variability of immunohistochemistry tissue samples and the subjectivity of the specialists to assess the samples. In addition, the immunohistochemistry process can produce diverse artifacts that difficult the HER2 overexpression assessment. In this paper we study the segmentation of HER2 overexpression in IHC stained breast cancer tissue images using a support vector machine (SVM) classifier. We asses the SVM performance using diverse color and texture pixel-level features including the RGB, CMYK, HSV, CIE L*a*b* color spaces, color deconvolution filter and Haralick features. We measure classification performance for three datasets containing a total of 153 IHC images that were previously labeled by a pathologist.

Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling.

PubMed

Yuan, Yinyin; Failmezger, Henrik; Rueda, Oscar M; Ali, H Raza; Gräf, Stefan; Chin, Suet-Feung; Schwarz, Roland F; Curtis, Christina; Dunning, Mark J; Bardwell, Helen; Johnson, Nicola; Doyle, Sarah; Turashvili, Gulisa; Provenzano, Elena; Aparicio, Sam; Caldas, Carlos; Markowetz, Florian

2012-10-24

Solid tumors are heterogeneous tissues composed of a mixture of cancer and normal cells, which complicates the interpretation of their molecular profiles. Furthermore, tissue architecture is generally not reflected in molecular assays, rendering this rich information underused. To address these challenges, we developed a computational approach based on standard hematoxylin and eosin-stained tissue sections and demonstrated its power in a discovery and validation cohort of 323 and 241 breast tumors, respectively. To deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy number profiles between samples. We next devised a predictor for survival in estrogen receptor-negative breast cancer that integrated both image-based and gene expression analyses and significantly outperformed classifiers that use single data types, such as microarray expression signatures. Image processing also allowed us to describe and validate an independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative, image-based method could benefit any large-scale cancer study by refining and complementing molecular assays of tumor samples.

Nonnegative constraint analysis of key fluorophores within human breast cancer using native fluorescence spectroscopy excited by selective wavelength of 300 nm

NASA Astrophysics Data System (ADS)

Pu, Yang; Sordillo, Laura A.; Alfano, Robert R.

2015-03-01

Native fluorescence spectroscopy offers an important role in cancer discrimination. It is widely acknowledged that the emission spectrum of tissue is a superposition of spectra of various salient fluorophores. In this study, the native fluorescence spectra of human cancerous and normal breast tissues excited by selected wavelength of 300 nm are used to investigate the key building block fluorophores: tryptophan and reduced nicotinamide adenine dinucleotide (NADH). The basis spectra of these key fluorophores' contribution to the tissue emission spectra are obtained by nonnegative constraint analysis. The emission spectra of human cancerous and normal tissue samples are projected onto the fluorophore spectral subspace. Since previous studies indicate that tryptophan and NADH are key fluorophores related with tumor evolution, it is essential to obtain their information from tissue fluorescence but discard the redundancy. To evaluate the efficacy of for cancer detection, linear discriminant analysis (LDA) classifier is used to evaluate the sensitivity, and specificity. This research demonstrates that the native fluorescence spectroscopy measurements are effective to detect changes of fluorophores' compositions in tissues due to the development of cancer.

Prediction of treatment outcome in soft tissue sarcoma based on radiologically defined habitats

NASA Astrophysics Data System (ADS)

Farhidzadeh, Hamidreza; Chaudhury, Baishali; Zhou, Mu; Goldgof, Dmitry B.; Hall, Lawrence O.; Gatenby, Robert A.; Gillies, Robert J.; Raghavan, Meera

2015-03-01

Soft tissue sarcomas are malignant tumors which develop from tissues like fat, muscle, nerves, fibrous tissue or blood vessels. They are challenging to physicians because of their relative infrequency and diverse outcomes, which have hindered development of new therapeutic agents. Additionally, assessing imaging response of these tumors to therapy is also difficult because of their heterogeneous appearance on magnetic resonance imaging (MRI). In this paper, we assessed standard of care MRI sequences performed before and after treatment using 36 patients with soft tissue sarcoma. Tumor tissue was identified by manually drawing a mask on contrast enhanced images. The Otsu segmentation method was applied to segment tumor tissue into low and high signal intensity regions on both T1 post-contrast and T2 without contrast images. This resulted in four distinctive subregions or "habitats." The features used to predict metastatic tumors and necrosis included the ratio of habitat size to whole tumor size and components of 2D intensity histograms. Individual cases were correctly classified as metastatic or non-metastatic disease with 80.55% accuracy and for necrosis ≥ 90 or necrosis <90 with 75.75% accuracy by using meta-classifiers which contained feature selectors and classifiers.

Combinational pixel-by-pixel and object-level classifying, segmenting, and agglomerating in performing quantitative image analysis that distinguishes between healthy non-cancerous and cancerous cell nuclei and delineates nuclear, cytoplasm, and stromal material objects from stained biological tissue materials

DOEpatents

Boucheron, Laura E

2013-07-16

Quantitative object and spatial arrangement-level analysis of tissue are detailed using expert (pathologist) input to guide the classification process. A two-step method is disclosed for imaging tissue, by classifying one or more biological materials, e.g. nuclei, cytoplasm, and stroma, in the tissue into one or more identified classes on a pixel-by-pixel basis, and segmenting the identified classes to agglomerate one or more sets of identified pixels into segmented regions. Typically, the one or more biological materials comprises nuclear material, cytoplasm material, and stromal material. The method further allows a user to markup the image subsequent to the classification to re-classify said materials. The markup is performed via a graphic user interface to edit designated regions in the image.

Differential expression patterns of housekeeping genes increase diagnostic and prognostic value in lung cancer

PubMed Central

Chang, Yu-Chun; Ding, Yan; Dong, Lingsheng; Zhu, Lang-Jing; Jensen, Roderick V.

2018-01-01

Background Using DNA microarrays, we previously identified 451 genes expressed in 19 different human tissues. Although ubiquitously expressed, the variable expression patterns of these “housekeeping genes” (HKGs) could separate one normal human tissue type from another. Current focus on identifying “specific disease markers” is problematic as single gene expression in a given sample represents the specific cellular states of the sample at the time of collection. In this study, we examine the diagnostic and prognostic potential of the variable expressions of HKGs in lung cancers. Methods Microarray and RNA-seq data for normal lungs, lung adenocarcinomas (AD), squamous cell carcinomas of the lung (SQCLC), and small cell carcinomas of the lung (SCLC) were collected from online databases. Using 374 of 451 HKGs, differentially expressed genes between pairs of sample types were determined via two-sided, homoscedastic t-test. Principal component analysis and hierarchical clustering classified normal lung and lung cancers subtypes according to relative gene expression variations. We used uni- and multi-variate cox-regressions to identify significant predictors of overall survival in AD patients. Classifying genes were selected using a set of training samples and then validated using an independent test set. Gene Ontology was examined by PANTHER. Results This study showed that the differential expression patterns of 242, 245, and 99 HKGs were able to distinguish normal lung from AD, SCLC, and SQCLC, respectively. From these, 70 HKGs were common across the three lung cancer subtypes. These HKGs have low expression variation compared to current lung cancer markers (e.g., EGFR, KRAS) and were involved in the most common biological processes (e.g., metabolism, stress response). In addition, the expression pattern of 106 HKGs alone was a significant classifier of AD versus SQCLC. We further highlighted that a panel of 13 HKGs was an independent predictor of overall survival and cumulative risk in AD patients. Discussion Here we report HKG expression patterns may be an effective tool for evaluation of lung cancer states. For example, the differential expression pattern of 70 HKGs alone can separate normal lung tissue from various lung cancers while a panel of 106 HKGs was a capable class predictor of subtypes of non-small cell carcinomas. We also reported that HKGs have significantly lower variance compared to traditional cancer markers across samples, highlighting the robustness of a panel of genes over any one specific biomarker. Using RNA-seq data, we showed that the expression pattern of 13 HKGs is a significant, independent predictor of overall survival for AD patients. This reinforces the predictive power of a HKG panel across different gene expression measurement platforms. Thus, we propose the expression patterns of HKGs alone may be sufficient for the diagnosis and prognosis of individuals with lung cancer. PMID:29761043

Secretory leukocyte protease inhibitor expression and high-risk HPV infection in anal lesions of HIV positive patients

PubMed Central

NUOVO, Gerard J.; GRINSZTEJN, Beatriz; FRIEDMAN, Ruth K.; VELOSO, Valdiléa G.; CUNHA, Cynthia B.; COUTINHO, José R.; VIANNA-ANDRADE, Cecilia; OLIVEIRA, Nathalia S.; WOODHAM, Andrew W.; DA SILVA, Diane M.; KAST, W. Martin

2016-01-01

Objective The aim of the current study was to evaluate secretory leukocyte protease inhibitor (SLPI) expression in anal biopsies from HIV-positive (HIV+) individuals, and compare that to anal intraepithelial neoplasia (AIN) diagnoses and human papillomavirus (HPV) status. Design This is a cross-sectional study of a cohort of 54 HIV+ (31 males and 23 females) from an AIDS clinic in Rio de Janeiro, Brazil. Methods The study material consisted of anorectal tissue biopsies obtained from HIV+ subjects, which were used to construct tissue microarray paraffin blocks for immunohistochemical analysis of SLPI expression. Biopsies were evaluated by an expert pathologist and classified as low-grade anal intraepithelial neoplasia (AIN1), high-grade anal intraepithelial neoplasia (AIN2/3), or normal squamous epithelium. Additionally, DNA from the biopsies was extracted and analyzed for the presence of low- or high-risk HPV DNA. Results Histologically normal squamous epithelium from the anorectal region showed strong positive SLPI staining in 17/20 (85%) samples. In comparison, 9/17 (53%) dysplastic squamous epithelial samples from AIN1 patients showed strong SLPI staining, and only 5/17 (29%) samples from AIN2-3 patients exhibited strong SPLI staining, which both were significantly fewer than those from normal tissue (p=0.005). Furthermore, there was a significantly higher proportion of samples in which oncogenic high-risk HPV genotypes were detected in low SLPI expressing tissues than that in tissues with high SLPI expression (p=0.040). Conclusion Taken together these results suggest that low SLPI expression is associated with high-risk HPV infections in the development of AIN. PMID:27149102

Malignant pleural mesothelioma and mesothelial hyperplasia: A new molecular tool for the differential diagnosis.

PubMed

Bruno, Rossella; Alì, Greta; Giannini, Riccardo; Proietti, Agnese; Lucchi, Marco; Chella, Antonio; Melfi, Franca; Mussi, Alfredo; Fontanini, Gabriella

2017-01-10

Malignant pleural mesothelioma (MPM) is a rare asbestos related cancer, aggressive and unresponsive to therapies. Histological examination of pleural lesions is the gold standard of MPM diagnosis, although it is sometimes hard to discriminate the epithelioid type of MPM from benign mesothelial hyperplasia (MH).This work aims to define a new molecular tool for the differential diagnosis of MPM, using the expression profile of 117 genes deregulated in this tumour.The gene expression analysis was performed by nanoString System on tumour tissues from 36 epithelioid MPM and 17 MH patients, and on 14 mesothelial pleural samples analysed in a blind way. Data analysis included raw nanoString data normalization, unsupervised cluster analysis by Pearson correlation, non-parametric Mann Whitney U-test and molecular classification by the Uncorrelated Shrunken Centroid (USC) Algorithm.The Mann-Whitney U-test found 35 genes upregulated and 31 downregulated in MPM. The unsupervised cluster analysis revealed two clusters, one composed only of MPM and one only of MH samples, thus revealing class-specific gene profiles. The Uncorrelated Shrunken Centroid algorithm identified two classifiers, one including 22 genes and the other 40 genes, able to properly classify all the samples as benign or malignant using gene expression data; both classifiers were also able to correctly determine, in a blind analysis, the diagnostic categories of all the 14 unknown samples.In conclusion we delineated a diagnostic tool combining molecular data (gene expression) and computational analysis (USC algorithm), which can be applied in the clinical practice for the differential diagnosis of MPM.

A minimum spanning forest based classification method for dedicated breast CT images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pike, Robert; Sechopoulos, Ioannis; Fei, Baowei, E-mail: bfei@emory.edu

Purpose: To develop and test an automated algorithm to classify different types of tissue in dedicated breast CT images. Methods: Images of a single breast of five different patients were acquired with a dedicated breast CT clinical prototype. The breast CT images were processed by a multiscale bilateral filter to reduce noise while keeping edge information and were corrected to overcome cupping artifacts. As skin and glandular tissue have similar CT values on breast CT images, morphologic processing is used to identify the skin based on its position information. A support vector machine (SVM) is trained and the resulting modelmore » used to create a pixelwise classification map of fat and glandular tissue. By combining the results of the skin mask with the SVM results, the breast tissue is classified as skin, fat, and glandular tissue. This map is then used to identify markers for a minimum spanning forest that is grown to segment the image using spatial and intensity information. To evaluate the authors’ classification method, they use DICE overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on five patient images. Results: Comparison between the automatic and the manual segmentation shows that the minimum spanning forest based classification method was able to successfully classify dedicated breast CT image with average DICE ratios of 96.9%, 89.8%, and 89.5% for fat, glandular, and skin tissue, respectively. Conclusions: A 2D minimum spanning forest based classification method was proposed and evaluated for classifying the fat, skin, and glandular tissue in dedicated breast CT images. The classification method can be used for dense breast tissue quantification, radiation dose assessment, and other applications in breast imaging.« less

Deep learning classifier with optical coherence tomography images for early dental caries detection

NASA Astrophysics Data System (ADS)

Karimian, Nima; Salehi, Hassan S.; Mahdian, Mina; Alnajjar, Hisham; Tadinada, Aditya

2018-02-01

Dental caries is a microbial disease that results in localized dissolution of the mineral content of dental tissue. Despite considerable decline in the incidence of dental caries, it remains a major health problem in many societies. Early detection of incipient lesions at initial stages of demineralization can result in the implementation of non-surgical preventive approaches to reverse the demineralization process. In this paper, we present a novel approach combining deep convolutional neural networks (CNN) and optical coherence tomography (OCT) imaging modality for classification of human oral tissues to detect early dental caries. OCT images of oral tissues with various densities were input to a CNN classifier to determine variations in tissue densities resembling the demineralization process. The CNN automatically learns a hierarchy of increasingly complex features and a related classifier directly from training data sets. The initial CNN layer parameters were randomly selected. The training set is split into minibatches, with 10 OCT images per batch. Given a batch of training patches, the CNN employs two convolutional and pooling layers to extract features and then classify each patch based on the probabilities from the SoftMax classification layer (output-layer). Afterward, the CNN calculates the error between the classification result and the reference label, and then utilizes the backpropagation process to fine-tune all the layer parameters to minimize this error using batch gradient descent algorithm. We validated our proposed technique on ex-vivo OCT images of human oral tissues (enamel, cortical-bone, trabecular-bone, muscular-tissue, and fatty-tissue), which attested to effectiveness of our proposed method.

Automated adipose study for assessing cancerous human breast tissue using optical coherence tomography (Conference Presentation)

NASA Astrophysics Data System (ADS)

Gan, Yu; Yao, Xinwen; Chang, Ernest W.; Bin Amir, Syed A.; Hibshoosh, Hanina; Feldman, Sheldon; Hendon, Christine P.

2017-02-01

Breast cancer is the third leading cause of death in women in the United States. In human breast tissue, adipose cells are infiltrated or replaced by cancer cells during the development of breast tumor. Therefore, an adipose map can be an indicator of identifying cancerous region. We developed an automated classification method to generate adipose map within human breast. To facilitate the automated classification, we first mask the B-scans from OCT volumes by comparing the signal noise ratio with a threshold. Then, the image was divided into multiple blocks with a size of 30 pixels by 30 pixels. In each block, we extracted texture features such as local standard deviation, entropy, homogeneity, and coarseness. The features of each block were input to a probabilistic model, relevance vector machine (RVM), which was trained prior to the experiment, to classify tissue types. For each block within the B-scan, RVM identified the region with adipose tissue. We calculated the adipose ratio as the number of blocks identified as adipose over the total number of blocks within the B-scan. We obtained OCT images from patients (n = 19) in Columbia medical center. We automatically generated the adipose maps from 24 B-scans including normal samples (n = 16) and cancerous samples (n = 8). We found the adipose regions show an isolated pattern that in cancerous tissue while a clustered pattern in normal tissue. Moreover, the adipose ratio (52.30 ± 29.42%) in normal tissue was higher than the that in cancerous tissue (12.41 ± 10.07%).

Cupping artifact correction and automated classification for high-resolution dedicated breast CT images.

PubMed

Yang, Xiaofeng; Wu, Shengyong; Sechopoulos, Ioannis; Fei, Baowei

2012-10-01

To develop and test an automated algorithm to classify the different tissues present in dedicated breast CT images. The original CT images are first corrected to overcome cupping artifacts, and then a multiscale bilateral filter is used to reduce noise while keeping edge information on the images. As skin and glandular tissues have similar CT values on breast CT images, morphologic processing is used to identify the skin mask based on its position information. A modified fuzzy C-means (FCM) classification method is then used to classify breast tissue as fat and glandular tissue. By combining the results of the skin mask with the FCM, the breast tissue is classified as skin, fat, and glandular tissue. To evaluate the authors' classification method, the authors use Dice overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on eight patient images. The correction method was able to correct the cupping artifacts and improve the quality of the breast CT images. For glandular tissue, the overlap ratios between the authors' automatic classification and manual segmentation were 91.6% ± 2.0%. A cupping artifact correction method and an automatic classification method were applied and evaluated for high-resolution dedicated breast CT images. Breast tissue classification can provide quantitative measurements regarding breast composition, density, and tissue distribution.

Cupping artifact correction and automated classification for high-resolution dedicated breast CT images

PubMed Central

Yang, Xiaofeng; Wu, Shengyong; Sechopoulos, Ioannis; Fei, Baowei

2012-01-01

Purpose: To develop and test an automated algorithm to classify the different tissues present in dedicated breast CT images. Methods: The original CT images are first corrected to overcome cupping artifacts, and then a multiscale bilateral filter is used to reduce noise while keeping edge information on the images. As skin and glandular tissues have similar CT values on breast CT images, morphologic processing is used to identify the skin mask based on its position information. A modified fuzzy C-means (FCM) classification method is then used to classify breast tissue as fat and glandular tissue. By combining the results of the skin mask with the FCM, the breast tissue is classified as skin, fat, and glandular tissue. To evaluate the authors’ classification method, the authors use Dice overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on eight patient images. Results: The correction method was able to correct the cupping artifacts and improve the quality of the breast CT images. For glandular tissue, the overlap ratios between the authors’ automatic classification and manual segmentation were 91.6% ± 2.0%. Conclusions: A cupping artifact correction method and an automatic classification method were applied and evaluated for high-resolution dedicated breast CT images. Breast tissue classification can provide quantitative measurements regarding breast composition, density, and tissue distribution. PMID:23039675

Investigating mechanically induced phase response of the tissue by using high-speed phase-resolved optical coherence tomography (Conference Presentation)

NASA Astrophysics Data System (ADS)

Ling, Yuye; Hendon, Christine P.

2017-02-01

Phase-resolved optical coherence tomography (OCT), a functional extension of OCT, provides depth-resolved phase information with extra contrast. In cardiology, changes in the mechanical properties have been associated with tissue remodeling and disease progression. Here we present the capability of profiling structural deformation of the sample in vivo by using a highly stable swept source OCT system The system, operating at 1300 nm, has an A-line acquisition rate of 200 kHz. We measured the phase noise floor to be 6.5 pm±3.2 pm by placing a cover slip in the sample arm, while blocking the reference arm. We then conducted a vibrational frequency test by measuring the phase response from a polymer membrane stimulated by a pure tone acoustic wave from 10 kHz to 80 kHz. The measured frequency response agreed with the known stimulation frequency with an error < 0.005%. We further measured the phase response of 7 fresh swine hearts obtained from Green Village Packing Company through a mechanical stretching test, within 24 hours of sacrifice. The heart tissue was cut into a 1 mm slices and fixed on two motorized stages. We acquired 100,000 consecutive M-scans, while the sample is stretched at a constant velocity of 10 um/s. The depth-resolved phase image presents linear phase response over time at each depth, but the slope varies among tissue types. Our future work includes refining our experiment protocol to quantitatively measured the elastic modulus of the tissue in vivo and building a tissue classifier based on depth-resolved phase information.

Extraction of tamoxifen and its metabolites from formalin-fixed, paraffin-embedded tissues: an innovative quantitation method using liquid chromatography and tandem mass spectrometry.

PubMed

Ng, Ella S M; Kangarloo, S Bill; Konno, Mie; Paterson, Alexander; Magliocco, Anthony M

2014-03-01

Tamoxifen is a key therapeutic option for breast cancer treatment. Understanding its complex metabolism and pharmacokinetics is important for dose optimization. We examined the possibility of utilizing archival formalin-fixed paraffin-embedded (FFPE) tissue as an alternative sample source for quantification since well-annotated retrospective samples were always limited. Six 15 μm sections of FFPE tissues were deparaffinized with xylene and purified using solid-phase extraction. Tamoxifen and its metabolites were separated and detected by liquid chromatography-tandem mass spectrometry using multiple-reaction monitoring. This method was linear between 0.4 and 200 ng/g for 4-hydroxy-tamoxifen and endoxifen, and 4-2,000 ng/g for tamoxifen and N-desmethyl-tamoxifen. Inter- and intra-assay precisions were <9 %, and mean accuracies ranged from 81 to 106 %. Extraction recoveries were between 83 and 88 %. The validated method was applied to FFPE tissues from two groups of patients, who received 20 mg/day of tamoxifen for >6 months, and were classified into breast tumor recurrence and non-recurrence. Our preliminary data show that levels of tamoxifen metabolites were significantly lower in patients with recurrent cancer, suggesting that inter-individual variability in tamoxifen metabolism might partly account for the development of cancer recurrence. Nevertheless, other causes such as non-compliance or stopping therapy of tamoxifen could possibly lead to the concentration differences. The ability to successfully study tamoxifen metabolism in such tissue samples will rapidly increase our knowledge of how tamoxifen's action, metabolism and tissue distribution contribute to breast cancer control. However, larger population studies are required to understand the underlying mechanism of tamoxifen metabolism for optimization of its treatment.

Spectral areas and ratios classifier algorithm for pancreatic tissue classification using optical spectroscopy

NASA Astrophysics Data System (ADS)

Chandra, Malavika; Scheiman, James; Simeone, Diane; McKenna, Barbara; Purdy, Julianne; Mycek, Mary-Ann

2010-01-01

Pancreatic adenocarcinoma is one of the leading causes of cancer death, in part because of the inability of current diagnostic methods to reliably detect early-stage disease. We present the first assessment of the diagnostic accuracy of algorithms developed for pancreatic tissue classification using data from fiber optic probe-based bimodal optical spectroscopy, a real-time approach that would be compatible with minimally invasive diagnostic procedures for early cancer detection in the pancreas. A total of 96 fluorescence and 96 reflectance spectra are considered from 50 freshly excised tissue sites-including human pancreatic adenocarcinoma, chronic pancreatitis (inflammation), and normal tissues-on nine patients. Classification algorithms using linear discriminant analysis are developed to distinguish among tissues, and leave-one-out cross-validation is employed to assess the classifiers' performance. The spectral areas and ratios classifier (SpARC) algorithm employs a combination of reflectance and fluorescence data and has the best performance, with sensitivity, specificity, negative predictive value, and positive predictive value for correctly identifying adenocarcinoma being 85, 89, 92, and 80%, respectively.

Meningioma and bone hyperostosis: expression of bone stimulating factors and review of the literature.

PubMed

Di Cristofori, Andrea; Del Bene, Massimiliano; Locatelli, Marco; Boggio, Francesca; Ercoli, Giulia; Ferrero, Stefano; Del Gobbo, Alessandro

2018-05-02

Several hypothesis have been proposed in order to understand the mechanisms underlying the meningioma related hyperostosis. Objective of our study was to investigate the role of osteoprotegerin(OPG), insulin-like growth factor-1(IGF-1), endothelin-1(ET-1), Bone Morphogenetic Protein(BMP-2 and -4). A total of 149 patients (39 males and 110 females with a mean age of 62 years) that received surgery were included. Depending on the relationship with the bone, meningiomas were classified in hyperostotic, osteolytic, infiltrative and without relation with the bone. Expression of BMP-2,-4; OPG and IGF-1 was evaluated with tissue microarray analysis on the surgical sample. Our series included 132 cases of grade I meningioma, 14 cases of grade II and 3 cases of grade III (according WHO). Relying on pre-operative CT scan the cases were classified as follow: hyperostotic(n=11), osteolytic(n=11), infiltrative(n=15), without relation with the bone(n=108). Four cases were excluded from the statistical analysis. Using ROC curves analysis, we identified a 2% cut-off for IGF-1 mean value that discriminated between osteolytic and osteoblastic lesions: cases with IGF-1 mean expression less than 2% were classified as osteolytic(p=0.0046);while cases with OPG mean expression less than 10% were classified as osteolytic(p=0.048). No other significant relations were found. OPG and IGF-1 found to be associated with development of hyeprostosis. Preliminary findings suggest that hyperostosis can be caused by an overexpression of osteogenic molecules that influence the osteoblast/osteoclast activity. Based on our results, further studies on hyperostotic bony tissue in meningiomas are needed in order to better understand how meningiomas influence the bone overproduction. Copyright © 2018 Elsevier Inc. All rights reserved.

[Methanotrophs and methylobacteria are found in woody plant tissues within a winter period].

PubMed

Doronina, N V; Ivanova, E G; Suzina, N F; Trotsenko, Iu A

2004-01-01

Samples of tree seeds, buds, and needles collected within a winter period at ambient temperatures from -11 to -17 degrees C were analyzed for the presence of methylotrophic microflora. Thin sections of blue spruce needles were found to contain bacteria morphologically close to pink-pigmented methylobacteria. The methylobacteria that were isolated in pure cultures from samples of linden seeds and buds, pine and blue spruce needles, as well as of lilac, maple, and apple buds, were classified into the genera Methylobacterium and Paracoccus based on the data of morphological studies, enzyme assay, and DNA-DNA hybridization analysis. The methanotrophs that were isolated in pure cultures from samples of linden buds and blue spruce needles were identified into the genus Methylocystis based on the data of morphological studies, enzyme assay, DNA-DNA hybridization, and the phylogenetic analysis of the particulate methane monooxygenase gene pmoA sequences. The inference is made that aerobic methylotrophic bacteria are permanently associated with plants. At the beginning of the vegetative period in spring, the phyllosphere of coniferous and deciduous trees is colonized by the methylotrophic bacteria that have wintered inside plant tissues.

In vitro chronic hepatic disease characterization with a multiparametric ultrasonic approach.

PubMed

Meziri, M; Pereira, W C A; Abdelwahab, A; Degott, C; Laugier, P

2005-03-01

Although, high resolution, real-time ultrasonic (US) imaging is routinely available, image interpretation is based on grey-level and texture and quantitative evaluation is limited. Other potentially useful diagnostic information from US echoes may include modifications in tissue acoustic parameters (speed, attenuation and backscattering) resulting from disease development. Changes in acoustical parameters can be detected using time-of-flight and spectral analysis techniques. The objective of this study is to explore the potential of three parameters together (attenuation coefficient, US speed and integrated backscatter coefficient-IBC) to discriminate healthy and fibrosis subgroups in liver tissue. Echoes from 21 fresh in vitro samples of human liver and from a plane reflector were obtained using a 20-MHz central frequency transducer (6-30 MHz bandpass). The scan plane was parallel to the reflector placed beneath the liver. A 30 x 20 matrix of A-scans was obtained, with a 200-microm step. The samples were classified according to the Metavir scale in five different degrees of fibrosis. US speed, attenuation and IBC were estimated from standard methods described in the literature. Statistical tests were applied to the results of each parameter individually and indicated that it was not possible to identify all the fibrosis groups. Then a discriminant analysis was performed for the three parameters together resulting in a reasonable separation of fibrotic groups. Although the number of tissue samples is limited, this study opens the possibility of enhancing the discriminant capability of ultrasonic parameters of liver tissue disease when they are combined together.

Neural network-based brain tissue segmentation in MR images using extracted features from intraframe coding in H.264

NASA Astrophysics Data System (ADS)

Jafari, Mehdi; Kasaei, Shohreh

2012-01-01

Automatic brain tissue segmentation is a crucial task in diagnosis and treatment of medical images. This paper presents a new algorithm to segment different brain tissues, such as white matter (WM), gray matter (GM), cerebral spinal fluid (CSF), background (BKG), and tumor tissues. The proposed technique uses the modified intraframe coding yielded from H.264/(AVC), for feature extraction. Extracted features are then imposed to an artificial back propagation neural network (BPN) classifier to assign each block to its appropriate class. Since the newest coding standard, H.264/AVC, has the highest compression ratio, it decreases the dimension of extracted features and thus yields to a more accurate classifier with low computational complexity. The performance of the BPN classifier is evaluated using the classification accuracy and computational complexity terms. The results show that the proposed technique is more robust and effective with low computational complexity compared to other recent works.

Neural network-based brain tissue segmentation in MR images using extracted features from intraframe coding in H.264

NASA Astrophysics Data System (ADS)

Jafari, Mehdi; Kasaei, Shohreh

2011-12-01

Automatic brain tissue segmentation is a crucial task in diagnosis and treatment of medical images. This paper presents a new algorithm to segment different brain tissues, such as white matter (WM), gray matter (GM), cerebral spinal fluid (CSF), background (BKG), and tumor tissues. The proposed technique uses the modified intraframe coding yielded from H.264/(AVC), for feature extraction. Extracted features are then imposed to an artificial back propagation neural network (BPN) classifier to assign each block to its appropriate class. Since the newest coding standard, H.264/AVC, has the highest compression ratio, it decreases the dimension of extracted features and thus yields to a more accurate classifier with low computational complexity. The performance of the BPN classifier is evaluated using the classification accuracy and computational complexity terms. The results show that the proposed technique is more robust and effective with low computational complexity compared to other recent works.

Characterization of coronary plaque regions in intravascular ultrasound images using a hybrid ensemble classifier.

PubMed

Hwang, Yoo Na; Lee, Ju Hwan; Kim, Ga Young; Shin, Eun Seok; Kim, Sung Min

2018-01-01

The purpose of this study was to propose a hybrid ensemble classifier to characterize coronary plaque regions in intravascular ultrasound (IVUS) images. Pixels were allocated to one of four tissues (fibrous tissue (FT), fibro-fatty tissue (FFT), necrotic core (NC), and dense calcium (DC)) through processes of border segmentation, feature extraction, feature selection, and classification. Grayscale IVUS images and their corresponding virtual histology images were acquired from 11 patients with known or suspected coronary artery disease using 20 MHz catheter. A total of 102 hybrid textural features including first order statistics (FOS), gray level co-occurrence matrix (GLCM), extended gray level run-length matrix (GLRLM), Laws, local binary pattern (LBP), intensity, and discrete wavelet features (DWF) were extracted from IVUS images. To select optimal feature sets, genetic algorithm was implemented. A hybrid ensemble classifier based on histogram and texture information was then used for plaque characterization in this study. The optimal feature set was used as input of this ensemble classifier. After tissue characterization, parameters including sensitivity, specificity, and accuracy were calculated to validate the proposed approach. A ten-fold cross validation approach was used to determine the statistical significance of the proposed method. Our experimental results showed that the proposed method had reliable performance for tissue characterization in IVUS images. The hybrid ensemble classification method outperformed other existing methods by achieving characterization accuracy of 81% for FFT and 75% for NC. In addition, this study showed that Laws features (SSV and SAV) were key indicators for coronary tissue characterization. The proposed method had high clinical applicability for image-based tissue characterization. Copyright © 2017 Elsevier B.V. All rights reserved.

Comparison of classification methods for voxel-based prediction of acute ischemic stroke outcome following intra-arterial intervention

NASA Astrophysics Data System (ADS)

Winder, Anthony J.; Siemonsen, Susanne; Flottmann, Fabian; Fiehler, Jens; Forkert, Nils D.

2017-03-01

Voxel-based tissue outcome prediction in acute ischemic stroke patients is highly relevant for both clinical routine and research. Previous research has shown that features extracted from baseline multi-parametric MRI datasets have a high predictive value and can be used for the training of classifiers, which can generate tissue outcome predictions for both intravenous and conservative treatments. However, with the recent advent and popularization of intra-arterial thrombectomy treatment, novel research specifically addressing the utility of predictive classi- fiers for thrombectomy intervention is necessary for a holistic understanding of current stroke treatment options. The aim of this work was to develop three clinically viable tissue outcome prediction models using approximate nearest-neighbor, generalized linear model, and random decision forest approaches and to evaluate the accuracy of predicting tissue outcome after intra-arterial treatment. Therefore, the three machine learning models were trained, evaluated, and compared using datasets of 42 acute ischemic stroke patients treated with intra-arterial thrombectomy. Classifier training utilized eight voxel-based features extracted from baseline MRI datasets and five global features. Evaluation of classifier-based predictions was performed via comparison to the known tissue outcome, which was determined in follow-up imaging, using the Dice coefficient and leave-on-patient-out cross validation. The random decision forest prediction model led to the best tissue outcome predictions with a mean Dice coefficient of 0.37. The approximate nearest-neighbor and generalized linear model performed equally suboptimally with average Dice coefficients of 0.28 and 0.27 respectively, suggesting that both non-linearity and machine learning are desirable properties of a classifier well-suited to the intra-arterial tissue outcome prediction problem.

Objective breast tissue image classification using Quantitative Transmission ultrasound tomography

NASA Astrophysics Data System (ADS)

Malik, Bilal; Klock, John; Wiskin, James; Lenox, Mark

2016-12-01

Quantitative Transmission Ultrasound (QT) is a powerful and emerging imaging paradigm which has the potential to perform true three-dimensional image reconstruction of biological tissue. Breast imaging is an important application of QT and allows non-invasive, non-ionizing imaging of whole breasts in vivo. Here, we report the first demonstration of breast tissue image classification in QT imaging. We systematically assess the ability of the QT images’ features to differentiate between normal breast tissue types. The three QT features were used in Support Vector Machines (SVM) classifiers, and classification of breast tissue as either skin, fat, glands, ducts or connective tissue was demonstrated with an overall accuracy of greater than 90%. Finally, the classifier was validated on whole breast image volumes to provide a color-coded breast tissue volume. This study serves as a first step towards a computer-aided detection/diagnosis platform for QT.

Ensemble Semi-supervised Frame-work for Brain Magnetic Resonance Imaging Tissue Segmentation.

PubMed

Azmi, Reza; Pishgoo, Boshra; Norozi, Narges; Yeganeh, Samira

2013-04-01

Brain magnetic resonance images (MRIs) tissue segmentation is one of the most important parts of the clinical diagnostic tools. Pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. Supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to obtain. Moreover, they cannot use unlabeled data to train classifiers. On the other hand, unsupervised segmentation methods have no prior knowledge and lead to low level of performance. However, semi-supervised learning which uses a few labeled data together with a large amount of unlabeled data causes higher accuracy with less trouble. In this paper, we propose an ensemble semi-supervised frame-work for segmenting of brain magnetic resonance imaging (MRI) tissues that it has been used results of several semi-supervised classifiers simultaneously. Selecting appropriate classifiers has a significant role in the performance of this frame-work. Hence, in this paper, we present two semi-supervised algorithms expectation filtering maximization and MCo_Training that are improved versions of semi-supervised methods expectation maximization and Co_Training and increase segmentation accuracy. Afterward, we use these improved classifiers together with graph-based semi-supervised classifier as components of the ensemble frame-work. Experimental results show that performance of segmentation in this approach is higher than both supervised methods and the individual semi-supervised classifiers.

The characterization of neural tissue ablation rate and corresponding heat affected zone of a 2 micron Tm3+ doped fiber laser(Conference Presentation)

NASA Astrophysics Data System (ADS)

Marques, Andrew J.; Jivraj, Jamil; Reyes, Robnier; Ramjist, Joel; Gu, Xijia J.; Yang, Victor X. D.

2017-02-01

Tissue removal using electrocautery is standard practice in neurosurgery since tissue can be cut and cauterized simultaneously. Thermally mediated tissue ablation using lasers can potentially possess the same benefits but with increased precision. However, given the critical nature of the spine, brain, and nerves, the effects of direct photo-thermal interaction on neural tissue needs to be known, yielding not only high precision of tissue removal but also increased control of peripheral heat damage. The proposed use of lasers as a neurosurgical tool requires that a common ground is found between ablation rates and resulting peripheral heat damage. Most surgical laser systems rely on the conversion of light energy into heat resulting in both desirable and undesirable thermal damage to the targeted tissue. Classifying the distribution of thermal energy in neural tissue, and thus characterizing the extent of undesirable thermal damage, can prove to be exceptionally challenging considering its highly inhomogenous composition when compared to other tissues such as muscle and bone. Here we present the characterization of neural tissue ablation rate and heat affected zone of a 1.94 micron thulium doped fiber laser for neural tissue ablation. In-Vivo ablation of porcine cerebral cortex is performed. Ablation volumes are studied in association with laser parameters. Histological samples are taken and examined to characterize the extent of peripheral heat damage.

Challenging the Cancer Molecular Stratification Dogma: Intratumoral Heterogeneity Undermines Consensus Molecular Subtypes and Potential Diagnostic Value in Colorectal Cancer.

PubMed

Dunne, Philip D; McArt, Darragh G; Bradley, Conor A; O'Reilly, Paul G; Barrett, Helen L; Cummins, Robert; O'Grady, Tony; Arthur, Ken; Loughrey, Maurice B; Allen, Wendy L; McDade, Simon S; Waugh, David J; Hamilton, Peter W; Longley, Daniel B; Kay, Elaine W; Johnston, Patrick G; Lawler, Mark; Salto-Tellez, Manuel; Van Schaeybroeck, Sandra

2016-08-15

A number of independent gene expression profiling studies have identified transcriptional subtypes in colorectal cancer with potential diagnostic utility, culminating in publication of a colorectal cancer Consensus Molecular Subtype classification. The worst prognostic subtype has been defined by genes associated with stem-like biology. Recently, it has been shown that the majority of genes associated with this poor prognostic group are stromal derived. We investigated the potential for tumor misclassification into multiple diagnostic subgroups based on tumoral region sampled. We performed multiregion tissue RNA extraction/transcriptomic analysis using colorectal-specific arrays on invasive front, central tumor, and lymph node regions selected from tissue samples from 25 colorectal cancer patients. We identified a consensus 30-gene list, which represents the intratumoral heterogeneity within a cohort of primary colorectal cancer tumors. Using a series of online datasets, we showed that this gene list displays prognostic potential HR = 2.914 (confidence interval 0.9286-9.162) in stage II/III colorectal cancer patients, but in addition, we demonstrated that these genes are stromal derived, challenging the assumption that poor prognosis tumors with stem-like biology have undergone a widespread epithelial-mesenchymal transition. Most importantly, we showed that patients can be simultaneously classified into multiple diagnostically relevant subgroups based purely on the tumoral region analyzed. Gene expression profiles derived from the nonmalignant stromal region can influence assignment of colorectal cancer transcriptional subtypes, questioning the current molecular classification dogma and highlighting the need to consider pathology sampling region and degree of stromal infiltration when employing transcription-based classifiers to underpin clinical decision making in colorectal cancer. Clin Cancer Res; 22(16); 4095-104. ©2016 AACRSee related commentary by Morris and Kopetz, p. 3989. ©2016 American Association for Cancer Research.

[Isolation and diversity analyses of endophytic fungi from Paris polyphylla var. yunnanensis].

PubMed

Wang, Qian; Shen, Shi-Kang; Zhang, Ai-Li; Wu, Chun-Yan; Wu, Fu-Qin; Zhang, Xin-Jun; Wang, Yue-Hua

2013-11-01

The paper is aimed at studying the diversity of endophytic fungi community from Paris polyphylla var. yunnanensis, and to provide a scientific basis for the utilization value of the endophytic fungi as bioactive material resources. In the present study, endophytic fungi were isolated from roots, rhizomes and leaves of wild P. polyphylla var. yunnanensis collected from Baoshan, Heqing county and Songming city of Yunnan province, and identified and classified by morphological methods together with its ITS sequence analysis. Seven and forty-nine strains of endophytic fungi were isolated from P. polyphylla var. yunnanensis. They were identified belonging to 41 genus. In these 41 genus, 3 genus exist in root only, 12 genus only exist in rhizome and 8 genus only exist in leaf. There was difference in endophytic fungi isolated from different sample sites. Endophytic fungi diversity from rhizomes of Heqing site was the highest. Endophytic fungi similarity coefficient was low among different sites and tissues. Based on these results, it is reasonable to propose that endophytic fungi of P. polyphylla var. yannanensis from different tissue and different sample sites has a certain difference which is possibly relate to their different habitats, different structure and composition of each tissue.

Probing microstructural information of anisotropic scattering media using rotation-independent polarization parameters.

PubMed

Sun, Minghao; He, Honghui; Zeng, Nan; Du, E; Guo, Yihong; Peng, Cheng; He, Yonghong; Ma, Hui

2014-05-10

Polarization parameters contain rich information on the micro- and macro-structure of scattering media. However, many of these parameters are sensitive to the spatial orientation of anisotropic media, and may not effectively reveal the microstructural information. In this paper, we take polarization images of different textile samples at different azimuth angles. The results demonstrate that the rotation insensitive polarization parameters from rotating linear polarization imaging and Mueller matrix transformation methods can be used to distinguish the characteristic features of different textile samples. Further examinations using both experiments and Monte Carlo simulations reveal that the residue rotation dependence in these polarization parameters is due to the oblique incidence illumination. This study shows that such rotation independent parameters are potentially capable of quantitatively classifying anisotropic samples, such as textiles or biological tissues.

Genotyping and pathobiologic characterization of canine parvovirus circulating in Nanjing, China

PubMed Central

2013-01-01

Background Canine parvovirus (CPV) is an important pathogen that causes acute enteric disease in dogs. It has mutated and spread throughout the world in dog populations. We provide an update on the molecular characterization of CPV that circulated in Nanjing, a provincial capital in China between 2009 and 2012. Results Seventy rectal swab samples were collected from the dogs diagnosed with CPV infection in 8 animal hospitals of Nanjing. Sequence analysis of VP2 genes of 31 samples revealed that 29 viral strains belonged to CPV-2a subtype, while other two strains were classified into CPV-2b. To investigate the pathogenicity of the prevalent virus, we isolated CPV-2a and performed the animal experiment. Nine beagles were inoculated with 105.86 of 50% tissue culture infectious doses (TCID50) of the virus. All the experimentally infected beagles exhibited mild to moderate mucoid or watery diarrhea on day 4 post-infection (p.i.). On day 9 p.i., characteristic histopathological lesions were clearly observed in multiple organs of infected dogs, including liver, spleen, kidney, brain and all segments of the small and large intestines, while viral DNA and antigen staining could be detected in the sampled tissues. It is notable that canine parvovirus was isolated in one from two brain samples processed. Conclusion Our results indicated that CPV-2a is the predominant subtype in Nanjing of China. And this virus caused extensive lesions in a variety of tissues, including the brain. PMID:23988202

Genotyping and pathobiologic characterization of canine parvovirus circulating in Nanjing, China.

PubMed

Zhao, Yanbing; Lin, Yan; Zeng, Xujian; Lu, Chengping; Hou, Jiafa

2013-08-29

Canine parvovirus (CPV) is an important pathogen that causes acute enteric disease in dogs. It has mutated and spread throughout the world in dog populations. We provide an update on the molecular characterization of CPV that circulated in Nanjing, a provincial capital in China between 2009 and 2012. Seventy rectal swab samples were collected from the dogs diagnosed with CPV infection in 8 animal hospitals of Nanjing. Sequence analysis of VP2 genes of 31 samples revealed that 29 viral strains belonged to CPV-2a subtype, while other two strains were classified into CPV-2b. To investigate the pathogenicity of the prevalent virus, we isolated CPV-2a and performed the animal experiment. Nine beagles were inoculated with 105.86 of 50% tissue culture infectious doses (TCID50) of the virus. All the experimentally infected beagles exhibited mild to moderate mucoid or watery diarrhea on day 4 post-infection (p.i.). On day 9 p.i., characteristic histopathological lesions were clearly observed in multiple organs of infected dogs, including liver, spleen, kidney, brain and all segments of the small and large intestines, while viral DNA and antigen staining could be detected in the sampled tissues. It is notable that canine parvovirus was isolated in one from two brain samples processed. Our results indicated that CPV-2a is the predominant subtype in Nanjing of China. And this virus caused extensive lesions in a variety of tissues, including the brain.

Automated classification of multiphoton microscopy images of ovarian tissue using deep learning.

PubMed

Huttunen, Mikko J; Hassan, Abdurahman; McCloskey, Curtis W; Fasih, Sijyl; Upham, Jeremy; Vanderhyden, Barbara C; Boyd, Robert W; Murugkar, Sangeeta

2018-06-01

Histopathological image analysis of stained tissue slides is routinely used in tumor detection and classification. However, diagnosis requires a highly trained pathologist and can thus be time-consuming, labor-intensive, and potentially risk bias. Here, we demonstrate a potential complementary approach for diagnosis. We show that multiphoton microscopy images from unstained, reproductive tissues can be robustly classified using deep learning techniques. We fine-train four pretrained convolutional neural networks using over 200 murine tissue images based on combined second-harmonic generation and two-photon excitation fluorescence contrast, to classify the tissues either as healthy or associated with high-grade serous carcinoma with over 95% sensitivity and 97% specificity. Our approach shows promise for applications involving automated disease diagnosis. It could also be readily applied to other tissues, diseases, and related classification problems. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Transplacental transfer of persistent organic pollutants in La Plata dolphins (Pontoporia blainvillei; Cetartiodactyla, Pontoporiidae).

PubMed

Barbosa, Ana Paula Moreno; Méndez-Fernandez, Paula; Dias, Patrick Simões; Santos, Marcos César Oliveira; Taniguchi, Satie; Montone, Rosalinda Carmela

2018-08-01

Persistent organic pollutants (POPs) accumulate in the fat tissue of living organisms and are found in relatively high concentrations in animals at the top of the food chain, such as dolphins. The ability of these compounds to interact with the endocrine system of marine mammals constitutes a risk for the reproduction and conservation of species. The La Plata dolphin, Pontoporia blainvillei, is exclusive to the southwestern Atlantic Ocean and is classified on the IUCN red list as a vulnerable species. Blubber, liver, kidney and muscle samples from four P. blainvillei mother-fetus pairs were analyzed to evaluate the transfer of POPs to fetal tissues through the placenta. The presence of POPs in fetal tissues indicates the maternal transfer of compounds. In the pregnant females, blubber was the tissue with POP highest concentration, followed by the liver, kidneys and muscles. In the fetuses, POP accumulation mainly occurred in the blubber followed by the muscles, liver and kidneys. Polychlorinated biphenyls (PCBs) and dichlorodiphenyltrichloroethane (DDTs) were found in all tissues analyzed and had the highest concentrations among all compounds. The main PCB congeners in the fetal samples had five to seven chlorine atoms. The only polybrominated diphenyl ether (PBDE) in the fetal samples was 47 and was found only in blubber. The main DDT metabolite in the fetuses was p,p'-DDE. POP transfer via the placenta occurs in the first months of gestation and increases with fetal development, according to fetus/mother (F/M) ratio: HCB>DDT>PCB>PBDE>Mirex, which may follow the order of the octanol/water partition coefficient (K ow ) values. Copyright © 2018 Elsevier B.V. All rights reserved.

Ex vivo optical coherence tomography and laser induced fluorescence spectroscopy imaging of murine gastrointestinal tract

NASA Astrophysics Data System (ADS)

Hariri, Lida; Tumlinson, Alexandre R.; Wade, Norman; Besselsen, David; Utzinger, Urs; Gerner, Eugene; Barton, Jennifer

2005-04-01

Optical Coherence Tomography (OCT) and Laser Induced Fluorescence Spectroscopy (LIF) have separately been found to have clinical potential in identifying human gastrointestinal (GI) pathologies, yet their diagnostic capability in mouse models of human disease is unknown. We combine the two modalities to survey the GI tract of a variety of mouse strains and sample dysplasias and inflammatory bowel disease (IBD) of the small and large intestine. Segments of duodenum and lower colon 2.5 cm in length and the entire esophagus from 10 mice each of two colon cancer models (ApcMin and AOM treated A/J) and two IBD models (Il-2 and Il-10) and 5 mice each of their respective controls were excised. OCT images and LIF spectra were obtained simultaneously from each tissue sample within 1 hour of extraction. Histology was used to classify tissue regions as normal, Peyer"s patch, dysplasia, adenoma, or IBD. Features in corresponding regions of OCT images were analyzed. Spectra from each of these categories were averaged and compared via the student's t-test. Features in OCT images correlated to histology in both normal and diseased tissue samples. In the diseased samples, OCT was able to identify early stages of mild colitis and dysplasia. In the sample of IBD, the LIF spectra displayed unique peaks at 635nm and 670nm, which were attributed to increased porphyrin production in the proliferating bacteria of the disease. These peaks have the potential to act as a diagnostic for IBD. OCT and LIF appear to be useful and complementary modalities for imaging mouse models.

TOFSIMS-P: a web-based platform for analysis of large-scale TOF-SIMS data.

PubMed

Yun, So Jeong; Park, Ji-Won; Choi, Il Ju; Kang, Byeongsoo; Kim, Hark Kyun; Moon, Dae Won; Lee, Tae Geol; Hwang, Daehee

2011-12-15

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) has been a useful tool to profile secondary ions from the near surface region of specimens with its high molecular specificity and submicrometer spatial resolution. However, the TOF-SIMS analysis of even a moderately large size of samples has been hampered due to the lack of tools for automatically analyzing the huge amount of TOF-SIMS data. Here, we present a computational platform to automatically identify and align peaks, find discriminatory ions, build a classifier, and construct networks describing differential metabolic pathways. To demonstrate the utility of the platform, we analyzed 43 data sets generated from seven gastric cancer and eight normal tissues using TOF-SIMS. A total of 87 138 ions were detected from the 43 data sets by TOF-SIMS. We selected and then aligned 1286 ions. Among them, we found the 66 ions discriminating gastric cancer tissues from normal ones. Using these 66 ions, we then built a partial least square-discriminant analysis (PLS-DA) model resulting in a misclassification error rate of 0.024. Finally, network analysis of the 66 ions showed disregulation of amino acid metabolism in the gastric cancer tissues. The results show that the proposed framework was effective in analyzing TOF-SIMS data from a moderately large size of samples, resulting in discrimination of gastric cancer tissues from normal tissues and identification of biomarker candidates associated with the amino acid metabolism.

Automatic tissue characterization from ultrasound imagery

NASA Astrophysics Data System (ADS)

Kadah, Yasser M.; Farag, Aly A.; Youssef, Abou-Bakr M.; Badawi, Ahmed M.

1993-08-01

In this work, feature extraction algorithms are proposed to extract the tissue characterization parameters from liver images. Then the resulting parameter set is further processed to obtain the minimum number of parameters representing the most discriminating pattern space for classification. This preprocessing step was applied to over 120 pathology-investigated cases to obtain the learning data for designing the classifier. The extracted features are divided into independent training and test sets and are used to construct both statistical and neural classifiers. The optimal criteria for these classifiers are set to have minimum error, ease of implementation and learning, and the flexibility for future modifications. Various algorithms for implementing various classification techniques are presented and tested on the data. The best performance was obtained using a single layer tensor model functional link network. Also, the voting k-nearest neighbor classifier provided comparably good diagnostic rates.

Applications of Mass Spectrometry Imaging to Cancer.

PubMed

Arentz, G; Mittal, P; Zhang, C; Ho, Y-Y; Briggs, M; Winderbaum, L; Hoffmann, M K; Hoffmann, P

2017-01-01

Pathologists play an essential role in the diagnosis and prognosis of benign and cancerous tumors. Clinicians provide tissue samples, for example, from a biopsy, which are then processed and thin sections are placed onto glass slides, followed by staining of the tissue with visible dyes. Upon processing and microscopic examination, a pathology report is provided, which relies on the pathologist's interpretation of the phenotypical presentation of the tissue. Targeted analysis of single proteins provide further insight and together with clinical data these results influence clinical decision making. Recent developments in mass spectrometry facilitate the collection of molecular information about such tissue specimens. These relatively new techniques generate label-free mass spectra across tissue sections providing nonbiased, nontargeted molecular information. At each pixel with spatial coordinates (x/y) a mass spectrum is acquired. The acquired mass spectrums can be visualized as intensity maps displaying the distribution of single m/z values of interest. Based on the sample preparation, proteins, peptides, lipids, small molecules, or glycans can be analyzed. The generated intensity maps/images allow new insights into tumor tissues. The technique has the ability to detect and characterize tumor cells and their environment in a spatial context and combined with histological staining, can be used to aid pathologists and clinicians in the diagnosis and management of cancer. Moreover, such data may help classify patients to aid therapy decisions and predict outcomes. The novel complementary mass spectrometry-based methods described in this chapter will contribute to the transformation of pathology services around the world. © 2017 Elsevier Inc. All rights reserved.

Automated high-throughput assessment of prostate biopsy tissue using infrared spectroscopic chemical imaging

NASA Astrophysics Data System (ADS)

Bassan, Paul; Sachdeva, Ashwin; Shanks, Jonathan H.; Brown, Mick D.; Clarke, Noel W.; Gardner, Peter

2014-03-01

Fourier transform infrared (FT-IR) chemical imaging has been demonstrated as a promising technique to complement histopathological assessment of biomedical tissue samples. Current histopathology practice involves preparing thin tissue sections and staining them using hematoxylin and eosin (H&E) after which a histopathologist manually assess the tissue architecture under a visible microscope. Studies have shown that there is disagreement between operators viewing the same tissue suggesting that a complementary technique for verification could improve the robustness of the evaluation, and improve patient care. FT-IR chemical imaging allows the spatial distribution of chemistry to be rapidly imaged at a high (diffraction-limited) spatial resolution where each pixel represents an area of 5.5 × 5.5 μm2 and contains a full infrared spectrum providing a chemical fingerprint which studies have shown contains the diagnostic potential to discriminate between different cell-types, and even the benign or malignant state of prostatic epithelial cells. We report a label-free (i.e. no chemical de-waxing, or staining) method of imaging large pieces of prostate tissue (typically 1 cm × 2 cm) in tens of minutes (at a rate of 0.704 × 0.704 mm2 every 14.5 s) yielding images containing millions of spectra. Due to refractive index matching between sample and surrounding paraffin, minimal signal processing is required to recover spectra with their natural profile as opposed to harsh baseline correction methods, paving the way for future quantitative analysis of biochemical signatures. The quality of the spectral information is demonstrated by building and testing an automated cell-type classifier based upon spectral features.

Ensemble Semi-supervised Frame-work for Brain Magnetic Resonance Imaging Tissue Segmentation

PubMed Central

Azmi, Reza; Pishgoo, Boshra; Norozi, Narges; Yeganeh, Samira

2013-01-01

Brain magnetic resonance images (MRIs) tissue segmentation is one of the most important parts of the clinical diagnostic tools. Pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. Supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to obtain. Moreover, they cannot use unlabeled data to train classifiers. On the other hand, unsupervised segmentation methods have no prior knowledge and lead to low level of performance. However, semi-supervised learning which uses a few labeled data together with a large amount of unlabeled data causes higher accuracy with less trouble. In this paper, we propose an ensemble semi-supervised frame-work for segmenting of brain magnetic resonance imaging (MRI) tissues that it has been used results of several semi-supervised classifiers simultaneously. Selecting appropriate classifiers has a significant role in the performance of this frame-work. Hence, in this paper, we present two semi-supervised algorithms expectation filtering maximization and MCo_Training that are improved versions of semi-supervised methods expectation maximization and Co_Training and increase segmentation accuracy. Afterward, we use these improved classifiers together with graph-based semi-supervised classifier as components of the ensemble frame-work. Experimental results show that performance of segmentation in this approach is higher than both supervised methods and the individual semi-supervised classifiers. PMID:24098863

Optical pathology of human brain metastasis of lung cancer using combined resonance Raman and spatial frequency spectroscopies

NASA Astrophysics Data System (ADS)

Zhou, Yan; Liu, Cheng-hui; Pu, Yang; Cheng, Gangge; Zhou, Lixin; Chen, Jun; Zhu, Ke; Alfano, Robert R.

2016-03-01

Raman spectroscopy has become widely used for diagnostic purpose of breast, lung and brain cancers. This report introduced a new approach based on spatial frequency spectra analysis of the underlying tissue structure at different stages of brain tumor. Combined spatial frequency spectroscopy (SFS), Resonance Raman (RR) spectroscopic method is used to discriminate human brain metastasis of lung cancer from normal tissues for the first time. A total number of thirty-one label-free micrographic images of normal and metastatic brain cancer tissues obtained from a confocal micro- Raman spectroscopic system synchronously with examined RR spectra of the corresponding samples were collected from the identical site of tissue. The difference of the randomness of tissue structures between the micrograph images of metastatic brain tumor tissues and normal tissues can be recognized by analyzing spatial frequency. By fitting the distribution of the spatial frequency spectra of human brain tissues as a Gaussian function, the standard deviation, σ, can be obtained, which was used to generate a criterion to differentiate human brain cancerous tissues from the normal ones using Support Vector Machine (SVM) classifier. This SFS-SVM analysis on micrograph images presents good results with sensitivity (85%), specificity (75%) in comparison with gold standard reports of pathology and immunology. The dual-modal advantages of SFS combined with RR spectroscopy method may open a new way in the neuropathology applications.

Multivariate classification of the infrared spectra of cell and tissue samples

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haaland, D.M.; Jones, H.D.; Thomas, E.V.

1997-03-01

Infrared microspectroscopy of biopsied canine lymph cells and tissue was performed to investigate the possibility of using IR spectra coupled with multivariate classification methods to classify the samples as normal, hyperplastic, or neoplastic (malignant). IR spectra were obtained in transmission mode through BaF{sub 2} windows and in reflection mode from samples prepared on gold-coated microscope slides. Cytology and histopathology samples were prepared by a variety of methods to identify the optimal methods of sample preparation. Cytospinning procedures that yielded a monolayer of cells on the BaF{sub 2} windows produced a limited set of IR transmission spectra. These transmission spectra weremore » converted to absorbance and formed the basis for a classification rule that yielded 100{percent} correct classification in a cross-validated context. Classifications of normal, hyperplastic, and neoplastic cell sample spectra were achieved by using both partial least-squares (PLS) and principal component regression (PCR) classification methods. Linear discriminant analysis applied to principal components obtained from the spectral data yielded a small number of misclassifications. PLS weight loading vectors yield valuable qualitative insight into the molecular changes that are responsible for the success of the infrared classification. These successful classification results show promise for assisting pathologists in the diagnosis of cell types and offer future potential for {ital in vivo} IR detection of some types of cancer. {copyright} {ital 1997} {ital Society for Applied Spectroscopy}« less

Mixture of learners for cancer stem cell detection using CD13 and H and E stained images

NASA Astrophysics Data System (ADS)

Oǧuz, Oǧuzhan; Akbaş, Cem Emre; Mallah, Maen; Taşdemir, Kasım.; Akhan Güzelcan, Ece; Muenzenmayer, Christian; Wittenberg, Thomas; Üner, Ayşegül; Cetin, A. E.; ćetin Atalay, Rengül

2016-03-01

In this article, algorithms for cancer stem cell (CSC) detection in liver cancer tissue images are developed. Conventionally, a pathologist examines of cancer cell morphologies under microscope. Computer aided diagnosis systems (CAD) aims to help pathologists in this tedious and repetitive work. The first algorithm locates CSCs in CD13 stained liver tissue images. The method has also an online learning algorithm to improve the accuracy of detection. The second family of algorithms classify the cancer tissues stained with H and E which is clinically routine and cost effective than immunohistochemistry (IHC) procedure. The algorithms utilize 1D-SIFT and Eigen-analysis based feature sets as descriptors. Normal and cancerous tissues can be classified with 92.1% accuracy in H and E stained images. Classification accuracy of low and high-grade cancerous tissue images is 70.4%. Therefore, this study paves the way for diagnosing the cancerous tissue and grading the level of it using H and E stained microscopic tissue images.

Discrimination of soft tissues using laser-induced breakdown spectroscopy in combination with k nearest neighbors (kNN) and support vector machine (SVM) classifiers

NASA Astrophysics Data System (ADS)

Li, Xiaohui; Yang, Sibo; Fan, Rongwei; Yu, Xin; Chen, Deying

2018-06-01

In this paper, discrimination of soft tissues using laser-induced breakdown spectroscopy (LIBS) in combination with multivariate statistical methods is presented. Fresh pork fat, skin, ham, loin and tenderloin muscle tissues are manually cut into slices and ablated using a 1064 nm pulsed Nd:YAG laser. Discrimination analyses between fat, skin and muscle tissues, and further between highly similar ham, loin and tenderloin muscle tissues, are performed based on the LIBS spectra in combination with multivariate statistical methods, including principal component analysis (PCA), k nearest neighbors (kNN) classification, and support vector machine (SVM) classification. Performances of the discrimination models, including accuracy, sensitivity and specificity, are evaluated using 10-fold cross validation. The classification models are optimized to achieve best discrimination performances. The fat, skin and muscle tissues can be definitely discriminated using both kNN and SVM classifiers, with accuracy of over 99.83%, sensitivity of over 0.995 and specificity of over 0.998. The highly similar ham, loin and tenderloin muscle tissues can also be discriminated with acceptable performances. The best performances are achieved with SVM classifier using Gaussian kernel function, with accuracy of 76.84%, sensitivity of over 0.742 and specificity of over 0.869. The results show that the LIBS technique assisted with multivariate statistical methods could be a powerful tool for online discrimination of soft tissues, even for tissues of high similarity, such as muscles from different parts of the animal body. This technique could be used for discrimination of tissues suffering minor clinical changes, thus may advance the diagnosis of early lesions and abnormalities.

Normal breast tissue DNA methylation differences at regulatory elements are associated with the cancer risk factor age.

PubMed

Johnson, Kevin C; Houseman, E Andres; King, Jessica E; Christensen, Brock C

2017-07-10

The underlying biological mechanisms through which epidemiologically defined breast cancer risk factors contribute to disease risk remain poorly understood. Identification of the molecular changes associated with cancer risk factors in normal tissues may aid in determining the earliest events of carcinogenesis and informing cancer prevention strategies. Here we investigated the impact cancer risk factors have on the normal breast epigenome by analyzing DNA methylation genome-wide (Infinium 450 K array) in cancer-free women from the Susan G. Komen Tissue Bank (n = 100). We tested the relation of established breast cancer risk factors, age, body mass index, parity, and family history of disease, with DNA methylation adjusting for potential variation in cell-type proportions. We identified 787 cytosine-guanine dinucleotide (CpG) sites that demonstrated significant associations (Q value <0.01) with subject age. Notably, DNA methylation was not strongly associated with the other evaluated breast cancer risk factors. Age-related DNA methylation changes are primarily increases in methylation enriched at breast epithelial cell enhancer regions (P = 7.1E-20), and binding sites of chromatin remodelers (MYC and CTCF). We validated the age-related associations in two independent populations, using normal breast tissue samples (n = 18) and samples of normal tissue adjacent to tumor tissue (n = 97). The genomic regions classified as age-related were more likely to be regions altered in both pre-invasive (n = 40, P = 3.0E-03) and invasive breast tumors (n = 731, P = 1.1E-13). DNA methylation changes with age occur at regulatory regions, and are further exacerbated in cancer, suggesting that age influences breast cancer risk in part through its contribution to epigenetic dysregulation in normal breast tissue.

Immunohistochemical and Biochemical Expression Patterns of TTF-1, RAGE, GLUT-1 and SOX2 in HCV-Associated Hepatocellular Carcinomas

PubMed Central

Aboushousha, Tarek; Mamdouh, Samah; Hamdy, Hussam; Helal, Noha; Khorshed, Fatma; Safwat, Gehan; Seleem, Mohamed

2018-01-01

Objective: To investigate the expression of TTF-1, RAGE, GLUT1 and SOX2 in HCV-associated HCCs and in surrounding non-tumorous liver tissue. Material and Methods: Tissue material from partial hepatectomy cases for HCC along with corresponding serum samples and 30 control serum samples from healthy volunteers were studied. Biopsies were classified into: non-tumor hepatic tissue (36 sections); HCC (33 sections) and liver cell dysplasia (LCD) (15 sections). All cases were positive for HCV. Immunohistochemistry (IHC), gene extraction and quantitative real-time reverse-transcription assays (qRT-PCR) were applied. Results: By IHC, LCD and HCC showed significantly high percentages of positive cases with all markers. SOX2 showed significant increase with higher HCC grades, while RAGE demonstrated an inverse relation and GLUT-1 and TTF-1 lacked any correlation. In nontumorous-HCV tissue, we found significantly high TTF-1, low RAGE and negative SOX2 expression. RAGE, GLUT-1 and SOX2 show non-significant elevation positivity in high grade HCV compared to low grade lesions. TTF-1, RAGE and SOX2 exhibited low expression in cirrhosis compared to fibrosis. Biochemical studies on serum and tissue extracts revealed significant down-regulation of RAGE, GLUT-1 and SOX2 genes, as well as significant up-regulation of the TTF-1 gene in HCC cases compared to controls. All studied genes show significant correlation with HCC grade. In non-tumor tissue, only TTF-1 gene expression had a significant correlation with the fibrosis score. Conclusion: Higher expression of TTF-1, RAGE, GLUT-1 and SOX2 in HCC and dysplasia compared to non-tumor tissues indicates up-regulation of these markers as early events during the development of HCV-associated HCC. PMID:29373917

Immunohistochemical and Biochemical Expression Patterns of TTF-1, RAGE, GLUT-1 and SOX2 in HCV-Associated Hepatocellular Carcinomas

PubMed

Aboushousha, Tarek; Mamdouh, Samah; Hamdy, Hussam; Helal, Noha; Khorshed, Fatma; Safwat, Gehan; Seleem, Mohamed

2018-01-27

Objective: To investigate the expression of TTF-1, RAGE, GLUT1 and SOX2 in HCV-associated HCCs and in surrounding non-tumorous liver tissue. Material and Methods: Tissue material from partial hepatectomy cases for HCC along with corresponding serum samples and 30 control serum samples from healthy volunteers were studied. Biopsies were classified into: non-tumor hepatic tissue (36 sections); HCC (33 sections) and liver cell dysplasia (LCD) (15 sections). All cases were positive for HCV. Immunohistochemistry (IHC), gene extraction and quantitative real-time reverse-transcription assays (qRT-PCR) were applied. Results: By IHC, LCD and HCC showed significantly high percentages of positive cases with all markers. SOX2 showed significant increase with higher HCC grades, while RAGE demonstrated an inverse relation and GLUT-1 and TTF-1 lacked any correlation. In nontumorous-HCV tissue, we found significantly high TTF-1, low RAGE and negative SOX2 expression. RAGE, GLUT-1 and SOX2 show non-significant elevation positivity in high grade HCV compared to low grade lesions. TTF-1, RAGE and SOX2 exhibited low expression in cirrhosis compared to fibrosis. Biochemical studies on serum and tissue extracts revealed significant down-regulation of RAGE, GLUT-1 and SOX2 genes, as well as significant up-regulation of the TTF-1 gene in HCC cases compared to controls. All studied genes show significant correlation with HCC grade. In non-tumor tissue, only TTF-1 gene expression had a significant correlation with the fibrosis score. Conclusion: Higher expression of TTF-1, RAGE, GLUT-1 and SOX2 in HCC and dysplasia compared to non-tumor tissues indicates up-regulation of these markers as early events during the development of HCV-associated HCC. Creative Commons Attribution License

Experimental and constitutive modeling approaches for a study of biomechanical properties of human coronary arteries.

PubMed

Jankowska, Malgorzata A; Bartkowiak-Jowsa, Magdalena; Bedzinski, Romuald

2015-10-01

The study concerns the determination of mechanical properties of human coronary arterial walls with both experimental and constitutive modeling approaches. The research material was harvested from 18 patients (range 50-84 years). On the basis of hospital records and visual observation, each tissue sample was classified according to the stage (0, I, II, III) of atherosclerosis development (SAD). Then, strip samples considered as a membrane with the shape of rectangular parallelepiped were preconditioned and subjected to uniaxial tensile tests in longitudinal (n=27) and circumferential (n=4) direction. With experimental data obtained, the stress-strain characteristics were prepared. Furthermore, tensile strengths and related strains, stiffness coefficients and tangent modules of elasticity were computed. For a constitutive model of passive mechanical behavior of coronary arteries, values of material parameters were computed. The studies led to the following conclusions. Most importantly, the atherosclerotic changes affect all the mechanical properties of arterial walls. A progress of arteriosclerosis contributes to an increase of vascular stiffness. The highest values of the stiffness coefficients are obtained for the tissues in the advanced stage of the disease. We were also able to observe that gradual calcification, progression of atherosclerosis and degradation of collagen in the tissue caused a decrease of tensile strengths and related strains. Finally, a comparison made for the tissues with the advanced SAD showed that the tensile strengths and strains were much higher in the case of the samples with the circumferential orientation rather than those with the longitudinal one. Copyright © 2015 Elsevier Ltd. All rights reserved.

Validation of a new classifier for the automated analysis of the human epidermal growth factor receptor 2 (HER2) gene amplification in breast cancer specimens

PubMed Central

2013-01-01

Amplification of the human epidermal growth factor receptor 2 (HER2) is a prognostic marker for poor clinical outcome and a predictive marker for therapeutic response to targeted therapies in breast cancer patients. With the introduction of anti-HER2 therapies, accurate assessment of HER2 status has become essential. Fluorescence in situ hybridization (FISH) is a widely used technique for the determination of HER2 status in breast cancer. However, the manual signal enumeration is time-consuming. Therefore, several companies like MetaSystem have developed automated image analysis software. Some of these signal enumeration software employ the so called “tile-sampling classifier”, a programming algorithm through which the software quantifies fluorescent signals in images on the basis of square tiles of fixed dimensions. Considering that the size of tile does not always correspond to the size of a single tumor cell nucleus, some users argue that this analysis method might not completely reflect the biology of cells. For that reason, MetaSystems has developed a new classifier which is able to recognize nuclei within tissue sections in order to determine the HER2 amplification status on nuclei basis. We call this new programming algorithm “nuclei-sampling classifier”. In this study, we evaluated the accuracy of the “nuclei-sampling classifier” in determining HER2 gene amplification by FISH in nuclei of breast cancer cells. To this aim, we randomly selected from our cohort 64 breast cancer specimens (32 nonamplified and 32 amplified) and we compared results obtained through manual scoring and through this new classifier. The new classifier automatically recognized individual nuclei. The automated analysis was followed by an optional human correction, during which the user interacted with the software in order to improve the selection of cell nuclei automatically selected. Overall concordance between manual scoring and automated nuclei-sampling analysis was 98.4% (100% for nonamplified cases and 96.9% for amplified cases). However, after human correction, concordance between the two methods was 100%. We conclude that the nuclei-based classifier is a new available tool for automated quantitative HER2 FISH signals analysis in nuclei in breast cancer specimen and it can be used for clinical purposes. PMID:23379971

[Measurement of the status of trace elements in cattle using liver biopsy samples].

PubMed

Ouweltjes, W; de Zeeuw, A C; Moen, A; Counotte, G H M

2007-02-01

Serum, plasma, or urine samples are usually used for the measurement of the trace elements copper; zinc, iron, selenium, because these samples are easy to obtain; however; these samples are not always appropriate. For example, it is not possible to measure molybdenum, the major antagonist of copper; in blood or urine. Therefore measurement of trace elements in liver tissue is considered the gold standard. For the assessment of selenium the method of choice remains determination of glutathion peroxidase in erythrocytes and for the assessment of magnesium determination of magnesium in urine. We determined the accuracy and repeatability of measuring trace elements in liver biopsies and whole liver homogenates. The levels of trace elements measured were similar in both preparations (92% agreement). Liver biopsy in live animals is a relatively simple procedure but not common in The Netherlands. Reference levels of trace elements, classified as too low, low, adequate, high, and too high, were established on the basis of our research and information in the literature. In a second study we investigated the practical aspects of obtaining liver tissue samples and their use. Samples were collected from cattle on a commercial dairy farm. Liver biopsy provided additional information to that obtained from serum and urine samples. We prepared a biopsy protocol and a test package, which we tested on 14 farms where an imbalance of trace minerals was suspected. Biopsy samples taken from 4 to 6 animals revealed extreme levels of trace elements.

Bayes estimation on parameters of the single-class classifier. [for remotely sensed crop data

NASA Technical Reports Server (NTRS)

Lin, G. C.; Minter, T. C.

1976-01-01

Normal procedures used for designing a Bayes classifier to classify wheat as the major crop of interest require not only training samples of wheat but also those of nonwheat. Therefore, ground truth must be available for the class of interest plus all confusion classes. The single-class Bayes classifier classifies data into the class of interest or the class 'other' but requires training samples only from the class of interest. This paper will present a procedure for Bayes estimation on the mean vector, covariance matrix, and a priori probability of the single-class classifier using labeled samples from the class of interest and unlabeled samples drawn from the mixture density function.

A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images

PubMed Central

Xu, Jun; Luo, Xiaofei; Wang, Guanhao; Gilmore, Hannah; Madabhushi, Anant

2016-01-01

Epithelial (EP) and stromal (ST) are two types of tissues in histological images. Automated segmentation or classification of EP and ST tissues is important when developing computerized system for analyzing the tumor microenvironment. In this paper, a Deep Convolutional Neural Networks (DCNN) based feature learning is presented to automatically segment or classify EP and ST regions from digitized tumor tissue microarrays (TMAs). Current approaches are based on handcraft feature representation, such as color, texture, and Local Binary Patterns (LBP) in classifying two regions. Compared to handcrafted feature based approaches, which involve task dependent representation, DCNN is an end-to-end feature extractor that may be directly learned from the raw pixel intensity value of EP and ST tissues in a data driven fashion. These high-level features contribute to the construction of a supervised classifier for discriminating the two types of tissues. In this work we compare DCNN based models with three handcraft feature extraction based approaches on two different datasets which consist of 157 Hematoxylin and Eosin (H&E) stained images of breast cancer and 1376 immunohistological (IHC) stained images of colorectal cancer, respectively. The DCNN based feature learning approach was shown to have a F1 classification score of 85%, 89%, and 100%, accuracy (ACC) of 84%, 88%, and 100%, and Matthews Correlation Coefficient (MCC) of 86%, 77%, and 100% on two H&E stained (NKI and VGH) and IHC stained data, respectively. Our DNN based approach was shown to outperform three handcraft feature extraction based approaches in terms of the classification of EP and ST regions. PMID:28154470

Automated prediction of tissue outcome after acute ischemic stroke in computed tomography perfusion images

NASA Astrophysics Data System (ADS)

Vos, Pieter C.; Bennink, Edwin; de Jong, Hugo; Velthuis, Birgitta K.; Viergever, Max A.; Dankbaar, Jan Willem

2015-03-01

Assessment of the extent of cerebral damage on admission in patients with acute ischemic stroke could play an important role in treatment decision making. Computed tomography perfusion (CTP) imaging can be used to determine the extent of damage. However, clinical application is hindered by differences among vendors and used methodology. As a result, threshold based methods and visual assessment of CTP images has not yet shown to be useful in treatment decision making and predicting clinical outcome. Preliminary results in MR studies have shown the benefit of using supervised classifiers for predicting tissue outcome, but this has not been demonstrated for CTP. We present a novel method for the automatic prediction of tissue outcome by combining multi-parametric CTP images into a tissue outcome probability map. A supervised classification scheme was developed to extract absolute and relative perfusion values from processed CTP images that are summarized by a trained classifier into a likelihood of infarction. Training was performed using follow-up CT scans of 20 acute stroke patients with complete recanalization of the vessel that was occluded on admission. Infarcted regions were annotated by expert neuroradiologists. Multiple classifiers were evaluated in a leave-one-patient-out strategy for their discriminating performance using receiver operating characteristic (ROC) statistics. Results showed that a RandomForest classifier performed optimally with an area under the ROC of 0.90 for discriminating infarct tissue. The obtained results are an improvement over existing thresholding methods and are in line with results found in literature where MR perfusion was used.

A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images.

PubMed

Xu, Jun; Luo, Xiaofei; Wang, Guanhao; Gilmore, Hannah; Madabhushi, Anant

2016-05-26

Epithelial (EP) and stromal (ST) are two types of tissues in histological images. Automated segmentation or classification of EP and ST tissues is important when developing computerized system for analyzing the tumor microenvironment. In this paper, a Deep Convolutional Neural Networks (DCNN) based feature learning is presented to automatically segment or classify EP and ST regions from digitized tumor tissue microarrays (TMAs). Current approaches are based on handcraft feature representation, such as color, texture, and Local Binary Patterns (LBP) in classifying two regions. Compared to handcrafted feature based approaches, which involve task dependent representation, DCNN is an end-to-end feature extractor that may be directly learned from the raw pixel intensity value of EP and ST tissues in a data driven fashion. These high-level features contribute to the construction of a supervised classifier for discriminating the two types of tissues. In this work we compare DCNN based models with three handcraft feature extraction based approaches on two different datasets which consist of 157 Hematoxylin and Eosin (H&E) stained images of breast cancer and 1376 immunohistological (IHC) stained images of colorectal cancer, respectively. The DCNN based feature learning approach was shown to have a F1 classification score of 85%, 89%, and 100%, accuracy (ACC) of 84%, 88%, and 100%, and Matthews Correlation Coefficient (MCC) of 86%, 77%, and 100% on two H&E stained (NKI and VGH) and IHC stained data, respectively. Our DNN based approach was shown to outperform three handcraft feature extraction based approaches in terms of the classification of EP and ST regions.

Trypanosoma cruzi discrete typing units in Chagas disease patients from endemic and non-endemic regions of Argentina.

PubMed

Cura, C I; Lucero, R H; Bisio, M; Oshiro, E; Formichelli, L B; Burgos, J M; Lejona, S; Brusés, B L; Hernández, D O; Severini, G V; Velazquez, E; Duffy, T; Anchart, E; Lattes, R; Altcheh, J; Freilij, H; Diez, M; Nagel, C; Vigliano, C; Favaloro, L; Favaloro, R R; Merino, D E; Sosa-Estani, S; Schijman, A G

2012-04-01

Genetic diversity of Trypanosoma cruzi may play a role in pathogenesis of Chagas disease forms. Natural populations are classified into 6 Discrete Typing Units (DTUs) Tc I-VI with taxonomical status. This study aimed to identify T. cruzi DTUs in bloodstream and tissue samples of Argentinean patients with Chagas disease. PCR-based strategies allowed DTU identification in 256 clinical samples from 239 Argentinean patients. Tc V prevailed in blood from both asymptomatic and symptomatic cases and Tc I was more frequent in bloodstream, cardiac tissues and chagoma samples from immunosuppressed patients. Tc II and VI were identified in a minority of cases, while Tc III and Tc IV were not detected in the studied population. Interestingly, Tc I and Tc II/VI sequences were amplified from the same skin biopsy slice from a kidney transplant patient suffering Chagas disease reactivation. Further data also revealed the occurrence of mixed DTU populations in the human chronic infection. In conclusion, our findings provide evidence of the complexity of the dynamics of T. cruzi diversity in the natural history of human Chagas disease and allege the pathogenic role of DTUs I, II, V and VI in the studied population.

Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B.

PubMed

Chen, Yang; Luo, Yan; Huang, Wei; Hu, Die; Zheng, Rong-Qin; Cong, Shu-Zhen; Meng, Fan-Kun; Yang, Hong; Lin, Hong-Jun; Sun, Yan; Wang, Xiu-Yan; Wu, Tao; Ren, Jie; Pei, Shu-Fang; Zheng, Ying; He, Yun; Hu, Yu; Yang, Na; Yan, Hongmei

2017-10-01

Hepatic fibrosis is a common middle stage of the pathological processes of chronic liver diseases. Clinical intervention during the early stages of hepatic fibrosis can slow the development of liver cirrhosis and reduce the risk of developing liver cancer. Performing a liver biopsy, the gold standard for viral liver disease management, has drawbacks such as invasiveness and a relatively high sampling error rate. Real-time tissue elastography (RTE), one of the most recently developed technologies, might be promising imaging technology because it is both noninvasive and provides accurate assessments of hepatic fibrosis. However, determining the stage of liver fibrosis from RTE images in a clinic is a challenging task. In this study, in contrast to the previous liver fibrosis index (LFI) method, which predicts the stage of diagnosis using RTE images and multiple regression analysis, we employed four classical classifiers (i.e., Support Vector Machine, Naïve Bayes, Random Forest and K-Nearest Neighbor) to build a decision-support system to improve the hepatitis B stage diagnosis performance. Eleven RTE image features were obtained from 513 subjects who underwent liver biopsies in this multicenter collaborative research. The experimental results showed that the adopted classifiers significantly outperformed the LFI method and that the Random Forest(RF) classifier provided the highest average accuracy among the four machine algorithms. This result suggests that sophisticated machine-learning methods can be powerful tools for evaluating the stage of hepatic fibrosis and show promise for clinical applications. Copyright © 2017 Elsevier Ltd. All rights reserved.

Segment fusion of ToF-SIMS images.

PubMed

Milillo, Tammy M; Miller, Mary E; Fischione, Remo; Montes, Angelina; Gardella, Joseph A

2016-06-08

The imaging capabilities of time-of-flight secondary ion mass spectrometry (ToF-SIMS) have not been used to their full potential in the analysis of polymer and biological samples. Imaging has been limited by the size of the dataset and the chemical complexity of the sample being imaged. Pixel and segment based image fusion algorithms commonly used in remote sensing, ecology, geography, and geology provide a way to improve spatial resolution and classification of biological images. In this study, a sample of Arabidopsis thaliana was treated with silver nanoparticles and imaged with ToF-SIMS. These images provide insight into the uptake mechanism for the silver nanoparticles into the plant tissue, giving new understanding to the mechanism of uptake of heavy metals in the environment. The Munechika algorithm was programmed in-house and applied to achieve pixel based fusion, which improved the spatial resolution of the image obtained. Multispectral and quadtree segment or region based fusion algorithms were performed using ecognition software, a commercially available remote sensing software suite, and used to classify the images. The Munechika fusion improved the spatial resolution for the images containing silver nanoparticles, while the segment fusion allowed classification and fusion based on the tissue types in the sample, suggesting potential pathways for the uptake of the silver nanoparticles.

Identification and Validation of a Diagnostic and Prognostic Multi-Gene Biomarker Panel for Pancreatic Ductal Adenocarcinoma.

PubMed

Klett, Hagen; Fuellgraf, Hannah; Levit-Zerdoun, Ella; Hussung, Saskia; Kowar, Silke; Küsters, Simon; Bronsert, Peter; Werner, Martin; Wittel, Uwe; Fritsch, Ralph; Busch, Hauke; Boerries, Melanie

2018-01-01

Late diagnosis and systemic dissemination essentially contribute to the invariably poor prognosis of pancreatic ductal adenocarcinoma (PDAC). Therefore, the development of diagnostic biomarkers for PDAC are urgently needed to improve patient stratification and outcome in the clinic. By studying the transcriptomes of independent PDAC patient cohorts of tumor and non-tumor tissues, we identified 81 robustly regulated genes, through a novel, generally applicable meta-analysis. Using consensus clustering on co-expression values revealed four distinct clusters with genes originating from exocrine/endocrine pancreas, stromal and tumor cells. Three clusters were strongly associated with survival of PDAC patients based on TCGA database underlining the prognostic potential of the identified genes. With the added information of impact of survival and the robustness within the meta-analysis, we extracted a 17-gene subset for further validation. We show that it did not only discriminate PDAC from non-tumor tissue and stroma in fresh-frozen as well as formalin-fixed paraffin embedded samples, but also detected pancreatic precursor lesions and singled out pancreatitis samples. Moreover, the classifier discriminated PDAC from other cancers in the TCGA database. In addition, we experimentally validated the classifier in PDAC patients on transcript level using qPCR and exemplify the usage on protein level for three proteins (AHNAK2, LAMC2, TFF1) using immunohistochemistry and for two secreted proteins (TFF1, SERPINB5) using ELISA-based protein detection in blood-plasma. In conclusion, we present a novel robust diagnostic and prognostic gene signature for PDAC with future potential applicability in the clinic.

Identification and Validation of a Diagnostic and Prognostic Multi-Gene Biomarker Panel for Pancreatic Ductal Adenocarcinoma

PubMed Central

Klett, Hagen; Fuellgraf, Hannah; Levit-Zerdoun, Ella; Hussung, Saskia; Kowar, Silke; Küsters, Simon; Bronsert, Peter; Werner, Martin; Wittel, Uwe; Fritsch, Ralph; Busch, Hauke; Boerries, Melanie

2018-01-01

Late diagnosis and systemic dissemination essentially contribute to the invariably poor prognosis of pancreatic ductal adenocarcinoma (PDAC). Therefore, the development of diagnostic biomarkers for PDAC are urgently needed to improve patient stratification and outcome in the clinic. By studying the transcriptomes of independent PDAC patient cohorts of tumor and non-tumor tissues, we identified 81 robustly regulated genes, through a novel, generally applicable meta-analysis. Using consensus clustering on co-expression values revealed four distinct clusters with genes originating from exocrine/endocrine pancreas, stromal and tumor cells. Three clusters were strongly associated with survival of PDAC patients based on TCGA database underlining the prognostic potential of the identified genes. With the added information of impact of survival and the robustness within the meta-analysis, we extracted a 17-gene subset for further validation. We show that it did not only discriminate PDAC from non-tumor tissue and stroma in fresh-frozen as well as formalin-fixed paraffin embedded samples, but also detected pancreatic precursor lesions and singled out pancreatitis samples. Moreover, the classifier discriminated PDAC from other cancers in the TCGA database. In addition, we experimentally validated the classifier in PDAC patients on transcript level using qPCR and exemplify the usage on protein level for three proteins (AHNAK2, LAMC2, TFF1) using immunohistochemistry and for two secreted proteins (TFF1, SERPINB5) using ELISA-based protein detection in blood-plasma. In conclusion, we present a novel robust diagnostic and prognostic gene signature for PDAC with future potential applicability in the clinic. PMID:29675033

How large a training set is needed to develop a classifier for microarray data?

PubMed

Dobbin, Kevin K; Zhao, Yingdong; Simon, Richard M

2008-01-01

A common goal of gene expression microarray studies is the development of a classifier that can be used to divide patients into groups with different prognoses, or with different expected responses to a therapy. These types of classifiers are developed on a training set, which is the set of samples used to train a classifier. The question of how many samples are needed in the training set to produce a good classifier from high-dimensional microarray data is challenging. We present a model-based approach to determining the sample size required to adequately train a classifier. It is shown that sample size can be determined from three quantities: standardized fold change, class prevalence, and number of genes or features on the arrays. Numerous examples and important experimental design issues are discussed. The method is adapted to address ex post facto determination of whether the size of a training set used to develop a classifier was adequate. An interactive web site for performing the sample size calculations is provided. We showed that sample size calculations for classifier development from high-dimensional microarray data are feasible, discussed numerous important considerations, and presented examples.

Reduction of Health Risks Due to Chromium(VI)Using Mesquite: A Potential Cr Phytoremediator

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gardea-Torresdey, Jorge L.; Aldrich, Mary V.; Peralta-Videa, Jose R.

Chromium is a transition metal extensively used in industry. Cr mining and industrial operations account for chromium wastes at Superfund sites in the United States. A study was performed to investigate the possibility of using mesquite (Prosopis spp.), which is an indigenous desert plant species, to remove Cr from contaminated sites. In this study, mesquite plants were grown in an agar-based medium containing 75 mg L-1 and 125 mg L-1 of Cr(VI). The Cr content of leaf tissue (992 mg kg-1 of dry weight, from 125 mg L-1 of Cr(VI)) indicated that mesquite could be classified as a chromium hyperaccumulator.more » X-ray absorption spectroscopy (XAS) studies performed to experimental samples showed that mesquite roots absorbed some of the supplied Cr(VI). However, the data analyses of plant tissues demonstrated that the absorbed Cr(VI) was fully reduced to Cr(III) in the leaf tissue.« less

Optical biopsy using fluorescence spectroscopy for prostate cancer diagnosis

NASA Astrophysics Data System (ADS)

Wu, Binlin; Gao, Xin; Smith, Jason; Bailin, Jacob

2017-02-01

Native fluorescence spectra are acquired from fresh normal and cancerous human prostate tissues. The fluorescence data are analyzed using a multivariate analysis algorithm such as non-negative matrix factorization. The nonnegative spectral components are retrieved and attributed to the native fluorophores such as collagen, reduced nicotinamide adenine dinucleotide (NADH), and flavin adenine dinucleotide (FAD) in tissue. The retrieved weights of the components, e.g. NADH and FAD are used to estimate the relative concentrations of the native fluorophores and the redox ratio. A machine learning algorithm such as support vector machine (SVM) is used for classification to distinguish normal and cancerous tissue samples based on either the relative concentrations of NADH and FAD or the redox ratio alone. The classification performance is shown based on statistical measures such as sensitivity, specificity, and accuracy, along with the area under receiver operating characteristic (ROC) curve. A cross validation method such as leave-one-out is used to evaluate the predictive performance of the SVM classifier to avoid bias due to overfitting.

On Evaluating Brain Tissue Classifiers without a Ground Truth

PubMed Central

Martin-Fernandez, Marcos; Ungar, Lida; Nakamura, Motoaki; Koo, Min-Seong; McCarley, Robert W.; Shenton, Martha E.

2009-01-01

In this paper, we present a set of techniques for the evaluation of brain tissue classifiers on a large data set of MR images of the head. Due to the difficulty of establishing a gold standard for this type of data, we focus our attention on methods which do not require a ground truth, but instead rely on a common agreement principle. Three different techniques are presented: the Williams’ index, a measure of common agreement; STAPLE, an Expectation Maximization algorithm which simultaneously estimates performance parameters and constructs an estimated reference standard; and Multidimensional Scaling, a visualization technique to explore similarity data. We apply these different evaluation methodologies to a set eleven different segmentation algorithms on forty MR images. We then validate our evaluation pipeline by building a ground truth based on human expert tracings. The evaluations with and without a ground truth are compared. Our findings show that comparing classifiers without a gold standard can provide a lot of interesting information. In particular, outliers can be easily detected, strongly consistent or highly variable techniques can be readily discriminated, and the overall similarity between different techniques can be assessed. On the other hand, we also find that some information present in the expert segmentations is not captured by the automatic classifiers, suggesting that common agreement alone may not be sufficient for a precise performance evaluation of brain tissue classifiers. PMID:17532646

Discrimination of premalignant lesions and cancer tissues from normal gastric tissues using Raman spectroscopy

NASA Astrophysics Data System (ADS)

Luo, Shuwen; Chen, Changshui; Mao, Hua; Jin, Shaoqin

2013-06-01

The feasibility of early detection of gastric cancer using near-infrared (NIR) Raman spectroscopy (RS) by distinguishing premalignant lesions (adenomatous polyp, n=27) and cancer tissues (adenocarcinoma, n=33) from normal gastric tissues (n=45) is evaluated. Significant differences in Raman spectra are observed among the normal, adenomatous polyp, and adenocarcinoma gastric tissues at 936, 1003, 1032, 1174, 1208, 1323, 1335, 1450, and 1655 cm-1. Diverse statistical methods are employed to develop effective diagnostic algorithms for classifying the Raman spectra of different types of ex vivo gastric tissues, including principal component analysis (PCA), linear discriminant analysis (LDA), and naive Bayesian classifier (NBC) techniques. Compared with PCA-LDA algorithms, PCA-NBC techniques together with leave-one-out, cross-validation method provide better discriminative results of normal, adenomatous polyp, and adenocarcinoma gastric tissues, resulting in superior sensitivities of 96.3%, 96.9%, and 96.9%, and specificities of 93%, 100%, and 95.2%, respectively. Therefore, NIR RS associated with multivariate statistical algorithms has the potential for early diagnosis of gastric premalignant lesions and cancer tissues in molecular level.

Serum Autoantibodies in Chronic Prostate Inflammation in Prostate Cancer Patients.

PubMed

Schlick, Bettina; Massoner, Petra; Lueking, Angelika; Charoentong, Pornpimol; Blattner, Mirjam; Schaefer, Georg; Marquart, Klaus; Theek, Carmen; Amersdorfer, Peter; Zielinski, Dirk; Kirchner, Matthias; Trajanoski, Zlatko; Rubin, Mark A; Müllner, Stefan; Schulz-Knappe, Peter; Klocker, Helmut

2016-01-01

Chronic inflammation is frequently observed on histological analysis of malignant and non-malignant prostate specimens. It is a suspected supporting factor for prostate diseases and their progression and a main cause of false positive PSA tests in cancer screening. We hypothesized that inflammation induces autoantibodies, which may be useful biomarkers. We aimed to identify and validate prostate inflammation associated serum autoantibodies in prostate cancer patients and evaluate the expression of corresponding autoantigens. Radical prostatectomy specimens of prostate cancer patients (N = 70) were classified into high and low inflammation groups according to the amount of tissue infiltrating lymphocytes. The corresponding pre-surgery blood serum samples were scrutinized for autoantibodies using a low-density protein array. Selected autoantigens were identified in prostate tissue and their expression pattern analyzed by immunohistochemistry and qPCR. The identified autoantibody profile was cross-checked in an independent sample set (N = 63) using the Luminex-bead protein array technology. Protein array screening identified 165 autoantibodies differentially abundant in the serum of high compared to low inflammation patients. The expression pattern of three corresponding antigens were established in benign and cancer tissue by immunohistochemistry and qPCR: SPAST (Spastin), STX18 (Syntaxin 18) and SPOP (speckle-type POZ protein). Of these, SPAST was significantly increased in prostate tissue with high inflammation. All three autoantigens were differentially expressed in primary and/or castration resistant prostate tumors when analyzed in an inflammation-independent tissue microarray. Cross-validation of the inflammation autoantibody profile on an independent sample set using a Luminex-bead protein array, retrieved 51 of the significantly discriminating autoantibodies. Three autoantibodies were significantly upregulated in both screens, MUT, RAB11B and CSRP2 (p>0.05), two, SPOP and ZNF671, close to statistical significance (p = 0.051 and 0.076). We provide evidence of an inflammation-specific autoantibody profile and confirm the expression of corresponding autoantigens in prostate tissue. This supports evaluation of autoantibodies as non-invasive markers for prostate inflammation.

Serum Autoantibodies in Chronic Prostate Inflammation in Prostate Cancer Patients

PubMed Central

Schlick, Bettina; Massoner, Petra; Lueking, Angelika; Charoentong, Pornpimol; Blattner, Mirjam; Schaefer, Georg; Marquart, Klaus; Theek, Carmen; Amersdorfer, Peter; Zielinski, Dirk; Kirchner, Matthias; Trajanoski, Zlatko; Rubin, Mark A.; Müllner, Stefan; Schulz-Knappe, Peter; Klocker, Helmut

2016-01-01

Background Chronic inflammation is frequently observed on histological analysis of malignant and non-malignant prostate specimens. It is a suspected supporting factor for prostate diseases and their progression and a main cause of false positive PSA tests in cancer screening. We hypothesized that inflammation induces autoantibodies, which may be useful biomarkers. We aimed to identify and validate prostate inflammation associated serum autoantibodies in prostate cancer patients and evaluate the expression of corresponding autoantigens. Methods Radical prostatectomy specimens of prostate cancer patients (N = 70) were classified into high and low inflammation groups according to the amount of tissue infiltrating lymphocytes. The corresponding pre-surgery blood serum samples were scrutinized for autoantibodies using a low-density protein array. Selected autoantigens were identified in prostate tissue and their expression pattern analyzed by immunohistochemistry and qPCR. The identified autoantibody profile was cross-checked in an independent sample set (N = 63) using the Luminex-bead protein array technology. Results Protein array screening identified 165 autoantibodies differentially abundant in the serum of high compared to low inflammation patients. The expression pattern of three corresponding antigens were established in benign and cancer tissue by immunohistochemistry and qPCR: SPAST (Spastin), STX18 (Syntaxin 18) and SPOP (speckle-type POZ protein). Of these, SPAST was significantly increased in prostate tissue with high inflammation. All three autoantigens were differentially expressed in primary and/or castration resistant prostate tumors when analyzed in an inflammation-independent tissue microarray. Cross-validation of the inflammation autoantibody profile on an independent sample set using a Luminex-bead protein array, retrieved 51 of the significantly discriminating autoantibodies. Three autoantibodies were significantly upregulated in both screens, MUT, RAB11B and CSRP2 (p>0.05), two, SPOP and ZNF671, close to statistical significance (p = 0.051 and 0.076). Conclusions We provide evidence of an inflammation-specific autoantibody profile and confirm the expression of corresponding autoantigens in prostate tissue. This supports evaluation of autoantibodies as non-invasive markers for prostate inflammation. PMID:26863016

Classification model based on Raman spectra of selected morphological and biochemical tissue constituents for identification of atherosclerosis in human coronary arteries.

PubMed

Peres, Marines Bertolo; Silveira, Landulfo; Zângaro, Renato Amaro; Pacheco, Marcos Tadeu Tavares; Pasqualucci, Carlos Augusto

2011-09-01

This study presents the results of Raman spectroscopy applied to the classification of arterial tissue based on a simplified model using basal morphological and biochemical information extracted from the Raman spectra of arteries. The Raman spectrograph uses an 830-nm diode laser, imaging spectrograph, and a CCD camera. A total of 111 Raman spectra from arterial fragments were used to develop the model, and those spectra were compared to the spectra of collagen, fat cells, smooth muscle cells, calcification, and cholesterol in a linear fit model. Non-atherosclerotic (NA), fatty and fibrous-fatty atherosclerotic plaques (A) and calcified (C) arteries exhibited different spectral signatures related to different morphological structures presented in each tissue type. Discriminant analysis based on Mahalanobis distance was employed to classify the tissue type with respect to the relative intensity of each compound. This model was subsequently tested prospectively in a set of 55 spectra. The simplified diagnostic model showed that cholesterol, collagen, and adipocytes were the tissue constituents that gave the best classification capability and that those changes were correlated to histopathology. The simplified model, using spectra obtained from a few tissue morphological and biochemical constituents, showed feasibility by using a small amount of variables, easily extracted from gross samples.

PCA-HOG symmetrical feature based diseased cell detection

NASA Astrophysics Data System (ADS)

Wan, Min-jie

2016-04-01

A histogram of oriented gradient (HOG) feature is applied to the field of diseased cell detection, which can detect diseased cells in high resolution tissue images rapidly, accurately and efficiently. Firstly, motivated by symmetrical cellular forms, a new HOG symmetrical feature based on the traditional HOG feature is proposed to meet the condition of cell detection. Secondly, considering the high feature dimension of traditional HOG feature leads to plenty of memory resources and long runtime in practical applications, a classical dimension reduction method called principal component analysis (PCA) is used to reduce the dimension of high-dimensional HOG descriptor. Because of that, computational speed is increased greatly, and the accuracy of detection can be controlled in a proper range at the same time. Thirdly, support vector machine (SVM) classifier is trained with PCA-HOG symmetrical features proposed above. At last, practical tissue images is detected and analyzed by SVM classifier. In order to verify the effectiveness of this new algorithm, it is practically applied to conduct diseased cell detection which takes 200 pieces of H&E (hematoxylin & eosin) high resolution staining histopathological images collected from 20 breast cancer patients as a sample. The experiment shows that the average processing rate can be 25 frames per second and the detection accuracy can be 92.1%.

Identification and Correction of Sample Mix-Ups in Expression Genetic Data: A Case Study

PubMed Central

Broman, Karl W.; Keller, Mark P.; Broman, Aimee Teo; Kendziorski, Christina; Yandell, Brian S.; Sen, Śaunak; Attie, Alan D.

2015-01-01

In a mouse intercross with more than 500 animals and genome-wide gene expression data on six tissues, we identified a high proportion (18%) of sample mix-ups in the genotype data. Local expression quantitative trait loci (eQTL; genetic loci influencing gene expression) with extremely large effect were used to form a classifier to predict an individual’s eQTL genotype based on expression data alone. By considering multiple eQTL and their related transcripts, we identified numerous individuals whose predicted eQTL genotypes (based on their expression data) did not match their observed genotypes, and then went on to identify other individuals whose genotypes did match the predicted eQTL genotypes. The concordance of predictions across six tissues indicated that the problem was due to mix-ups in the genotypes (although we further identified a small number of sample mix-ups in each of the six panels of gene expression microarrays). Consideration of the plate positions of the DNA samples indicated a number of off-by-one and off-by-two errors, likely the result of pipetting errors. Such sample mix-ups can be a problem in any genetic study, but eQTL data allow us to identify, and even correct, such problems. Our methods have been implemented in an R package, R/lineup. PMID:26290572

Identification and Correction of Sample Mix-Ups in Expression Genetic Data: A Case Study.

PubMed

Broman, Karl W; Keller, Mark P; Broman, Aimee Teo; Kendziorski, Christina; Yandell, Brian S; Sen, Śaunak; Attie, Alan D

2015-08-19

In a mouse intercross with more than 500 animals and genome-wide gene expression data on six tissues, we identified a high proportion (18%) of sample mix-ups in the genotype data. Local expression quantitative trait loci (eQTL; genetic loci influencing gene expression) with extremely large effect were used to form a classifier to predict an individual's eQTL genotype based on expression data alone. By considering multiple eQTL and their related transcripts, we identified numerous individuals whose predicted eQTL genotypes (based on their expression data) did not match their observed genotypes, and then went on to identify other individuals whose genotypes did match the predicted eQTL genotypes. The concordance of predictions across six tissues indicated that the problem was due to mix-ups in the genotypes (although we further identified a small number of sample mix-ups in each of the six panels of gene expression microarrays). Consideration of the plate positions of the DNA samples indicated a number of off-by-one and off-by-two errors, likely the result of pipetting errors. Such sample mix-ups can be a problem in any genetic study, but eQTL data allow us to identify, and even correct, such problems. Our methods have been implemented in an R package, R/lineup. Copyright © 2015 Broman et al.

Fuzzy logic modeling of bioaccumulation pattern of metals in coastal biota of Ondo State, Nigeria.

PubMed

Agunbiade, Foluso O; Olu-Owolabi, Bamidele I; Adebowale, Kayode O

2012-01-01

The accumulation patterns of ten metals in tissues of plant, Eichornia crassipes, and fishes, Hydrocynus forskahlii and Oreochromis mossambicus, were modeled with simple fuzzy classification (SFC) to assess toxic effects of anthropogenic activities on the coastal biota. The plant sample was separated into root, stem, and leaves and the fishes into bones, internal tissues, and muscles. They were analyzed for As, Cd, Cr, Cu, Ni, Pb, V, Fe, Mn, and Zn after wet oxidation of their dried samples. The results were converted into membership functions of five accumulation classes and aggregated with SFC. The classification results showed that there was no metal accumulation in the plant parts while the fishes were classified into low accumulation category. The internal tissues of the fishes had higher metal accumulation than the other parts. Generally, Fe and Mn had highest concentrations in the biota but are natural to the area and may not constitute significant risk. Cr had the highest transfer and accumulation from the coastal water into the aquatic lives and may be indicative of risk prone system being a toxic metal. Metal contaminations in the zone had not significantly accumulated in the biota making them less prone to risk associated with metal accumulation.

Rapid detection of bacterial contamination in cell or tissue cultures based on Raman spectroscopy

NASA Astrophysics Data System (ADS)

Bolwien, Carsten; Sulz, Gerd; Becker, Sebastian; Thielecke, Hagen; Mertsching, Heike; Koch, Steffen

2008-02-01

Monitoring the sterility of cell or tissue cultures is an essential task, particularly in the fields of regenerative medicine and tissue engineering when implanting cells into the human body. We present a system based on a commercially available microscope equipped with a microfluidic cell that prepares the particles found in the solution for analysis, a Raman-spectrometer attachment optimized for non-destructive, rapid recording of Raman spectra, and a data acquisition and analysis tool for identification of the particles. In contrast to conventional sterility testing in which samples are incubated over weeks, our system is able to analyze milliliters of supernatant or cell suspension within hours by filtering relevant particles and placing them on a Raman-friendly substrate in the microfluidic cell. Identification of critical particles via microscopic imaging and subsequent image analysis is carried out before micro-Raman analysis of those particles is then carried out with an excitation wavelength of 785 nm. The potential of this setup is demonstrated by results of artificial contamination of samples with a pool of bacteria, fungi, and spores: single-channel spectra of the critical particles are automatically baseline-corrected without using background data and classified via hierarchical cluster analysis, showing great promise for accurate and rapid detection and identification of contaminants.

Comprehensive quality assessment based specific chemical profiles for geographic and tissue variation in Gentiana rigescens using HPLC and FTIR method combined with principal component analysis

NASA Astrophysics Data System (ADS)

Li, Jie; Zhang, Ji; Zhao, Yan-Li; Huang, Heng-Yu; Wang, Yuan-Zhong

2017-12-01

Roots, stems, leaves and flowers of Longdan (Gentiana rigescens Franch. ex Hemsl) were collected from six geographic origins of Yunnan Province (n = 240) to implement the quality assessment based on contents of gentiopicroside, loganic acid, sweroside and swertiamarin and chemical profile using HPLC-DAD and FTIR method combined with principal component analysis (PCA). The content of gentiopicroside (major iridoid glycoside) was the highest in G. rigescens, regardless of tissue and geographic origin. The level of swertiamarin was the lowest, even unable to be detected in samples from Kunming and Qujing. Significant correlations (p < 0.05) between gentiopicroside, loganic acid, sweroside and swertiamarin were found at inter- or intra-tissues, which were highly depended on geographic origins, indicating the influence of environmental conditions on the conversion and transport of secondary metabolites in G. rigescens. Furthermore, samples were reasonably classified as three clusters along large producing areas where have similar climate conditions, characterized by carbohydrates, phenols, benzoates, terpenoids, aliphatic alcohols, aromatic hydrocarbons, and so forth. The present work provided global information on the chemical profile and contents of major iridoid glycosides in G. rigescens originated from six different origins, which is helpful for controlling quality of herbal medicines systematically.

Comprehensive Quality Assessment Based Specific Chemical Profiles for Geographic and Tissue Variation in Gentiana rigescens Using HPLC and FTIR Method Combined with Principal Component Analysis

PubMed Central

Li, Jie; Zhang, Ji; Zhao, Yan-Li; Huang, Heng-Yu; Wang, Yuan-Zhong

2017-01-01

Roots, stems, leaves, and flowers of Longdan (Gentiana rigescens Franch. ex Hemsl) were collected from six geographic origins of Yunnan Province (n = 240) to implement the quality assessment based on contents of gentiopicroside, loganic acid, sweroside and swertiamarin and chemical profile using HPLC-DAD and FTIR method combined with principal component analysis (PCA). The content of gentiopicroside (major iridoid glycoside) was the highest in G. rigescens, regardless of tissue and geographic origin. The level of swertiamarin was the lowest, even unable to be detected in samples from Kunming and Qujing. Significant correlations (p < 0.05) between gentiopicroside, loganic acid, sweroside, and swertiamarin were found at inter- or intra-tissues, which were highly depended on geographic origins, indicating the influence of environmental conditions on the conversion and transport of secondary metabolites in G. rigescens. Furthermore, samples were reasonably classified as three clusters along large producing areas where have similar climate conditions, characterized by carbohydrates, phenols, benzoates, terpenoids, aliphatic alcohols, aromatic hydrocarbons, and so forth. The present work provided global information on the chemical profile and contents of major iridoid glycosides in G. rigescens originated from six different origins, which is helpful for controlling quality of herbal medicines systematically. PMID:29312929

Fluorescence Spectroscopy as a Tool for the Assessment of Liver Samples with Several Stages of Fibrosis.

PubMed

Fabila-Bustos, Diego A; Arroyo-Camarena, Úrsula D; López-Vancell, María D; Durán-Padilla, Marco A; Azuceno-García, Itzel; Stolik-Isakina, Suren; Valor-Reed, Alma; Ibarra-Coronado, Elizabeth; Hernández-Quintanar, Luis F; Escobedo, Galileo; de la Rosa-Vázquez, José M

2018-03-01

During the last years, fluorescence spectroscopy has been used as a potential tool for the evaluation and characterization of tissues with different disease conditions due to its low cost, high sensitivity, and minimally or noninvasive character. In this study, fluorescence spectroscopy was used to study 19 paraffin blocks containing human liver tissue from biopsies. All samples were previously analyzed by two senior pathologists in a single-blind trial. After their evaluation, four liver samples were classified as nonfibrosis (F0), four as initial fibrosis (F1-F2), four as advanced fibrosis (F3), and six as cirrhosis (F4). The fluorescence was induced at different wavelengths as follows: 330, 365, and 405 nm using a portable fiber-optic system. The fluorescence spectra were recorded in the range of 400-750 nm. A distinctive correlation between the shape of each spectrum and the level of fibrosis in the liver sample was detected. A multi-variate statistical analysis based on principal component analysis followed by linear discrimination analysis was applied to develop algorithms able to distinguish different stages of fibrosis based on the characteristics of fluorescence spectra. Pairwise comparisons were performed: F0 versus F1-F2, F1-F2 versus F3, F3 versus F4, and F1-F2 versus F4. The algorithms applied to each set of data yielded values of sensitivity and specificity that were higher than 90% and 95%, respectively, in all the analyzed cases. With this study, it is concluded that fluorescence spectroscopy can be used as a complementary tool for the assessment of liver fibrosis in liver tissue samples, which sets the stage for subsequent clinical trials.

Sample entropy analysis of cervical neoplasia gene-expression signatures

PubMed Central

Botting, Shaleen K; Trzeciakowski, Jerome P; Benoit, Michelle F; Salama, Salama A; Diaz-Arrastia, Concepcion R

2009-01-01

Background We introduce Approximate Entropy as a mathematical method of analysis for microarray data. Approximate entropy is applied here as a method to classify the complex gene expression patterns resultant of a clinical sample set. Since Entropy is a measure of disorder in a system, we believe that by choosing genes which display minimum entropy in normal controls and maximum entropy in the cancerous sample set we will be able to distinguish those genes which display the greatest variability in the cancerous set. Here we describe a method of utilizing Approximate Sample Entropy (ApSE) analysis to identify genes of interest with the highest probability of producing an accurate, predictive, classification model from our data set. Results In the development of a diagnostic gene-expression profile for cervical intraepithelial neoplasia (CIN) and squamous cell carcinoma of the cervix, we identified 208 genes which are unchanging in all normal tissue samples, yet exhibit a random pattern indicative of the genetic instability and heterogeneity of malignant cells. This may be measured in terms of the ApSE when compared to normal tissue. We have validated 10 of these genes on 10 Normal and 20 cancer and CIN3 samples. We report that the predictive value of the sample entropy calculation for these 10 genes of interest is promising (75% sensitivity, 80% specificity for prediction of cervical cancer over CIN3). Conclusion The success of the Approximate Sample Entropy approach in discerning alterations in complexity from biological system with such relatively small sample set, and extracting biologically relevant genes of interest hold great promise. PMID:19232110

Comparison of risk classification between EndoPredict and MammaPrint in ER-positive/HER2-negative primary invasive breast cancer

PubMed Central

Peláez-García, Alberto; Yébenes, Laura; Berjón, Alberto; Angulo, Antonia; Zamora, Pilar; Sánchez-Méndez, José Ignacio; Espinosa, Enrique; Redondo, Andrés; Heredia-Soto, Victoria; Mendiola, Marta; Feliú, Jaime

2017-01-01

Purpose To compare the concordance in risk classification between the EndoPredict and the MammaPrint scores obtained for the same cancer samples on 40 estrogen-receptor positive/HER2-negative breast carcinomas. Methods Formalin-fixed, paraffin-embedded invasive breast carcinoma tissues that were previously analyzed with MammaPrint as part of routine care of the patients, and were classified as high-risk (20 patients) and low-risk (20 patients), were selected to be analyzed by the EndoPredict assay, a second generation gene expression test that combines expression of 8 genes (EP score) with two clinicopathological variables (tumor size and nodal status, EPclin score). Results The EP score classified 15 patients as low-risk and 25 patients as high-risk. EPclin re-classified 5 of the 25 EP high-risk patients into low-risk, resulting in a total of 20 high-risk and 20 low-risk tumors. EP score and MammaPrint score were significantly correlated (p = 0.008). Twelve of 20 samples classified as low-risk by MammaPrint were also low-risk by EP score (60%). 17 of 20 MammaPrint high-risk tumors were also high-risk by EP score. The overall concordance between EP score and MammaPrint was 72.5% (κ = 0.45, (95% CI, 0.182 to 0.718)). EPclin score also correlated with MammaPrint results (p = 0.004). Discrepancies between both tests occurred in 10 cases: 5 MammaPrint low-risk patients were classified as EPclin high-risk and 5 high-risk MammaPrint were classified as low-risk by EPclin and overall concordance of 75% (κ = 0.5, (95% CI, 0.232 to 0.768)). Conclusions This pilot study demonstrates a limited concordance between MammaPrint and EndoPredict. Differences in results could be explained by the inclusion of different gene sets in each platform, the use of different methodology, and the inclusion of clinicopathological parameters, such as tumor size and nodal status, in the EndoPredict test. PMID:28886093

Automated assessment of breast tissue density in non-contrast 3D CT images without image segmentation based on a deep CNN

NASA Astrophysics Data System (ADS)

Zhou, Xiangrong; Kano, Takuya; Koyasu, Hiromi; Li, Shuo; Zhou, Xinxin; Hara, Takeshi; Matsuo, Masayuki; Fujita, Hiroshi

2017-03-01

This paper describes a novel approach for the automatic assessment of breast density in non-contrast three-dimensional computed tomography (3D CT) images. The proposed approach trains and uses a deep convolutional neural network (CNN) from scratch to classify breast tissue density directly from CT images without segmenting the anatomical structures, which creates a bottleneck in conventional approaches. Our scheme determines breast density in a 3D breast region by decomposing the 3D region into several radial 2D-sections from the nipple, and measuring the distribution of breast tissue densities on each 2D section from different orientations. The whole scheme is designed as a compact network without the need for post-processing and provides high robustness and computational efficiency in clinical settings. We applied this scheme to a dataset of 463 non-contrast CT scans obtained from 30- to 45-year-old-women in Japan. The density of breast tissue in each CT scan was assigned to one of four categories (glandular tissue within the breast <25%, 25%-50%, 50%-75%, and >75%) by a radiologist as ground truth. We used 405 CT scans for training a deep CNN and the remaining 58 CT scans for testing the performance. The experimental results demonstrated that the findings of the proposed approach and those of the radiologist were the same in 72% of the CT scans among the training samples and 76% among the testing samples. These results demonstrate the potential use of deep CNN for assessing breast tissue density in non-contrast 3D CT images.

Role of Insulin-like growth factors in initiation of follicle growth in normal and polycystic human ovaries.

PubMed

Stubbs, Sharron A; Webber, Lisa J; Stark, Jaroslav; Rice, Suman; Margara, Raul; Lavery, Stuart; Trew, Geoffrey H; Hardy, Kate; Franks, Stephen

2013-08-01

Polycystic ovary syndrome (PCOS), the commonest cause of anovulatory infertility, is characterized by disordered follicle development including increased activation and accelerated growth of preantral follicles. Data from experimental animals and preliminary results from studies of human ovarian tissue suggest that IGFs affect preantral follicle development. Our objectives were to investigate the expression of the type-1 IGF receptor (IGFR-1) in the human ovary and to determine whether IGFs are involved in stimulating the transition of follicles from primordial to primary stage in normal and polycystic ovaries. We used archived ovarian tissue for protein expression studies and small cortical biopsies for follicle isolation and for tissue culture. This was a laboratory-based study, using clinical tissue samples. A total of 54 women, 33 with normal ovaries and 21 with polycystic ovaries, were classified by reference to menstrual cycle history and ultrasonography. We evaluated expression of IGFR-1 mRNA in isolated preantral follicles and of IGFR-1 protein in archived ovarian tissue samples from normal and polycystic ovaries and effects of exogenous IGF-1 on preantral follicle development and survival in cultured fragments of normal and polycystic ovaries. IGFR-1 mRNA and protein was expressed in preantral follicles at all stages of development and enhanced expression was noted in PCOS follicles during early preantral development. IGF-1 stimulated initiation of follicle growth in normal tissue but had little effect on preantral follicle growth in polycystic ovaries in which, characteristically, there was a higher proportion of follicles that had entered the growing phase even before culture. IGFs are plausible candidates in regulation of initiation of human follicle growth, and accelerated preantral follicle growth in PCOS may be due to increased activity of endogenous IGFs.

Study of normal, fibrous and calcified aortic valve tissue by Raman and reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Rodrigues, Kátia Calligaris; Munin, Egberto; Alves, Leandro P.; Silveira, Fabrício L.; Junior, Landulfo S.; De Lima, Carlos J.; Lázzaro, João C.; De Souza, Genivaldo C.; Piotto, José A. B.; Pacheco, Marcos T. T.; Zângaro, Renato A.

2007-02-01

Several studies have identified the degree of aortic valve calcification as a strong predictor both for the progression and outcome of aortic stenosis. In industrialized countries, aortic valve stenosis is most frequently caused by progressive calcification and degeneration of aortic cusps. However, there are no accurate methods to quantify the extent of aortic valve calcification. To provide a non-invasive alternative to biopsy, a range of optical methods have been investigated, including Raman and reflectance spectroscopy. A Raman spectrum can be used to access the molecular constitution of a particular tissue and classify it. Raman spectroscopy is largely used in the quantification and evaluation of human atherosclerosis, being a powerful technique for performing biochemical analysis without tissue removal. Nevertheless, increased thickness and disorganization of the collagen fibre network and extracellular matrix are known to affect the diffuse spectral reflectance of the tissue. A catheter with the "6 around 1" configuration, the central fiber transmit laser radiation to the sample and the scattered light is collected by the other six surrounding fibers, was used both for Raman and reflectance spectroscopy. A white light (krypton lamp, flashtube Model FX 1160 Perkin Elmer, USA) excitation was used for reflectance measurements. A Ti-sapphire (785nm, Spectra Physics, model 3900S, USA) laser, pumped by an argon laser (Spectra Physics, model Stabilite 2017, USA) was used as the near infrared Raman set up. Several ex-vivo spectra of aortic valve samples were analyzed. The results show a promising way to differentiate normal, fibrous and calcified tissue in aortic valve.

Multiscale Rotation-Invariant Convolutional Neural Networks for Lung Texture Classification.

PubMed

Wang, Qiangchang; Zheng, Yuanjie; Yang, Gongping; Jin, Weidong; Chen, Xinjian; Yin, Yilong

2018-01-01

We propose a new multiscale rotation-invariant convolutional neural network (MRCNN) model for classifying various lung tissue types on high-resolution computed tomography. MRCNN employs Gabor-local binary pattern that introduces a good property in image analysis-invariance to image scales and rotations. In addition, we offer an approach to deal with the problems caused by imbalanced number of samples between different classes in most of the existing works, accomplished by changing the overlapping size between the adjacent patches. Experimental results on a public interstitial lung disease database show a superior performance of the proposed method to state of the art.

Comparison of Hybrid Classifiers for Crop Classification Using Normalized Difference Vegetation Index Time Series: A Case Study for Major Crops in North Xinjiang, China

PubMed Central

Hao, Pengyu; Wang, Li; Niu, Zheng

2015-01-01

A range of single classifiers have been proposed to classify crop types using time series vegetation indices, and hybrid classifiers are used to improve discriminatory power. Traditional fusion rules use the product of multi-single classifiers, but that strategy cannot integrate the classification output of machine learning classifiers. In this research, the performance of two hybrid strategies, multiple voting (M-voting) and probabilistic fusion (P-fusion), for crop classification using NDVI time series were tested with different training sample sizes at both pixel and object levels, and two representative counties in north Xinjiang were selected as study area. The single classifiers employed in this research included Random Forest (RF), Support Vector Machine (SVM), and See 5 (C 5.0). The results indicated that classification performance improved (increased the mean overall accuracy by 5%~10%, and reduced standard deviation of overall accuracy by around 1%) substantially with the training sample number, and when the training sample size was small (50 or 100 training samples), hybrid classifiers substantially outperformed single classifiers with higher mean overall accuracy (1%~2%). However, when abundant training samples (4,000) were employed, single classifiers could achieve good classification accuracy, and all classifiers obtained similar performances. Additionally, although object-based classification did not improve accuracy, it resulted in greater visual appeal, especially in study areas with a heterogeneous cropping pattern. PMID:26360597

Genome-wide prediction and analysis of human tissue-selective genes using microarray expression data

PubMed Central

2013-01-01

Background Understanding how genes are expressed specifically in particular tissues is a fundamental question in developmental biology. Many tissue-specific genes are involved in the pathogenesis of complex human diseases. However, experimental identification of tissue-specific genes is time consuming and difficult. The accurate predictions of tissue-specific gene targets could provide useful information for biomarker development and drug target identification. Results In this study, we have developed a machine learning approach for predicting the human tissue-specific genes using microarray expression data. The lists of known tissue-specific genes for different tissues were collected from UniProt database, and the expression data retrieved from the previously compiled dataset according to the lists were used for input vector encoding. Random Forests (RFs) and Support Vector Machines (SVMs) were used to construct accurate classifiers. The RF classifiers were found to outperform SVM models for tissue-specific gene prediction. The results suggest that the candidate genes for brain or liver specific expression can provide valuable information for further experimental studies. Our approach was also applied for identifying tissue-selective gene targets for different types of tissues. Conclusions A machine learning approach has been developed for accurately identifying the candidate genes for tissue specific/selective expression. The approach provides an efficient way to select some interesting genes for developing new biomedical markers and improve our knowledge of tissue-specific expression. PMID:23369200

Utilizing spatial and spectral features of photoacoustic imaging for ovarian cancer detection and diagnosis

NASA Astrophysics Data System (ADS)

Li, Hai; Kumavor, Patrick; Salman Alqasemi, Umar; Zhu, Quing

2015-01-01

A composite set of ovarian tissue features extracted from photoacoustic spectral data, beam envelope, and co-registered ultrasound and photoacoustic images are used to characterize malignant and normal ovaries using logistic and support vector machine (SVM) classifiers. Normalized power spectra were calculated from the Fourier transform of the photoacoustic beamformed data, from which the spectral slopes and 0-MHz intercepts were extracted. Five features were extracted from the beam envelope and another 10 features were extracted from the photoacoustic images. These 17 features were ranked by their p-values from t-tests on which a filter type of feature selection method was used to determine the optimal feature number for final classification. A total of 169 samples from 19 ex vivo ovaries were randomly distributed into training and testing groups. Both classifiers achieved a minimum value of the mean misclassification error when the seven features with lowest p-values were selected. Using these seven features, the logistic and SVM classifiers obtained sensitivities of 96.39±3.35% and 97.82±2.26%, and specificities of 98.92±1.39% and 100%, respectively, for the training group. For the testing group, logistic and SVM classifiers achieved sensitivities of 92.71±3.55% and 92.64±3.27%, and specificities of 87.52±8.78% and 98.49±2.05%, respectively.

Optimal culture incubation time in orthopedic device-associated infections: a retrospective analysis of prolonged 14-day incubation.

PubMed

Schwotzer, Nora; Wahl, Peter; Fracheboud, Dominique; Gautier, Emanuel; Chuard, Christian

2014-01-01

Accurate diagnosis of orthopedic device-associated infections can be challenging. Culture of tissue biopsy specimens is often considered the gold standard; however, there is currently no consensus on the ideal incubation time for specimens. The aim of our study was to assess the yield of a 14-day incubation protocol for tissue biopsy specimens from revision surgery (joint replacements and internal fixation devices) in a general orthopedic and trauma surgery setting. Medical records were reviewed retrospectively in order to identify cases of infection according to predefined diagnostic criteria. From August 2009 to March 2012, 499 tissue biopsy specimens were sampled from 117 cases. In 70 cases (59.8%), at least one sample showed microbiological growth. Among them, 58 cases (82.9%) were considered infections and 12 cases (17.1%) were classified as contaminations. The median time to positivity in the cases of infection was 1 day (range, 1 to 10 days), compared to 6 days (range, 1 to 11 days) in the cases of contamination (P < 0.001). Fifty-six (96.6%) of the infection cases were diagnosed within 7 days of incubation. In conclusion, the results of our study show that the incubation of tissue biopsy specimens beyond 7 days is not productive in a general orthopedic and trauma surgery setting. Prolonged 14-day incubation might be of interest in particular situations, however, in which the prevalence of slow-growing microorganisms and anaerobes is higher.

A Tissue Systems Pathology Test Detects Abnormalities Associated with Prevalent High-Grade Dysplasia and Esophageal Cancer in Barrett's Esophagus.

PubMed

Critchley-Thorne, Rebecca J; Davison, Jon M; Prichard, Jeffrey W; Reese, Lia M; Zhang, Yi; Repa, Kathleen; Li, Jinhong; Diehl, David L; Jhala, Nirag C; Ginsberg, Gregory G; DeMarshall, Maureen; Foxwell, Tyler; Jobe, Blair A; Zaidi, Ali H; Duits, Lucas C; Bergman, Jacques J G H M; Rustgi, Anil; Falk, Gary W

2017-02-01

There is a need for improved tools to detect high-grade dysplasia (HGD) and esophageal adenocarcinoma (EAC) in patients with Barrett's esophagus. In previous work, we demonstrated that a 3-tier classifier predicted risk of incident progression in Barrett's esophagus. Our aim was to determine whether this risk classifier could detect a field effect in nondysplastic (ND), indefinite for dysplasia (IND), or low-grade dysplasia (LGD) biopsies from Barrett's esophagus patients with prevalent HGD/EAC. We performed a multi-institutional case-control study to evaluate a previously developed risk classifier that is based upon quantitative image features derived from 9 biomarkers and morphology, and predicts risk for HGD/EAC in Barrett's esophagus patients. The risk classifier was evaluated in ND, IND, and LGD biopsies from Barrett's esophagus patients diagnosed with HGD/EAC on repeat endoscopy (prevalent cases, n = 30, median time to HGD/EAC diagnosis 140.5 days) and nonprogressors (controls, n = 145, median HGD/EAC-free surveillance time 2,015 days). The risk classifier stratified prevalent cases and non-progressor patients into low-, intermediate-, and high-risk classes [OR, 46.0; 95% confidence interval, 14.86-169 (high-risk vs. low-risk); P < 0.0001]. The classifier also provided independent prognostic information that outperformed the subspecialist and generalist diagnosis. A tissue systems pathology test better predicts prevalent HGD/EAC in Barrett's esophagus patients than pathologic variables. The results indicate that molecular and cellular changes associated with malignant transformation in Barrett's esophagus may be detectable as a field effect using the test. A tissue systems pathology test may provide an objective method to facilitate earlier identification of Barrett's esophagus patients requiring therapeutic intervention. Cancer Epidemiol Biomarkers Prev; 26(2); 240-8. ©2016 AACR. ©2016 American Association for Cancer Research.

Far-red to near infrared emission and scattering spectroscopy for biomedical applications

NASA Astrophysics Data System (ADS)

Zhang, Gang

2001-06-01

The thesis investigates the far-red and near infrared (NIR) spectral region from biomedical tissue samples for monitoring the state of tissues. The NIR emission wing intensity is weak in comparison to the emission in the visible spectral region. The wing emission from biomedical samples has revealed meaningful information about the state of the tissues. A model is presented to explain the shape of the spectral wing based on a continuum of energy levels. The wing can be used to classify different kinds of tissues; especially it can be used to differentiate cancer part from normal human breast tissues. The research work of the far-red emission from thermal damaged tissue samples shows that the emission intensity in this spectral region is proportional to the extent of the thermal damage of the tissue. Near infrared spectral absorption method is used to investigate blood hemodynamics (perfusion and oxygenation) in brain during sleep-wake transition. The result of the research demonstrates that the continuous wave (CW) type near infrared spectroscopy (NIRS) device can be used to investigate brain blood perfusion and oxygenation with a similar precision with frequency domain (FD) type device. The human subject sleep and wake transition, has been monitored by CW type NIRS instrument with traditional electroencephalograph (EEG) method. Parallel change in oxy-Hb and deoxy-Hb is a discrete event that occurs in the transition from both sleep to wakefulness and wakefulness to sleep. These hemodynamic switches are generally about few seconds delayed from the human decided transition point between sleep and wake on the polygraph EEG recording paper. The combination of NIRS and EEG methods monitor the brain activity, gives more information about the brain activity. The sleep apnea investigation was associated with recurrent apneas, insufficient nasal continuous positive airway pressure (CPAP) and the different response of the peripheral and central compartments to breathing events. The different results with finger pulse oximetry and NIRS suggest that optical monitoring of the brain may have advantages that may help clarify the morbidity of obstructive sleep apnea (OSA) Syndrome.

Genome-scale approaches to the epigenetics of common human disease

PubMed Central

2011-01-01

Traditionally, the pathology of human disease has been focused on microscopic examination of affected tissues, chemical and biochemical analysis of biopsy samples, other available samples of convenience, such as blood, and noninvasive or invasive imaging of varying complexity, in order to classify disease and illuminate its mechanistic basis. The molecular age has complemented this armamentarium with gene expression arrays and selective analysis of individual genes. However, we are entering a new era of epigenomic profiling, i.e., genome-scale analysis of cell-heritable nonsequence genetic change, such as DNA methylation. The epigenome offers access to stable measurements of cellular state and to biobanked material for large-scale epidemiological studies. Some of these genome-scale technologies are beginning to be applied to create the new field of epigenetic epidemiology. PMID:19844740

Multi-class texture analysis in colorectal cancer histology

NASA Astrophysics Data System (ADS)

Kather, Jakob Nikolas; Weis, Cleo-Aron; Bianconi, Francesco; Melchers, Susanne M.; Schad, Lothar R.; Gaiser, Timo; Marx, Alexander; Zöllner, Frank Gerrit

2016-06-01

Automatic recognition of different tissue types in histological images is an essential part in the digital pathology toolbox. Texture analysis is commonly used to address this problem; mainly in the context of estimating the tumour/stroma ratio on histological samples. However, although histological images typically contain more than two tissue types, only few studies have addressed the multi-class problem. For colorectal cancer, one of the most prevalent tumour types, there are in fact no published results on multiclass texture separation. In this paper we present a new dataset of 5,000 histological images of human colorectal cancer including eight different types of tissue. We used this set to assess the classification performance of a wide range of texture descriptors and classifiers. As a result, we found an optimal classification strategy that markedly outperformed traditional methods, improving the state of the art for tumour-stroma separation from 96.9% to 98.6% accuracy and setting a new standard for multiclass tissue separation (87.4% accuracy for eight classes). We make our dataset of histological images publicly available under a Creative Commons license and encourage other researchers to use it as a benchmark for their studies.

Classification of Genes and Putative Biomarker Identification Using Distribution Metrics on Expression Profiles

PubMed Central

Huang, Hung-Chung; Jupiter, Daniel; VanBuren, Vincent

2010-01-01

Background Identification of genes with switch-like properties will facilitate discovery of regulatory mechanisms that underlie these properties, and will provide knowledge for the appropriate application of Boolean networks in gene regulatory models. As switch-like behavior is likely associated with tissue-specific expression, these gene products are expected to be plausible candidates as tissue-specific biomarkers. Methodology/Principal Findings In a systematic classification of genes and search for biomarkers, gene expression profiles (GEPs) of more than 16,000 genes from 2,145 mouse array samples were analyzed. Four distribution metrics (mean, standard deviation, kurtosis and skewness) were used to classify GEPs into four categories: predominantly-off, predominantly-on, graded (rheostatic), and switch-like genes. The arrays under study were also grouped and examined by tissue type. For example, arrays were categorized as ‘brain group’ and ‘non-brain group’; the Kolmogorov-Smirnov distance and Pearson correlation coefficient were then used to compare GEPs between brain and non-brain for each gene. We were thus able to identify tissue-specific biomarker candidate genes. Conclusions/Significance The methodology employed here may be used to facilitate disease-specific biomarker discovery. PMID:20140228

Discriminating solitary cysts from soft tissue lesions in mammography using a pretrained deep convolutional neural network.

PubMed

Kooi, Thijs; van Ginneken, Bram; Karssemeijer, Nico; den Heeten, Ard

2017-03-01

It is estimated that 7% of women in the western world will develop palpable breast cysts in their lifetime. Even though cysts have been correlated with risk of developing breast cancer, many of them are benign and do not require follow-up. We develop a method to discriminate benign solitary cysts from malignant masses in digital mammography. We think a system like this can have merit in the clinic as a decision aid or complementary to specialized modalities. We employ a deep convolutional neural network (CNN) to classify cyst and mass patches. Deep CNNs have been shown to be powerful classifiers, but need a large amount of training data for which medical problems are often difficult to come by. The key contribution of this paper is that we show good performance can be obtained on a small dataset by pretraining the network on a large dataset of a related task. We subsequently investigate the following: (a) when a mammographic exam is performed, two different views of the same breast are recorded. We investigate the merit of combining the output of the classifier from these two views. (b) We evaluate the importance of the resolution of the patches fed to the network. (c) A method dubbed tissue augmentation is subsequently employed, where we extract normal tissue from normal patches and superimpose this onto the actual samples aiming for a classifier invariant to occluding tissue. (d) We combine the representation extracted using the deep CNN with our previously developed features. We show that using the proposed deep learning method, an area under the ROC curve (AUC) value of 0.80 can be obtained on a set of benign solitary cysts and malignant mass findings recalled in screening. We find that it works significantly better than our previously developed approach by comparing the AUC of the ROC using bootstrapping. By combining views, the results can be further improved, though this difference was not found to be significant. We find no significant difference between using a resolution of 100 versus 200 micron. The proposed tissue augmentations give a small improvement in performance, but this improvement was also not found to be significant. The final system obtained an AUC of 0.80 with 95% confidence interval [0.78, 0.83], calculated using bootstrapping. The system works best for lesions larger than 27 mm where it obtains an AUC value of 0.87. We have presented a computer-aided diagnosis (CADx) method to discriminate cysts from solid lesion in mammography using features from a deep CNN trained on a large set of mass candidates, obtaining an AUC of 0.80 on a set of diagnostic exams recalled from screening. We believe the system shows great potential and comes close to the performance of recently developed spectral mammography. We think the system can be further improved when more data and computational power becomes available. © 2017 American Association of Physicists in Medicine.

Textural analysis of optical coherence tomography skin images: quantitative differentiation between healthy and cancerous tissues

NASA Astrophysics Data System (ADS)

Adabi, Saba; Conforto, Silvia; Hosseinzadeh, Matin; Noe, Shahryar; Daveluy, Steven; Mehregan, Darius; Nasiriavanaki, Mohammadreza

2017-02-01

Optical Coherence Tomography (OCT) offers real-time high-resolution three-dimensional images of tissue microstructures. In this study, we used OCT skin images acquired from ten volunteers, neither of whom had any skin conditions addressing the features of their anatomic location. OCT segmented images are analyzed based on their optical properties (attenuation coefficient) and textural image features e.g., contrast, correlation, homogeneity, energy, entropy, etc. Utilizing the information and referring to their clinical insight, we aim to make a comprehensive computational model for the healthy skin. The derived parameters represent the OCT microstructural morphology and might provide biological information for generating an atlas of normal skin from different anatomic sites of human skin and may allow for identification of cell microstructural changes in cancer patients. We then compared the parameters of healthy samples with those of abnormal skin and classified them using a linear Support Vector Machines (SVM) with 82% accuracy.

Veterinary Medicine and Multi-Omics Research for Future Nutrition Targets: Metabolomics and Transcriptomics of the Common Degenerative Mitral Valve Disease in Dogs.

PubMed

Li, Qinghong; Freeman, Lisa M; Rush, John E; Huggins, Gordon S; Kennedy, Adam D; Labuda, Jeffrey A; Laflamme, Dorothy P; Hannah, Steven S

2015-08-01

Canine degenerative mitral valve disease (DMVD) is the most common form of heart disease in dogs. The objective of this study was to identify cellular and metabolic pathways that play a role in DMVD by performing metabolomics and transcriptomics analyses on serum and tissue (mitral valve and left ventricle) samples previously collected from dogs with DMVD or healthy hearts. Gas or liquid chromatography followed by mass spectrophotometry were used to identify metabolites in serum. Transcriptomics analysis of tissue samples was completed using RNA-seq, and selected targets were confirmed by RT-qPCR. Random Forest analysis was used to classify the metabolites that best predicted the presence of DMVD. Results identified 41 known and 13 unknown serum metabolites that were significantly different between healthy and DMVD dogs, representing alterations in fat and glucose energy metabolism, oxidative stress, and other pathways. The three metabolites with the greatest single effect in the Random Forest analysis were γ-glutamylmethionine, oxidized glutathione, and asymmetric dimethylarginine. Transcriptomics analysis identified 812 differentially expressed transcripts in left ventricle samples and 263 in mitral valve samples, representing changes in energy metabolism, antioxidant function, nitric oxide signaling, and extracellular matrix homeostasis pathways. Many of the identified alterations may benefit from nutritional or medical management. Our study provides evidence of the growing importance of integrative approaches in multi-omics research in veterinary and nutritional sciences.

Genome-wide DNA methylation sequencing reveals miR-663a is a novel epimutation candidate in CIMP-high endometrial cancer

PubMed Central

Yanokura, Megumi; Banno, Kouji; Adachi, Masataka; Aoki, Daisuke; Abe, Kuniya

2017-01-01

Aberrant DNA methylation is widely observed in many cancers. Concurrent DNA methylation of multiple genes occurs in endometrial cancer and is referred to as the CpG island methylator phenotype (CIMP). However, the features and causes of CIMP-positive endometrial cancer are not well understood. To investigate DNA methylation features characteristic to CIMP-positive endometrial cancer, we first classified samples from 25 patients with endometrial cancer based on the methylation status of three genes, i.e. MLH1, CDH1 (E-cadherin) and APC: CIMP-high (CIMP-H, 2/25, 8.0%), CIMP-low (CIMP-L, 7/25, 28.0%) and CIMP-negative (CIMP(-), 16/25, 64.0%). We then selected two samples each from CIMP-H and CIMP(-) classes, and analyzed DNA methylation status of both normal (peripheral blood cells: PBCs) and cancer tissues by genome-wide, targeted bisulfite sequencing. Genomes of the CIMP-H cancer tissues were significantly hypermethylated compared to those of the CIMP(-). Surprisingly, in normal tissues of the CIMP-H patients, promoter region of the miR-663a locus is hypermethylated relative to CIMP(-) samples. Consistent with this finding, miR-663a expression was lower in the CIMP-H PBCs than in the CIMP(-) PBCs. The same region of the miR663a locus is found to be highly methylated in cancer tissues of both CIMP-H and CIMP(-) cases. This is the first report showing that aberrant DNA methylation of the miR-663a promoter can occur in normal tissue of the cancer patients, suggesting a possible link between this epigenetic abnormality and endometrial cancer. This raises the possibility that the hypermethylation of the miR-663a promoter represents an epimutation associated with the CIMP-H endometrial cancers. Based on these findings, relationship of the aberrant DNA methylation and CIMP-H phenotype is discussed. PMID:28440489

Genome-wide DNA methylation sequencing reveals miR-663a is a novel epimutation candidate in CIMP-high endometrial cancer.

PubMed

Yanokura, Megumi; Banno, Kouji; Adachi, Masataka; Aoki, Daisuke; Abe, Kuniya

2017-06-01

Aberrant DNA methylation is widely observed in many cancers. Concurrent DNA methylation of multiple genes occurs in endometrial cancer and is referred to as the CpG island methylator phenotype (CIMP). However, the features and causes of CIMP-positive endometrial cancer are not well understood. To investigate DNA methylation features characteristic to CIMP-positive endometrial cancer, we first classified samples from 25 patients with endometrial cancer based on the methylation status of three genes, i.e. MLH1, CDH1 (E-cadherin) and APC: CIMP-high (CIMP-H, 2/25, 8.0%), CIMP-low (CIMP-L, 7/25, 28.0%) and CIMP-negative (CIMP(-), 16/25, 64.0%). We then selected two samples each from CIMP-H and CIMP(-) classes, and analyzed DNA methylation status of both normal (peripheral blood cells: PBCs) and cancer tissues by genome-wide, targeted bisulfite sequencing. Genomes of the CIMP-H cancer tissues were significantly hypermethylated compared to those of the CIMP(-). Surprisingly, in normal tissues of the CIMP-H patients, promoter region of the miR-663a locus is hypermethylated relative to CIMP(-) samples. Consistent with this finding, miR-663a expression was lower in the CIMP-H PBCs than in the CIMP(-) PBCs. The same region of the miR663a locus is found to be highly methylated in cancer tissues of both CIMP-H and CIMP(-) cases. This is the first report showing that aberrant DNA methylation of the miR-663a promoter can occur in normal tissue of the cancer patients, suggesting a possible link between this epigenetic abnormality and endometrial cancer. This raises the possibility that the hypermethylation of the miR-663a promoter represents an epimutation associated with the CIMP-H endometrial cancers. Based on these findings, relationship of the aberrant DNA methylation and CIMP-H phenotype is discussed.

Targeted MS Assay Predicting Tamoxifen Resistance in Estrogen-Receptor-Positive Breast Cancer Tissues and Sera.

PubMed

De Marchi, Tommaso; Kuhn, Erik; Dekker, Lennard J; Stingl, Christoph; Braakman, Rene B H; Opdam, Mark; Linn, Sabine C; Sweep, Fred C G J; Span, Paul N; Luider, Theo M; Foekens, John A; Martens, John W M; Carr, Steven A; Umar, Arzu

2016-04-01

We recently reported on the development of a 4-protein-based classifier (PDCD4, CGN, G3BP2, and OCIAD1) capable of predicting outcome to tamoxifen treatment in recurrent, estrogen-receptor-positive breast cancer based on high-resolution MS data. A precise and high-throughput assay to measure these proteins in a multiplexed, targeted fashion would be favorable to measure large numbers of patient samples to move these findings toward a clinical setting. By coupling immunoprecipitation to multiple reaction monitoring (MRM) MS and stable isotope dilution, we developed a high-precision assay to measure the 4-protein signature in 38 primary breast cancer whole tissue lysates (WTLs). Furthermore, we evaluated the presence and patient stratification capabilities of our signature in an independent set of 24 matched (pre- and post-therapy) sera. We compared the performance of immuno-MRM (iMRM) with direct MRM in the absence of fractionation and shotgun proteomics in combination with label-free quantification (LFQ) on both WTL and laser capture microdissected (LCM) tissues. Measurement of the 4-proteins by iMRM showed not only higher accuracy in measuring proteotypic peptides (Spearman r: 0.74 to 0.93) when compared with MRM (Spearman r: 0.0 to 0.76) but also significantly discriminated patient groups based on treatment outcome (hazard ratio [HR]: 10.96; 95% confidence interval [CI]: 4.33 to 27.76; Log-rank P < 0.001) when compared with LCM (HR: 2.85; 95% CI: 1.24 to 6.54; Log-rank P = 0.013) and WTL (HR: 1.16; 95% CI: 0.57 to 2.33; Log-rank P = 0.680) LFQ-based predictors. Serum sample analysis by iMRM confirmed the detection of the four proteins in these samples. We hereby report that iMRM outperformed regular MRM, confirmed our previous high-resolution MS results in tumor tissues, and has shown that the 4-protein signature is measurable in serum samples.

Algorithms and Results of Eye Tissues Differentiation Based on RF Ultrasound

PubMed Central

Jurkonis, R.; Janušauskas, A.; Marozas, V.; Jegelevičius, D.; Daukantas, S.; Patašius, M.; Paunksnis, A.; Lukoševičius, A.

2012-01-01

Algorithms and software were developed for analysis of B-scan ultrasonic signals acquired from commercial diagnostic ultrasound system. The algorithms process raw ultrasonic signals in backscattered spectrum domain, which is obtained using two time-frequency methods: short-time Fourier and Hilbert-Huang transformations. The signals from selected regions of eye tissues are characterized by parameters: B-scan envelope amplitude, approximated spectral slope, approximated spectral intercept, mean instantaneous frequency, mean instantaneous bandwidth, and parameters of Nakagami distribution characterizing Hilbert-Huang transformation output. The backscattered ultrasound signal parameters characterizing intraocular and orbit tissues were processed by decision tree data mining algorithm. The pilot trial proved that applied methods are able to correctly classify signals from corpus vitreum blood, extraocular muscle, and orbit tissues. In 26 cases of ocular tissues classification, one error occurred, when tissues were classified into classes of corpus vitreum blood, extraocular muscle, and orbit tissue. In this pilot classification parameters of spectral intercept and Nakagami parameter for instantaneous frequencies distribution of the 1st intrinsic mode function were found specific for corpus vitreum blood, orbit and extraocular muscle tissues. We conclude that ultrasound data should be further collected in clinical database to establish background for decision support system for ocular tissue noninvasive differentiation. PMID:22654643

Soy sauce classification by geographic region and fermentation based on artificial neural network and genetic algorithm.

PubMed

Xu, Libin; Li, Yang; Xu, Ning; Hu, Yong; Wang, Chao; He, Jianjun; Cao, Yueze; Chen, Shigui; Li, Dongsheng

2014-12-24

This work demonstrated the possibility of using artificial neural networks to classify soy sauce from China. The aroma profiles of different soy sauce samples were differentiated using headspace solid-phase microextraction. The soy sauce samples were analyzed by gas chromatography-mass spectrometry, and 22 and 15 volatile aroma compounds were selected for sensitivity analysis to classify the samples by fermentation and geographic region, respectively. The 15 selected samples can be classified by fermentation and geographic region with a prediction success rate of 100%. Furans and phenols represented the variables with the greatest contribution in classifying soy sauce samples by fermentation and geographic region, respectively.

Molecular Signature for Lymphatic Invasion Associated with Survival of Epithelial Ovarian Cancer.

PubMed

Paik, E Sun; Choi, Hyun Jin; Kim, Tae-Joong; Lee, Jeong-Won; Kim, Byoung-Gie; Bae, Duk-Soo; Choi, Chel Hun

2018-04-01

We aimed to develop molecular classifier that can predict lymphatic invasion and their clinical significance in epithelial ovarian cancer (EOC) patients. We analyzed gene expression (mRNA, methylated DNA) in data from The Cancer Genome Atlas. To identify molecular signatures for lymphatic invasion, we found differentially expressed genes. The performance of classifier was validated by receiver operating characteristics analysis, logistic regression, linear discriminant analysis (LDA), and support vector machine (SVM). We assessed prognostic role of classifier using random survival forest (RSF) model and pathway deregulation score (PDS). For external validation,we analyzed microarray data from 26 EOC samples of Samsung Medical Center and curatedOvarianData database. We identified 21 mRNAs, and seven methylated DNAs from primary EOC tissues that predicted lymphatic invasion and created prognostic models. The classifier predicted lymphatic invasion well, which was validated by logistic regression, LDA, and SVM algorithm (C-index of 0.90, 0.71, and 0.74 for mRNA and C-index of 0.64, 0.68, and 0.69 for DNA methylation). Using RSF model, incorporating molecular data with clinical variables improved prediction of progression-free survival compared with using only clinical variables (p < 0.001 and p=0.008). Similarly, PDS enabled us to classify patients into high-risk and low-risk group, which resulted in survival difference in mRNA profiles (log-rank p-value=0.011). In external validation, gene signature was well correlated with prediction of lymphatic invasion and patients' survival. Molecular signature model predicting lymphatic invasion was well performed and also associated with survival of EOC patients.

Using X-Ray In-Line Phase-Contrast Imaging for the Investigation of Nude Mouse Hepatic Tumors

PubMed Central

Zhang, Lu; Luo, Shuqian

2012-01-01

The purpose of this paper is to report the noninvasive imaging of hepatic tumors without contrast agents. Both normal tissues and tumor tissues can be detected, and tumor tissues in different stages can be classified quantitatively. We implanted BEL-7402 human hepatocellular carcinoma cells into the livers of nude mice and then imaged the livers using X-ray in-line phase-contrast imaging (ILPCI). The projection images' texture feature based on gray level co-occurrence matrix (GLCM) and dual-tree complex wavelet transforms (DTCWT) were extracted to discriminate normal tissues and tumor tissues. Different stages of hepatic tumors were classified using support vector machines (SVM). Images of livers from nude mice sacrificed 6 days after inoculation with cancer cells show diffuse distribution of the tumor tissue, but images of livers from nude mice sacrificed 9, 12, or 15 days after inoculation with cancer cells show necrotic lumps in the tumor tissue. The results of the principal component analysis (PCA) of the texture features based on GLCM of normal regions were positive, but those of tumor regions were negative. The results of PCA of the texture features based on DTCWT of normal regions were greater than those of tumor regions. The values of the texture features in low-frequency coefficient images increased monotonically with the growth of the tumors. Different stages of liver tumors can be classified using SVM, and the accuracy is 83.33%. Noninvasive and micron-scale imaging can be achieved by X-ray ILPCI. We can observe hepatic tumors and small vessels from the phase-contrast images. This new imaging approach for hepatic cancer is effective and has potential use in the early detection and classification of hepatic tumors. PMID:22761929

Detection and characterization of glaucoma-like canine retinal tissues using Raman spectroscopy

NASA Astrophysics Data System (ADS)

Wang, Qi; Grozdanic, Sinisa D.; Harper, Matthew M.; Hamouche, Karl; Hamouche, Nicholas; Kecova, Helga; Lazic, Tatjana; Hernandez-Merino, Elena; Yu, Chenxu

2013-06-01

Early detection of pathological changes and progression in glaucoma and other neuroretinal diseases remains a great challenge and is critical to reduce permanent structural and functional retina and optic nerve damage. Raman spectroscopy is a sensitive technique that provides rapid biochemical characterization of tissues in a nondestructive and noninvasive fashion. In this study, spectroscopic analysis was conducted on the retinal tissues of seven beagles with acute elevation of intraocular pressure (AEIOP), six beagles with compressive optic neuropathy (CON), and five healthy beagles. Spectroscopic markers were identified associated with the different neuropathic conditions. Furthermore, the Raman spectra were subjected to multivariate discriminate analysis to classify independent tissue samples into diseased/healthy categories. The multivariate discriminant model yielded an average optimal classification accuracy of 72.6% for AEIOP and 63.4% for CON with 20 principal components being used that accounted for 87% of the total variance in the data set. A strong correlation (R2>0.92) was observed between pattern electroretinography characteristics of AEIOP dogs and Raman separation distance that measures the separation of spectra of diseased tissues from normal tissues; however, the underlining mechanism of this correlation remains to be understood. Since AEIOP mimics the pathological symptoms of acute/early-stage glaucoma, it was demonstrated that Raman spectroscopic screening has the potential to become a powerful tool for the detection and characterization of early-stage disease.

Long non-coding RNA expression profile in cervical cancer tissues

PubMed Central

Zhu, Hua; Chen, Xiangjian; Hu, Yan; Shi, Zhengzheng; Zhou, Qing; Zheng, Jingjie; Wang, Yifeng

2017-01-01

Cervical cancer (CC), one of the most common types of cancer of the female population, presents an enormous challenge in diagnosis and treatment. Long non-coding (lnc)RNAs, non-coding (nc)RNAs with length >200 nucleotides, have been identified to be associated with multiple types of cancer, including CC. This class of nc transcripts serves an important role in tumor suppression and oncogenic signaling pathways. In the present study, the microarray method was used to obtain the expression profile of lncRNAs and protein-coding mRNAs and to compare the expression of lncRNAs between CC tissues and corresponding adjacent non-cancerous tissues in order to screen potential lncRNAs for associations with CC. Overall, 3356 lncRNAs with significantly different expression pattern in CC tissues compared with adjacent non-cancerous tissues were identified, while 1,857 of them were upregulated. These differentially expressed lncRNAs were additionally classified into 5 subgroups. Reverse transcription quantitative polymerase chain reactions were performed to validate the expression pattern of 5 random selected lncRNAs, and 2lncRNAs were identified to have significantly different expression in CC samples compared with adjacent non-cancerous tissues. This finding suggests that those lncRNAs with different expression may serve important roles in the development of CC, and the expression data may provide information for additional study on the involvement of lncRNAs in CC. PMID:28789353

Identification of DEP domain-containing proteins by a machine learning method and experimental analysis of their expression in human HCC tissues

NASA Astrophysics Data System (ADS)

Liao, Zhijun; Wang, Xinrui; Zeng, Yeting; Zou, Quan

2016-12-01

The Dishevelled/EGL-10/Pleckstrin (DEP) domain-containing (DEPDC) proteins have seven members. However, whether this superfamily can be distinguished from other proteins based only on the amino acid sequences, remains unknown. Here, we describe a computational method to segregate DEPDCs and non-DEPDCs. First, we examined the Pfam numbers of the known DEPDCs and used the longest sequences for each Pfam to construct a phylogenetic tree. Subsequently, we extracted 188-dimensional (188D) and 20D features of DEPDCs and non-DEPDCs and classified them with random forest classifier. We also mined the motifs of human DEPDCs to find the related domains. Finally, we designed experimental verification methods of human DEPDC expression at the mRNA level in hepatocellular carcinoma (HCC) and adjacent normal tissues. The phylogenetic analysis showed that the DEPDCs superfamily can be divided into three clusters. Moreover, the 188D and 20D features can both be used to effectively distinguish the two protein types. Motif analysis revealed that the DEP and RhoGAP domain was common in human DEPDCs, human HCC and the adjacent tissues that widely expressed DEPDCs. However, their regulation was not identical. In conclusion, we successfully constructed a binary classifier for DEPDCs and experimentally verified their expression in human HCC tissues.

Melanoma metastases in regional lymph nodes are accurately detected by proton magnetic resonance spectroscopy of fine-needle aspirate biopsy samples.

PubMed

Stretch, Jonathan R; Somorjai, Ray; Bourne, Roger; Hsiao, Edward; Scolyer, Richard A; Dolenko, Brion; Thompson, John F; Mountford, Carolyn E; Lean, Cynthia L

2005-11-01

Nonsurgical assessment of sentinel nodes (SNs) would offer advantages over surgical SN excision by reducing morbidity and costs. Proton magnetic resonance spectroscopy (MRS) of fine-needle aspirate biopsy (FNAB) specimens identifies melanoma lymph node metastases. This study was undertaken to determine the accuracy of the MRS method and thereby establish a basis for the future development of a nonsurgical technique for assessing SNs. FNAB samples were obtained from 118 biopsy specimens from 77 patients during SN biopsy and regional lymphadenectomy. The specimens were histologically evaluated and correlated with MRS data. Histopathologic analysis established that 56 specimens contained metastatic melanoma and that 62 specimens were benign. A linear discriminant analysis-based classifier was developed for benign tissues and metastases. The presence of metastatic melanoma in lymph nodes was predicted with a sensitivity of 92.9%, a specificity of 90.3%, and an accuracy of 91.5% in a primary data set. In a second data set that used FNAB samples separate from the original tissue samples, melanoma metastases were predicted with a sensitivity of 87.5%, a specificity of 90.3%, and an accuracy of 89.1%, thus supporting the reproducibility of the method. Proton MRS of FNAB samples may provide a robust and accurate diagnosis of metastatic disease in the regional lymph nodes of melanoma patients. These data indicate the potential for SN staging of melanoma without surgical biopsy and histopathological evaluation.

Multiband tissue classification for ultrasonic transmission tomography using spectral profile detection

NASA Astrophysics Data System (ADS)

Jeong, Jeong-Won; Kim, Tae-Seong; Shin, Dae-Chul; Do, Synho; Marmarelis, Vasilis Z.

2004-04-01

Recently it was shown that soft tissue can be differentiated with spectral unmixing and detection methods that utilize multi-band information obtained from a High-Resolution Ultrasonic Transmission Tomography (HUTT) system. In this study, we focus on tissue differentiation using the spectral target detection method based on Constrained Energy Minimization (CEM). We have developed a new tissue differentiation method called "CEM filter bank". Statistical inference on the output of each CEM filter of a filter bank is used to make a decision based on the maximum statistical significance rather than the magnitude of each CEM filter output. We validate this method through 3-D inter/intra-phantom soft tissue classification where target profiles obtained from an arbitrary single slice are used for differentiation in multiple tomographic slices. Also spectral coherence between target and object profiles of an identical tissue at different slices and phantoms is evaluated by conventional cross-correlation analysis. The performance of the proposed classifier is assessed using Receiver Operating Characteristic (ROC) analysis. Finally we apply our method to classify tiny structures inside a beef kidney such as Styrofoam balls (~1mm), chicken tissue (~5mm), and vessel-duct structures.

Predicting membrane protein types using various decision tree classifiers based on various modes of general PseAAC for imbalanced datasets.

PubMed

Sankari, E Siva; Manimegalai, D

2017-12-21

Predicting membrane protein types is an important and challenging research area in bioinformatics and proteomics. Traditional biophysical methods are used to classify membrane protein types. Due to large exploration of uncharacterized protein sequences in databases, traditional methods are very time consuming, expensive and susceptible to errors. Hence, it is highly desirable to develop a robust, reliable, and efficient method to predict membrane protein types. Imbalanced datasets and large datasets are often handled well by decision tree classifiers. Since imbalanced datasets are taken, the performance of various decision tree classifiers such as Decision Tree (DT), Classification And Regression Tree (CART), C4.5, Random tree, REP (Reduced Error Pruning) tree, ensemble methods such as Adaboost, RUS (Random Under Sampling) boost, Rotation forest and Random forest are analysed. Among the various decision tree classifiers Random forest performs well in less time with good accuracy of 96.35%. Another inference is RUS boost decision tree classifier is able to classify one or two samples in the class with very less samples while the other classifiers such as DT, Adaboost, Rotation forest and Random forest are not sensitive for the classes with fewer samples. Also the performance of decision tree classifiers is compared with SVM (Support Vector Machine) and Naive Bayes classifier. Copyright © 2017 Elsevier Ltd. All rights reserved.

Brain tissue analysis using texture features based on optical coherence tomography images

NASA Astrophysics Data System (ADS)

Lenz, Marcel; Krug, Robin; Dillmann, Christopher; Gerhardt, Nils C.; Welp, Hubert; Schmieder, Kirsten; Hofmann, Martin R.

2018-02-01

Brain tissue differentiation is highly demanded in neurosurgeries, i.e. tumor resection. Exact navigation during the surgery is essential in order to guarantee best life quality afterwards. So far, no suitable method has been found that perfectly covers this demands. With optical coherence tomography (OCT), fast three dimensional images can be obtained in vivo and contactless with a resolution of 1-15 μm. With these specifications OCT is a promising tool to support neurosurgeries. Here, we investigate ex vivo samples of meningioma, healthy white and healthy gray matter in a preliminary study towards in vivo brain tumor removal assistance. Raw OCT images already display structural variations for different tissue types, especially meningioma. But, in order to achieve neurosurgical guidance directly during resection, an automated differentiation approach is desired. For this reason, we employ different texture feature based algorithms, perform a Principal Component Analysis afterwards and then train a Support Vector Machine classifier. In the future we will try different combinations of texture features and perform in vivo measurements in order to validate our findings.

Facial soft tissue thickness of Brazilian adults.

PubMed

Tedeschi-Oliveira, Sílvia Virginia; Melani, Rodolfo Francisco Haltenhoff; de Almeida, Natalie Haddad; de Paiva, Luiz Airton Saavedra

2009-12-15

The auxiliary technique known as Facial Reconstruction enables one to reestablish the contours of the soft tissues over the skull, therefore producing a face and increasing the probability of a facial recognition. The reliability of this technique depends on the evaluation of the mean values of soft tissue thicknesses observed in a given population. Measurements were evaluated in autopsied corpses in "Section of Technical Verification of Deaths" in Guarulhos, São Paulo, Brazil. Thickness was measured manually by puncturing 10 midline craniometrical points and 11 bilateral points on a sample of 40 corpses of both sexes aged between 17 and 90 years, classified by skin color and the nutritional state. The results for the average thickness values are higher for males, variations related to the nutritional state are proportional to the increased fat on the face and age was not significant. The ethnic variable related to skin color when compared to studies with other populations showed differences, with the need for a reference table for a given population application of Facial Reconstruction technique in skulls of non-attributable identity.

Investigation of c-KIT and Ki67 expression in normal, preneoplastic and neoplastic canine prostate.

PubMed

Fonseca-Alves, Carlos Eduardo; Kobayashi, Priscilla Emiko; Palmieri, Chiara; Laufer-Amorim, Renée

2017-12-06

c-KIT expression has been related to bone metastasis in human prostate cancer, but whether c-KIT expression can be similarly classified in canine prostatic tissue is unknown. This study assessed c-KIT and Ki67 expression in canine prostate cancer (PC). c-KIT gene and protein expression and Ki67 expression were evaluated in forty-four canine prostatic tissues by immunohistochemistry, RT-qPCR and western blot. Additionally, we have investigated c-KIT protein expression by immunoblotting in two primary canine prostate cancer cell lines. Eleven normal prostates, 12 proliferative inflammatory atrophy (PIA) prostates, 18 PC, 3 metastatic lesions and two prostate cancer cell cultures (PC1 and PC2) were analysed. The prostatic tissue exhibited varying degrees of membranous, cytoplasmic or membranous/cytoplasmic c-KIT staining. Four normal prostates, 4 PIA and 5 prostatic carcinomas showed positive c-KIT expression. No c-KIT immunoexpression was observed in metastases. Canine prostate cancer and PIA samples contained a higher number of Ki67-positive cells compared to normal samples. The median relative quantification (RQ) for c-KIT expression in normal, PIA and prostate cancer and metastatic samples were 0.6 (0.1-2.5), 0.7 (0.09-2.1), 0.7 (0.09-5.1) and 0.1 (0.07-0.6), respectively. A positive correlation between the number of Ki67-positive cells and c-KIT transcript levels was observed in prostate cancer samples. In the cell line, PC1 was negative for c-KIT protein expression, while PC2 was weakly positive. The present study identified a strong correlation between c-KIT expression and proliferative index, suggesting that c-KIT may influence cell proliferation. Therefore, c-KIT heterogeneous protein expression among the samples (five positive and thirteen negative prostate cancer samples) indicates a personalized approach for canine prostate cancer.

The AEO, an Ontology of Anatomical Entities for Classifying Animal Tissues and Organs

PubMed Central

Bard, Jonathan B. L.

2012-01-01

This paper describes the AEO, an ontology of anatomical entities that expands the common anatomy reference ontology (CARO) and whose major novel feature is a type hierarchy of ~160 anatomical terms. The breadth of the AEO is wider than CARO as it includes both developmental and gender-specific classes, while the granularity of the AEO terms is at a level adequate to classify simple-tissues (~70 classes) characterized by their containing a predominantly single cell-type. For convenience and to facilitate interoperability, the AEO contains an abbreviated version of the ontology of cell-types (~100 classes) that is linked to these simple-tissue types. The AEO was initially based on an analysis of a broad range of animal anatomy ontologies and then upgraded as it was used to classify the ~2500 concepts in a new version of the ontology of human developmental anatomy (www.obofoundry.org/), a process that led to significant improvements in its structure and content, albeit with a possible focus on mammalian embryos. The AEO is intended to provide the formal classification expected in contemporary ontologies as well as capturing knowledge about anatomical structures not currently included in anatomical ontologies. The AEO may thus be useful in increasing the amount of tissue and cell-type knowledge in other anatomy ontologies, facilitating annotation of tissues that share common features, and enabling interoperability across anatomy ontologies. The AEO can be downloaded from http://www.obofoundry.org/. PMID:22347883

The AEO, an Ontology of Anatomical Entities for Classifying Animal Tissues and Organs.

PubMed

Bard, Jonathan B L

2012-01-01

This paper describes the AEO, an ontology of anatomical entities that expands the common anatomy reference ontology (CARO) and whose major novel feature is a type hierarchy of ~160 anatomical terms. The breadth of the AEO is wider than CARO as it includes both developmental and gender-specific classes, while the granularity of the AEO terms is at a level adequate to classify simple-tissues (~70 classes) characterized by their containing a predominantly single cell-type. For convenience and to facilitate interoperability, the AEO contains an abbreviated version of the ontology of cell-types (~100 classes) that is linked to these simple-tissue types. The AEO was initially based on an analysis of a broad range of animal anatomy ontologies and then upgraded as it was used to classify the ~2500 concepts in a new version of the ontology of human developmental anatomy (www.obofoundry.org/), a process that led to significant improvements in its structure and content, albeit with a possible focus on mammalian embryos. The AEO is intended to provide the formal classification expected in contemporary ontologies as well as capturing knowledge about anatomical structures not currently included in anatomical ontologies. The AEO may thus be useful in increasing the amount of tissue and cell-type knowledge in other anatomy ontologies, facilitating annotation of tissues that share common features, and enabling interoperability across anatomy ontologies. The AEO can be downloaded from http://www.obofoundry.org/.

Endoglin (CD105) expression differentiates between aseptic loosening and periprosthetic joint infection after total joint arthroplasty.

PubMed

Jansen, Philipp; Mumme, Torsten; Randau, Thomas; Gravius, Sascha; Hermanns-Sachweh, Benita

2014-01-01

The differentiation between aseptic loosening and periprosthetic joint infection (PJI) after total joint arthroplasty is essential for successful therapy. A better understanding of pathogenesis of aseptic loosening and PJI may help to prevent or treat these complications. Previous investigations revealed an increased vascularization in the periprosthetic membrane in cases of PJI via PET signals. Based on these findings our hypothesis was that PJI is associated with an increased neovascularization in the periprosthetic membrane. Tissue samples from periprosthetic membranes of the bone-implant interface were investigated histologically for inflammation, wear particles, vascularization and fibrosis. To identify vascular structures antibodies against CD 31, CD 34, factor VIII and CD 105 (endoglin) were applied for immunohistochemical investigations. According to a consensus classification of Morawietz the tissue samples were divided into four types: type I (wear particle induced type, n = 11), type II (infectious type, n = 7), type III (combined type, n = 7) and type IV (indeterminate type, n = 7). Patients with PJI (type II) showed a pronounced infiltration of neutrophil granulocytes in the periprosthetic membrane and an enhanced neovascularization indicated by positive immunoreaction with antibodies against CD 105 (endoglin). Tissue samples classified as type I, type III and type IV showed significantly less immune reaction for CD 105. In cases of aseptic loosening and PJI vascularization is found in different expression in periprosthetic membranes. However, in aseptic loosening, there is nearly no neovascularization with CD 105-positive immune reaction. Therefore, endoglin (CD 105) expression allows for differentiation between aseptic loosening and PJI.

Tissue Equivalent Phantom Design for Characterization of a Coherent Scatter X-ray Imaging System

NASA Astrophysics Data System (ADS)

Albanese, Kathryn Elizabeth

Scatter in medical imaging is typically cast off as image-related noise that detracts from meaningful diagnosis. It is therefore typically rejected or removed from medical images. However, it has been found that every material, including cancerous tissue, has a unique X-ray coherent scatter signature that can be used to identify the material or tissue. Such scatter-based tissue-identification provides the advantage of locating and identifying particular materials over conventional anatomical imaging through X-ray radiography. A coded aperture X-ray coherent scatter spectral imaging system has been developed in our group to classify different tissue types based on their unique scatter signatures. Previous experiments using our prototype have demonstrated that the depth-resolved coherent scatter spectral imaging system (CACSSI) can discriminate healthy and cancerous tissue present in the path of a non-destructive x-ray beam. A key to the successful optimization of CACSSI as a clinical imaging method is to obtain anatomically accurate phantoms of the human body. This thesis describes the development and fabrication of 3D printed anatomical scatter phantoms of the breast and lung. The purpose of this work is to accurately model different breast geometries using a tissue equivalent phantom, and to classify these tissues in a coherent x-ray scatter imaging system. Tissue-equivalent anatomical phantoms were designed to assess the capability of the CACSSI system to classify different types of breast tissue (adipose, fibroglandular, malignant). These phantoms were 3D printed based on DICOM data obtained from CT scans of prone breasts. The phantoms were tested through comparison of measured scatter signatures with those of adipose and fibroglandular tissue from literature. Tumors in the phantom were modeled using a variety of biological tissue including actual surgically excised benign and malignant tissue specimens. Lung based phantoms have also been printed for future testing. Our imaging system has been able to define the location and composition of the various materials in the phantom. These phantoms were used to characterize the CACSSI system in terms of beam width and imaging technique. The result of this work showed accurate modeling and characterization of the phantoms through comparison of the tissue-equivalent form factors to those from literature. The physical construction of the phantoms, based on actual patient anatomy, was validated using mammography and computed tomography to visually compare the clinical images to those of actual patient anatomy.

Consensus Classification Using Non-Optimized Classifiers.

PubMed

Brownfield, Brett; Lemos, Tony; Kalivas, John H

2018-04-03

Classifying samples into categories is a common problem in analytical chemistry and other fields. Classification is usually based on only one method, but numerous classifiers are available with some being complex, such as neural networks, and others are simple, such as k nearest neighbors. Regardless, most classification schemes require optimization of one or more tuning parameters for best classification accuracy, sensitivity, and specificity. A process not requiring exact selection of tuning parameter values would be useful. To improve classification, several ensemble approaches have been used in past work to combine classification results from multiple optimized single classifiers. The collection of classifications for a particular sample are then combined by a fusion process such as majority vote to form the final classification. Presented in this Article is a method to classify a sample by combining multiple classification methods without specifically classifying the sample by each method, that is, the classification methods are not optimized. The approach is demonstrated on three analytical data sets. The first is a beer authentication set with samples measured on five instruments, allowing fusion of multiple instruments by three ways. The second data set is composed of textile samples from three classes based on Raman spectra. This data set is used to demonstrate the ability to classify simultaneously with different data preprocessing strategies, thereby reducing the need to determine the ideal preprocessing method, a common prerequisite for accurate classification. The third data set contains three wine cultivars for three classes measured at 13 unique chemical and physical variables. In all cases, fusion of nonoptimized classifiers improves classification. Also presented are atypical uses of Procrustes analysis and extended inverted signal correction (EISC) for distinguishing sample similarities to respective classes.

Digital immunohistochemistry platform for the staining variation monitoring based on integration of image and statistical analyses with laboratory information system.

PubMed

Laurinaviciene, Aida; Plancoulaine, Benoit; Baltrusaityte, Indra; Meskauskas, Raimundas; Besusparis, Justinas; Lesciute-Krilaviciene, Daiva; Raudeliunas, Darius; Iqbal, Yasir; Herlin, Paulette; Laurinavicius, Arvydas

2014-01-01

Digital immunohistochemistry (IHC) is one of the most promising applications brought by new generation image analysis (IA). While conventional IHC staining quality is monitored by semi-quantitative visual evaluation of tissue controls, IA may require more sensitive measurement. We designed an automated system to digitally monitor IHC multi-tissue controls, based on SQL-level integration of laboratory information system with image and statistical analysis tools. Consecutive sections of TMA containing 10 cores of breast cancer tissue were used as tissue controls in routine Ki67 IHC testing. Ventana slide label barcode ID was sent to the LIS to register the serial section sequence. The slides were stained and scanned (Aperio ScanScope XT), IA was performed by the Aperio/Leica Colocalization and Genie Classifier/Nuclear algorithms. SQL-based integration ensured automated statistical analysis of the IA data by the SAS Enterprise Guide project. Factor analysis and plot visualizations were performed to explore slide-to-slide variation of the Ki67 IHC staining results in the control tissue. Slide-to-slide intra-core IHC staining analysis revealed rather significant variation of the variables reflecting the sample size, while Brown and Blue Intensity were relatively stable. To further investigate this variation, the IA results from the 10 cores were aggregated to minimize tissue-related variance. Factor analysis revealed association between the variables reflecting the sample size detected by IA and Blue Intensity. Since the main feature to be extracted from the tissue controls was staining intensity, we further explored the variation of the intensity variables in the individual cores. MeanBrownBlue Intensity ((Brown+Blue)/2) and DiffBrownBlue Intensity (Brown-Blue) were introduced to better contrast the absolute intensity and the colour balance variation in each core; relevant factor scores were extracted. Finally, tissue-related factors of IHC staining variance were explored in the individual tissue cores. Our solution enabled to monitor staining of IHC multi-tissue controls by the means of IA, followed by automated statistical analysis, integrated into the laboratory workflow. We found that, even in consecutive serial tissue sections, tissue-related factors affected the IHC IA results; meanwhile, less intense blue counterstain was associated with less amount of tissue, detected by the IA tools.

Digital immunohistochemistry platform for the staining variation monitoring based on integration of image and statistical analyses with laboratory information system

PubMed Central

2014-01-01

Background Digital immunohistochemistry (IHC) is one of the most promising applications brought by new generation image analysis (IA). While conventional IHC staining quality is monitored by semi-quantitative visual evaluation of tissue controls, IA may require more sensitive measurement. We designed an automated system to digitally monitor IHC multi-tissue controls, based on SQL-level integration of laboratory information system with image and statistical analysis tools. Methods Consecutive sections of TMA containing 10 cores of breast cancer tissue were used as tissue controls in routine Ki67 IHC testing. Ventana slide label barcode ID was sent to the LIS to register the serial section sequence. The slides were stained and scanned (Aperio ScanScope XT), IA was performed by the Aperio/Leica Colocalization and Genie Classifier/Nuclear algorithms. SQL-based integration ensured automated statistical analysis of the IA data by the SAS Enterprise Guide project. Factor analysis and plot visualizations were performed to explore slide-to-slide variation of the Ki67 IHC staining results in the control tissue. Results Slide-to-slide intra-core IHC staining analysis revealed rather significant variation of the variables reflecting the sample size, while Brown and Blue Intensity were relatively stable. To further investigate this variation, the IA results from the 10 cores were aggregated to minimize tissue-related variance. Factor analysis revealed association between the variables reflecting the sample size detected by IA and Blue Intensity. Since the main feature to be extracted from the tissue controls was staining intensity, we further explored the variation of the intensity variables in the individual cores. MeanBrownBlue Intensity ((Brown+Blue)/2) and DiffBrownBlue Intensity (Brown-Blue) were introduced to better contrast the absolute intensity and the colour balance variation in each core; relevant factor scores were extracted. Finally, tissue-related factors of IHC staining variance were explored in the individual tissue cores. Conclusions Our solution enabled to monitor staining of IHC multi-tissue controls by the means of IA, followed by automated statistical analysis, integrated into the laboratory workflow. We found that, even in consecutive serial tissue sections, tissue-related factors affected the IHC IA results; meanwhile, less intense blue counterstain was associated with less amount of tissue, detected by the IA tools. PMID:25565007

Liquid-Based Medium Used to Prepare Cytological Breast Nipple Fluid Improves the Quality of Cellular Samples Automatic Collection

PubMed Central

Zonta, Marco Antonio; Velame, Fernanda; Gema, Samara; Filassi, Jose Roberto; Longatto-Filho, Adhemar

2014-01-01

Background Breast cancer is the second cause of death in women worldwide. The spontaneous breast nipple discharge may contain cells that can be analyzed for malignancy. Halo® Mamo Cyto Test (HMCT) was recently developed as an automated system indicated to aspirate cells from the breast ducts. The objective of this study was to standardize the methodology of sampling and sample preparation of nipple discharge obtained by the automated method Halo breast test and perform cytological evaluation in samples preserved in liquid medium (SurePath™). Methods We analyzed 564 nipple fluid samples, from women between 20 and 85 years old, without history of breast disease and neoplasia, no pregnancy, and without gynecologic medical history, collected by HMCT method and preserved in two different vials with solutions for transport. Results From 306 nipple fluid samples from method 1, 199 (65%) were classified as unsatisfactory (class 0), 104 (34%) samples were classified as benign findings (class II), and three (1%) were classified as undetermined to neoplastic cells (class III). From 258 samples analyzed in method 2, 127 (49%) were classified as class 0, 124 (48%) were classified as class II, and seven (2%) were classified as class III. Conclusion Our study suggests an improvement in the quality and quantity of cellular samples when the association of the two methodologies is performed, Halo breast test and the method in liquid medium. PMID:29147397

Proteoglycan depletion and size reduction in lesions of early grade chondromalacia of the patella.

PubMed

Väätäinen, U; Häkkinen, T; Kiviranta, I; Jaroma, H; Inkinen, R; Tammi, M

1995-10-01

To determine the content and molecular size of proteoglycans (PGs) in patellar chondromalacia (CM) and control cartilages as a first step in investigating the role of matrix alterations in the pathogenesis of this disease. Chondromalacia tissue from 10 patients was removed with a surgical knife. Using identical techniques, apparently healthy cartilage of the same site was obtained from 10 age matched cadavers (mean age 31 years in both groups). Additional pathological cartilage was collected from 67 patients with grades II-IV CM (classified according to Outerbridge) using a motorised shaver under arthroscopic control. The shaved cartilage chips were collected with a dense net from the irrigation fluid of the shaver. The content of tissue PGs was determined by Safranin O precipitation or uronic acid content, and the molecular size by mobility on agarose gel electrophoresis. The mean PG content of the CM tissue samples with a knife was dramatically reduced, being only 15% of that in controls. The cartilage chips collected from shaving operations of grades II, III, and IV CM showed a decreasing PG content: 9%, 5%, and 1% of controls, respectively. Electrophoretic analysis of PGs extracted with guanidium chloride from the shaved tissue samples suggested a significantly reduced size of aggrecans in the mild (grade II) lesions. These data show that there is already a dramatic and progressive depletion of PGs in CM grade II lesions. This explains the softening of cartilage, a typical finding in the arthroscopic examination of CM. The PG size reduction observed in grade II implicates proteolytic attack as a factor in the pathogenesis of CM.

The differentiation of oral soft- and hard tissues using laser induced breakdown spectroscopy - a prospect for tissue specific laser surgery.

PubMed

Rohde, Maximilian; Mehari, Fanuel; Klämpfl, Florian; Adler, Werner; Neukam, Friedrich-Wilhelm; Schmidt, Michael; Stelzle, Florian

2017-10-01

Compared to conventional techniques, Laser surgery procedures provide a number of advantages, but may be associated with an increased risk of iatrogenic damage to important anatomical structures. The type of tissue ablated in the focus spot is unknown. Laser-Induced Breakdown-Spectroscopy (LIBS) has the potential to gain information about the type of material that is being ablated by the laser beam. This may form the basis for tissue selective laser surgery. In the present study, 7 different porcine tissues (cortical and cancellous bone, nerve, mucosa, enamel, dentine and pulp) from 6 animals were analyzed for their qualitative and semiquantitative molecular composition using LIBS. The so gathered data was used to first differentiate between the soft- and hard-tissues using a Calcium-Carbon emission based classifier. The tissues were then further classified using emission-ratio based analysis, principal component analysis (PCA) and linear discriminant analysis (LDA). The relatively higher concentration of Calcium in the hard tissues allows for an accurate first differentiation of soft- and hard tissues (100% sensitivity and specificity). The ratio based statistical differentiation approach yields results in the range from 65% (enamel-dentine pair) to 100% (nerve-pulp, cancellous bone-dentine, cancellous bone-enamel pairs) sensitivity and specificity. Experimental LIBS measuring setup. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

[The composition of the gastrointestinal bacterial flora of mouse embryos and the placenta tissue bacterial flora].

PubMed

Lei, D; Lin, Y; Jiang, X; Lan, L; Zhang, W; Wang, B X

2017-03-02

Objective: To explore the composition of the gastrointestinal bacterial flora of mouse embryos and the placenta tissue bacterial flora. Method: Twenty-four specimens were collected from pregnant Kunming mouse including 8 mice of early embryonic (12-13 days) gastrointestinal tissues, 8 cases of late embryonic (19-20 days)gastrointestinal tissues, 8 of late pregnancy placental tissues.The 24 samples were extracted by DNeasy Blood & Tissue kit for high-throughput DNA sequencing. Result: The level of Proteobacteria, Bacteroidetes, Actino-bacteria and Firmicutes were predominantin all specimens.The relative content of predominant bacterial phyla in each group: Proteobacteria (95.00%, 88.14%, 87.26%), Bacteroidetes(1.71%, 2.15%, 2.63%), Actino-Bacteria(1.16%, 4.10%, 3.38%), Firmicutes(0.75%, 2.62%, 2.01%). At the level of family, there were nine predominant bacterial families in which Enterobacteriaeae , Shewanel laceae and Moraxellaceae were dominant.The relative content of dominant bacterial family in eachgroup: Enterobacteriaeae (46.99%, 44.34%, 41.08%), Shewanellaceae (21.99%, 21.10%, 19.05%), Moraxellaceae (9.18%, 7.09%, 5.64%). From the species of flora, the flora from fetal gastrointestinal in early pregnancy and late pregnancy (65.44% and 62.73%) were the same as that from placenta tissue in the late pregnancy.From the abundance of bacteria, at the level of family, the same content of bacteria in three groups accounted for 78.16%, 72.53% and 65.78% respectively. Conclusion: It was proved that the gastrointestinal bacterial flora of mouse embryos and the placenta tissue bacterial flora were colonized. At the same time the bacteria are classified.

Raman spectroscopy differentiates squamous cell carcinoma (SCC) from normal skin following treatment with a high-powered CO2 laser.

PubMed

Fox, Sara A; Shanblatt, Ashley A; Beckman, Hugh; Strasswimmer, John; Terentis, Andrew C

2014-12-01

The number of cases of non-melanoma skin cancer (NMSC), which include squamous cell carcinoma (SCC) and basal cell carcinoma (BCC), continues to rise as the aging population grows. Mohs micrographic surgery has become the treatment of choice in many cases but is not always necessary or feasible. Ablation with a high-powered CO2 laser offers the advantage of highly precise, hemostatic tissue removal. However, confirmation of complete cancer removal following ablation is difficult. In this study we tested for the first time the feasibility of using Raman spectroscopy as an in situ diagnostic method to differentiate NMSC from normal tissue following partial ablation with a high-powered CO2 laser. Twenty-five tissue samples were obtained from eleven patients undergoing Mohs micrographic surgery to remove NMSC tumors. Laser treatment was performed with a SmartXide DOT Fractional CO2 Laser (DEKA Laser Technologies, Inc.) emitting a wavelength of 10.6 μm. Treatment levels ranged from 20 mJ to 1200 mJ total energy delivered per laser treatment spot (350 μm spot size). Raman spectra were collected from both untreated and CO2 laser-treated samples using a 785 nm diode laser. Principal Component Analysis (PCA) and Binary Logistic Regression (LR) were used to classify spectra as originating from either normal or NMSC tissue, and from treated or untreated tissue. Partial laser ablation did not adversely affect the ability of Raman spectroscopy to differentiate normal from cancerous residual tissue, with the spectral classification model correctly identifying SCC tissue with 95% sensitivity and 100% specificity following partial laser ablation, compared with 92% sensitivity and 60% selectivity for untreated NMSC tissue. The main biochemical difference identified between normal and NMSC tissue was high levels of collagen in the normal tissue, which was lacking in the NMSC tissue. The feasibility of a combined high-powered CO2 laser ablation, Raman diagnostic procedure for the treatment of NMSC is demonstrated since CO2 laser treatment does not hinder the ability of Raman spectroscopy to differentiate normal from diseased tissue. This combined approach could be employed clinically to greatly enhance the speed and effectiveness of NMSC treatment in many cases. © 2014 Wiley Periodicals, Inc.

Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery

PubMed Central

Thanh Noi, Phan; Kappas, Martin

2017-01-01

In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost classifiers at producing high accuracies. However, only a few studies have compared the performances of these classifiers with different training sample sizes for the same remote sensing images, particularly the Sentinel-2 Multispectral Imager (MSI). In this study, we examined and compared the performances of the RF, kNN, and SVM classifiers for land use/cover classification using Sentinel-2 image data. An area of 30 × 30 km2 within the Red River Delta of Vietnam with six land use/cover types was classified using 14 different training sample sizes, including balanced and imbalanced, from 50 to over 1250 pixels/class. All classification results showed a high overall accuracy (OA) ranging from 90% to 95%. Among the three classifiers and 14 sub-datasets, SVM produced the highest OA with the least sensitivity to the training sample sizes, followed consecutively by RF and kNN. In relation to the sample size, all three classifiers showed a similar and high OA (over 93.85%) when the training sample size was large enough, i.e., greater than 750 pixels/class or representing an area of approximately 0.25% of the total study area. The high accuracy was achieved with both imbalanced and balanced datasets. PMID:29271909

Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery.

PubMed

Thanh Noi, Phan; Kappas, Martin

2017-12-22

In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost classifiers at producing high accuracies. However, only a few studies have compared the performances of these classifiers with different training sample sizes for the same remote sensing images, particularly the Sentinel-2 Multispectral Imager (MSI). In this study, we examined and compared the performances of the RF, kNN, and SVM classifiers for land use/cover classification using Sentinel-2 image data. An area of 30 × 30 km² within the Red River Delta of Vietnam with six land use/cover types was classified using 14 different training sample sizes, including balanced and imbalanced, from 50 to over 1250 pixels/class. All classification results showed a high overall accuracy (OA) ranging from 90% to 95%. Among the three classifiers and 14 sub-datasets, SVM produced the highest OA with the least sensitivity to the training sample sizes, followed consecutively by RF and kNN. In relation to the sample size, all three classifiers showed a similar and high OA (over 93.85%) when the training sample size was large enough, i.e., greater than 750 pixels/class or representing an area of approximately 0.25% of the total study area. The high accuracy was achieved with both imbalanced and balanced datasets.

Quantification of photoacoustic microscopy images for ovarian cancer detection

NASA Astrophysics Data System (ADS)

Wang, Tianheng; Yang, Yi; Alqasemi, Umar; Kumavor, Patrick D.; Wang, Xiaohong; Sanders, Melinda; Brewer, Molly; Zhu, Quing

2014-03-01

In this paper, human ovarian tissues with malignant and benign features were imaged ex vivo by using an opticalresolution photoacoustic microscopy (OR-PAM) system. Several features were quantitatively extracted from PAM images to describe photoacoustic signal distributions and fluctuations. 106 PAM images from 18 human ovaries were classified by applying those extracted features to a logistic prediction model. 57 images from 9 ovaries were used as a training set to train the logistic model, and 49 images from another 9 ovaries were used to test our prediction model. We assumed that if one image from one malignant ovary was classified as malignant, it is sufficient to classify this ovary as malignant. For the training set, we achieved 100% sensitivity and 83.3% specificity; for testing set, we achieved 100% sensitivity and 66.7% specificity. These preliminary results demonstrate that PAM could be extremely valuable in assisting and guiding surgeons for in vivo evaluation of ovarian tissue.

Prevalence of Ectopic Breast Tissue and Tumor: A 20-Year Single Center Experience.

PubMed

Famá, Fausto; Cicciú, Marco; Sindoni, Alessandro; Scarfó, Paola; Pollicino, Andrea; Giacobbe, Giuseppa; Buccheri, Giancarlo; Taranto, Filippo; Palella, Jessica; Gioffré-Florio, Maria

2016-08-01

Ectopic breast tissue, which includes both supernumerary breast and aberrant breast tissue, is the most common congenital breast abnormality. Ectopic breast cancers are rare neoplasms that occur in 0.3% to 0.6% of all cases of breast cancer. We retrospectively report, using a large series of breast abnormalities diagnosed and treated, our clinical experience on the management of the ectopic breast cancer. In 2 decades, we observed 327 (2.7%) patients with ectopic breast tissue out of a total of 12,177 subjects undergoing a breast visit for lesions. All patients were classified into 8 classes, according to the classification of Kajava, and assessed by a physician examination, ultrasounds, and, when appropriate, further studies with fine needle aspiration cytology and mammography. All specimens were submitted to the anatomo-pathologist. The most frequent benign histological diagnosis was fibrocystic disease. A rare granulosa cell tumor was also found in the right anterior thoracic wall of 1 patient. Four malignancies were also diagnosed in 4 women: an infiltrating lobular cancer in 1 patient with a lesion classified as class I, and an infiltrating apocrine carcinoma, an infiltrating ductal cancer, and an infiltrating ductal cancer with tubular pattern, occurring in 3 patients with lesions classified as class IV. Only 1 recurrence was observed. We recommend an earlier surgical approach for patients with lesions from class I to IV. Copyright © 2016 Elsevier Inc. All rights reserved.

CFS-SMO based classification of breast density using multiple texture models.

PubMed

Sharma, Vipul; Singh, Sukhwinder

2014-06-01

It is highly acknowledged in the medical profession that density of breast tissue is a major cause for the growth of breast cancer. Increased breast density was found to be linked with an increased risk of breast cancer growth, as high density makes it difficult for radiologists to see an abnormality which leads to false negative results. Therefore, there is need for the development of highly efficient techniques for breast tissue classification based on density. This paper presents a hybrid scheme for classification of fatty and dense mammograms using correlation-based feature selection (CFS) and sequential minimal optimization (SMO). In this work, texture analysis is done on a region of interest selected from the mammogram. Various texture models have been used to quantify the texture of parenchymal patterns of breast. To reduce the dimensionality and to identify the features which differentiate between breast tissue densities, CFS is used. Finally, classification is performed using SMO. The performance is evaluated using 322 images of mini-MIAS database. Highest accuracy of 96.46% is obtained for two-class problem (fatty and dense) using proposed approach. Performance of selected features by CFS is also evaluated by Naïve Bayes, Multilayer Perceptron, RBF Network, J48 and kNN classifier. The proposed CFS-SMO method outperforms all other classifiers giving a sensitivity of 100%. This makes it suitable to be taken as a second opinion in classifying breast tissue density.

Urine cell-based DNA methylation classifier for monitoring bladder cancer.

PubMed

van der Heijden, Antoine G; Mengual, Lourdes; Ingelmo-Torres, Mercedes; Lozano, Juan J; van Rijt-van de Westerlo, Cindy C M; Baixauli, Montserrat; Geavlete, Bogdan; Moldoveanud, Cristian; Ene, Cosmin; Dinney, Colin P; Czerniak, Bogdan; Schalken, Jack A; Kiemeney, Lambertus A L M; Ribal, Maria J; Witjes, J Alfred; Alcaraz, Antonio

2018-01-01

Current standard methods used to detect and monitor bladder cancer (BC) are invasive or have low sensitivity. This study aimed to develop a urine methylation biomarker classifier for BC monitoring and validate this classifier in patients in follow-up for bladder cancer (PFBC). Voided urine samples ( N  = 725) from BC patients, controls, and PFBC were prospectively collected in four centers. Finally, 626 urine samples were available for analysis. DNA was extracted from the urinary cells and bisulfite modificated, and methylation status was analyzed using pyrosequencing. Cytology was available from a subset of patients ( N  = 399). In the discovery phase, seven selected genes from the literature ( CDH13 , CFTR , NID2 , SALL3 , TMEFF2 , TWIST1 , and VIM2 ) were studied in 111 BC and 57 control samples. This training set was used to develop a gene classifier by logistic regression and was validated in 458 PFBC samples (173 with recurrence). A three-gene methylation classifier containing CFTR , SALL3 , and TWIST1 was developed in the training set (AUC 0.874). The classifier achieved an AUC of 0.741 in the validation series. Cytology results were available for 308 samples from the validation set. Cytology achieved AUC 0.696 whereas the classifier in this subset of patients reached an AUC 0.768. Combining the methylation classifier with cytology results achieved an AUC 0.86 in the validation set, with a sensitivity of 96%, a specificity of 40%, and a positive and negative predictive value of 56 and 92%, respectively. The combination of the three-gene methylation classifier and cytology results has high sensitivity and high negative predictive value in a real clinical scenario (PFBC). The proposed classifier is a useful test for predicting BC recurrence and decrease the number of cystoscopies in the follow-up of BC patients. If only patients with a positive combined classifier result would be cystoscopied, 36% of all cystoscopies can be prevented.

Classification of inflammatory bowel diseases by means of Raman spectroscopic imaging of epithelium cells

NASA Astrophysics Data System (ADS)

Bielecki, Christiane; Bocklitz, Thomas W.; Schmitt, Michael; Krafft, Christoph; Marquardt, Claudio; Gharbi, Akram; Knösel, Thomas; Stallmach, Andreas; Popp, Juergen

2012-07-01

We report on a Raman microspectroscopic characterization of the inflammatory bowel diseases (IBD) Crohn's disease (CD) and ulcerative colitis (UC). Therefore, Raman maps of human colon tissue sections were analyzed by utilizing innovative chemometric approaches. First, support vector machines were applied to highlight the tissue morphology (=Raman spectroscopic histopathology). In a second step, the biochemical tissue composition has been studied by analyzing the epithelium Raman spectra of sections of healthy control subjects (n=11), subjects with CD (n=14), and subjects with UC (n=13). These three groups exhibit significantly different molecular specific Raman signatures, allowing establishment of a classifier (support-vector-machine). By utilizing this classifier it was possible to separate between healthy control patients, patients with CD, and patients with UC with an accuracy of 98.90%. The automatic design of both classification steps (visualization of the tissue morphology and molecular classification of IBD) paves the way for an objective clinical diagnosis of IBD by means of Raman spectroscopy in combination with chemometric approaches.

Cocoa content influences chocolate molecular profile investigated by MALDI-TOF mass spectrometry.

PubMed

Bonatto, Cínthia C; Silva, Luciano P

2015-06-01

Chocolate authentication is a key aspect of quality control and safety. Matrix-assisted laser desorption ionization time-of flight (MALDI-TOF) mass spectrometry (MS) has been demonstrated to be useful for molecular profiling of cells, tissues, and even food. The present study evaluated if MALDI-TOF MS analysis on low molecular mass profile may classify chocolate samples according to the cocoa content. The molecular profiles of seven processed commercial chocolate samples were compared by using MALDI-TOF MS. Some ions detected exclusively in chocolate samples corresponded to the metabolites of cocoa or other constituents. This method showed the presence of three distinct clusters according to confectionery and sensorial features of the chocolates and was used to establish a mass spectra database. Also, novel chocolate samples were evaluated in order to check the validity of the method and to challenge the database created with the mass spectra of the primary samples. Thus, the method was shown to be reliable for clustering unknown samples into the main chocolate categories. Simple sample preparation of the MALDI-TOF MS approach described will allow the surveillance and monitoring of constituents during the molecular profiling of chocolates. © 2014 Society of Chemical Industry.

Highly sensitive molecular diagnosis of prostate cancer using surplus material washed off from biopsy needles

PubMed Central

Bermudo, R; Abia, D; Mozos, A; García-Cruz, E; Alcaraz, A; Ortiz, Á R; Thomson, T M; Fernández, P L

2011-01-01

Introduction: Currently, final diagnosis of prostate cancer (PCa) is based on histopathological analysis of needle biopsies, but this process often bears uncertainties due to small sample size, tumour focality and pathologist's subjective assessment. Methods: Prostate cancer diagnostic signatures were generated by applying linear discriminant analysis to microarray and real-time RT–PCR (qRT–PCR) data from normal and tumoural prostate tissue samples. Additionally, after removal of biopsy tissues, material washed off from transrectal biopsy needles was used for molecular profiling and discriminant analysis. Results: Linear discriminant analysis applied to microarray data for a set of 318 genes differentially expressed between non-tumoural and tumoural prostate samples produced 26 gene signatures, which classified the 84 samples used with 100% accuracy. To identify signatures potentially useful for the diagnosis of prostate biopsies, surplus material washed off from routine biopsy needles from 53 patients was used to generate qRT–PCR data for a subset of 11 genes. This analysis identified a six-gene signature that correctly assigned the biopsies as benign or tumoural in 92.6% of the cases, with 88.8% sensitivity and 96.1% specificity. Conclusion: Surplus material from prostate needle biopsies can be used for minimal-size gene signature analysis for sensitive and accurate discrimination between non-tumoural and tumoural prostates, without interference with current diagnostic procedures. This approach could be a useful adjunct to current procedures in PCa diagnosis. PMID:22009027

Steady-state MR imaging sequences: physics, classification, and clinical applications.

PubMed

Chavhan, Govind B; Babyn, Paul S; Jankharia, Bhavin G; Cheng, Hai-Ling M; Shroff, Manohar M

2008-01-01

Steady-state sequences are a class of rapid magnetic resonance (MR) imaging techniques based on fast gradient-echo acquisitions in which both longitudinal magnetization (LM) and transverse magnetization (TM) are kept constant. Both LM and TM reach a nonzero steady state through the use of a repetition time that is shorter than the T2 relaxation time of tissue. When TM is maintained as multiple radiofrequency excitation pulses are applied, two types of signal are formed once steady state is reached: preexcitation signal (S-) from echo reformation; and postexcitation signal (S+), which consists of free induction decay. Depending on the signal sampled and used to form an image, steady-state sequences can be classified as (a) postexcitation refocused (only S+ is sampled), (b) preexcitation refocused (only S- is sampled), and (c) fully refocused (both S+ and S- are sampled) sequences. All tissues with a reasonably long T2 relaxation time will show additional signals due to various refocused echo paths. Steady-state sequences have revolutionized cardiac imaging and have become the standard for anatomic functional cardiac imaging and for the assessment of myocardial viability because of their good signal-to-noise ratio and contrast-to-noise ratio and increased speed of acquisition. They are also useful in abdominal and fetal imaging and hold promise for interventional MR imaging. Because steady-state sequences are now commonly used in MR imaging, radiologists will benefit from understanding the underlying physics, classification, and clinical applications of these sequences.

GBM heterogeneity characterization by radiomic analysis of phenotype anatomical planes

NASA Astrophysics Data System (ADS)

Chaddad, Ahmad; Desrosiers, Christian; Toews, Matthew

2016-03-01

Glioblastoma multiforme (GBM) is the most common malignant primary tumor of the central nervous system, characterized among other traits by rapid metastatis. Three tissue phenotypes closely associated with GBMs, namely, necrosis (N), contrast enhancement (CE), and edema/invasion (E), exhibit characteristic patterns of texture heterogeneity in magnetic resonance images (MRI). In this study, we propose a novel model to characterize GBM tissue phenotypes using gray level co-occurrence matrices (GLCM) in three anatomical planes. The GLCM encodes local image patches in terms of informative, orientation-invariant texture descriptors, which are used here to sub-classify GBM tissue phenotypes. Experiments demonstrate the model on MRI data of 41 GBM patients, obtained from the cancer genome atlas (TCGA). Intensity-based automatic image registration is applied to align corresponding pairs of fixed T1˗weighted (T1˗WI) post-contrast and fluid attenuated inversion recovery (FLAIR) images. GBM tissue regions are then segmented using the 3D Slicer tool. Texture features are computed from 12 quantifier functions operating on GLCM descriptors, that are generated from MRI intensities within segmented GBM tissue regions. Various classifier models are used to evaluate the effectiveness of texture features for discriminating between GBM phenotypes. Results based on T1-WI scans showed a phenotype classification accuracy of over 88.14%, a sensitivity of 85.37% and a specificity of 96.1%, using the linear discriminant analysis (LDA) classifier. This model has the potential to provide important characteristics of tumors, which can be used for the sub-classification of GBM phenotypes.

Modeling of Tool-Tissue Interactions for Computer-Based Surgical Simulation: A Literature Review

PubMed Central

Misra, Sarthak; Ramesh, K. T.; Okamura, Allison M.

2009-01-01

Surgical simulators present a safe and potentially effective method for surgical training, and can also be used in robot-assisted surgery for pre- and intra-operative planning. Accurate modeling of the interaction between surgical instruments and organs has been recognized as a key requirement in the development of high-fidelity surgical simulators. Researchers have attempted to model tool-tissue interactions in a wide variety of ways, which can be broadly classified as (1) linear elasticity-based, (2) nonlinear (hyperelastic) elasticity-based finite element (FE) methods, and (3) other techniques that not based on FE methods or continuum mechanics. Realistic modeling of organ deformation requires populating the model with real tissue data (which are difficult to acquire in vivo) and simulating organ response in real time (which is computationally expensive). Further, it is challenging to account for connective tissue supporting the organ, friction, and topological changes resulting from tool-tissue interactions during invasive surgical procedures. Overcoming such obstacles will not only help us to model tool-tissue interactions in real time, but also enable realistic force feedback to the user during surgical simulation. This review paper classifies the existing research on tool-tissue interactions for surgical simulators specifically based on the modeling techniques employed and the kind of surgical operation being simulated, in order to inform and motivate future research on improved tool-tissue interaction models. PMID:20119508

FT-IR spectroscopy characterization of schwannoma: a case study

NASA Astrophysics Data System (ADS)

Ferreira, Isabelle; Neto, Lazaro P. M.; das Chagas, Maurilio José; Carvalho, Luís. Felipe C. S.; dos Santos, Laurita; Ribas, Marcelo; Loddi, Vinicius; Martin, Airton A.

2016-03-01

Schwannoma are rare benign neural neoplasia. The clinical diagnosis could be improved if novel optical techniques are performed. Among these techniques, FT-IR is one of the currently techniques which has been applied for samples discrimination using biochemical information with minimum sample preparation. In this work, we report a case of a schwannoma in the cervical region. A histological examination described a benign process. An immunohistochemically examination demonstrated positivity to anti-S100 protein antibody, indicating a diagnosis of schwannoma. The aim of this analysis was to characterize FT-IR spectrum of the neoplastic and normal tissue in the fingerprint (1000-1800 cm-1) and high wavenumber region (2800-3600 cm-1). The IR spectra were collect from tumor tissue and normal nerve samples by a FT-IR spectrophotometer (Spotlight Perkin Elmer 400, USA) with 64 scans, and resolution of 4 cm-1. A total of twenty spectra were recorded (10 from schwannoma and 10 from nerve). Multivariate Analysis was used to classify the data. Through average and standard deviation analysis we observed that the main spectral change occurs at ≍1600 cm-1 (amide I) and ≍1400 cm-1 (amide III) in the fingerprint region, and in CH2/CH3 protein-lipids and OH-water vibrations for the high wavenumber region. In conclusion, FT-IR could be used as a technique for schwannoma analysis helping to establish specific diagnostic.

Deaths Associated With Brotizolam Poisoning From a Single Drug Overdose: Four Reported Cases.

PubMed

Sakai, Kentaro; Saito, Kazuyuki; Takada, Aya; Hikiji, Wakako; Kikuchi, Yosuke; Fukunaga, Tatsushige

2018-03-01

Brotizolam is a short-acting hypnotic in the benzodiazepine family, and fatal poisonings by an overdose of brotizolam are rare. This report describes 4 cases of deaths associated with brotizolam poisoning from a single drug overdose. The ages ranged from 51 to 90 years, and the postmortem interval between death and tissue sampling was 1.5 to 2.5 days. These deaths were classified as 1 homicide and 3 suicides. The concentration of the brotizolam ranged from 0.05 to 0.21 mg/L in the blood samples. Ethanol, which could cause mild alcohol intoxication, was detected in the blood samples from 2 cases. Postmortem examinations did not find any significant pathologic conditions, except for a case of death by drowning in a bathtub due to brotizolam poisoning. These 4 cases suggest that a brotizolam overdose should not be underestimated in terms of its fatal effects, particularly when situations involve alcohol intoxication, injury subsequent to the poisoning, or underlying medical conditions including aging.

Confidence Preserving Machine for Facial Action Unit Detection

PubMed Central

Zeng, Jiabei; Chu, Wen-Sheng; De la Torre, Fernando; Cohn, Jeffrey F.; Xiong, Zhang

2016-01-01

Facial action unit (AU) detection from video has been a long-standing problem in automated facial expression analysis. While progress has been made, accurate detection of facial AUs remains challenging due to ubiquitous sources of errors, such as inter-personal variability, pose, and low-intensity AUs. In this paper, we refer to samples causing such errors as hard samples, and the remaining as easy samples. To address learning with the hard samples, we propose the Confidence Preserving Machine (CPM), a novel two-stage learning framework that combines multiple classifiers following an “easy-to-hard” strategy. During the training stage, CPM learns two confident classifiers. Each classifier focuses on separating easy samples of one class from all else, and thus preserves confidence on predicting each class. During the testing stage, the confident classifiers provide “virtual labels” for easy test samples. Given the virtual labels, we propose a quasi-semi-supervised (QSS) learning strategy to learn a person-specific (PS) classifier. The QSS strategy employs a spatio-temporal smoothness that encourages similar predictions for samples within a spatio-temporal neighborhood. In addition, to further improve detection performance, we introduce two CPM extensions: iCPM that iteratively augments training samples to train the confident classifiers, and kCPM that kernelizes the original CPM model to promote nonlinearity. Experiments on four spontaneous datasets GFT [15], BP4D [56], DISFA [42], and RU-FACS [3] illustrate the benefits of the proposed CPM models over baseline methods and state-of-the-art semisupervised learning and transfer learning methods. PMID:27479964

Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks.

PubMed

Bandeira Diniz, João Otávio; Bandeira Diniz, Pedro Henrique; Azevedo Valente, Thales Levi; Corrêa Silva, Aristófanes; de Paiva, Anselmo Cardoso; Gattass, Marcelo

2018-03-01

The processing of medical image is an important tool to assist in minimizing the degree of uncertainty of the specialist, while providing specialists with an additional source of detect and diagnosis information. Breast cancer is the most common type of cancer that affects the female population around the world. It is also the most deadly type of cancer among women. It is the second most common type of cancer among all others. The most common examination to diagnose breast cancer early is mammography. In the last decades, computational techniques have been developed with the purpose of automatically detecting structures that maybe associated with tumors in mammography examination. This work presents a computational methodology to automatically detection of mass regions in mammography by using a convolutional neural network. The materials used in this work is the DDSM database. The method proposed consists of two phases: training phase and test phase. The training phase has 2 main steps: (1) create a model to classify breast tissue into dense and non-dense (2) create a model to classify regions of breast into mass and non-mass. The test phase has 7 step: (1) preprocessing; (2) registration; (3) segmentation; (4) first reduction of false positives; (5) preprocessing of regions segmented; (6) density tissue classification (7) second reduction of false positives where regions will be classified into mass and non-mass. The proposed method achieved 95.6% of accuracy in classify non-dense breasts tissue and 97,72% accuracy in classify dense breasts. To detect regions of mass in non-dense breast, the method achieved a sensitivity value of 91.5%, and specificity value of 90.7%, with 91% accuracy. To detect regions in dense breasts, our method achieved 90.4% of sensitivity and 96.4% of specificity, with accuracy of 94.8%. According to the results achieved by CNN, we demonstrate the feasibility of using convolutional neural networks on medical image processing techniques for classification of breast tissue and mass detection. Copyright © 2018 Elsevier B.V. All rights reserved.

An Investigation into the Use of Spatially-Filtered Fourier Transforms to Classify Mammary Lesions.

DTIC Science & Technology

difference in Fourier space between lesioned breast tissue which would enable accurate computer classification of benign and malignant lesions. Low...separate benign and malignant breast tissue. However, no success was achieved when using two-dimensional Fourier transform and power spectrum analysis. (Author)

Faster, More Reproducible DESI-MS for Biological Tissue Imaging

NASA Astrophysics Data System (ADS)

Tillner, Jocelyn; Wu, Vincen; Jones, Emrys A.; Pringle, Steven D.; Karancsi, Tamas; Dannhorn, Andreas; Veselkov, Kirill; McKenzie, James S.; Takats, Zoltan

2017-10-01

A new, more robust sprayer for desorption electrospray ionization (DESI) mass spectrometry imaging is presented. The main source of variability in DESI is thought to be the uncontrolled variability of various geometric parameters of the sprayer, primarily the position of the solvent capillary, or more specifically, its positioning within the gas capillary or nozzle. If the solvent capillary is off-center, the sprayer becomes asymmetrical, making the geometry difficult to control and compromising reproducibility. If the stiffness, tip quality, and positioning of the capillary are improved, sprayer reproducibility can be improved by an order of magnitude. The quality of the improved sprayer and its potential for high spatial resolution imaging are demonstrated on human colorectal tissue samples by acquisition of images at pixel sizes of 100, 50, and 20 μm, which corresponds to a lateral resolution of 40-60 μm, similar to the best values published in the literature. The high sensitivity of the sprayer also allows combination with a fast scanning quadrupole time-of-flight mass spectrometer. This provides up to 30 times faster DESI acquisition, reducing the overall acquisition time for a 10 mm × 10 mm rat brain sample to approximately 1 h. Although some spectral information is lost with increasing analysis speed, the resulting data can still be used to classify tissue types on the basis of a previously constructed model. This is particularly interesting for clinical applications, where fast, reliable diagnosis is required. [Figure not available: see fulltext.

Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks

NASA Astrophysics Data System (ADS)

Cruz-Roa, Angel; Basavanhally, Ajay; González, Fabio; Gilmore, Hannah; Feldman, Michael; Ganesan, Shridar; Shih, Natalie; Tomaszewski, John; Madabhushi, Anant

2014-03-01

This paper presents a deep learning approach for automatic detection and visual analysis of invasive ductal carcinoma (IDC) tissue regions in whole slide images (WSI) of breast cancer (BCa). Deep learning approaches are learn-from-data methods involving computational modeling of the learning process. This approach is similar to how human brain works using different interpretation levels or layers of most representative and useful features resulting into a hierarchical learned representation. These methods have been shown to outpace traditional approaches of most challenging problems in several areas such as speech recognition and object detection. Invasive breast cancer detection is a time consuming and challenging task primarily because it involves a pathologist scanning large swathes of benign regions to ultimately identify the areas of malignancy. Precise delineation of IDC in WSI is crucial to the subsequent estimation of grading tumor aggressiveness and predicting patient outcome. DL approaches are particularly adept at handling these types of problems, especially if a large number of samples are available for training, which would also ensure the generalizability of the learned features and classifier. The DL framework in this paper extends a number of convolutional neural networks (CNN) for visual semantic analysis of tumor regions for diagnosis support. The CNN is trained over a large amount of image patches (tissue regions) from WSI to learn a hierarchical part-based representation. The method was evaluated over a WSI dataset from 162 patients diagnosed with IDC. 113 slides were selected for training and 49 slides were held out for independent testing. Ground truth for quantitative evaluation was provided via expert delineation of the region of cancer by an expert pathologist on the digitized slides. The experimental evaluation was designed to measure classifier accuracy in detecting IDC tissue regions in WSI. Our method yielded the best quantitative results for automatic detection of IDC regions in WSI in terms of F-measure and balanced accuracy (71.80%, 84.23%), in comparison with an approach using handcrafted image features (color, texture and edges, nuclear textural and architecture), and a machine learning classifier for invasive tumor classification using a Random Forest. The best performing handcrafted features were fuzzy color histogram (67.53%, 78.74%) and RGB histogram (66.64%, 77.24%). Our results also suggest that at least some of the tissue classification mistakes (false positives and false negatives) were less due to any fundamental problems associated with the approach, than the inherent limitations in obtaining a very highly granular annotation of the diseased area of interest by an expert pathologist.

Altered intragenic DNA methylation of HOOK2 gene in adipose tissue from individuals with obesity and type 2 diabetes

PubMed Central

Fernández-Bayón, Gustavo; Morales-Sánchez, Paula; Sanz, Lourdes; Turienzo, Estrella; González, Juan José; Martinez-Faedo, Ceferino; Suarez-Gutiérrez, Lorena; Ares, Jessica; Díaz-Naya, Lucia; Martin-Nieto, Alicia; Fernández-Morera, Juan L.; Fraga, Mario F.

2017-01-01

Aims/Hypothesis Failure in glucose response to insulin is a common pathology associated with obesity. In this study, we analyzed the genome wide DNA methylation profile of visceral adipose tissue (VAT) samples in a population of individuals with obesity and assessed whether differential methylation profiles are associated with the presence of type 2 diabetes (T2D). Methods More than 485,000 CpG genome sites from VAT samples from women with obesity undergoing gastric bypass (n = 18), and classified as suffering from type 2 diabetes (T2D) or not (no type 2 diabetes, NT2D), were analyzed using DNA methylation arrays. Results We found significant differential methylation between T2D and NT2D samples in 24 CpGs that map with sixteen genes, one of which, HOOK2, demonstrated a significant correlation between differentially hypermethylated regions on the gene body and the presence of type 2 diabetes. This was validated by pyrosequencing in a population of 91 samples from both males and females with obesity. Furthermore, when these results were analyzed by gender, female T2D samples were found hypermethylated at the cg04657146-region and the cg 11738485-region of HOOK2 gene, whilst, interestingly, male samples were found hypomethylated in this latter region. Conclusion The differential methylation profile of the HOOK2 gene in individuals with T2D and obesity might be related to the attendant T2D, but further studies are required to identify the potential role of HOOK2 gene in T2D disease. The finding of gender differences in T2D methylation of HOOK2 also warrants further investigation. PMID:29228058

An atlas of active enhancers across human cell types and tissues

NASA Astrophysics Data System (ADS)

Andersson, Robin; Gebhard, Claudia; Miguel-Escalada, Irene; Hoof, Ilka; Bornholdt, Jette; Boyd, Mette; Chen, Yun; Zhao, Xiaobei; Schmidl, Christian; Suzuki, Takahiro; Ntini, Evgenia; Arner, Erik; Valen, Eivind; Li, Kang; Schwarzfischer, Lucia; Glatz, Dagmar; Raithel, Johanna; Lilje, Berit; Rapin, Nicolas; Bagger, Frederik Otzen; Jørgensen, Mette; Andersen, Peter Refsing; Bertin, Nicolas; Rackham, Owen; Burroughs, A. Maxwell; Baillie, J. Kenneth; Ishizu, Yuri; Shimizu, Yuri; Furuhata, Erina; Maeda, Shiori; Negishi, Yutaka; Mungall, Christopher J.; Meehan, Terrence F.; Lassmann, Timo; Itoh, Masayoshi; Kawaji, Hideya; Kondo, Naoto; Kawai, Jun; Lennartsson, Andreas; Daub, Carsten O.; Heutink, Peter; Hume, David A.; Jensen, Torben Heick; Suzuki, Harukazu; Hayashizaki, Yoshihide; Müller, Ferenc; Consortium, The Fantom; Forrest, Alistair R. R.; Carninci, Piero; Rehli, Michael; Sandelin, Albin

2014-03-01

Enhancers control the correct temporal and cell-type-specific activation of gene expression in multicellular eukaryotes. Knowing their properties, regulatory activity and targets is crucial to understand the regulation of differentiation and homeostasis. Here we use the FANTOM5 panel of samples, covering the majority of human tissues and cell types, to produce an atlas of active, in vivo-transcribed enhancers. We show that enhancers share properties with CpG-poor messenger RNA promoters but produce bidirectional, exosome-sensitive, relatively short unspliced RNAs, the generation of which is strongly related to enhancer activity. The atlas is used to compare regulatory programs between different cells at unprecedented depth, to identify disease-associated regulatory single nucleotide polymorphisms, and to classify cell-type-specific and ubiquitous enhancers. We further explore the utility of enhancer redundancy, which explains gene expression strength rather than expression patterns. The online FANTOM5 enhancer atlas represents a unique resource for studies on cell-type-specific enhancers and gene regulation.

Micro-RNAs as diagnostic or prognostic markers in human epithelial malignancies

PubMed Central

2011-01-01

Micro-RNAs (miRs) are important regulators of mRNA and protein expression; the ability of miR expression profilings to distinguish different cancer types and classify their sub-types has been well-described. They also represent a novel biological entity with potential value as tumour biomarkers, which can improve diagnosis, prognosis, and monitoring of treatment response for human cancers. This endeavour has been greatly facilitated by the stability of miRs in formalin-fixed paraffin-embedded (FFPE) tissues, and their detection in circulation. This review will summarize some of the key dysregulated miRs described to date in human epithelial malignancies, and their potential value as molecular bio-markers in FFPE tissues and blood samples. There remain many challenges in this domain, however, with the evolution of different platforms, the complexities of normalizing miR profiling data, and the importance of evaluating sufficiently-powered training and validation cohorts. Nonetheless, well-conducted miR profiling studies should contribute important insights into the molecular aberrations driving human cancer development and progression. PMID:22128797

Morphological and immunohistochemical characterization of spontaneous mammary tumours in European hedgehogs (Erinaceus europaeus).

PubMed

Döpke, C; Fehr, M; Thiele, A; Pohlenz, J; Wohlsein, P

2007-07-01

Mammary tumour samples (11 surgical and five post-mortem) from 16 adult European hedgehogs submitted between 1980 and 2004 were examined. Histologically, the tumours were classified as simple tubulo-papillary carcinomas with local invasive growth. In six cases, tumour cell emboli were present in blood vessels or lymphatic vessels, or both. However, metastasis to regional lymph nodes was found only in one hedgehog. Malignant neoplastic epithelial cells were immunolabelled by antibodies specific for various cytokeratins (CKs), including CK1-8, 10, 13-16, 19 and 20. CK expression did not differ from that in normal mammary gland tissue. CK20 was expressed in the mammary tissue of hedgehogs, in contrast to that of dogs and cats; CK7 immunolabelling, however, which commonly occurs in mammary epithelial cells, was negative. CK20 expression, together with the lack of CK7 as determined by a protein-specific antibody, represented an important difference from the CK profile shown by mammary epithelial cells of other mammalian species, including the dog and cat.

Rare calcium oxalate monohydrate calculus attached to the wall of the renal pelvis.

PubMed

Grases, Felix; Costa-Bauza, Antonia; Prieto, Rafael M; Saus, Carlos; Servera, Antonio; García-Miralles, Reyes; Benejam, Joan

2011-04-01

Most renal calculi can be classified using well-established criteria in a manner that reflects both composition and fine structure under specific pathophysiological conditions. However, when a large patient population is considered, rare renal calculi invariably appear, some of which have never been classified; careful study is required to establish stone etiology in such cases. The patient in the present case report formed two types of calculi. One was attached on the wall of the renal pelvis near the ureter and part of the calculus was embedded inside pelvic renal tissue. The calculus developed on an ossified calcification located in the pelvis tissue. Current knowledge on the development of calcification in soft tissues suggests a pre-existing injury as an inducer of its development. A mechanism of calculus formation is proposed. The second stone was a typical jack-stone calculus. © 2011 The Japanese Urological Association.

The potential of nanofibers in tissue engineering and stem cell therapy.

PubMed

Gholizadeh-Ghaleh Aziz, Shiva; Gholizadeh-Ghaleh Aziz, Sara; Akbarzadeh, Abolfazl

2016-08-01

Electrospinning is a technique in which materials in solution are shaped into continuous nano- and micro-sized fibers. Combining stem cells with biomaterial scaffolds and nanofibers affords a favorable approach for bone tissue engineering, stem cell growth and transfer, ocular surface reconstruction, and treatment of congenital corneal diseases. This review seeks to describe the current examples of the use of scaffolds in stem cell therapy. Stem cells are classified as adult or embryonic stem (ES) cells, and the advantages and drawbacks of each group are detailed. The nanofibers and scaffolds are further classified in Tables I and II , which describe specific examples from the literature. Finally, the current applications of biomaterial scaffolds containing stem cells for tissue engineering applications are presented. Overall, this review seeks to give an overview of the biomaterials available for use in combination with stem cells, and the application of nanofibers in stem cell therapy.

Depletion of isoeugenol residues from the fillet tissue of AQUI-S™ exposed rainbow trout (Oncorhynchus mykiss)

USGS Publications Warehouse

Meinertz, Jeffery R.; Schreier, Theresa M.

2009-01-01

There is a critical need in U.S. public aquaculture and fishery management for an approved sedative that allows for the immediate release of fish after sedation. AQUI-STM is a fish anesthetic/sedative approved for use in several countries and until recently was being developed in the U.S. as a sedative for immediate release of fish after sedation. The U.S. National Toxicology Program reported that isoeugenol (the active ingredient in AQUI-STM) exposed male mice showed clear evidence of carcinogenicity, therefore efforts within the U.S. Department of Interior to develop AQUI-STM as a sedative that allows for immediate release ceased. Despite the ruling, AQUI-STM still has the potential to be approved as an anesthetic with a short withdrawal time. Among the data required to gain approval for use in the U.S. are data describing the composition and depletion of all AQUI-STM residues from fish fillet tissue. A total residue depletion study for AQUI-STM was conducted by exposing market-sized rainbow trout, Oncorhynchus mykiss (mean weight, 502.7 ± 54 g; s.d.) to 8.9 mg/L of 14C-[URL]-isoeugenol for 60 min in 17 °C water. The 14C-[URL]-isoeugenol was mixed with a surfactant resulting in a mixture that mimicked AQUI-STM. Groups of fish (n = 6) were sampled immediately after the exposure (0-h sample group) and at 0.5, 1, 2, and 4 h thereafter. Total isoeugenol-equivalent residue concentrations in the fillet tissue were determined by oxidizing triplicate subsamples of homogenized skin-on fillet tissue from each fish to 14CO2 and enumerating the radioactivity by static liquid scintillation counting. Isoeugenol concentrations in fillet tissue were determined by extracting homogenized fillet tissue with solvents and determining the isoeugenol concentrations in the extracts with high performance liquid chromatography techniques. The mean total isoeugenol-equivalent residue concentrations in the 0, 0.5, 1, 2, and 4-h sample groups were 55.4, 32.0, 19.8, 11.3, and 4.9 µg/g, respectively. The primary chemical residue in fillet tissue from all exposed fish was isoeugenol. The mean isoeugenol concentrations in the 0, 0.5, 1, 2, and 4-h sample groups were 48.9, 26.5, 15.3, 7.2, and 2.2 µg/g, respectively. The percents of the total radioactivity classified as isoeugenol in the 0, 0.5, 1, 2, and 4-h tissue extracts were 95, 73, 73, 64, and 48%, respectively.

Centre-based restricted nearest feature plane with angle classifier for face recognition

NASA Astrophysics Data System (ADS)

Tang, Linlin; Lu, Huifen; Zhao, Liang; Li, Zuohua

2017-10-01

An improved classifier based on the nearest feature plane (NFP), called the centre-based restricted nearest feature plane with the angle (RNFPA) classifier, is proposed for the face recognition problems here. The famous NFP uses the geometrical information of samples to increase the number of training samples, but it increases the computation complexity and it also has an inaccuracy problem coursed by the extended feature plane. To solve the above problems, RNFPA exploits a centre-based feature plane and utilizes a threshold of angle to restrict extended feature space. By choosing the appropriate angle threshold, RNFPA can improve the performance and decrease computation complexity. Experiments in the AT&T face database, AR face database and FERET face database are used to evaluate the proposed classifier. Compared with the original NFP classifier, the nearest feature line (NFL) classifier, the nearest neighbour (NN) classifier and some other improved NFP classifiers, the proposed one achieves competitive performance.

Tissue response and biodegradation of composite scaffolds prepared from Thai silk fibroin, gelatin and hydroxyapatite.

PubMed

Tungtasana, Hathairat; Shuangshoti, Somruetai; Shuangshoti, Shanop; Kanokpanont, Sorada; Kaplan, David L; Bunaprasert, Tanom; Damrongsakkul, Siriporn

2010-12-01

This work aimed to investigate tissue responses and biodegradation, both in vitro and in vivo, of four types of Bombyx mori Thai silk fibroin based-scaffolds. Thai silk fibroin (SF), conjugated gelatin/Thai silk fibroin (CGSF), hydroxyapatite/Thai silk fibroin (SF4), and hydroxyapatite/conjugated gelatin/Thai silk fibroin (CGSF4) scaffolds were fabricated using salt-porogen leaching, dehydrothermal/chemical crosslinking and an alternate soaking technique for mineralization. In vitro biodegradation in collagenase showed that CGSF scaffolds had the slowest biodegradability, due to the double crosslinking by dehydrothermal and chemical treatments. The hydroxyapatite deposited from alternate soaking separated from the surface of the protein scaffolds when immersed in collagenase. From in vivo biodegradation studies, all scaffolds could still be observed after 12 weeks of implantation in subcutaneous tissue of Wistar rats and also following ISO10993-6: Biological evaluation of medical devices. At 2 and 4 weeks of implantation the four types of Thai silk fibroin based-scaffolds were classified as "non-irritant" to "slight-irritant", compared to Gelfoam(®) (control samples). These natural Thai silk fibroin-based scaffolds may provide suitable biomaterials for clinical applications.

The human laryngeal microbiome: effects of cigarette smoke and reflux.

PubMed

Jetté, Marie E; Dill-McFarland, Kimberly A; Hanshew, Alissa S; Suen, Garret; Thibeault, Susan L

2016-10-24

Prolonged diffuse laryngeal inflammation from smoking and/or reflux is commonly diagnosed as chronic laryngitis and treated empirically with expensive drugs that have not proven effective. Shifts in microbiota have been associated with many inflammatory diseases, though little is known about how resident microbes may contribute to chronic laryngitis. We sought to characterize the core microbiota of disease-free human laryngeal tissue and to investigate shifts in microbial community membership associated with exposure to cigarette smoke and reflux. Using 454 pyrosequencing of the 16S rRNA gene, we compared bacterial communities of laryngeal tissue biopsies collected from 97 non-treatment-seeking volunteers based on reflux and smoking status. The core community was characterized by a highly abundant OTU within the family Comamonadaceae found in all laryngeal tissues. Smokers demonstrated less microbial diversity than nonsmokers, with differences in relative abundances of OTUs classified as Streptococcus, unclassified Comamonadaceae, Cloacibacterium, and Helicobacter. Reflux status did not affect microbial diversity nor community structure nor composition. Comparison of healthy laryngeal microbial communities to benign vocal fold disease samples revealed greater abundance of Streptococcus in benign vocal fold disease suggesting that mucosal dominance by Streptococcus may be a factor in disease etiology.

Staging Liver Fibrosis with Statistical Observers

NASA Astrophysics Data System (ADS)

Brand, Jonathan Frieman

Chronic liver disease is a worldwide health problem, and hepatic fibrosis (HF) is one of the hallmarks of the disease. Pathology diagnosis of HF is based on textural change in the liver as a lobular collagen network that develops within portal triads. The scale of collagen lobules is characteristically on order of 1mm, which close to the resolution limit of in vivo Gd-enhanced MRI. In this work the methods to collect training and testing images for a Hotelling observer are covered. An observer based on local texture analysis is trained and tested using wet-tissue phantoms. The technique is used to optimize the MRI sequence based on task performance. The final method developed is a two stage model observer to classify fibrotic and healthy tissue in both phantoms and in vivo MRI images. The first stage observer tests for the presence of local texture. Test statistics from the first observer are used to train the second stage observer to globally sample the local observer results. A decision of the disease class is made for an entire MRI image slice using test statistics collected from the second observer. The techniques are tested on wet-tissue phantoms and in vivo clinical patient data.

Photoacoustic Image Analysis for Cancer Detection and Building a Novel Ultrasound Imaging System

NASA Astrophysics Data System (ADS)

Sinha, Saugata

Photoacoustic (PA) imaging is a rapidly emerging non-invasive soft tissue imaging modality which has the potential to detect tissue abnormality at early stage. Photoacoustic images map the spatially varying optical absorption property of tissue. In multiwavelength photoacoustic imaging, the soft tissue is imaged with different wavelengths, tuned to the absorption peaks of the specific light absorbing tissue constituents or chromophores to obtain images with different contrasts of the same tissue sample. From those images, spatially varying concentration of the chromophores can be recovered. As multiwavelength PA images can provide important physiological information related to function and molecular composition of the tissue, so they can be used for diagnosis of cancer lesions and differentiation of malignant tumors from benign tumors. In this research, a number of parameters have been extracted from multiwavelength 3D PA images of freshly excised human prostate and thyroid specimens, imaged at five different wavelengths. Using marked histology slides as ground truths, region of interests (ROI) corresponding to cancer, benign and normal regions have been identified in the PA images. The extracted parameters belong to different categories namely chromophore concentration, frequency parameters and PA image pixels and they represent different physiological and optical properties of the tissue specimens. Statistical analysis has been performed to test whether the extracted parameters are significantly different between cancer, benign and normal regions. A multidimensional [29 dimensional] feature set, built with the extracted parameters from the 3D PA images, has been divided randomly into training and testing sets. The training set has been used to train support vector machine (SVM) and neural network (NN) classifiers while the performance of the classifiers in differentiating different tissue pathologies have been determined by the testing dataset. Using the NN classifier, performance of parameters belonging to different categories in differentiating malignant tissue from nonmalignant tissue has been determined. It has been found that, among different categories, the frequency parameters performed best in differentiating malignant from nonmalignant tissue [sensitivity and specificity with testing dataset are 85% and 84%] while performance of all the categories combined was better than that [sensitivity and specificity with testing dataset are 93% and 91%]. However, PA imaging cannot be used to provide the anatomical cues required to determine the position of the detected or suspected malignant tumor region relative to familiar organ landmarks. On the other hand, although accuracy of Ultrasound (US) imaging in detecting cancer lesions is low, major anatomical cues like organ boundaries or presence of nearby major organs are visible in US images. A dual mode PA and US imaging system can potentially detect as well as localize cancer lesions with high accuracy. In this study, we have developed a novel pulse echo US imaging system which can be easily integrated with our existing ex-vivo PA imaging system to produce the dual mode imaging system. Here a Polyvinylidene fluoride (PVDF) film has been used as US transmitter. To improve the anticipated low signal to noise ratio (SNR) of the received US signal due to the low electromechanical coupling coefficient of the PVDF film, we implemented pulse compression technique using chirp signals. Comparisons among the different SNR values obtained with short pulse and after pulse compression with chirp signal show a clear improvement of the SNR for the compressed pulse. The axial resolution of the imaging system improved with increasing sweep bandwidth of input chirp signals, whereas the lateral resolution remained almost constant. This work demonstrates the feasibility of using a PVDF film transducer as an US transmitter and implementing pulse compression technique in an acoustic lens focusing based imaging system.

Proteoglycan depletion and size reduction in lesions of early grade chondromalacia of the patella.

PubMed Central

Väätäinen, U; Häkkinen, T; Kiviranta, I; Jaroma, H; Inkinen, R; Tammi, M

1995-01-01

OBJECTIVE--To determine the content and molecular size of proteoglycans (PGs) in patellar chondromalacia (CM) and control cartilages as a first step in investigating the role of matrix alterations in the pathogenesis of this disease. METHODS--Chondromalacia tissue from 10 patients was removed with a surgical knife. Using identical techniques, apparently healthy cartilage of the same site was obtained from 10 age matched cadavers (mean age 31 years in both groups). Additional pathological cartilage was collected from 67 patients with grades II-IV CM (classified according to Outerbridge) using a motorised shaver under arthroscopic control. The shaved cartilage chips were collected with a dense net from the irrigation fluid of the shaver. The content of tissue PGs was determined by Safranin O precipitation or uronic acid content, and the molecular size by mobility on agarose gel electrophoresis. RESULTS--The mean PG content of the CM tissue samples with a knife was dramatically reduced, being only 15% of that in controls. The cartilage chips collected from shaving operations of grades II, III, and IV CM showed a decreasing PG content: 9%, 5%, and 1% of controls, respectively. Electrophoretic analysis of PGs extracted with guanidium chloride from the shaved tissue samples suggested a significantly reduced size of aggrecans in the mild (grade II) lesions. CONCLUSION--These data show that there is already a dramatic and progressive depletion of PGs in CM grade II lesions. This explains the softening of cartilage, a typical finding in the arthroscopic examination of CM. The PG size reduction observed in grade II implicates proteolytic attack as a factor in the pathogenesis of CM. Images PMID:7492223

Methodological requirements for valid tissue-based biomarker studies that can be used in clinical practice.

PubMed

True, Lawrence D

2014-03-01

Paralleling the growth of ever more cost efficient methods to sequence the whole genome in minute fragments of tissue has been the identification of increasingly numerous molecular abnormalities in cancers--mutations, amplifications, insertions and deletions of genes, and patterns of differential gene expression, i.e., overexpression of growth factors and underexpression of tumor suppressor genes. These abnormalities can be translated into assays to be used in clinical decision making. In general terms, the result of such an assay is subject to a large number of variables regarding the characteristics of the available sample, particularities of the used assay, and the interpretation of the results. This review discusses the effects of these variables on assays of tissue-based biomarkers, classified by macromolecule--DNA, RNA (including micro RNA, messenger RNA, long noncoding RNA, protein, and phosphoprotein). Since the majority of clinically applicable biomarkers are immunohistochemically detectable proteins this review focuses on protein biomarkers. However, the principles outlined are mostly applicable to any other analyte. A variety of preanalytical variables impacts on the results obtained, including analyte stability (which is different for different analytes, i.e., DNA, RNA, or protein), period of warm and of cold ischemia, fixation time, tissue processing, sample storage time, and storage conditions. In addition, assay variables play an important role, including reagent specificity (notably but not uniquely an issue concerning antibodies used in immunohistochemistry), technical components of the assay, quantitation, and assay interpretation. Finally, appropriateness of an assay for clinical application is an important issue. Reference is made to publicly available guidelines to improve on biomarker development in general and requirements for clinical use in particular. Strategic goals are formulated in order to improve on the quality of biomarker reporting, including issues of analyte quality, experimental detail, assay efficiency and precision, and assay appropriateness.

Threshold Setting for Likelihood Function for Elasticity-Based Tissue Classification of Arterial Walls by Evaluating Variance in Measurement of Radial Strain

NASA Astrophysics Data System (ADS)

Tsuzuki, Kentaro; Hasegawa, Hideyuki; Kanai, Hiroshi; Ichiki, Masataka; Tezuka, Fumiaki

2008-05-01

Pathologic changes in arterial walls significantly influence their mechanical properties. We have developed a correlation-based method, the phased tracking method [H. Kanai et al.: IEEE Trans. Ultrason. Ferroelectr. Freq. Control 43 (1996) 791], for measurement of the regional elasticity of the arterial wall. Using this method, elasticity distributions of lipids, blood clots, fibrous tissue, and calcified tissue were measured in vitro by experiments on excised arteries (mean±SD: lipids 89±47 kPa, blood clots 131 ±56 kPa, fibrous tissue 1022±1040 kPa, calcified tissue 2267 ±1228 kPa) [H. Kanai et al.: Circulation 107 (2003) 3018; J. Inagaki et al.: Jpn. J. Appl. Phys. 44 (2005) 4593]. It was found that arterial tissues can be classified into soft tissues (lipids and blood clots) and hard tissues (fibrous tissue and calcified tissue) on the basis of their elasticity. However, there are large overlaps between elasticity distributions of lipids and blood clots and those of fibrous tissue and calcified tissue. Thus, it was difficult to differentiate lipids from blood clots and fibrous tissue from calcified tissue by simply thresholding elasticity value. Therefore, we previously proposed a method by classifying the elasticity distribution in each region of interest (ROI) (not a single pixel) in an elasticity image into lipids, blood clots, fibrous tissue, or calcified tissue based on a likelihood function for each tissue [J. Inagaki et al.: Jpn. J. Appl. Phys. 44 (2006) 4732]. In our previous study, the optimum size of an ROI was determined to be 1,500 µm in the arterial radial direction and 1,500 µm in the arterial longitudinal direction [K. Tsuzuki et al.: Ultrasound Med. Biol. 34 (2008) 573]. In this study, the threshold for the likelihood function used in the tissue classification was set by evaluating the variance in the ultrasonic measurement of radial strain. The recognition rate was improved from 50 to 54% by the proposed thresholding.

Ensemble-based classification approach for micro-RNA mining applied on diverse metagenomic sequences.

PubMed

ElGokhy, Sherin M; ElHefnawi, Mahmoud; Shoukry, Amin

2014-05-06

MicroRNAs (miRNAs) are endogenous ∼22 nt RNAs that are identified in many species as powerful regulators of gene expressions. Experimental identification of miRNAs is still slow since miRNAs are difficult to isolate by cloning due to their low expression, low stability, tissue specificity and the high cost of the cloning procedure. Thus, computational identification of miRNAs from genomic sequences provide a valuable complement to cloning. Different approaches for identification of miRNAs have been proposed based on homology, thermodynamic parameters, and cross-species comparisons. The present paper focuses on the integration of miRNA classifiers in a meta-classifier and the identification of miRNAs from metagenomic sequences collected from different environments. An ensemble of classifiers is proposed for miRNA hairpin prediction based on four well-known classifiers (Triplet SVM, Mipred, Virgo and EumiR), with non-identical features, and which have been trained on different data. Their decisions are combined using a single hidden layer neural network to increase the accuracy of the predictions. Our ensemble classifier achieved 89.3% accuracy, 82.2% f-measure, 74% sensitivity, 97% specificity, 92.5% precision and 88.2% negative predictive value when tested on real miRNA and pseudo sequence data. The area under the receiver operating characteristic curve of our classifier is 0.9 which represents a high performance index.The proposed classifier yields a significant performance improvement relative to Triplet-SVM, Virgo and EumiR and a minor refinement over MiPred.The developed ensemble classifier is used for miRNA prediction in mine drainage, groundwater and marine metagenomic sequences downloaded from the NCBI sequence reed archive. By consulting the miRBase repository, 179 miRNAs have been identified as highly probable miRNAs. Our new approach could thus be used for mining metagenomic sequences and finding new and homologous miRNAs. The paper investigates a computational tool for miRNA prediction in genomic or metagenomic data. It has been applied on three metagenomic samples from different environments (mine drainage, groundwater and marine metagenomic sequences). The prediction results provide a set of extremely potential miRNA hairpins for cloning prediction methods. Among the ensemble prediction obtained results there are pre-miRNA candidates that have been validated using miRbase while they have not been recognized by some of the base classifiers.

Improved characterization of molecular phenotypes in breast lesions using 18F-FDG PET image homogeneity

NASA Astrophysics Data System (ADS)

Cao, Kunlin; Bhagalia, Roshni; Sood, Anup; Brogi, Edi; Mellinghoff, Ingo K.; Larson, Steven M.

2015-03-01

Positron emission tomography (PET) using uorodeoxyglucose (18F-FDG) is commonly used in the assessment of breast lesions by computing voxel-wise standardized uptake value (SUV) maps. Simple metrics derived from ensemble properties of SUVs within each identified breast lesion are routinely used for disease diagnosis. The maximum SUV within the lesion (SUVmax) is the most popular of these metrics. However these simple metrics are known to be error-prone and are susceptible to image noise. Finding reliable SUV map-based features that correlate to established molecular phenotypes of breast cancer (viz. estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) expression) will enable non-invasive disease management. This study investigated 36 SUV features based on first and second order statistics, local histograms and texture of segmented lesions to predict ER and PR expression in 51 breast cancer patients. True ER and PR expression was obtained via immunohistochemistry (IHC) of tissue samples from each lesion. A supervised learning, adaptive boosting-support vector machine (AdaBoost-SVM), framework was used to select a subset of features to classify breast lesions into distinct phenotypes. Performance of the trained multi-feature classifier was compared against the baseline single-feature SUVmax classifier using receiver operating characteristic (ROC) curves. Results show that texture features encoding local lesion homogeneity extracted from gray-level co-occurrence matrices are the strongest discriminator of lesion ER expression. In particular, classifiers including these features increased prediction accuracy from 0.75 (baseline) to 0.82 and the area under the ROC curve from 0.64 (baseline) to 0.75.

Novel computer-aided diagnosis of mesothelioma using nuclear structure of mesothelial cells in effusion cytology specimens

NASA Astrophysics Data System (ADS)

Tosun, Akif Burak; Yergiyev, Oleksandr; Kolouri, Soheil; Silverman, Jan F.; Rohde, Gustavo K.

2014-03-01

diagnostic standard is a pleural biopsy with subsequent histologic examination of the tissue demonstrating invasion by the tumor. The diagnostic tissue is obtained through thoracoscopy or open thoracotomy, both being highly invasive procedures. Thoracocenthesis, or removal of effusion fluid from the pleural space, is a far less invasive procedure that can provide material for cytological examination. However, it is insufficient to definitively confirm or exclude the diagnosis of malignant mesothelioma, since tissue invasion cannot be determined. In this study, we present a computerized method to detect and classify malignant mesothelioma based on the nuclear chromatin distribution from digital images of mesothelial cells in effusion cytology specimens. Our method aims at determining whether a set of nuclei belonging to a patient, obtained from effusion fluid images using image segmentation, is benign or malignant, and has a potential to eliminate the need for tissue biopsy. This method is performed by quantifying chromatin morphology of cells using the optimal transportation (Kantorovich-Wasserstein) metric in combination with the modified Fisher discriminant analysis, a k-nearest neighborhood classification, and a simple voting strategy. Our results show that we can classify the data of 10 different human cases with 100% accuracy after blind cross validation. We conclude that nuclear structure alone contains enough information to classify the malignant mesothelioma. We also conclude that the distribution of chromatin seems to be a discriminating feature between nuclei of benign and malignant mesothelioma cells.

Automatic recognition of fundamental tissues on histology images of the human cardiovascular system.

PubMed

Mazo, Claudia; Trujillo, Maria; Alegre, Enrique; Salazar, Liliana

2016-10-01

Cardiovascular disease is the leading cause of death worldwide. Therefore, techniques for improving diagnosis and treatment in this field have become key areas for research. In particular, approaches for tissue image processing may support education system and medical practice. In this paper, an approach to automatic recognition and classification of fundamental tissues, using morphological information is presented. Taking a 40× or 10× histological image as input, three clusters are created with the k-means algorithm using a structural tensor and the red and the green channels. Loose connective tissue, light regions and cell nuclei are recognised on 40× images. Then, the cell nuclei's features - shape and spatial projection - and light regions are used to recognise and classify epithelial cells and tissue into flat, cubic and cylindrical. In a similar way, light regions, loose connective and muscle tissues are recognised on 10× images. Finally, the tissue's function and composition are used to refine muscle tissue recognition. Experimental validation is then carried out by histologist following expert criteria, along with manually annotated images that are used as a ground-truth. The results revealed that the proposed approach classified the fundamental tissues in a similar way to the conventional method employed by histologists. The proposed automatic recognition approach provides for epithelial tissues a sensitivity of 0.79 for cubic, 0.85 for cylindrical and 0.91 for flat. Furthermore, the experts gave our method an average score of 4.85 out of 5 in the recognition of loose connective tissue and 4.82 out of 5 for muscle tissue recognition. Copyright © 2016 Elsevier Ltd. All rights reserved.

Brain tissue segmentation in 4D CT using voxel classification

NASA Astrophysics Data System (ADS)

van den Boom, R.; Oei, M. T. H.; Lafebre, S.; Oostveen, L. J.; Meijer, F. J. A.; Steens, S. C. A.; Prokop, M.; van Ginneken, B.; Manniesing, R.

2012-02-01

A method is proposed to segment anatomical regions of the brain from 4D computer tomography (CT) patient data. The method consists of a three step voxel classification scheme, each step focusing on structures that are increasingly difficult to segment. The first step classifies air and bone, the second step classifies vessels and the third step classifies white matter, gray matter and cerebrospinal fluid. As features the time averaged intensity value and the temporal intensity change value were used. In each step, a k-Nearest-Neighbor classifier was used to classify the voxels. Training data was obtained by placing regions of interest in reconstructed 3D image data. The method has been applied to ten 4D CT cerebral patient data. A leave-one-out experiment showed consistent and accurate segmentation results.

Effect of separate sampling on classification accuracy.

PubMed

Shahrokh Esfahani, Mohammad; Dougherty, Edward R

2014-01-15

Measurements are commonly taken from two phenotypes to build a classifier, where the number of data points from each class is predetermined, not random. In this 'separate sampling' scenario, the data cannot be used to estimate the class prior probabilities. Moreover, predetermined class sizes can severely degrade classifier performance, even for large samples. We employ simulations using both synthetic and real data to show the detrimental effect of separate sampling on a variety of classification rules. We establish propositions related to the effect on the expected classifier error owing to a sampling ratio different from the population class ratio. From these we derive a sample-based minimax sampling ratio and provide an algorithm for approximating it from the data. We also extend to arbitrary distributions the classical population-based Anderson linear discriminant analysis minimax sampling ratio derived from the discriminant form of the Bayes classifier. All the codes for synthetic data and real data examples are written in MATLAB. A function called mmratio, whose output is an approximation of the minimax sampling ratio of a given dataset, is also written in MATLAB. All the codes are available at: http://gsp.tamu.edu/Publications/supplementary/shahrokh13b.

Force Trends and Pulsatility for Catheter Contact Identification in Intracardiac Electrograms during Arrhythmia Ablation.

PubMed

Rivas-Lalaleo, David; Muñoz-Romero, Sergio; Huerta, Mónica; Erazo-Rodas, Mayra; Sánchez-Muñoz, Juan José; Rojo-Álvarez, José Luis; García-Alberola, Arcadi

2018-05-02

The intracardiac electrical activation maps are commonly used as a guide in the ablation of cardiac arrhythmias. The use of catheters with force sensors has been proposed in order to know if the electrode is in contact with the tissue during the registration of intracardiac electrograms (EGM). Although threshold criteria on force signals are often used to determine the catheter contact, this may be a limited criterion due to the complexity of the heart dynamics and cardiac vorticity. The present paper is devoted to determining the criteria and force signal profiles that guarantee the contact of the electrode with the tissue. In this study, we analyzed 1391 force signals and their associated EGM recorded during 2 and 8 s, respectively, in 17 patients (82 ± 60 points per patient). We aimed to establish a contact pattern by first visually examining and classifying the signals, according to their likely-contact joint profile and following the suggestions from experts in the doubtful cases. First, we used Principal Component Analysis to scrutinize the force signal dynamics by analyzing the main eigen-directions, first globally and then grouped according to the certainty of their tissue-catheter contact. Second, we used two different linear classifiers (Fisher discriminant and support vector machines) to identify the most relevant components of the previous signal models. We obtained three main types of eigenvectors, namely, pulsatile relevant, non-pulsatile relevant, and irrelevant components. The classifiers reached a moderate to sufficient discrimination capacity (areas under the curve between 0.84 and 0.95 depending on the contact certainty and on the classifier), which allowed us to analyze the relevant properties in the force signals. We conclude that the catheter-tissue contact profiles in force recordings are complex and do not depend only on the signal intensity being above a threshold at a single time instant, but also on time pulsatility and trends. These findings pave the way towards a subsystem which can be included in current intracardiac navigation systems assisted by force contact sensors, and it can provide the clinician with an estimate of the reliability on the tissue-catheter contact in the point-by-point EGM acquisition procedure.

Force Trends and Pulsatility for Catheter Contact Identification in Intracardiac Electrograms during Arrhythmia Ablation

PubMed Central

Muñoz-Romero, Sergio; Erazo-Rodas, Mayra; Sánchez-Muñoz, Juan José; García-Alberola, Arcadi

2018-01-01

The intracardiac electrical activation maps are commonly used as a guide in the ablation of cardiac arrhythmias. The use of catheters with force sensors has been proposed in order to know if the electrode is in contact with the tissue during the registration of intracardiac electrograms (EGM). Although threshold criteria on force signals are often used to determine the catheter contact, this may be a limited criterion due to the complexity of the heart dynamics and cardiac vorticity. The present paper is devoted to determining the criteria and force signal profiles that guarantee the contact of the electrode with the tissue. In this study, we analyzed 1391 force signals and their associated EGM recorded during 2 and 8 s, respectively, in 17 patients (82 ± 60 points per patient). We aimed to establish a contact pattern by first visually examining and classifying the signals, according to their likely-contact joint profile and following the suggestions from experts in the doubtful cases. First, we used Principal Component Analysis to scrutinize the force signal dynamics by analyzing the main eigen-directions, first globally and then grouped according to the certainty of their tissue-catheter contact. Second, we used two different linear classifiers (Fisher discriminant and support vector machines) to identify the most relevant components of the previous signal models. We obtained three main types of eigenvectors, namely, pulsatile relevant, non-pulsatile relevant, and irrelevant components. The classifiers reached a moderate to sufficient discrimination capacity (areas under the curve between 0.84 and 0.95 depending on the contact certainty and on the classifier), which allowed us to analyze the relevant properties in the force signals. We conclude that the catheter-tissue contact profiles in force recordings are complex and do not depend only on the signal intensity being above a threshold at a single time instant, but also on time pulsatility and trends. These findings pave the way towards a subsystem which can be included in current intracardiac navigation systems assisted by force contact sensors, and it can provide the clinician with an estimate of the reliability on the tissue-catheter contact in the point-by-point EGM acquisition procedure. PMID:29724033

Recognition Using Hybrid Classifiers.

PubMed

Osadchy, Margarita; Keren, Daniel; Raviv, Dolev

2016-04-01

A canonical problem in computer vision is category recognition (e.g., find all instances of human faces, cars etc., in an image). Typically, the input for training a binary classifier is a relatively small sample of positive examples, and a huge sample of negative examples, which can be very diverse, consisting of images from a large number of categories. The difficulty of the problem sharply increases with the dimension and size of the negative example set. We propose to alleviate this problem by applying a "hybrid" classifier, which replaces the negative samples by a prior, and then finds a hyperplane which separates the positive samples from this prior. The method is extended to kernel space and to an ensemble-based approach. The resulting binary classifiers achieve an identical or better classification rate than SVM, while requiring far smaller memory and lower computational complexity to train and apply.

A two-dimensional matrix image based feature extraction method for classification of sEMG: A comparative analysis based on SVM, KNN and RBF-NN.

PubMed

Wen, Tingxi; Zhang, Zhongnan; Qiu, Ming; Zeng, Ming; Luo, Weizhen

2017-01-01

The computer mouse is an important human-computer interaction device. But patients with physical finger disability are unable to operate this device. Surface EMG (sEMG) can be monitored by electrodes on the skin surface and is a reflection of the neuromuscular activities. Therefore, we can control limbs auxiliary equipment by utilizing sEMG classification in order to help the physically disabled patients to operate the mouse. To develop a new a method to extract sEMG generated by finger motion and apply novel features to classify sEMG. A window-based data acquisition method was presented to extract signal samples from sEMG electordes. Afterwards, a two-dimensional matrix image based feature extraction method, which differs from the classical methods based on time domain or frequency domain, was employed to transform signal samples to feature maps used for classification. In the experiments, sEMG data samples produced by the index and middle fingers at the click of a mouse button were separately acquired. Then, characteristics of the samples were analyzed to generate a feature map for each sample. Finally, the machine learning classification algorithms (SVM, KNN, RBF-NN) were employed to classify these feature maps on a GPU. The study demonstrated that all classifiers can identify and classify sEMG samples effectively. In particular, the accuracy of the SVM classifier reached up to 100%. The signal separation method is a convenient, efficient and quick method, which can effectively extract the sEMG samples produced by fingers. In addition, unlike the classical methods, the new method enables to extract features by enlarging sample signals' energy appropriately. The classical machine learning classifiers all performed well by using these features.

Classification of coronary artery tissues using optical coherence tomography imaging in Kawasaki disease

NASA Astrophysics Data System (ADS)

Abdolmanafi, Atefeh; Prasad, Arpan Suravi; Duong, Luc; Dahdah, Nagib

2016-03-01

Intravascular imaging modalities, such as Optical Coherence Tomography (OCT) allow nowadays improving diagnosis, treatment, follow-up, and even prevention of coronary artery disease in the adult. OCT has been recently used in children following Kawasaki disease (KD), the most prevalent acquired coronary artery disease during childhood with devastating complications. The assessment of coronary artery layers with OCT and early detection of coronary sequelae secondary to KD is a promising tool for preventing myocardial infarction in this population. More importantly, OCT is promising for tissue quantification of the inner vessel wall, including neo intima luminal myofibroblast proliferation, calcification, and fibrous scar deposits. The goal of this study is to classify the coronary artery layers of OCT imaging obtained from a series of KD patients. Our approach is focused on developing a robust Random Forest classifier built on the idea of randomly selecting a subset of features at each node and based on second- and higher-order statistical texture analysis which estimates the gray-level spatial distribution of images by specifying the local features of each pixel and extracting the statistics from their distribution. The average classification accuracy for intima and media are 76.36% and 73.72% respectively. Random forest classifier with texture analysis promises for classification of coronary artery tissue.

Non-invasive optical estimate of tissue composition to differentiate malignant from benign breast lesions: A pilot study

NASA Astrophysics Data System (ADS)

Taroni, Paola; Paganoni, Anna Maria; Ieva, Francesca; Pifferi, Antonio; Quarto, Giovanna; Abbate, Francesca; Cassano, Enrico; Cubeddu, Rinaldo

2017-01-01

Several techniques are being investigated as a complement to screening mammography, to reduce its false-positive rate, but results are still insufficient to draw conclusions. This initial study explores time domain diffuse optical imaging as an adjunct method to classify non-invasively malignant vs benign breast lesions. We estimated differences in tissue composition (oxy- and deoxyhemoglobin, lipid, water, collagen) and absorption properties between lesion and average healthy tissue in the same breast applying a perturbative approach to optical images collected at 7 red-near infrared wavelengths (635-1060 nm) from subjects bearing breast lesions. The Discrete AdaBoost procedure, a machine-learning algorithm, was then exploited to classify lesions based on optically derived information (either tissue composition or absorption) and risk factors obtained from patient’s anamnesis (age, body mass index, familiarity, parity, use of oral contraceptives, and use of Tamoxifen). Collagen content, in particular, turned out to be the most important parameter for discrimination. Based on the initial results of this study the proposed method deserves further investigation.

A review of medical robotics for minimally invasive soft tissue surgery.

PubMed

Dogangil, G; Davies, B L; Rodriguez y Baena, F

2010-01-01

This paper provides an overview of recent trends and developments in medical robotics for minimally invasive soft tissue surgery, with a view to highlight some of the issues posed and solutions proposed in the literature. The paper includes a thorough review of the literature, which focuses on soft tissue surgical robots developed and published in the last five years (between 2004 and 2008) in indexed journals and conference proceedings. Only surgical systems were considered; imaging and diagnostic devices were excluded from the review. The systems included in this paper are classified according to the following surgical specialties: neurosurgery; eye surgery and ear, nose, and throat (ENT); general, thoracic, and cardiac surgery; gastrointestinal and colorectal surgery; and urologic surgery. The systems are also cross-classified according to their engineering design and robotics technology, which is included in tabular form at the end of the paper. The review concludes with an overview of the field, along with some statistical considerations about the size, geographical spread, and impact of medical robotics for soft tissue surgery today.

Development of a computer aided diagnosis model for prostate cancer classification on multi-parametric MRI

NASA Astrophysics Data System (ADS)

Alfano, R.; Soetemans, D.; Bauman, G. S.; Gibson, E.; Gaed, M.; Moussa, M.; Gomez, J. A.; Chin, J. L.; Pautler, S.; Ward, A. D.

2018-02-01

Multi-parametric MRI (mp-MRI) is becoming a standard in contemporary prostate cancer screening and diagnosis, and has shown to aid physicians in cancer detection. It offers many advantages over traditional systematic biopsy, which has shown to have very high clinical false-negative rates of up to 23% at all stages of the disease. However beneficial, mp-MRI is relatively complex to interpret and suffers from inter-observer variability in lesion localization and grading. Computer-aided diagnosis (CAD) systems have been developed as a solution as they have the power to perform deterministic quantitative image analysis. We measured the accuracy of such a system validated using accurately co-registered whole-mount digitized histology. We trained a logistic linear classifier (LOGLC), support vector machine (SVC), k-nearest neighbour (KNN) and random forest classifier (RFC) in a four part ROI based experiment against: 1) cancer vs. non-cancer, 2) high-grade (Gleason score ≥4+3) vs. low-grade cancer (Gleason score <4+3), 3) high-grade vs. other tissue components and 4) high-grade vs. benign tissue by selecting the classifier with the highest AUC using 1-10 features from forward feature selection. The CAD model was able to classify malignant vs. benign tissue and detect high-grade cancer with high accuracy. Once fully validated, this work will form the basis for a tool that enhances the radiologist's ability to detect malignancies, potentially improving biopsy guidance, treatment selection, and focal therapy for prostate cancer patients, maximizing the potential for cure and increasing quality of life.

Molecular Diagnosis of Orthopedic-Device-Related Infection Directly from Sonication Fluid by Metagenomic Sequencing

PubMed Central

Sanderson, Nicholas D.; Atkins, Bridget L.; Brent, Andrew J.; Cole, Kevin; Foster, Dona; McNally, Martin A.; Oakley, Sarah; Peto, Leon; Taylor, Adrian; Peto, Tim E. A.; Crook, Derrick W.; Eyre, David W.

2017-01-01

ABSTRACT Culture of multiple periprosthetic tissue samples is the current gold standard for microbiological diagnosis of prosthetic joint infections (PJI). Additional diagnostic information may be obtained through culture of sonication fluid from explants. However, current techniques can have relatively low sensitivity, with prior antimicrobial therapy and infection by fastidious organisms influencing results. We assessed if metagenomic sequencing of total DNA extracts obtained direct from sonication fluid can provide an alternative rapid and sensitive tool for diagnosis of PJI. We compared metagenomic sequencing with standard aerobic and anaerobic culture in 97 sonication fluid samples from prosthetic joint and other orthopedic device infections. Reads from Illumina MiSeq sequencing were taxonomically classified using Kraken. Using 50 derivation samples, we determined optimal thresholds for the number and proportion of bacterial reads required to identify an infection and confirmed our findings in 47 independent validation samples. Compared to results from sonication fluid culture, the species-level sensitivity of metagenomic sequencing was 61/69 (88%; 95% confidence interval [CI], 77 to 94%; for derivation samples 35/38 [92%; 95% CI, 79 to 98%]; for validation samples, 26/31 [84%; 95% CI, 66 to 95%]), and genus-level sensitivity was 64/69 (93%; 95% CI, 84 to 98%). Species-level specificity, adjusting for plausible fastidious causes of infection, species found in concurrently obtained tissue samples, and prior antibiotics, was 85/97 (88%; 95% CI, 79 to 93%; for derivation samples, 43/50 [86%; 95% CI, 73 to 94%]; for validation samples, 42/47 [89%; 95% CI, 77 to 96%]). High levels of human DNA contamination were seen despite the use of laboratory methods to remove it. Rigorous laboratory good practice was required to minimize bacterial DNA contamination. We demonstrate that metagenomic sequencing can provide accurate diagnostic information in PJI. Our findings, combined with the increasing availability of portable, random-access sequencing technology, offer the potential to translate metagenomic sequencing into a rapid diagnostic tool in PJI. PMID:28490492

Superoxide dismutases in chronic gastritis.

PubMed

Švagelj, Dražen; Terzić, Velimir; Dovhanj, Jasna; Švagelj, Marija; Cvrković, Mirta; Švagelj, Ivan

2016-04-01

Human gastric diseases have shown significant changes in the activity and expression of superoxide dismutase (SOD) isoforms. The aim of this study was to detect Mn-SOD activity and expression in the tissue of gastric mucosa, primarily in chronic gastritis (immunohistochemical Helicobacter pylori-negative gastritis, without other pathohistological changes) and to evaluate their possible connection with pathohistological diagnosis. We examined 51 consecutive outpatients undergoing endoscopy for upper gastrointestinal symptoms. Patients were classified based on their histopathological examinations and divided into three groups: 51 patients (archive samples between 2004-2009) with chronic immunohistochemical Helicobacter pylori-negative gastritis (mononuclear cells infiltration were graded as absent, moderate, severe) divided into three groups. Severity of gastritis was graded according to the updated Sydney system. Gastric tissue samples were used to determine the expression of Mn-SOD with anti-Mn-SOD Ab immunohistochemically. The Mn-SOD expression was more frequently present in specimens with severe and moderate inflammation of gastric mucosa than in those with normal mucosa. In patients with normal histological finding, positive immunoreactivity of Mn-SOD was not found. Our results determine the changes in Mn-SOD expression occurring in the normal gastric mucosa that had undergone changes in the intensity of chronic inflammatory infiltrates in the lamina propria. © 2016 APMIS. Published by John Wiley & Sons Ltd.

Trends in tissue engineering research.

PubMed

Hacker, Michael C; Mikos, Antonios G

2006-08-01

For more than a decade, Tissue Engineering has been devoted to the reporting and discussion of scientific advances in the interdisciplinary field of tissue engineering. In this study, 779 original articles published in the journal since its inception were analyzed and classified according to different attributes, such as focus of research and tissue of interest, to reveal trends in tissue engineering research. In addition, the use of different biomaterials, scaffold architectures, surface and bulk modification agents, cells, differentiation factors, gene delivery vectors, and animal models was examined. The results of this survey show interesting trends over time and by continental origin.

Fiber-probe optical spectroscopy discriminates normal brain from focal cortical dysplasia in pediatric subjects

NASA Astrophysics Data System (ADS)

Anand, Suresh; Cicchi, Riccardo; Giordano, Flavio; Conti, Valerio; Buccoliero, Anna Maria; Guerrini, Renzo; Pavone, Francesco S.

2017-02-01

Focal cortical dysplasia (FCD) is an abnormality in the cerebral cortex that is caused by malformations during cortical development. Currently, magnetic resonance imaging (MRI) and electro-corticography (ECoG) are used for detecting FCD. On the downside, MRI is very much insensitive to small malformations in the brain, while ECoG is an invasive and time consuming procedure. Recently, optical techniques were widely exploited as a minimally invasive and quantitative approaches for disease diagnosis. These techniques include fluorescence and Raman spectroscopy. The aim of this investigation is to study the diagnostic performances of optical spectroscopy incorporating fluorescence (at 378 nm and 445 nm excitation wavelengths) and Raman spectroscopy (at 785 nm excitation) for the discrimination of FCD from normal brain in pediatric subjects. The study included 10 normal and 17 FCD tissue sites from 3 normal and 7 FCD samples. The emission spectra of FCD at 378 nm excitation wavelength presented a blue-shifted peak with respect to normal tissue. Prominent spectral differences between normal and FCD tissue were observed at 1298 cm-1, 1302 cm-1, 1445 cm-1 and 1660 cm-1 using Raman spectroscopy. Tissue classification models were developed using a multivariate statistical method, principal component analysis. This study demonstrates that a combined spectroscopic approach can provide a better diagnostic capability for classifying normal and FCD tissues. Further, the implementation of the technology within a fiber probe could open the way for in vivo diagnostics and intra-operative surgical guidance.

Local classifier weighting by quadratic programming.

PubMed

Cevikalp, Hakan; Polikar, Robi

2008-10-01

It has been widely accepted that the classification accuracy can be improved by combining outputs of multiple classifiers. However, how to combine multiple classifiers with various (potentially conflicting) decisions is still an open problem. A rich collection of classifier combination procedures -- many of which are heuristic in nature -- have been developed for this goal. In this brief, we describe a dynamic approach to combine classifiers that have expertise in different regions of the input space. To this end, we use local classifier accuracy estimates to weight classifier outputs. Specifically, we estimate local recognition accuracies of classifiers near a query sample by utilizing its nearest neighbors, and then use these estimates to find the best weights of classifiers to label the query. The problem is formulated as a convex quadratic optimization problem, which returns optimal nonnegative classifier weights with respect to the chosen objective function, and the weights ensure that locally most accurate classifiers are weighted more heavily for labeling the query sample. Experimental results on several data sets indicate that the proposed weighting scheme outperforms other popular classifier combination schemes, particularly on problems with complex decision boundaries. Hence, the results indicate that local classification-accuracy-based combination techniques are well suited for decision making when the classifiers are trained by focusing on different regions of the input space.

An exploratory study of a text classification framework for Internet-based surveillance of emerging epidemics

PubMed Central

Torii, Manabu; Yin, Lanlan; Nguyen, Thang; Mazumdar, Chand T.; Liu, Hongfang; Hartley, David M.; Nelson, Noele P.

2014-01-01

Purpose Early detection of infectious disease outbreaks is crucial to protecting the public health of a society. Online news articles provide timely information on disease outbreaks worldwide. In this study, we investigated automated detection of articles relevant to disease outbreaks using machine learning classifiers. In a real-life setting, it is expensive to prepare a training data set for classifiers, which usually consists of manually labeled relevant and irrelevant articles. To mitigate this challenge, we examined the use of randomly sampled unlabeled articles as well as labeled relevant articles. Methods Naïve Bayes and Support Vector Machine (SVM) classifiers were trained on 149 relevant and 149 or more randomly sampled unlabeled articles. Diverse classifiers were trained by varying the number of sampled unlabeled articles and also the number of word features. The trained classifiers were applied to 15 thousand articles published over 15 days. Top-ranked articles from each classifier were pooled and the resulting set of 1337 articles was reviewed by an expert analyst to evaluate the classifiers. Results Daily averages of areas under ROC curves (AUCs) over the 15-day evaluation period were 0.841 and 0.836, respectively, for the naïve Bayes and SVM classifier. We referenced a database of disease outbreak reports to confirm that this evaluation data set resulted from the pooling method indeed covered incidents recorded in the database during the evaluation period. Conclusions The proposed text classification framework utilizing randomly sampled unlabeled articles can facilitate a cost-effective approach to training machine learning classifiers in a real-life Internet-based biosurveillance project. We plan to examine this framework further using larger data sets and using articles in non-English languages. PMID:21134784

Use of Unlabeled Samples for Mitigating the Hughes Phenomenon

NASA Technical Reports Server (NTRS)

Landgrebe, David A.; Shahshahani, Behzad M.

1993-01-01

The use of unlabeled samples in improving the performance of classifiers is studied. When the number of training samples is fixed and small, additional feature measurements may reduce the performance of a statistical classifier. It is shown that by using unlabeled samples, estimates of the parameters can be improved and therefore this phenomenon may be mitigated. Various methods for using unlabeled samples are reviewed and experimental results are provided.

Azithromycin metabolite identification in plasma, bile, and tissues of the ball python (Python regius).

PubMed

Hunter, R P; Koch, D E; Coke, R L; Goatley, M A; Isaza, R

2003-04-01

Azithromycin is the first of a class of antibiotics classified as azalides. Six ball pythons (Python regius) were given a single dose of azithromycin at 10 mg/kg p.o. and i.v. in a crossover design. Serial blood samples were collected for unchanged azithromycin and to determine, if possible, the structure and number of circulating azithromycin metabolites. After a 4-month wash-out period, the snakes were given azithromycin p.o. as a single dose of 10 mg/kg for the study of azithromycin metabolism and metabolite tissue distribution. Bile, liver, lung, kidney, and skin samples were analyzed for the metabolites identified from the first experiment. Unchanged azithromycin accounted for 80, 68, and 60% of the total material at 12, 24, and 48 h postadministration in plasma, independent of route of administration. At both 24 and 72 h postadministration, azithromycin accounted for 70% of total azithromycin- associated material in bile. In liver and kidney, unchanged azithromycin accounted for 40% of the total azithromycin-associated material; this doubled in lung and skin. Fifteen metabolites were positively or tentatively identified in plasma, bile, or tissues of all snakes. Four of these possible metabolites: 3'-desamine-3-ene-azithromycin, descladinose dehydroxy-2-ene-azithromycin, 3'-desamine-3-ene descladinose-azithromycin, and 3'-N-nitroso,9a-N-desmethyl-azithromycin are unique to this species. Descladinose-azithromycin, 3'-N-desmethyl,9a-N-desmethyl-azithromycin, and 3'-N-desmethyl, 3'-O-desmethyl-azithromycin were the only metabolites identified in skin. Kidney tissue contained a greater number of metabolites than liver tissue, with 3'-N-didesmethyl-azithromycin being identified only in the kidney. Compared with the dog and cat, a greater number of metabolites were identified in ball python plasma. The percentage of unchanged azithromycin in bile is not different between the three species.

Identification of Carbonic Anhydrase IX as a Novel Target for Endoscopic Molecular Imaging of Human Bladder Cancer.

PubMed

Wang, Jiaqi; Fang, Ruizhe; Wang, Lu; Chen, Guang; Wang, Hongzhi; Wang, Zhichao; Zhao, Danfeng; Pavlov, Valentin N; Kabirov, Ildar; Wang, Ziqi; Guo, Pengyu; Peng, Li; Xu, Wanhai

2018-06-27

Emerging novel optical imaging techniques with cancer-specific molecular imaging agents offer a powerful and promising platform for cancer detection and resection. White-light cystoscopy and random bladder biopsies remain the most appropriate but nonetheless suboptimal diagnostic technique for bladder cancer, which is associated with high morbidity and recurrence. However, white-light cystoscopy has intrinsic shortcomings. Although current optical imaging technologies hold great potential for improved diagnostic accuracy, there are few imaging agents for specific molecular targeting. Carbonic anhydrase IX (CAIX) plays a pivotal role in tumorigenesis and tumor progression with potential value as an imaging target. Here, we investigated the feasibility of CAIX as a target and validated the diagnostic performance and significance of CAIX as an imaging agent. We first analyzed the data from The Cancer Genome Atlas (TCGA). Pairs of samples comprising bladder cancer and adjacent normal tissue were collected. All tissue samples were used for real-time PCR and immunohistochemistry to compare CAIX expression in normal and cancer tissue. Using blue-light cystoscopy, we observed the optical distribution of fluorescently labeled CAIX antibody in freshly excised human bladders and obtained random bladder biopsies to assess sensitivity and specificity. The TCGA data revealed that CAIX expression was significantly higher in bladder cancer specimens than in normal tissue. The outcome was similar in quantitative real-time PCR analysis. In immunohistochemical analysis, bladder cancer specimens classified in four pathological subtypes presented a variety of positive staining intensities, whereas no benign specimens showed CAIX staining. Using blue-light cystoscopy, we distinguished bladder cancers that were mainly papillary, some variants of urothelial carcinoma, and less carcinoma in situ, from benign tissue, despite the presence of suspicious-appearing mucosa. The sensitivity and specificity for CAIX-targeted imaging were 88.00% and 93.75%, respectively. CAIX-targeted molecular imaging could be a feasible and adaptive alternative approach for the accurate diagnosis and complete resection of bladder cancer. © 2018 The Author(s). Published by S. Karger AG, Basel.

The biobanking research infrastructure BBMRI_CZ: a critical tool to enhance translational cancer research.

PubMed

Holub, P; Greplova, K; Knoflickova, D; Nenutil, R; Valik, D

2012-01-01

We introduce the national research biobanking infrastructure, BBMRI_CZ. The infrastructure has been founded by the Ministry of Education and became a partner of the European biobanking infrastructure BBMRI.eu. It is designed as a network of individual biobanks where each biobank stores samples obtained from associated healthcare providers. The biobanks comprise long term storage (various types of tissues classified by diagnosis, serum at surgery, genomic DNA and RNA) and short term storage (longitudinally sampled patient sera). We discuss the operation workflow of the infrastructure that needs to be the distributed system: transfer of the samples to the biobank needs to be accompanied by extraction of data from the hospital information systems and this data must be stored in a central index serving mainly for sample lookup. Since BBMRI_CZ is designed solely for research purposes, the data is anonymised prior to their integration into the central BBMRI_CZ index. The index is then available for registered researchers to seek for samples of interest and to request the samples from biobank managers. The paper provides an overview of the structure of data stored in the index. We also discuss monitoring system for the biobanks, incorporated to ensure quality of the stored samples.

A consensus prognostic gene expression classifier for ER positive breast cancer

PubMed Central

Teschendorff, Andrew E; Naderi, Ali; Barbosa-Morais, Nuno L; Pinder, Sarah E; Ellis, Ian O; Aparicio, Sam; Brenton, James D; Caldas, Carlos

2006-01-01

Background A consensus prognostic gene expression classifier is still elusive in heterogeneous diseases such as breast cancer. Results Here we perform a combined analysis of three major breast cancer microarray data sets to hone in on a universally valid prognostic molecular classifier in estrogen receptor (ER) positive tumors. Using a recently developed robust measure of prognostic separation, we further validate the prognostic classifier in three external independent cohorts, confirming the validity of our molecular classifier in a total of 877 ER positive samples. Furthermore, we find that molecular classifiers may not outperform classical prognostic indices but that they can be used in hybrid molecular-pathological classification schemes to improve prognostic separation. Conclusion The prognostic molecular classifier presented here is the first to be valid in over 877 ER positive breast cancer samples and across three different microarray platforms. Larger multi-institutional studies will be needed to fully determine the added prognostic value of molecular classifiers when combined with standard prognostic factors. PMID:17076897

Simultaneous determination of polysaccharides and 21 nucleosides and amino acids in different tissues of Salvia miltiorrhiza from different areas by UV-visible spectrophotometry and UHPLC with triple quadrupole MS/MS.

PubMed

Xiang, Xiang; Sha, Xiuxiu; Su, Shulan; Zhu, Zhenhua; Guo, Sheng; Yan, Hui; Qian, Dawei; Duan, Jin-Ao

2018-03-01

Salvia miltiorrhiza, a traditional Chinese medicine, is a widely used herbal medicine to treat cardiovascular and cerebrovascular diseases. In this study, ultraviolet (UV)-visible spectrophotometry and ultra-high performance liquid chromatography with triple quadrupole tandem mass spectrometry analytical methods were used for rapid quantification of polysaccharides and 21 nucleosides and amino acids in S. miltiorrhiza to determine 17 samples of different tissues from different areas. Based on the total contents, hierarchical clustering analysis and principal components analysis were performed to classify these samples. The established methods were validated with good linearity, precision, repeatability, stability, and recovery. Chemical analysis revealed a higher content of total analytes in the sample of inflorescence from Nanjing (34.17 mg/g), sample of root and rhizome from Shaanxi (34.13 mg/g) and sample of stem and leaf from Nanjing (31.14 mg/g), respectively, indicating that root and rhizome from Shaanxi and the aerial parts from Nanjing exhibited the highest quality due to their highest content. In addition, contents of nucleosides and amino acids in the aerial parts (14.67 mg/g) were much higher than that in roots and rhizomes (9.17 mg/g). This study suggested that UV-visible spectrophotometry and ultra-high performance liquid chromatography with triple quadrupole tandem mass spectrometry are effective techniques to analyze polysaccharides, nucleosides, and amino acids in plants, and they provided valuable information for the development and utilization value of the aerial parts of S. miltiorrhiza. This analysis would also provide useful information for the quality control of S. miltiorrhiza. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Tissue characterization by time-resolved fluorescence spectroscopy of endogenous and exogenous fluorochromes: apparatus design and preliminary results

NASA Astrophysics Data System (ADS)

Glanzmann, Thomas M.; Ballini, Jean-Pierre; Jichlinski, Patrice; van den Bergh, Hubert; Wagnieres, Georges A.

1996-12-01

The biomedical use of an optical fiber-based spectro- temporal fluorometer that can endoscopically record the fluorescence decay of an entire spectrum without scanning is presented. The detector consists of a streak camera coupled to a spectrograph. A mode-locked argon ion pumped dye laser or a nitrogen laser-pumped dye laser are used as pulsed excitation light sources. We measured the fluorescence decays of endogenous fluorophores and of ALA-induced- protoporphyrin IX(PPIX) in an excised human bladder with a carcinoma in situ (CIS). Each autofluorescence decay can be decomposed in at least three exponential components for all tissue samples investigated if the excitation is at 425 nm. The decays of the autofluorescence of all normal sites of the human bladder are similar and they differ significantly from the decays measured on the CIS and the necrotic tissue. The fluorescence of the ALA-induced PPIX in the bladder is monoexponential with a lifetime of 15 (plus or minus 1) ns and this fluorescence lifetime does not change significantly between the normal urothelium and the CIS. A photoproduct of ALA-PPIX with a fluorescence maximum at 670 nm and a lifetime of 8 (plus or minus 1) ns was observed. The measurement of the decay of the autofluorescence allowed to correctly identify a normal tissue site that was classified as abnormal by the measurement of the ALA-PPIX fluorescence intensity.

Classification of cardiovascular tissues using LBP based descriptors and a cascade SVM.

PubMed

Mazo, Claudia; Alegre, Enrique; Trujillo, Maria

2017-08-01

Histological images have characteristics, such as texture, shape, colour and spatial structure, that permit the differentiation of each fundamental tissue and organ. Texture is one of the most discriminative features. The automatic classification of tissues and organs based on histology images is an open problem, due to the lack of automatic solutions when treating tissues without pathologies. In this paper, we demonstrate that it is possible to automatically classify cardiovascular tissues using texture information and Support Vector Machines (SVM). Additionally, we realised that it is feasible to recognise several cardiovascular organs following the same process. The texture of histological images was described using Local Binary Patterns (LBP), LBP Rotation Invariant (LBPri), Haralick features and different concatenations between them, representing in this way its content. Using a SVM with linear kernel, we selected the more appropriate descriptor that, for this problem, was a concatenation of LBP and LBPri. Due to the small number of the images available, we could not follow an approach based on deep learning, but we selected the classifier who yielded the higher performance by comparing SVM with Random Forest and Linear Discriminant Analysis. Once SVM was selected as the classifier with a higher area under the curve that represents both higher recall and precision, we tuned it evaluating different kernels, finding that a linear SVM allowed us to accurately separate four classes of tissues: (i) cardiac muscle of the heart, (ii) smooth muscle of the muscular artery, (iii) loose connective tissue, and (iv) smooth muscle of the large vein and the elastic artery. The experimental validation was conducted using 3000 blocks of 100 × 100 sized pixels, with 600 blocks per class and the classification was assessed using a 10-fold cross-validation. using LBP as the descriptor, concatenated with LBPri and a SVM with linear kernel, the main four classes of tissues were recognised with an AUC higher than 0.98. A polynomial kernel was then used to separate the elastic artery and vein, yielding an AUC in both cases superior to 0.98. Following the proposed approach, it is possible to separate with very high precision (AUC greater than 0.98) the fundamental tissues of the cardiovascular system along with some organs, such as the heart, arteries and veins. Copyright © 2017 Elsevier B.V. All rights reserved.

Histopathological and Ultrastructural Studies of Liver Tissue from TCDD-Exposed Beach Mice (Peromyscus polionotus).

DTIC Science & Technology

1980-03-01

TQuantitative ultrastructural studies were conducted on liver tissue f ran beach Lj mice, Per~ ascus polionotus, exposed to the toxin 2,3, 7f8...weights per se was not attempted since the ages of the beach mice were not known and the animals could only be classified by sex and treatment. The

The role of histopathology in forensic practice: an overview.

PubMed

Dettmeyer, R B

2014-09-01

The role of forensic histopathology in routine practice is to establish the cause of death in particular cases. This is achieved on the basis of microscopic analysis of representative cell and tissue samples taken from the major internal organs and from abnormal findings made at autopsy. A prerequisite of this is adherence to the quality standards set out for conventional histological/cytological staining and enzyme histochemical and immunohistochemical methods. The interpretation of histological findings is performed by taking into account macroscopic autopsy findings and information on previous history. Histological analysis may prompt postmortem biochemical and chemical-toxicological investigations. The results of histological analysis need to be classified by experts in the context of the available information and the need to withstand the scrutiny of other experts.

Towards intraoperative surgical margin assessment and visualization using bioimpedance properties of the tissue

NASA Astrophysics Data System (ADS)

Khan, Shadab; Mahara, Aditya; Hyams, Elias S.; Schned, Alan; Halter, Ryan

2015-03-01

Prostate cancer (PCa) has a high 10-year recurrence rate, making PCa the second leading cause of cancer-specific mortality among men in the USA. PCa recurrences are often predicted by assessing the status of surgical margins (SM) with positive surgical margins (PSM) increasing the chances of biochemical recurrence by 2-4 times. To this end, an SM assessment system using Electrical Impedance Spectroscopy (EIS) was developed with a microendoscopic probe. This system measures the tissue bioimpedance over a range of frequencies (1 kHz to 1MHz), and computes a Composite Impedance Metric (CIM). CIM can be used to classify tissue as benign or cancerous. The system was used to collect the impedance spectra from excised prostates, which were obtained from men undergoing radical prostatectomy. The data revealed statistically significant (p<0.05) differences in the impedance properties of the benign and tumorous tissues, and between different tissue morphologies. To visualize the results of SM-assessment, a visualization tool using da Vinci stereo laparoscope is being developed. Together with the visualization tool, the EIS-based SM assessment system can be potentially used to intraoperatively classify tissues and display the results on the surgical console with a video feed of the surgical site, thereby augmenting a surgeon's view of the site and providing a potential solution to the intraoperative SM assessment needs.

Auto-simultaneous laser treatment and Ohshiro's classification of laser treatment

NASA Astrophysics Data System (ADS)

Ohshiro, Toshio

2005-07-01

When the laser was first applied in medicine and surgery in the late 1960"s and early 1970"s, early adopters reported better wound healing and less postoperative pain with laser procedures compared with the same procedure performed with the cold scalpel or with electrothermy, and multiple surgical effects such as incision, vaporization and hemocoagulation could be achieved with the same laser beam. There was thus an added beneficial component which was associated only with laser surgery. This was first recognized as the `?-effect", was then classified by the author as simultaneous laser therapy, but is now more accurately classified by the author as part of the auto-simultaneous aspect of laser treatment. Indeed, with the dramatic increase of the applications of the laser in surgery and medicine over the last 2 decades there has been a parallel increase in the need for a standardized classification of laser treatment. Some classifications have been machine-based, and thus inaccurate because at appropriate parameters, a `low-power laser" can produce a surgical effect and a `high power laser", a therapeutic one . A more accurate classification based on the tissue reaction is presented, developed by the author. In addition to this, the author has devised a graphical representation of laser surgical and therapeutic beams whereby the laser type, parameters, penetration depth, and tissue reaction can all be shown in a single illustration, which the author has termed the `Laser Apple", due to the typical pattern generated when a laser beam is incident on tissue. Laser/tissue reactions fall into three broad groups. If the photoreaction in the tissue is irreversible, then it is classified as high-reactive level laser treatment (HLLT). If some irreversible damage occurs together with reversible photodamage, as in tissue welding, the author refers to this as mid-reactive level laser treatment (MLLT). If the level of reaction in the target tissue is lower than the cells" survival threshold, then this is low reactive-level laser therapy (LLLT). All three of these classifications can occur simultaneously in the one target, and fall under the umbrella of laser treatment (LT). LT is further subdivided into three main types: mono-type LT (Mo-LT, treatment with a single laser system; multi-type LT (Mu-LT, treatment with multiple laser systems); and concomitant LT (Cc-LT), laser treatment in combination, each of which is further subdivided by tissue reaction to give an accurate, treatment-based categorization of laser treatment. When this effect-based classification is combined with and illustrated by the appropriate laser apple pattern, an accurate and simple method of classifying laser/tissue reactions by the reaction, rather than by the laser used to produce the reaction, is achieved. Examples will be given to illustrate the author"s new approach to this important concept.

Tumor margin assessment in Mohs surgery using reflectance, fluorescence and Raman spectroscopy

NASA Astrophysics Data System (ADS)

Nguyen, Hieu T. M.; Moy, Austin J.; Zhang, Yao; Feng, Xu; Reichenberg, Jason S.; Fox, Matthew; Tunnell, James W.

2017-02-01

Mohs surgery is the current gold standard to treat large, aggressive or high-risk non-melanoma skin cancer (NMSC) cases. While Mohs surgery is an effective treatment, the procedure is time-consuming and expensive for physicians as well as burdensome for patients as they wait for frozen section histology. Our group has recently demonstrated high diagnostic accuracy using a noninvasive "spectral biopsy" (combination of diffuse reflectance (DRS), fluorescence (FS) and Raman spectroscopy (RS)) to classify NMSC vs. normal lesion in a screening setting of intact tissue. Here, we examine the sensitivity of spectral biopsy to pathology in excised Mohs sections. The system is designed with three modalities integrated into one fiber probe, which is utilized to measure DRS, FS, and RS of freshly excised skin from patients with various NMSC pathologies including basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), where each measurement location is correlated to histopathology. The spectral biopsy provides complimentary physiological information including the reduced scattering coefficient, hemoglobin content and oxygen saturation from DRS, NADH and collagen contribution from FS and information regarding multiple proteins and lipids from RS. We then apply logistic regression model to the extracted physiological parameters to classify NMSC vs. normal tissue. The results on the excised tissue are generally consistent with in vivo measurements showing decreased scattering within the tumor and reduced fluorescence. Due to the high sensitivity of RS to lipids, subcutaneous fat often dominates the RS signal. This pilot study demonstrates the potential for a spectral biopsy to classify NMSC vs. normal tissue, indicating the opportunity to guide Mohs excisions.

The maximum entropy method of moments and Bayesian probability theory

NASA Astrophysics Data System (ADS)

Bretthorst, G. Larry

2013-08-01

The problem of density estimation occurs in many disciplines. For example, in MRI it is often necessary to classify the types of tissues in an image. To perform this classification one must first identify the characteristics of the tissues to be classified. These characteristics might be the intensity of a T1 weighted image and in MRI many other types of characteristic weightings (classifiers) may be generated. In a given tissue type there is no single intensity that characterizes the tissue, rather there is a distribution of intensities. Often this distributions can be characterized by a Gaussian, but just as often it is much more complicated. Either way, estimating the distribution of intensities is an inference problem. In the case of a Gaussian distribution, one must estimate the mean and standard deviation. However, in the Non-Gaussian case the shape of the density function itself must be inferred. Three common techniques for estimating density functions are binned histograms [1, 2], kernel density estimation [3, 4], and the maximum entropy method of moments [5, 6]. In the introduction, the maximum entropy method of moments will be reviewed. Some of its problems and conditions under which it fails will be discussed. Then in later sections, the functional form of the maximum entropy method of moments probability distribution will be incorporated into Bayesian probability theory. It will be shown that Bayesian probability theory solves all of the problems with the maximum entropy method of moments. One gets posterior probabilities for the Lagrange multipliers, and, finally, one can put error bars on the resulting estimated density function.

Comparison of whole-body post mortem 3D CT and autopsy evaluation in accidental blunt force traumatic death using the abbreviated injury scale classification.

PubMed

Daly, Barry; Abboud, Samir; Ali, Zabiullah; Sliker, Clint; Fowler, David

2013-02-10

Although 3D CT imaging data are available on survivors of accidental blunt trauma, little similar data has been collected and classified on major injuries in victims of fatal injuries. This study compared the sensitivity of post mortem computed tomography (PMCT) with that of conventional autopsy for major trauma findings classified according to the trauma Abbreviated Injury Scale (AIS). Whole-body 3D PMCT imaging data and full autopsy findings were analyzed on 21 victims of accidental blunt force trauma death. All major injuries were classified on the AIS scale with ratings from 3 (serious) to 6 (unsurvivable). Agreement between sensitivity of autopsy and PMCT for major injuries was determined. A total of 195 major injuries were detected (mean per fatality, 9.3; range, 1-14). Skeletal injuries by AIS grade included 37 grade 3, 45 grade 4, 12 grade 5, and 2 grade 6 major findings. Soft tissue injuries included 10 grade 3, 68 grade 4, 16 grade 5, and 5 grade 6 major findings. Of these, PMCT detected 165 (88 skeletal, 77 soft tissue), and autopsy detected 127 (59 skeletal, 68 soft tissue). PMCT agreed with autopsy in 86% and 76% of skeletal and soft tissue injuries, respectively. PMCT detected an additional 37 skeletal and 31 soft tissue injuries that were not identified at autopsy. Autopsy detected 8 skeletal and 22 soft tissue injuries that were not detected by PMCT. PMCT was more sensitive for skeletal (P=0.05) and head and neck region injury (P=0.043) detection. PMCT showed a trend for greater sensitivity than autopsy, but this did not reach statistical significance (P=0.083). 3D PMCT detected significantly more skeletal injuries than autopsy and a similar number of soft tissue injuries to autopsy and promises to be a sensitive tool for detection and classification of skeletal injuries in fatal blunt force accidental trauma. Use of the AIS scale allows standardized categorization and quantification of injuries that contribute to death in such cases and allows more objective comparison between autopsy and PMCT. Copyright © 2012. Published by Elsevier Ireland Ltd.

Absolute cosine-based SVM-RFE feature selection method for prostate histopathological grading.

PubMed

Sahran, Shahnorbanun; Albashish, Dheeb; Abdullah, Azizi; Shukor, Nordashima Abd; Hayati Md Pauzi, Suria

2018-04-18

Feature selection (FS) methods are widely used in grading and diagnosing prostate histopathological images. In this context, FS is based on the texture features obtained from the lumen, nuclei, cytoplasm and stroma, all of which are important tissue components. However, it is difficult to represent the high-dimensional textures of these tissue components. To solve this problem, we propose a new FS method that enables the selection of features with minimal redundancy in the tissue components. We categorise tissue images based on the texture of individual tissue components via the construction of a single classifier and also construct an ensemble learning model by merging the values obtained by each classifier. Another issue that arises is overfitting due to the high-dimensional texture of individual tissue components. We propose a new FS method, SVM-RFE(AC), that integrates a Support Vector Machine-Recursive Feature Elimination (SVM-RFE) embedded procedure with an absolute cosine (AC) filter method to prevent redundancy in the selected features of the SV-RFE and an unoptimised classifier in the AC. We conducted experiments on H&E histopathological prostate and colon cancer images with respect to three prostate classifications, namely benign vs. grade 3, benign vs. grade 4 and grade 3 vs. grade 4. The colon benchmark dataset requires a distinction between grades 1 and 2, which are the most difficult cases to distinguish in the colon domain. The results obtained by both the single and ensemble classification models (which uses the product rule as its merging method) confirm that the proposed SVM-RFE(AC) is superior to the other SVM and SVM-RFE-based methods. We developed an FS method based on SVM-RFE and AC and successfully showed that its use enabled the identification of the most crucial texture feature of each tissue component. Thus, it makes possible the distinction between multiple Gleason grades (e.g. grade 3 vs. grade 4) and its performance is far superior to other reported FS methods. Copyright © 2018 Elsevier B.V. All rights reserved.

The developmental basis for germline mosaicism in mouse and Drosophila melanogaster.

PubMed

Drost, J B; Lee, W R

1998-01-01

Data involving germline mosaics in Drosophila melanogaster and mouse are reconciled with developmental observations. Mutations that become fixed in the early embryo before separation of soma from the germline may, by the sampling process of development, continue as part of germline and/or differentiate into any somatic tissue. The cuticle of adult D. melanogaster, because of segmental development, can be used to estimate the proportion of mutant nuclei in the early embryo, but most somatic tissues and the germlines of both species continue from samples too small to be representative of the early embryo. Because of the small sample of cells/nuclei that remain in the germline after separation of soma in both species, mosaic germlines have percentages of mutant cells that vary widely, with a mean of 50% and an unusual platykurtic, flat-topped distribution. While the sampling process leads to similar statistical results for both species, their patterns of development are very different. In D. melanogaster the first differentiation is the separation of soma from germline with the germline continuing from a sample of only two to four nuclei, whereas the adult cuticle is a representative sample of cleavage nuclei. The presence of mosaicism in D. melanogaster germline is independent of mosaicism in the eye, head, and thorax. This independence was used to determine that mutations can occur at any of the early embryonic cell divisions and still average 50% mutant germ cells when the germline is mosaic; however, the later the mutation occurs, the higher the proportion of completely nonmutant germlines. In contrast to D. melanogaster, the first differentiation in the mouse does not separate soma from germline but produces the inner cell mass that is representative of the cleavage nuclei. Following formation of the primitive streak, the primordial germ cells develop at the base of the allantois and among a clonally related sample of cells, providing the same statistical distribution in the mouse germlines as in D. melanogaster. The proportion of mutations that are fixed during early embryonic development is greatly underestimated. For example, a DNA lesion in a postmeiotic gamete that becomes fixed as a dominant mutation during early embryonic development of the F1 may produce an individual completely mutant in the germ line and relevant somatic tissue or, alternatively, the F1 germline may be completely mutant but with no relevant somatic tissues for detecting the mutation until the F2. In both cases the mutation would be classified as complete in the F1 and F2, respectively, and not recognized as embryonic in origin. Because germ cells differentiate later in mammalian development, there are more opportunities for correlation between germline and soma in the mammal than Drosophila. However, because the germ cells and any somatic tissue, like blood, are derived from small samples, there may be many individuals that test negative in blood but have germlines that are either mosaic or entirely mutant.

Experiments on automatic classification of tissue malignancy in the field of digital pathology

NASA Astrophysics Data System (ADS)

Pereira, J.; Barata, R.; Furtado, Pedro

2017-06-01

Automated analysis of histological images helps diagnose and further classify breast cancer. Totally automated approaches can be used to pinpoint images for further analysis by the medical doctor. But tissue images are especially challenging for either manual or automated approaches, due to mixed patterns and textures, where malignant regions are sometimes difficult to detect unless they are in very advanced stages. Some of the major challenges are related to irregular and very diffuse patterns, as well as difficulty to define winning features and classifier models. Although it is also hard to segment correctly into regions, due to the diffuse nature, it is still crucial to take low-level features over individualized regions instead of the whole image, and to select those with the best outcomes. In this paper we report on our experiments building a region classifier with a simple subspace division and a feature selection model that improves results over image-wide and/or limited feature sets. Experimental results show modest accuracy for a set of classifiers applied over the whole image, while the conjunction of image division, per-region low-level extraction of features and selection of features, together with the use of a neural network classifier achieved the best levels of accuracy for the dataset and settings we used in the experiments. Future work involves deep learning techniques, adding structures semantics and embedding the approach as a tumor finding helper in a practical Medical Imaging Application.

Phenotypic characterization of glioblastoma identified through shape descriptors

NASA Astrophysics Data System (ADS)

Chaddad, Ahmad; Desrosiers, Christian; Toews, Matthew

2016-03-01

This paper proposes quantitatively describing the shape of glioblastoma (GBM) tissue phenotypes as a set of shape features derived from segmentations, for the purposes of discriminating between GBM phenotypes and monitoring tumor progression. GBM patients were identified from the Cancer Genome Atlas, and quantitative MR imaging data were obtained from the Cancer Imaging Archive. Three GBM tissue phenotypes are considered including necrosis, active tumor and edema/invasion. Volumetric tissue segmentations are obtained from registered T1˗weighted (T1˗WI) postcontrast and fluid-attenuated inversion recovery (FLAIR) MRI modalities. Shape features are computed from respective tissue phenotype segmentations, and a Kruskal-Wallis test was employed to select features capable of classification with a significance level of p < 0.05. Several classifier models are employed to distinguish phenotypes, where a leave-one-out cross-validation was performed. Eight features were found statistically significant for classifying GBM phenotypes with p <0.05, orientation is uninformative. Quantitative evaluations show the SVM results in the highest classification accuracy of 87.50%, sensitivity of 94.59% and specificity of 92.77%. In summary, the shape descriptors proposed in this work show high performance in predicting GBM tissue phenotypes. They are thus closely linked to morphological characteristics of GBM phenotypes and could potentially be used in a computer assisted labeling system.

Convolutional neural networks for prostate cancer recurrence prediction

NASA Astrophysics Data System (ADS)

Kumar, Neeraj; Verma, Ruchika; Arora, Ashish; Kumar, Abhay; Gupta, Sanchit; Sethi, Amit; Gann, Peter H.

2017-03-01

Accurate prediction of the treatment outcome is important for cancer treatment planning. We present an approach to predict prostate cancer (PCa) recurrence after radical prostatectomy using tissue images. We used a cohort whose case vs. control (recurrent vs. non-recurrent) status had been determined using post-treatment follow up. Further, to aid the development of novel biomarkers of PCa recurrence, cases and controls were paired based on matching of other predictive clinical variables such as Gleason grade, stage, age, and race. For this cohort, tissue resection microarray with up to four cores per patient was available. The proposed approach is based on deep learning, and its novelty lies in the use of two separate convolutional neural networks (CNNs) - one to detect individual nuclei even in the crowded areas, and the other to classify them. To detect nuclear centers in an image, the first CNN predicts distance transform of the underlying (but unknown) multi-nuclear map from the input HE image. The second CNN classifies the patches centered at nuclear centers into those belonging to cases or controls. Voting across patches extracted from image(s) of a patient yields the probability of recurrence for the patient. The proposed approach gave 0.81 AUC for a sample of 30 recurrent cases and 30 non-recurrent controls, after being trained on an independent set of 80 case-controls pairs. If validated further, such an approach might help in choosing between a combination of treatment options such as active surveillance, radical prostatectomy, radiation, and hormone therapy. It can also generalize to the prediction of treatment outcomes in other cancers.

Mass type-specific sparse representation for mass classification in computer-aided detection on mammograms

PubMed Central

2013-01-01

Background Breast cancer is the leading cause of both incidence and mortality in women population. For this reason, much research effort has been devoted to develop Computer-Aided Detection (CAD) systems for early detection of the breast cancers on mammograms. In this paper, we propose a new and novel dictionary configuration underpinning sparse representation based classification (SRC). The key idea of the proposed algorithm is to improve the sparsity in terms of mass margins for the purpose of improving classification performance in CAD systems. Methods The aim of the proposed SRC framework is to construct separate dictionaries according to the types of mass margins. The underlying idea behind our method is that the separated dictionaries can enhance the sparsity of mass class (true-positive), leading to an improved performance for differentiating mammographic masses from normal tissues (false-positive). When a mass sample is given for classification, the sparse solutions based on corresponding dictionaries are separately solved and combined at score level. Experiments have been performed on both database (DB) named as Digital Database for Screening Mammography (DDSM) and clinical Full Field Digital Mammogram (FFDM) DBs. In our experiments, sparsity concentration in the true class (SCTC) and area under the Receiver operating characteristic (ROC) curve (AUC) were measured for the comparison between the proposed method and a conventional single dictionary based approach. In addition, a support vector machine (SVM) was used for comparing our method with state-of-the-arts classifier extensively used for mass classification. Results Comparing with the conventional single dictionary configuration, the proposed approach is able to improve SCTC of up to 13.9% and 23.6% on DDSM and FFDM DBs, respectively. Moreover, the proposed method is able to improve AUC with 8.2% and 22.1% on DDSM and FFDM DBs, respectively. Comparing to SVM classifier, the proposed method improves AUC with 2.9% and 11.6% on DDSM and FFDM DBs, respectively. Conclusions The proposed dictionary configuration is found to well improve the sparsity of dictionaries, resulting in an enhanced classification performance. Moreover, the results show that the proposed method is better than conventional SVM classifier for classifying breast masses subject to various margins from normal tissues. PMID:24564973

Secretory glands and microvascular systems imaged in aqueous solution by atmospheric scanning electron microscopy (ASEM).

PubMed

Yamazawa, Toshiko; Nakamura, Naotoshi; Sato, Mari; Sato, Chikara

2016-12-01

Exocrine glands, e.g., salivary and pancreatic glands, play an important role in digestive enzyme secretion, while endocrine glands, e.g., pancreatic islets, secrete hormones that regulate blood glucose levels. The dysfunction of these secretory organs immediately leads to various diseases, such as diabetes or Sjögren's syndrome, by poorly understood mechanisms. Gland-related diseases have been studied by optical microscopy (OM), and at higher resolution by transmission electron microscopy (TEM) of Epon embedded samples, which necessitates hydrophobic sample pretreatment. Here, we report the direct observation of tissue in aqueous solution by atmospheric scanning electron microscopy (ASEM). Salivary glands, lacrimal glands, and pancreas were fixed, sectioned into slabs, stained with phosphotungstic acid (PTA), and inspected in radical scavenger d-glucose solution from below by an inverted scanning electron microscopy (SEM), guided by optical microscopy from above to target the tissue substructures. A 2- to 3-µm specimen thickness was visualized by the SEM. In secretory cells, cytoplasmic vesicles and other organelles were clearly imaged at high resolution, and the former could be classified according to the degree of PTA staining. In islets of Langerhans, the microvascular system used as an outlet by the secretory cells was also clearly observed. Microvascular system is also critically involved in the onset of diabetic complications and was clearly visible in subcutaneous tissue imaged by ASEM. The results suggest the use of in-solution ASEM for histology and to study vesicle secretion systems. Further, the high-throughput of ASEM makes it a potential tool for the diagnosis of exocrine and endocrine-related diseases. © 2016 Wiley Periodicals, Inc.

Molecular differences in transition zone and peripheral zone prostate tumors

PubMed Central

Sinnott, Jennifer A.; Rider, Jennifer R.; Carlsson, Jessica; Gerke, Travis; Tyekucheva, Svitlana; Penney, Kathryn L.; Sesso, Howard D.; Loda, Massimo; Fall, Katja; Stampfer, Meir J.; Mucci, Lorelei A.; Pawitan, Yudi; Andersson, Sven-Olof; Andrén, Ove

2015-01-01

Prostate tumors arise primarily in the peripheral zone (PZ) of the prostate, but 20–30% arise in the transition zone (TZ). Zone of origin may have prognostic value or reflect distinct molecular subtypes; however, it can be difficult to determine in practice. Using whole-genome gene expression, we built a signature of zone using normal tissue from five individuals and found that it successfully classified nine tumors of known zone. Hypothesizing that this signature captures tumor zone of origin, we assessed its relationship with clinical factors among 369 tumors of unknown zone from radical prostatectomies (RPs) and found that tumors that molecularly resembled TZ tumors showed lower mortality (P = 0.09) that was explained by lower Gleason scores (P = 0.009). We further applied the signature to an earlier study of 88 RP and 333 transurethral resection of the prostate (TURP) tumor samples, also of unknown zone, with gene expression on ~6000 genes. We had observed previously substantial expression differences between RP and TURP specimens, and hypothesized that this might be because RPs capture primarily PZ tumors, whereas TURPs capture more TZ tumors. Our signature distinguished these two groups, with an area under the receiver operating characteristic curve of 87% (P < 0.0001). Our findings that zonal differences in normal tissue persist in tumor tissue and that these differences are associated with Gleason score and sample type suggest that subtypes potentially resulting from different etiologic pathways might arise in these zones. Zone of origin may be important to consider in prostate tumor biomarker research. PMID:25870172

Automatic tissue segmentation of breast biopsies imaged by QPI

NASA Astrophysics Data System (ADS)

Majeed, Hassaan; Nguyen, Tan; Kandel, Mikhail; Marcias, Virgilia; Do, Minh; Tangella, Krishnarao; Balla, Andre; Popescu, Gabriel

2016-03-01

The current tissue evaluation method for breast cancer would greatly benefit from higher throughput and less inter-observer variation. Since quantitative phase imaging (QPI) measures physical parameters of tissue, it can be used to find quantitative markers, eliminating observer subjectivity. Furthermore, since the pixel values in QPI remain the same regardless of the instrument used, classifiers can be built to segment various tissue components without need for color calibration. In this work we use a texton-based approach to segment QPI images of breast tissue into various tissue components (epithelium, stroma or lumen). A tissue microarray comprising of 900 unstained cores from 400 different patients was imaged using Spatial Light Interference Microscopy. The training data were generated by manually segmenting the images for 36 cores and labelling each pixel (epithelium, stroma or lumen.). For each pixel in the data, a response vector was generated by the Leung-Malik (LM) filter bank and these responses were clustered using the k-means algorithm to find the centers (called textons). A random forest classifier was then trained to find the relationship between a pixel's label and the histogram of these textons in that pixel's neighborhood. The segmentation was carried out on the validation set by calculating the texton histogram in a pixel's neighborhood and generating a label based on the model learnt during training. Segmentation of the tissue into various components is an important step toward efficiently computing parameters that are markers of disease. Automated segmentation, followed by diagnosis, can improve the accuracy and speed of analysis leading to better health outcomes.

White matter lesion extension to automatic brain tissue segmentation on MRI.

PubMed

de Boer, Renske; Vrooman, Henri A; van der Lijn, Fedde; Vernooij, Meike W; Ikram, M Arfan; van der Lugt, Aad; Breteler, Monique M B; Niessen, Wiro J

2009-05-01

A fully automated brain tissue segmentation method is optimized and extended with white matter lesion segmentation. Cerebrospinal fluid (CSF), gray matter (GM) and white matter (WM) are segmented by an atlas-based k-nearest neighbor classifier on multi-modal magnetic resonance imaging data. This classifier is trained by registering brain atlases to the subject. The resulting GM segmentation is used to automatically find a white matter lesion (WML) threshold in a fluid-attenuated inversion recovery scan. False positive lesions are removed by ensuring that the lesions are within the white matter. The method was visually validated on a set of 209 subjects. No segmentation errors were found in 98% of the brain tissue segmentations and 97% of the WML segmentations. A quantitative evaluation using manual segmentations was performed on a subset of 6 subjects for CSF, GM and WM segmentation and an additional 14 for the WML segmentations. The results indicated that the automatic segmentation accuracy is close to the interobserver variability of manual segmentations.

A Minimum Spanning Forest Based Method for Noninvasive Cancer Detection with Hyperspectral Imaging

PubMed Central

Pike, Robert; Lu, Guolan; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

2016-01-01

Goal The purpose of this paper is to develop a classification method that combines both spectral and spatial information for distinguishing cancer from healthy tissue on hyperspectral images in an animal model. Methods An automated algorithm based on a minimum spanning forest (MSF) and optimal band selection has been proposed to classify healthy and cancerous tissue on hyperspectral images. A support vector machine (SVM) classifier is trained to create a pixel-wise classification probability map of cancerous and healthy tissue. This map is then used to identify markers that are used to compute mutual information for a range of bands in the hyperspectral image and thus select the optimal bands. An MSF is finally grown to segment the image using spatial and spectral information. Conclusion The MSF based method with automatically selected bands proved to be accurate in determining the tumor boundary on hyperspectral images. Significance Hyperspectral imaging combined with the proposed classification technique has the potential to provide a noninvasive tool for cancer detection. PMID:26285052

Facial soft tissue thickness differences among three skeletal classes in Japanese population.

PubMed

Utsuno, Hajime; Kageyama, Toru; Uchida, Keiichi; Kibayashi, Kazuhiko

2014-03-01

Facial reconstruction is used in forensic anthropology to recreate the face from unknown human skeletal remains, and to elucidate the antemortem facial appearance. This requires accurate assessment of the skull (age, sex, ancestry, etc.) and thickness data. However, additional information is required to reconstruct the face as the information obtained from the skull is limited. Here, we aimed to examine the information from the skull that is required for accurate facial reconstruction. The human facial profile is classified into 3 shapes: straight, convex, and concave. These facial profiles facilitate recognition of individuals. The skeletal classes used in orthodontics are classified according to these 3 facial types. We have previously reported the differences between Japanese females. In the present study, we applied this classification for facial tissue measurement, compared the differences in tissue depth of each skeletal class for both sexes in the Japanese population, and elucidated the differences between the skeletal classes. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Age determination of bottled Chinese rice wine by VIS-NIR spectroscopy

NASA Astrophysics Data System (ADS)

Yu, Haiyan; Lin, Tao; Ying, Yibin; Pan, Xingxiang

2006-10-01

The feasibility of non-invasive visible and near infrared (VIS-NIR) spectroscopy for determining wine age (1, 2, 3, 4, and 5 years) of Chinese rice wine was investigated. Samples of Chinese rice wine were analyzed in 600 mL square brown glass bottles with side length of approximately 64 mm at room temperature. VIS-NIR spectra of 100 bottled Chinese rice wine samples were collected in transmission mode in the wavelength range of 350-1200 nm by a fiber spectrometer system. Discriminant models were developed based on discriminant analysis (DA) together with raw, first and second derivative spectra. The concentration of alcoholic degree, total acid, and °Brix was determined to validate the NIR results. The calibration result for raw spectra was better than that for first and second derivative spectra. The percentage of samples correctly classified for raw spectra was 98%. For 1-, 2-, and 3-year-old sample groups, the sample were all correctly classified, and for 4- and 5-year-old sample groups, the percentage of samples correctly classified was 92.9%, respectively. In validation analysis, the percentage of samples correctly classified was 100%. The results demonstrated that VIS-NIR spectroscopic technique could be used as a non-invasive, rapid and reliable method for predicting wine age of bottled Chinese rice wine.

Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers

PubMed Central

2018-01-01

Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a large generalization error. To overcome the said problem, we propose a fuzziness-based active learning framework (FALF), in which we implement the idea of selecting optimal training samples to enhance generalization performance for two different kinds of classifiers, discriminative and generative (e.g. SVM and KNN). The optimal samples are selected by first estimating the boundary of each class and then calculating the fuzziness-based distance between each sample and the estimated class boundaries. Those samples that are at smaller distances from the boundaries and have higher fuzziness are chosen as target candidates for the training set. Through detailed experimentation on three publically available datasets, we showed that when trained with the proposed sample selection framework, both classifiers achieved higher classification accuracy and lower processing time with the small amount of training data as opposed to the case where the training samples were selected randomly. Our experiments demonstrate the effectiveness of our proposed method, which equates favorably with the state-of-the-art methods. PMID:29304512

Microarray gene expression profiling using core biopsies of renal neoplasia.

PubMed

Rogers, Craig G; Ditlev, Jonathon A; Tan, Min-Han; Sugimura, Jun; Qian, Chao-Nan; Cooper, Jeff; Lane, Brian; Jewett, Michael A; Kahnoski, Richard J; Kort, Eric J; Teh, Bin T

2009-01-01

We investigate the feasibility of using microarray gene expression profiling technology to analyze core biopsies of renal tumors for classification of tumor histology. Core biopsies were obtained ex-vivo from 7 renal tumors-comprised of four histological subtypes-following radical nephrectomy using 18-gauge biopsy needles. RNA was isolated from these samples and, in the case of biopsy samples, amplified by in vitro transcription. Microarray analysis was then used to quantify the mRNA expression patterns in these samples relative to non-diseased renal tissue mRNA. Genes with significant variation across all non-biopsy tumor samples were identified, and the relationship between tumor and biopsy samples in terms of expression levels of these genes was then quantified in terms of Euclidean distance, and visualized by complete linkage clustering. Final pathologic assessment of kidney tumors demonstrated clear cell renal cell carcinoma (4), oncocytoma (1), angiomyolipoma (1) and adrenalcortical carcinoma (1). Five of the seven biopsy samples were most similar in terms of gene expression to the resected tumors from which they were derived in terms of Euclidean distance. All seven biopsies were assigned to the correct histological class by hierarchical clustering. We demonstrate the feasibility of gene expression profiling of core biopsies of renal tumors to classify tumor histology.

Microarray gene expression profiling using core biopsies of renal neoplasia

PubMed Central

Rogers, Craig G.; Ditlev, Jonathon A.; Tan, Min-Han; Sugimura, Jun; Qian, Chao-Nan; Cooper, Jeff; Lane, Brian; Jewett, Michael A.; Kahnoski, Richard J.; Kort, Eric J.; Teh, Bin T.

2009-01-01

We investigate the feasibility of using microarray gene expression profiling technology to analyze core biopsies of renal tumors for classification of tumor histology. Core biopsies were obtained ex-vivo from 7 renal tumors—comprised of four histological subtypes—following radical nephrectomy using 18-gauge biopsy needles. RNA was isolated from these samples and, in the case of biopsy samples, amplified by in vitro transcription. Microarray analysis was then used to quantify the mRNA expression patterns in these samples relative to non-diseased renal tissue mRNA. Genes with significant variation across all non-biopsy tumor samples were identified, and the relationship between tumor and biopsy samples in terms of expression levels of these genes was then quantified in terms of Euclidean distance, and visualized by complete linkage clustering. Final pathologic assessment of kidney tumors demonstrated clear cell renal cell carcinoma (4), oncocytoma (1), angiomyolipoma (1) and adrenalcortical carcinoma (1). Five of the seven biopsy samples were most similar in terms of gene expression to the resected tumors from which they were derived in terms of Euclidean distance. All seven biopsies were assigned to the correct histological class by hierarchical clustering. We demonstrate the feasibility of gene expression profiling of core biopsies of renal tumors to classify tumor histology. PMID:19966938

A Tissue Systems Pathology Assay for High-Risk Barrett's Esophagus.

PubMed

Critchley-Thorne, Rebecca J; Duits, Lucas C; Prichard, Jeffrey W; Davison, Jon M; Jobe, Blair A; Campbell, Bruce B; Zhang, Yi; Repa, Kathleen A; Reese, Lia M; Li, Jinhong; Diehl, David L; Jhala, Nirag C; Ginsberg, Gregory; DeMarshall, Maureen; Foxwell, Tyler; Zaidi, Ali H; Lansing Taylor, D; Rustgi, Anil K; Bergman, Jacques J G H M; Falk, Gary W

2016-06-01

Better methods are needed to predict risk of progression for Barrett's esophagus. We aimed to determine whether a tissue systems pathology approach could predict progression in patients with nondysplastic Barrett's esophagus, indefinite for dysplasia, or low-grade dysplasia. We performed a nested case-control study to develop and validate a test that predicts progression of Barrett's esophagus to high-grade dysplasia (HGD) or esophageal adenocarcinoma (EAC), based upon quantification of epithelial and stromal variables in baseline biopsies. Data were collected from Barrett's esophagus patients at four institutions. Patients who progressed to HGD or EAC in ≥1 year (n = 79) were matched with patients who did not progress (n = 287). Biopsies were assigned randomly to training or validation sets. Immunofluorescence analyses were performed for 14 biomarkers and quantitative biomarker and morphometric features were analyzed. Prognostic features were selected in the training set and combined into classifiers. The top-performing classifier was assessed in the validation set. A 3-tier, 15-feature classifier was selected in the training set and tested in the validation set. The classifier stratified patients into low-, intermediate-, and high-risk classes [HR, 9.42; 95% confidence interval, 4.6-19.24 (high-risk vs. low-risk); P < 0.0001]. It also provided independent prognostic information that outperformed predictions based on pathology analysis, segment length, age, sex, or p53 overexpression. We developed a tissue systems pathology test that better predicts risk of progression in Barrett's esophagus than clinicopathologic variables. The test has the potential to improve upon histologic analysis as an objective method to risk stratify Barrett's esophagus patients. Cancer Epidemiol Biomarkers Prev; 25(6); 958-68. ©2016 AACR. ©2016 American Association for Cancer Research.

An Optimized Set of Fluorescence In Situ Hybridization Probes for Detection of Pancreatobiliary Tract Cancer in Cytology Brush Samples.

PubMed

Barr Fritcher, Emily G; Voss, Jesse S; Brankley, Shannon M; Campion, Michael B; Jenkins, Sarah M; Keeney, Matthew E; Henry, Michael R; Kerr, Sarah M; Chaiteerakij, Roongruedee; Pestova, Ekaterina V; Clayton, Amy C; Zhang, Jun; Roberts, Lewis R; Gores, Gregory J; Halling, Kevin C; Kipp, Benjamin R

2015-12-01

Pancreatobiliary cancer is detected by fluorescence in situ hybridization (FISH) of pancreatobiliary brush samples with UroVysion probes, originally designed to detect bladder cancer. We designed a set of new probes to detect pancreatobiliary cancer and compared its performance with that of UroVysion and routine cytology analysis. We tested a set of FISH probes on tumor tissues (cholangiocarcinoma or pancreatic carcinoma) and non-tumor tissues from 29 patients. We identified 4 probes that had high specificity for tumor vs non-tumor tissues; we called this set of probes pancreatobiliary FISH. We performed a retrospective analysis of brush samples from 272 patients who underwent endoscopic retrograde cholangiopancreatography for evaluation of malignancy at the Mayo Clinic; results were available from routine cytology and FISH with UroVysion probes. Archived residual specimens were retrieved and used to evaluate the pancreatobiliary FISH probes. Cutoff values for FISH with the pancreatobiliary probes were determined using 89 samples and validated in the remaining 183 samples. Clinical and pathologic evidence of malignancy in the pancreatobiliary tract within 2 years of brush sample collection was used as the standard; samples from patients without malignancies were used as negative controls. The validation cohort included 85 patients with malignancies (46.4%) and 114 patients with primary sclerosing cholangitis (62.3%). Samples containing cells above the cutoff for polysomy (copy number gain of ≥2 probes) were classified as positive in FISH with the UroVysion and pancreatobiliary probes. Multivariable logistic regression was used to estimate associations between clinical and pathology findings and results from FISH. The combination of FISH probes 1q21, 7p12, 8q24, and 9p21 identified cancer cells with 93% sensitivity and 100% specificity in pancreatobiliary tissue samples and were therefore included in the pancreatobiliary probe set. In the validation cohort of brush samples, pancreatobiliary FISH identified samples from patients with malignancy with a significantly higher level of sensitivity (64.7%) than the UroVysion probes (45.9%) (P < .001) or routine cytology analysis (18.8%) (P < .001), but similar specificity (92.9%, 90.8%, and 100.0% respectively). Factors significantly associated with detection of carcinoma, in adjusted analyses, included detection of polysomy by pancreatobiliary FISH (P < .001), a mass by cross-sectional imaging (P < .001), cancer cells by routine cytology (overall P = .003), as well as absence of primary sclerosing cholangitis (P = .011). We identified a set of FISH probes that detects cancer cells in pancreatobiliary brush samples from patients with and without primary sclerosing cholangitis with higher levels of sensitivity than UroVysion probes. Cytologic brushing test results and clinical features were independently associated with detection of cancer and might be used to identify patients with pancreatobiliary cancers. Copyright © 2015 AGA Institute. Published by Elsevier Inc. All rights reserved.

Soluble Mediators and Clinical Features Discern Risk of Transitioning to Classified Disease in Relatives of Systemic Lupus Erythematosus Patients

PubMed Central

Munroe, Melissa E.; Young, Kendra A.; Kamen, Diane L.; Guthridge, Joel M.; Niewold, Timothy B.; Costenbader, Karen H.; Weisman, Michael H.; Ishimori, Mariko L.; Wallace, Daniel J.; Gilkeson, Gary S.; Karp, David R.; Harley, John B.; Norris, Jill M.; James, Judith A.

2016-01-01

Objective Systemic lupus erythematosus (SLE) and other autoimmune diseases cause significant morbidity. Identifying populations at risk of developing SLE is essential to curtail irreversible inflammatory damage. The objective of this study was to identify factors associated with transition to classified disease that inform SLE risk. Methods Previously identified lupus patient blood relatives with < 4 American College of Rheumatology SLE classification criteria at baseline (n=409) were enrolled in this follow-up study. Participants provided detailed family, demographic, and clinical information, including the SLE-specific portion of the Connective Tissue Disease Screening Questionnaire (SLE-CSQ). Plasma samples were tested for the presence of lupus-associated autoantibodies and 52 soluble mediators. Generalized estimating equations (GEE) were applied to identify factors anticipating disease transition. Results Forty-five relatives (11%) transitioned to classified SLE during follow-up (mean time=6.4 years). Relatives who transitioned displayed more lupus-associated autoantibody specificities and higher SLE-CSQ scores (p<0.0001) at baseline than non-transitioned relatives. Importantly, they also had elevated baseline plasma levels of inflammatory mediators, including B-lymphocyte stimulator (BLyS), stem cell factor (SCF), and interferon-associated chemokines (p≤0.02), with concurrent decreases in levels of regulatory mediators, tumor growth factor (TGF)-β and interleukin (IL)-10 (p≤0.03). GEE revealed that baseline SLE-CSQ or ACR scores and plasma levels of SCF and TGF-β (p≤0.03), but not autoantibodies, were significant and independent predictors of SLE transition. Conclusions Altered levels of soluble mediators anticipate transition to classified disease in lupus relatives. Thus, immune perturbations precede SLE classification and can help identify high-risk relatives for rheumatology referral and potential enrollment in prevention trials. PMID:27863174

Correcting Classifiers for Sample Selection Bias in Two-Phase Case-Control Studies

PubMed Central

Theis, Fabian J.

2017-01-01

Epidemiological studies often utilize stratified data in which rare outcomes or exposures are artificially enriched. This design can increase precision in association tests but distorts predictions when applying classifiers on nonstratified data. Several methods correct for this so-called sample selection bias, but their performance remains unclear especially for machine learning classifiers. With an emphasis on two-phase case-control studies, we aim to assess which corrections to perform in which setting and to obtain methods suitable for machine learning techniques, especially the random forest. We propose two new resampling-based methods to resemble the original data and covariance structure: stochastic inverse-probability oversampling and parametric inverse-probability bagging. We compare all techniques for the random forest and other classifiers, both theoretically and on simulated and real data. Empirical results show that the random forest profits from only the parametric inverse-probability bagging proposed by us. For other classifiers, correction is mostly advantageous, and methods perform uniformly. We discuss consequences of inappropriate distribution assumptions and reason for different behaviors between the random forest and other classifiers. In conclusion, we provide guidance for choosing correction methods when training classifiers on biased samples. For random forests, our method outperforms state-of-the-art procedures if distribution assumptions are roughly fulfilled. We provide our implementation in the R package sambia. PMID:29312464

Effects of the Exxon Valdez oil spill on migrant shorebirds using rocky intertidal habitats of Prince William Sound, Alaska, during spring, 1989. Exxon Valdez oil spill state/federal natural resource damage assessment final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martin, P.D.

1993-12-01

A minimum of a few 10,000`s of surfbirds (Aphriza virgata) and black turnstones (Arenaria melanocephala) used rocky intertidal habitats of southwestern Prince William sound in spring 1989. Virtually all the shorebirds were found using shorelines, primarily on northern Montague Island, subjectively classified in the field as lightly oiled or unoiled. Surfbirds and black turnstones preyed mainly on herring eggs, blue mussels, and barnacles. Samples of these prey items from oiled areas contained petroleum-derived hydrocarbons, as did at some of the samples from the relatively clean portions of Montague Island. The results of chemical analysis of a small sample of shorebirdmore » liver tissues provided only limited support for the hypothesis that shorebirds had ingested significant quantities of petroleum-derived hydrocarbons. Surfbirds and black turnstones probably escaped significant population impacts as a result of the EVOs because shorelines which received heavy use by these species were largely spared contamination.« less

Molecular Epidemiology of Nontyphoidal Salmonella in Poultry and Poultry Products in India: Implications for Human Health.

PubMed

Saravanan, Sellappan; Purushothaman, Venketaraman; Murthy, Thippichettypalayam Ramasamy Gopala Krishna; Sukumar, Kuppannan; Srinivasan, Palani; Gowthaman, Vasudevan; Balusamy, Mohan; Atterbury, Robert; Kuchipudi, Suresh V

2015-09-01

Human infections with non-typhoidal Salmonella (NTS) serovars are increasingly becoming a threat to human health globally. While all motile Salmonellae have zoonotic potential, Salmonella Enteritidis and Salmonella Typhimurium are most commonly associated with human disease, for which poultry are a major source. Despite the increasing number of human NTS infections, the epidemiology of NTS in poultry in India has not been fully understood. Hence, as a first step, we carried out epidemiological analysis to establish the incidence of NTS in poultry to evaluate the risk to human health. A total of 1215 samples (including poultry meat, tissues, egg and environmental samples) were collected from 154 commercial layer farms from southern India and screened for NTS. Following identification by cultural and biochemical methods, Salmonella isolates were further characterized by multiplex PCR, allele-specific PCR, enterobacterial repetitive intergenic consensus (ERIC) PCR and pulse field gel electrophoresis (PFGE). In the present study, 21/1215 (1.73 %) samples tested positive for NTS. We found 12/392 (3.06 %) of tissue samples, 7/460 (1.52 %) of poultry products, and 2/363 (0.55 %) of environmental samples tested positive for NTS. All the Salmonella isolates were resistant to oxytetracycline, which is routinely used as poultry feed additive. The multiplex PCR results allowed 16/21 isolates to be classified as S. Typhimurium, and five isolates as S. Enteritidis. Of the five S. Enteritidis isolates, four were identified as group D Salmonella by allele-specific PCR. All of the isolates produced different banding patterns in ERIC PCR. Of the thirteen macro restriction profiles (MRPs) obtained by PFGE, MRP 6 was predominant which included 6 (21 %) isolates. In conclusion, the findings of the study revealed higher incidence of contamination of NTS Salmonella in poultry tissue and animal protein sources used for poultry. The results of the study warrants further investigation on different type of animal feed sources, food market chains, processing plants, live bird markets etc., to evaluate the risk factors, transmission and effective control measures of human Salmonella infection from poultry products.

The effect of enzymes upon metabolism, storage, and release of carbohydrates in normal and abnormal endometria.

PubMed

Hughes, E C

1976-07-01

This paper presents preliminary data concerning the relationship of various components of glandular epithelium and effect of enzymes on metabolism, storage, and release of certain substances in normal and abnormal endometria. Activity of these endometrial enzymes has been compared between two groups: 252 patients with normal menstrual histories and 156 patients, all over the age of 40, with abnormal uterine bleeding. Material was obtained by endometrial biopsy or curettage. In the pathologic classification of the group of 156, 30 patients had secretory endometria, 88 patients had endometria classified as proliferative, 24 were classified as endometrial hyperplasia, and 14 were classified as adenocarcinoma. All tissue was studied by histologic, histochemical, and biochemical methods. Glycogen synthetase activity caused synthesis of glucose to glycogen, increasing in amount until midcycle, when glycogen phosphorylase activity caused the breakdown to glucose during the regressive stage of endometrial activity. This normal cyclic activity did not occur in the abnormal endometria, where activity of both enzymes continued at low constant tempo. Only the I form of glycogen synthetase increased as the tissue became more hyperplastic. With the constant glycogen content and the increased activity of both the TPN isocitric dehydrogenase and glucose-6-phosphate dehydrogenase in the hyperplastic and cancerous endometria, tissue energy was created, resulting in abnormal cell proliferation. These altered biochemical and cellular activities may be the basis for malignant cell growth.

Detection of radiation-induced brain necrosis in live rats using label-free time-resolved fluorescence spectroscopy (TRFS) (Conference Presentation)

NASA Astrophysics Data System (ADS)

Hartl, Brad A.; Ma, Htet S. W.; Sridharan, Shamira; Hansen, Katherine; Klich, Melanie; Perks, Julian; Kent, Michael; Kim, Kyoungmi; Fragoso, Ruben; Marcu, Laura

2017-02-01

Differentiating radiation-induced necrosis from recurrent tumor in the brain remains a significant challenge to the neurosurgeon. Clinical imaging modalities are not able to reliably discriminate the two tissue types, making biopsy location selection and surgical management difficult. Label-free fluorescence lifetime techniques have previously been shown to be able to delineate human brain tumor from healthy tissues. Thus, fluorescence lifetime techniques represent a potential means to discriminate the two tissues in real-time during surgery. This study aims to characterize the endogenous fluorescence lifetime signatures from radiation induced brain necrosis in a tumor-free rat model. Fischer rats received a single fraction of 60 Gy of radiation to the right hemisphere using a linear accelerator. Animals underwent a terminal live surgery after gross necrosis had developed, as verified with MRI. During surgery, healthy and necrotic brain tissue was measured with a fiber optic needle connected to a multispectral fluorescence lifetime system. Measurements of the necrotic tissue showed a 48% decrease in intensity and 20% increase in lifetimes relative to healthy tissue. Using a support vector machine classifier and leave-one-out validation technique, the necrotic tissue was correctly classified with 94% sensitivity and 97% specificity. Spectral contribution analysis also confirmed that the primary source of fluorescence contrast lies within the redox and bound-unbound population shifts of nicotinamide adenine dinucleotide. A clinical trial is presently underway to measure these tissue types in humans. These results show for the first time that radiation-induced necrotic tissue in the brain contains significantly different metabolic signatures that are detectable with label-free fluorescence lifetime techniques.

Simulation techniques for estimating error in the classification of normal patterns

NASA Technical Reports Server (NTRS)

Whitsitt, S. J.; Landgrebe, D. A.

1974-01-01

Methods of efficiently generating and classifying samples with specified multivariate normal distributions were discussed. Conservative confidence tables for sample sizes are given for selective sampling. Simulation results are compared with classified training data. Techniques for comparing error and separability measure for two normal patterns are investigated and used to display the relationship between the error and the Chernoff bound.

Determination of Minimum Training Sample Size for Microarray-Based Cancer Outcome Prediction–An Empirical Assessment

PubMed Central

Cheng, Ningtao; Wu, Leihong; Cheng, Yiyu

2013-01-01

The promise of microarray technology in providing prediction classifiers for cancer outcome estimation has been confirmed by a number of demonstrable successes. However, the reliability of prediction results relies heavily on the accuracy of statistical parameters involved in classifiers. It cannot be reliably estimated with only a small number of training samples. Therefore, it is of vital importance to determine the minimum number of training samples and to ensure the clinical value of microarrays in cancer outcome prediction. We evaluated the impact of training sample size on model performance extensively based on 3 large-scale cancer microarray datasets provided by the second phase of MicroArray Quality Control project (MAQC-II). An SSNR-based (scale of signal-to-noise ratio) protocol was proposed in this study for minimum training sample size determination. External validation results based on another 3 cancer datasets confirmed that the SSNR-based approach could not only determine the minimum number of training samples efficiently, but also provide a valuable strategy for estimating the underlying performance of classifiers in advance. Once translated into clinical routine applications, the SSNR-based protocol would provide great convenience in microarray-based cancer outcome prediction in improving classifier reliability. PMID:23861920

A novel approach for detection and classification of mammographic microcalcifications using wavelet analysis and extreme learning machine.

PubMed

Malar, E; Kandaswamy, A; Chakravarthy, D; Giri Dharan, A

2012-09-01

The objective of this paper is to reveal the effectiveness of wavelet based tissue texture analysis for microcalcification detection in digitized mammograms using Extreme Learning Machine (ELM). Microcalcifications are tiny deposits of calcium in the breast tissue which are potential indicators for early detection of breast cancer. The dense nature of the breast tissue and the poor contrast of the mammogram image prohibit the effectiveness in identifying microcalcifications. Hence, a new approach to discriminate the microcalcifications from the normal tissue is done using wavelet features and is compared with different feature vectors extracted using Gray Level Spatial Dependence Matrix (GLSDM) and Gabor filter based techniques. A total of 120 Region of Interests (ROIs) extracted from 55 mammogram images of mini-Mias database, including normal and microcalcification images are used in the current research. The network is trained with the above mentioned features and the results denote that ELM produces relatively better classification accuracy (94%) with a significant reduction in training time than the other artificial neural networks like Bayesnet classifier, Naivebayes classifier, and Support Vector Machine. ELM also avoids problems like local minima, improper learning rate, and over fitting. Copyright © 2012 Elsevier Ltd. All rights reserved.

Melanoma-specific marker expression in skin biopsy tissues as a tool to facilitate melanoma diagnosis.

PubMed

Alexandrescu, Doru T; Kauffman, C Lisa; Jatkoe, Timothy A; Hartmann, Dan P; Vener, Tatiana; Wang, Haiying; Derecho, Carlo; Rajpurohit, Yashoda; Wang, Yixin; Palma, John F

2010-07-01

Diagnosis of cutaneous melanoma requires accurate differentiation of true malignant tumors from highly atypical lesions, which lack the capacity to develop uncontrolled proliferation and to metastasize. We used melanoma markers from previous work to differentiate benign and atypical lesions from melanoma using paraffin-embedded tissue. This critical step in diagnosis generates the most uncertainty and discrepancy between dermatopathologists. A total of 193 biopsy tissues were selected: 47 melanomas, 48 benign nevi, and 98 atypical/suspicious, including 48 atypical nevi and 50 melanomas as later assigned by expert dermatopathologists. Performance for SILV, GDF15, and L1CAM normalized to TYR in unequivocal melanoma versus benign nevi resulted in an area under the curve (AUC) of 0.94, 0.67, and 0.5, respectively. SILV also differentiated atypical cases classified as melanoma from atypical nevi with an AUC=0.74. Furthermore, SILV showed a significant difference between suspicious melanoma and each suspicious atypia group: melanoma versus severe atypia and melanoma versus moderate atypia had P-values of 0.0077 and 0.0009, respectively. SILV showed clear discrimination between melanoma and benign unequivocal cases as well as between different atypia subgroups in the group of suspicious samples. The role and potential utility of this molecular assay as an adjunct to the morphological diagnosis of melanoma are discussed.

Towards multispectral endoscopic imaging of cardiac lesion assessment and classification for cardiac ablation therapy

NASA Astrophysics Data System (ADS)

Park, Soo Young; Singh-Moon, Rajinder P.; Hendon, Christine P.

2018-02-01

Pulmonary vein (PV) isolation is a critical procedure for the treatment and termination of atrial fibrillation (AF). The success of such treatment depends on the extent of tissue damage, where partial lesions can allow abnormal electrical conduction and risk relapse of AF. Proper evaluation of lesion delivery and ablation line continuity remains challenging with current techniques and in part limit procedural efficacy. A tool for direct visualization of endo-myocardial lesions in vivo could potentially reduce ambiguity in treatment location and extent and improve the overall fidelity of lesion sets. In this work, we introduce a method for wide-field visualization of myocardial tissue including the discernment of ablated and non-ablated regions using an endoscopic multispectral imaging system (EMIS). The system was designed to fit the working channel of most commercial sheathes (<4 Fr) and supported quadruple-wavelength reflectance imaging through a flexible fiber-bundle. A total of 50 endocardial lesions were created and imaged on nine swine hearts, ex vivo in addition to 15 lesions on human LA samples near PV regions. A pixel-wise linear discriminant analysis algorithm was developed to classify regions of ablation treatment based on calibrated EMI maps. Results show good agreement of treatment severity and spatial extent compared to post-hoc tissue vital staining.

New methods for time-resolved fluorescence spectroscopy data analysis based on the Laguerre expansion technique--applications in tissue diagnosis.

PubMed

Jo, J A; Marcu, L; Fang, Q; Papaioannou, T; Qiao, J H; Fishbein, M C; Beseth, B; Dorafshar, A H; Reil, T; Baker, D; Freischlag, J

2007-01-01

A new deconvolution method for the analysis of time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) data is introduced and applied for tissue diagnosis. The intrinsic TR-LIFS decays are expanded on a Laguerre basis, and the computed Laguerre expansion coefficients (LEC) are used to characterize the sample fluorescence emission. The method was applied for the diagnosis of atherosclerotic vulnerable plaques. At a first stage, using a rabbit atherosclerotic model, 73 TR-LIFS in-vivo measurements from the normal and atherosclerotic aorta segments of eight rabbits were taken. The Laguerre deconvolution technique was able to accurately deconvolve the TR-LIFS measurements. More interesting, the LEC reflected the changes in the arterial biochemical composition and provided discrimination of lesions rich in macrophages/foam-cells with high sensitivity (> 85%) and specificity (> 95%). At a second stage, 348 TR-LIFS measurements were obtained from the explanted carotid arteries of 30 patients. Lesions with significant inflammatory cells (macrophages/foam-cells and lymphocytes) were detected with high sensitivity (> 80%) and specificity (> 90%), using LEC-based classifiers. This study has demonstrated the potential of using TR-LIFS information by means of LEC for in vivo tissue diagnosis, and specifically for detecting inflammation in atherosclerotic lesions, a key marker of plaque vulnerability.

Method to characterize inorganic particulates in lung tissue biopsies using field emission scanning electron microscopy

USGS Publications Warehouse

Lowers, Heather; Breit, George N.; Strand, Matthew; Pillers, Renee M.; Meeker, Gregory P.; Todorov, Todor I.; Plumlee, Geoffrey S.; Wolf, Ruth E.; Robinson, Maura; Parr, Jane; Miller, Robert J.; Groshong, Steve; Green, Francis; Rose, Cecile

2018-01-01

Humans accumulate large numbers of inorganic particles in their lungs over a lifetime. Whether this causes or contributes to debilitating disease over a normal lifespan depends on the type and concentration of the particles. We developed and tested a protocol for in situ characterization of the types and distribution of inorganic particles in biopsied lung tissue from three human groups using field emission scanning electron microscopy (FE-SEM) combined with energy dispersive spectroscopy (EDS). Many distinct particle types were recognized among the 13 000 particles analyzed. Silica, feldspars, clays, titanium dioxides, iron oxides and phosphates were the most common constituents in all samples. Particles were classified into three general groups: endogenous, which form naturally in the body; exogenic particles, natural earth materials; and anthropogenic particles, attributed to industrial sources. These in situ results were compared with those using conventional sodium hypochlorite tissue digestion and particle filtration. With the exception of clays and phosphates, the relative abundances of most common particle types were similar in both approaches. Nonetheless, the digestion/filtration method was determined to alter the texture and relative abundances of some particle types. SEM/EDS analysis of digestion filters could be automated in contrast to the more time intensive in situ analyses.

Resonance Raman of BCC and normal skin

NASA Astrophysics Data System (ADS)

Liu, Cheng-hui; Sriramoju, Vidyasagar; Boydston-White, Susie; Wu, Binlin; Zhang, Chunyuan; Pei, Zhe; Sordillo, Laura; Beckman, Hugh; Alfano, Robert R.

2017-02-01

The Resonance Raman (RR) spectra of basal cell carcinoma (BCC) and normal human skin tissues were analyzed using 532nm laser excitation. RR spectral differences in vibrational fingerprints revealed skin normal and cancerous states tissues. The standard diagnosis criterion for BCC tissues are created by native RR biomarkers and its changes at peak intensity. The diagnostic algorithms for the classification of BCC and normal were generated based on SVM classifier and PCA statistical method. These statistical methods were used to analyze the RR spectral data collected from skin tissues, yielding a diagnostic sensitivity of 98.7% and specificity of 79% compared with pathological reports.

A novel modular ANN architecture for efficient monitoring of gases/odours in real-time

NASA Astrophysics Data System (ADS)

Mishra, A.; Rajput, N. S.

2018-04-01

Data pre-processing is tremendously used for enhanced classification of gases. However, it suppresses the concentration variances of different gas samples. A classical solution of using single artificial neural network (ANN) architecture is also inefficient and renders degraded quantification. In this paper, a novel modular ANN design has been proposed to provide an efficient and scalable solution in real–time. Here, two separate ANN blocks viz. classifier block and quantifier block have been used to provide efficient and scalable gas monitoring in real—time. The classifier ANN consists of two stages. In the first stage, the Net 1-NDSRT has been trained to transform raw sensor responses into corresponding virtual multi-sensor responses using normalized difference sensor response transformation (NDSRT). These responses have been fed to the second stage (i.e., Net 2-classifier ). The Net 2-classifier has been trained to classify various gas samples to their respective class. Further, the quantifier block has parallel ANN modules, multiplexed to quantify each gas. Therefore, the classifier ANN decides class and quantifier ANN decides the exact quantity of the gas/odor present in the respective sample of that class.

Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI

NASA Astrophysics Data System (ADS)

Niaf, Emilie; Rouvière, Olivier; Mège-Lechevallier, Florence; Bratan, Flavie; Lartizien, Carole

2012-06-01

This study evaluated a computer-assisted diagnosis (CADx) system for determining a likelihood measure of prostate cancer presence in the peripheral zone (PZ) based on multiparametric magnetic resonance (MR) imaging, including T2-weighted, diffusion-weighted and dynamic contrast-enhanced MRI at 1.5 T. Based on a feature set derived from grey-level images, including first-order statistics, Haralick features, gradient features, semi-quantitative and quantitative (pharmacokinetic modelling) dynamic parameters, four kinds of classifiers were trained and compared : nonlinear support vector machine (SVM), linear discriminant analysis, k-nearest neighbours and naïve Bayes classifiers. A set of feature selection methods based on t-test, mutual information and minimum-redundancy-maximum-relevancy criteria were also compared. The aim was to discriminate between the relevant features as well as to create an efficient classifier using these features. The diagnostic performances of these different CADx schemes were evaluated based on a receiver operating characteristic (ROC) curve analysis. The evaluation database consisted of 30 sets of multiparametric MR images acquired from radical prostatectomy patients. Using histologic sections as the gold standard, both cancer and nonmalignant (but suspicious) tissues were annotated in consensus on all MR images by two radiologists, a histopathologist and a researcher. Benign tissue regions of interest (ROIs) were also delineated in the remaining prostate PZ. This resulted in a series of 42 cancer ROIs, 49 benign but suspicious ROIs and 124 nonsuspicious benign ROIs. From the outputs of all evaluated feature selection methods on the test bench, a restrictive set of about 15 highly informative features coming from all MR sequences was discriminated, thus confirming the validity of the multiparametric approach. Quantitative evaluation of the diagnostic performance yielded a maximal area under the ROC curve (AUC) of 0.89 (0.81-0.94) for the discrimination of the malignant versus nonmalignant tissues and 0.82 (0.73-0.90) for the discrimination of the malignant versus suspicious tissues when combining the t-test feature selection approach with a SVM classifier. A preliminary comparison showed that the optimal CADx scheme mimicked, in terms of AUC, the human experts in differentiating malignant from suspicious tissues, thus demonstrating its potential for assisting cancer identification in the PZ.

Effective Sequential Classifier Training for SVM-Based Multitemporal Remote Sensing Image Classification

NASA Astrophysics Data System (ADS)

Guo, Yiqing; Jia, Xiuping; Paull, David

2018-06-01

The explosive availability of remote sensing images has challenged supervised classification algorithms such as Support Vector Machines (SVM), as training samples tend to be highly limited due to the expensive and laborious task of ground truthing. The temporal correlation and spectral similarity between multitemporal images have opened up an opportunity to alleviate this problem. In this study, a SVM-based Sequential Classifier Training (SCT-SVM) approach is proposed for multitemporal remote sensing image classification. The approach leverages the classifiers of previous images to reduce the required number of training samples for the classifier training of an incoming image. For each incoming image, a rough classifier is firstly predicted based on the temporal trend of a set of previous classifiers. The predicted classifier is then fine-tuned into a more accurate position with current training samples. This approach can be applied progressively to sequential image data, with only a small number of training samples being required from each image. Experiments were conducted with Sentinel-2A multitemporal data over an agricultural area in Australia. Results showed that the proposed SCT-SVM achieved better classification accuracies compared with two state-of-the-art model transfer algorithms. When training data are insufficient, the overall classification accuracy of the incoming image was improved from 76.18% to 94.02% with the proposed SCT-SVM, compared with those obtained without the assistance from previous images. These results demonstrate that the leverage of a priori information from previous images can provide advantageous assistance for later images in multitemporal image classification.

Texture Analysis and Synthesis of Malignant and Benign Mediastinal Lymph Nodes in Patients with Lung Cancer on Computed Tomography

NASA Astrophysics Data System (ADS)

Pham, Tuan D.; Watanabe, Yuzuru; Higuchi, Mitsunori; Suzuki, Hiroyuki

2017-02-01

Texture analysis of computed tomography (CT) imaging has been found useful to distinguish subtle differences, which are in- visible to human eyes, between malignant and benign tissues in cancer patients. This study implemented two complementary methods of texture analysis, known as the gray-level co-occurrence matrix (GLCM) and the experimental semivariogram (SV) with an aim to improve the predictive value of evaluating mediastinal lymph nodes in lung cancer. The GLCM was explored with the use of a rich set of its derived features, whereas the SV feature was extracted on real and synthesized CT samples of benign and malignant lymph nodes. A distinct advantage of the computer methodology presented herein is the alleviation of the need for an automated precise segmentation of the lymph nodes. Using the logistic regression model, a sensitivity of 75%, specificity of 90%, and area under curve of 0.89 were obtained in the test population. A tenfold cross-validation of 70% accuracy of classifying between benign and malignant lymph nodes was obtained using the support vector machines as a pattern classifier. These results are higher than those recently reported in literature with similar studies.

Prevalence of boar taint in commercial pigs from Spanish farms.

PubMed

Borrisser-Pairó, F; Panella-Riera, N; Zammerini, D; Olivares, A; Garrido, M D; Martínez, B; Gil, M; García-Regueiro, J A; Oliver, M A

2016-01-01

The presence of boar taint can affect the sensory quality of pork because the "off" odours and flavours can be detected by consumers. The aim of this study was to determine the prevalence of boar taint in pig carcasses from 30 Spanish farms located in different regions of the country. Hot carcass weight and subcutaneous fat thickness means were 79.4±8.19 kg and 18.4±5.09 mm, respectively. Subcutaneous fat samples were classified into different levels according to androstenone and skatole concentrations in adipose tissue measured using GC-MS and HPLC. Androstenone results were: 87.4% of the carcasses below 0.50 μg/g, 7.1% from 0.50 to 1.00 μg/g (medium level), and 5.5% ≥1.00 μg/g (high level). Skatole results were: 88.9% of the carcasses below 0.10 μg/g, 4.5% from 0.10 to 0.20 μg/g (medium level), and 6.6% ≥0.20 μg/g (high level). Given these results, a future online method to classify carcasses according to boar taint is strongly recommended. Copyright © 2015 Elsevier Ltd. All rights reserved.

Cytological Findings of Malignant and Benign Head and Neck Masses in Somalia.

PubMed

Baş, Yılmaz

2018-04-11

There are no up-to-date records on head and neck masses (HNMs) in Somalia. This cytological study is the first to demonstrate the benefits and findings of fine-needle aspiration cytology in evaluating HNMs in the adult population of Somalia. A total of 116 aspiration samples were taken from different levels of the neck region, except for the thyroid. Cases were classified as salivary gland, lymph node, or soft tissue/cystic lesions. They were classified according to age, gender, and cytological diagnosis. Patients included 54 (46.6%) males and 62 (53.4%) females, with a mean age of 40.6 years. Seventy-two patients (62.1%) had benign lesions, while 44 (37.9%) had malignant lesions. Necrotizing granulomatous lymphadenitis (n = 51, 70.8% of the benign findings) and lymph node metastasis of squamous cell carcinoma (n = 13, 29.5% of the malignant findings) were the most frequent findings. Fine-needle aspiration is a useful procedure in the diagnosis of neck masses. It is a cheap and easy guiding method for diagnosing granulomatous lymphadenitis and advanced-stage metastatic cancers, which are common in this country. © 2018 S. Karger AG, Basel.

Histopathological retrospective study of canine renal disease in Korea, 2003~2008

PubMed Central

Yhee, Ji-Young; Yu, Chi-Ho; Kim, Jong-Hyuk; Im, Keum-Soon; Chon, Seung-Ki

2010-01-01

Renal disease includes conditions affecting the glomeruli, tubules, interstitium, pelvis, and vasculature. Diseases of the kidney include glomerular diseases, diseases of the tubules and interstitium, diseases of renal pelvis, and developmental abnormalities. Renal tissue samples (n = 70) submitted to the Department of Veterinary Pathology of Konkuk University from 2003 to 2008 were included in this study. Tissue histopathology was performed using light microscopy with hematoxylin and eosin stains. Masson's trichrome, Congo Red, and Warthin starry silver staining were applied in several individual cases. Glomerular diseases (22.9%), tubulointerstitial diseases (8.6%), neoplastic diseases (8.6%), conditions secondary to urinary obstruction (24.3%), and other diseases (35.7%) were identified. Glomerulonephritis (GN) cases were classified as acute proliferative GN (5.7%), membranous GN (4.3%), membranoproliferative GN (4.3%), focal segmental GN (2.9%), and other GN (4.2%). The proportion of canine GN cases presently identified was not as high as the proportions identified in human studies. Conversely, urinary obstruction and end-stage renal disease cases were relatively higher in dogs than in human populations. PMID:21113095

Hyperspectral Image Analysis for Skin Tumor Detection

NASA Astrophysics Data System (ADS)

Kong, Seong G.; Park, Lae-Jeong

This chapter presents hyperspectral imaging of fluorescence for nonin-vasive detection of tumorous tissue on mouse skin. Hyperspectral imaging sensors collect two-dimensional (2D) image data of an object in a number of narrow, adjacent spectral bands. This high-resolution measurement of spectral information reveals a continuous emission spectrum for each image pixel useful for skin tumor detection. The hyperspectral image data used in this study are fluorescence intensities of a mouse sample consisting of 21 spectral bands in the visible spectrum of wavelengths ranging from 440 to 640 nm. Fluorescence signals are measured using a laser excitation source with the center wavelength of 337 nm. An acousto-optic tunable filter is used to capture individual spectral band images at a 10-nm resolution. All spectral band images are spatially registered with the reference band image at 490 nm to obtain exact pixel correspondences by compensating the offsets caused during the image capture procedure. The support vector machines with polynomial kernel functions provide decision boundaries with a maximum separation margin to classify malignant tumor and normal tissue from the observed fluorescence spectral signatures for skin tumor detection.

Bayesian approach to MSD-based analysis of particle motion in live cells.

PubMed

Monnier, Nilah; Guo, Syuan-Ming; Mori, Masashi; He, Jun; Lénárt, Péter; Bathe, Mark

2012-08-08

Quantitative tracking of particle motion using live-cell imaging is a powerful approach to understanding the mechanism of transport of biological molecules, organelles, and cells. However, inferring complex stochastic motion models from single-particle trajectories in an objective manner is nontrivial due to noise from sampling limitations and biological heterogeneity. Here, we present a systematic Bayesian approach to multiple-hypothesis testing of a general set of competing motion models based on particle mean-square displacements that automatically classifies particle motion, properly accounting for sampling limitations and correlated noise while appropriately penalizing model complexity according to Occam's Razor to avoid over-fitting. We test the procedure rigorously using simulated trajectories for which the underlying physical process is known, demonstrating that it chooses the simplest physical model that explains the observed data. Further, we show that computed model probabilities provide a reliability test for the downstream biological interpretation of associated parameter values. We subsequently illustrate the broad utility of the approach by applying it to disparate biological systems including experimental particle trajectories from chromosomes, kinetochores, and membrane receptors undergoing a variety of complex motions. This automated and objective Bayesian framework easily scales to large numbers of particle trajectories, making it ideal for classifying the complex motion of large numbers of single molecules and cells from high-throughput screens, as well as single-cell-, tissue-, and organism-level studies. Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Classification of pulmonary emphysema from chest CT scans using integral geometry descriptors

NASA Astrophysics Data System (ADS)

van Rikxoort, E. M.; Goldin, J. G.; Galperin-Aizenberg, M.; Brown, M. S.

2011-03-01

To gain insight into the underlying pathways of emphysema and monitor the effect of treatment, methods to quantify and phenotype the different types of emphysema from chest CT scans are of crucial importance. Current standard measures rely on density thresholds for individual voxels, which is influenced by inspiration level and does not take into account the spatial relationship between voxels. Measures based on texture analysis do take the interrelation between voxels into account and therefore might be useful for distinguishing different types of emphysema. In this study, we propose to use Minkowski functionals combined with rotation invariant Gaussian features to distinguish between healthy and emphysematous tissue and classify three different types of emphysema. Minkowski functionals characterize binary images in terms of geometry and topology. In 3D, four Minkowski functionals are defined. By varying the threshold and size of neighborhood around a voxel, a set of Minkowski functionals can be defined for each voxel. Ten chest CT scans with 1810 annotated regions were used to train the method. A set of 108 features was calculated for each training sample from which 10 features were selected to be most informative. A linear discriminant classifier was trained to classify each voxel in the lungs into a subtype of emphysema or normal lung. The method was applied to an independent test set of 30 chest CT scans with varying amounts and types of emphysema with 4347 annotated regions of interest. The method is shown to perform well, with an overall accuracy of 95%.

Automated pixel-wise brain tissue segmentation of diffusion-weighted images via machine learning.

PubMed

Ciritsis, Alexander; Boss, Andreas; Rossi, Cristina

2018-04-26

The diffusion-weighted (DW) MR signal sampled over a wide range of b-values potentially allows for tissue differentiation in terms of cellularity, microstructure, perfusion, and T 2 relaxivity. This study aimed to implement a machine learning algorithm for automatic brain tissue segmentation from DW-MRI datasets, and to determine the optimal sub-set of features for accurate segmentation. DWI was performed at 3 T in eight healthy volunteers using 15 b-values and 20 diffusion-encoding directions. The pixel-wise signal attenuation, as well as the trace and fractional anisotropy (FA) of the diffusion tensor, were used as features to train a support vector machine classifier for gray matter, white matter, and cerebrospinal fluid classes. The datasets of two volunteers were used for validation. For each subject, tissue classification was also performed on 3D T 1 -weighted data sets with a probabilistic framework. Confusion matrices were generated for quantitative assessment of image classification accuracy in comparison with the reference method. DWI-based tissue segmentation resulted in an accuracy of 82.1% on the validation dataset and of 82.2% on the training dataset, excluding relevant model over-fitting. A mean Dice coefficient (DSC) of 0.79 ± 0.08 was found. About 50% of the classification performance was attributable to five features (i.e. signal measured at b-values of 5/10/500/1200 s/mm 2 and the FA). This reduced set of features led to almost identical performances for the validation (82.2%) and the training (81.4%) datasets (DSC = 0.79 ± 0.08). Machine learning techniques applied to DWI data allow for accurate brain tissue segmentation based on both morphological and functional information. Copyright © 2018 John Wiley & Sons, Ltd.

A simple model for cell type recognition using 2D-correlation analysis of FTIR images from breast cancer tissue

NASA Astrophysics Data System (ADS)

Ali, Mohamed H.; Rakib, Fazle; Al-Saad, Khalid; Al-Saady, Rafif; Lyng, Fiona M.; Goormaghtigh, Erik

2018-07-01

Breast cancer is the second most common cancer after lung cancer. So far, in clinical practice, most cancer parameters originating from histopathology rely on the visualization by a pathologist of microscopic structures observed in stained tissue sections, including immunohistochemistry markers. Fourier transform infrared spectroscopy (FTIR) spectroscopy provides a biochemical fingerprint of a biopsy sample and, together with advanced data analysis techniques, can accurately classify cell types. Yet, one of the challenges when dealing with FTIR imaging is the slow recording of the data. One cm2 tissue section requires several hours of image recording. We show in the present paper that 2D covariance analysis singles out only a few wavenumbers where both variance and covariance are large. Simple models could be built using 4 wavenumbers to identify the 4 main cell types present in breast cancer tissue sections. Decision trees provide particularly simple models to reach discrimination between the 4 cell types. The robustness of these simple decision-tree models were challenged with FTIR spectral data obtained using different recording conditions. One test set was recorded by transflection on tissue sections in the presence of paraffin while the training set was obtained on dewaxed tissue sections by transmission. Furthermore, the test set was collected with a different brand of FTIR microscope and a different pixel size. Despite the different recording conditions, separating extracellular matrix (ECM) from carcinoma spectra was 100% successful, underlying the robustness of this univariate model and the utility of covariance analysis for revealing efficient wavenumbers. We suggest that 2D covariance maps using the full spectral range could be most useful to select the interesting wavenumbers and achieve very fast data acquisition on quantum cascade laser infrared imaging microscopes.

Touch imprint cytology with massively parallel sequencing (TIC-seq): a simple and rapid method to snapshot genetic alterations in tumors.

PubMed

Amemiya, Kenji; Hirotsu, Yosuke; Goto, Taichiro; Nakagomi, Hiroshi; Mochizuki, Hitoshi; Oyama, Toshio; Omata, Masao

2016-12-01

Identifying genetic alterations in tumors is critical for molecular targeting of therapy. In the clinical setting, formalin-fixed paraffin-embedded (FFPE) tissue is usually employed for genetic analysis. However, DNA extracted from FFPE tissue is often not suitable for analysis because of its low levels and poor quality. Additionally, FFPE sample preparation is time-consuming. To provide early treatment for cancer patients, a more rapid and robust method is required for precision medicine. We present a simple method for genetic analysis, called touch imprint cytology combined with massively paralleled sequencing (touch imprint cytology [TIC]-seq), to detect somatic mutations in tumors. We prepared FFPE tissues and TIC specimens from tumors in nine lung cancer patients and one patient with breast cancer. We found that the quality and quantity of TIC DNA was higher than that of FFPE DNA, which requires microdissection to enrich DNA from target tissues. Targeted sequencing using a next-generation sequencer obtained sufficient sequence data using TIC DNA. Most (92%) somatic mutations in lung primary tumors were found to be consistent between TIC and FFPE DNA. We also applied TIC DNA to primary and metastatic tumor tissues to analyze tumor heterogeneity in a breast cancer patient, and showed that common and distinct mutations among primary and metastatic sites could be classified into two distinct histological subtypes. TIC-seq is an alternative and feasible method to analyze genomic alterations in tumors by simply touching the cut surface of specimens to slides. © 2016 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

Transcriptome-Wide Mega-Analyses Reveal Joint Dysregulation of Immunologic Genes and Transcription Regulators in Brain and Blood in Schizophrenia

PubMed Central

Hess, Jonathan L.; Tylee, Daniel S.; Barve, Rahul; de Jong, Simone; Ophoff, Roel A.; Kumarasinghe, Nishantha; Tooney, Paul; Schall, Ulrich; Gardiner, Erin; Beveridge, Natalie Jane; Scott, Rodney J.; Yasawardene, Surangi; Perera, Antionette; Mendis, Jayan; Carr, Vaughan; Kelly, Brian; Cairns, Murray; Tsuang, Ming T.; Glatt, Stephen J.

2016-01-01

The application of microarray technology in schizophrenia research was heralded as paradigm-shifting, as it allowed for high-throughput assessment of cell and tissue function. This technology was widely adopted, initially in studies of postmortem brain tissue, and later in studies of peripheral blood. The collective body of schizophrenia microarray literature contains apparent inconsistencies between studies, with failures to replicate top hits, in part due to small sample sizes, cohort-specific effects, differences in array types, and other confounders. In an attempt to summarize existing studies of schizophrenia cases and non-related comparison subjects, we performed two mega-analyses of a combined set of microarray data from postmortem prefrontal cortices (n = 315) and from ex-vivo blood tissues (n = 578). We adjusted regression models per gene to remove non-significant covariates, providing best-estimates of transcripts dysregulated in schizophrenia. We also examined dysregulation of functionally related gene sets and gene co-expression modules, and assessed enrichment of cell types and genetic risk factors. The identities of the most significantly dysregulated genes were largely distinct for each tissue, but the findings indicated common emergent biological functions (e.g. immunity) and regulatory factors (e.g., predicted targets of transcription factors and miRNA species across tissues). Our network-based analyses converged upon similar patterns of heightened innate immune gene expression in both brain and blood in schizophrenia. We also constructed generalizable machine-learning classifiers using the blood-based microarray data. Our study provides an informative atlas for future pathophysiologic and biomarker studies of schizophrenia. PMID:27450777

Transcriptome-wide mega-analyses reveal joint dysregulation of immunologic genes and transcription regulators in brain and blood in schizophrenia.

PubMed

Hess, Jonathan L; Tylee, Daniel S; Barve, Rahul; de Jong, Simone; Ophoff, Roel A; Kumarasinghe, Nishantha; Tooney, Paul; Schall, Ulrich; Gardiner, Erin; Beveridge, Natalie Jane; Scott, Rodney J; Yasawardene, Surangi; Perera, Antionette; Mendis, Jayan; Carr, Vaughan; Kelly, Brian; Cairns, Murray; Tsuang, Ming T; Glatt, Stephen J

2016-10-01

The application of microarray technology in schizophrenia research was heralded as paradigm-shifting, as it allowed for high-throughput assessment of cell and tissue function. This technology was widely adopted, initially in studies of postmortem brain tissue, and later in studies of peripheral blood. The collective body of schizophrenia microarray literature contains apparent inconsistencies between studies, with failures to replicate top hits, in part due to small sample sizes, cohort-specific effects, differences in array types, and other confounders. In an attempt to summarize existing studies of schizophrenia cases and non-related comparison subjects, we performed two mega-analyses of a combined set of microarray data from postmortem prefrontal cortices (n=315) and from ex-vivo blood tissues (n=578). We adjusted regression models per gene to remove non-significant covariates, providing best-estimates of transcripts dysregulated in schizophrenia. We also examined dysregulation of functionally related gene sets and gene co-expression modules, and assessed enrichment of cell types and genetic risk factors. The identities of the most significantly dysregulated genes were largely distinct for each tissue, but the findings indicated common emergent biological functions (e.g. immunity) and regulatory factors (e.g., predicted targets of transcription factors and miRNA species across tissues). Our network-based analyses converged upon similar patterns of heightened innate immune gene expression in both brain and blood in schizophrenia. We also constructed generalizable machine-learning classifiers using the blood-based microarray data. Our study provides an informative atlas for future pathophysiologic and biomarker studies of schizophrenia. Published by Elsevier B.V.

Authentication of animal origin of heparin and low molecular weight heparin including ovine, porcine and bovine species using 1D NMR spectroscopy and chemometric tools.

PubMed

Monakhova, Yulia B; Diehl, Bernd W K; Fareed, Jawed

2018-02-05

High resolution (600MHz) nuclear magnetic resonance (NMR) spectroscopy is used to distinguish heparin and low-molecular weight heparins (LMWHs) produced from porcine, bovine and ovine mucosal tissues as well as their blends. For multivariate analysis several statistical methods such as principal component analysis (PCA), factor discriminant analysis (FDA), partial least squares - discriminant analysis (PLS-DA), linear discriminant analysis (LDA) were utilized for the modeling of NMR data of more than 100 authentic samples. Heparin and LMWH samples from the independent test set (n=15) were 100% correctly classified according to its animal origin. Moreover, by using 1 H NMR coupled with chemometrics and several batches of bovine heparins from two producers were differentiated. Thus, NMR spectroscopy combined with chemometrics is an efficient tool for simultaneous identification of animal origin and process based manufacturing difference in heparin products. Copyright © 2017 Elsevier B.V. All rights reserved.

Is there a direct relationship between stress biomarkers in oysters and the amount of metals in the sediments where they inhabit?

PubMed

Rodriguez-Iruretagoiena, A; Rementeria, A; Zaldibar, B; de Vallejuelo, S Fdez-Ortiz; Gredilla, A; Arana, G; de Diego, A

2016-10-15

The effects exerted by metals in oysters are still a matter of debate and require more detailed studies. In this work we have investigated whether the health status of oysters are affected by the amount of metals present in the sediments of their habitat. Sediments and oysters were collected in the tidal part of the estuary of the Oka River (Basque Country), representative of other mesotidal, well mixed and short estuaries of the European Atlantic coast. The concentrations of 14 elements were determined in all the samples. Several biomarkers were also measured in the soft tissues of oysters. According to the concentrations found, the sediments were classified as non-toxic or slightly toxic. In good agreement, the histological alterations observed in oysters were not severe. Interestingly, in those sampling sites where the sediments showed relatively high metal concentrations, the metallic content in oysters was lower, and vice versa. Copyright © 2016 Elsevier Ltd. All rights reserved.

DTWscore: differential expression and cell clustering analysis for time-series single-cell RNA-seq data.

PubMed

Wang, Zhuo; Jin, Shuilin; Liu, Guiyou; Zhang, Xiurui; Wang, Nan; Wu, Deliang; Hu, Yang; Zhang, Chiping; Jiang, Qinghua; Xu, Li; Wang, Yadong

2017-05-23

The development of single-cell RNA sequencing has enabled profound discoveries in biology, ranging from the dissection of the composition of complex tissues to the identification of novel cell types and dynamics in some specialized cellular environments. However, the large-scale generation of single-cell RNA-seq (scRNA-seq) data collected at multiple time points remains a challenge to effective measurement gene expression patterns in transcriptome analysis. We present an algorithm based on the Dynamic Time Warping score (DTWscore) combined with time-series data, that enables the detection of gene expression changes across scRNA-seq samples and recovery of potential cell types from complex mixtures of multiple cell types. The DTWscore successfully classify cells of different types with the most highly variable genes from time-series scRNA-seq data. The study was confined to methods that are implemented and available within the R framework. Sample datasets and R packages are available at https://github.com/xiaoxiaoxier/DTWscore .

Prevalence of different periapical lesions associated with human teeth and their correlation with the presence and extension of apical external root resorption.

PubMed

Vier, F V; Figueiredo, J A P

2002-08-01

The aim of this study was to determine the prevalence of various periapical pathologies and their association with the presence and extent of apical external inflammatory root resorption in human teeth. One hundred and four root apices from extracted teeth with periapical lesions were examined. Semi-serial sections of soft tissue lesions were stained with HE. The lesions were classified as noncystic or cystic, each with different degrees of acute inflammation: 0, 1, 2 and 3, increasing in severity. The root apices were analysed by SEM. External root resorption was classified according to site, as periforaminal or foraminal, and the extension of the resorbed area graded in increasing area as 0, 1, 2 or 3. Cysts accounted for 24.5% of the samples, 84% of which were associated with marked inflammation. The most prevalent diagnosis was noncystic periapical abscess with varying degrees of severity (63.7%). Periapical granuloma was not a frequent finding. SEM analysis showed that 42.2% of the root apices had periforaminal resorption extending over 50% of their circumference. When the foraminal resorption was evaluated, 28.7% had resorption affecting >50% of the periphery. Only 8.9% of the samples showed no periforaminal or foraminal resorption. In the sample of extracted teeth investigated, 24.5% of the periapical lesions were cysts. Most periapical lesions (84.3%) displayed acute inflammation, whether cystic or not. Periforaminal resorption was present in 87.3% of the cases, and foraminal resorption in 83.2%. Periforaminal and foraminal resorptions were independent entities. There was no association between external root resorption and the nature of the periapical lesions.

Differentiation between microcystin contaminated and uncontaminated fish by determination of unconjugated MCs using an ELISA anti-Adda test based on receiver-operating characteristic curves threshold values: application to Tinca tinca from natural ponds.

PubMed

Moreno, Isabel María; Herrador, M Ángeles; Atencio, Loyda; Puerto, María; González, A Gustavo; Cameán, Ana María

2011-02-01

The aim of this study was to evaluate whether the enzyme-linked immunosorbent assay (ELISA) anti-Adda technique could be used to monitor free microcystins (MCs) in biological samples from fish naturally exposed to toxic cyanobacteria by using receiver operating characteristic (ROC) curve software to establish an optimal cut-off value for MCs. The cut-off value determined by ROC curve analysis in tench (Tinca tinca) exposed to MCs under laboratory conditions by ROC curve analysis was 5.90-μg MCs/kg tissue dry weight (d.w.) with a sensitivity of 93.3%. This value was applied in fish samples from natural ponds (Extremadura, Spain) in order to asses its potential MCs bioaccumulation by classifying samples as either true positive (TP), false positive (FP), true negative (TN), or false negative (FN). In this work, it has been demonstrated that toxic cyanobacteria, mainly Microcystis aeruginosa, Aphanizomenon issatchenkoi, and Anabaena spiroides, were present in two of these ponds, Barruecos de Abajo (BDown) and Barruecos de Arriba (BUp). The MCs levels were detected in waters from both ponds with an anti-MC-LR ELISA immunoassay and were of similar values (between 3.8-6.5-μg MC-LR equivalent/L in BDown pond and 4.8-6.0-μg MC-LR equivalent/L in BUp). The MCs cut-off values were applied in livers from fish collected from these two ponds using the ELISA anti-Adda technique. A total of 83% of samples from BDown pond and only 42% from BUp were TP with values of free MCs higher than 8.8-μg MCs/kg tissue (d.w.). Copyright © 2009 Wiley Periodicals, Inc.

Automated classification and visualization of healthy and pathological dental tissues based on near-infrared hyper-spectral imaging

NASA Astrophysics Data System (ADS)

Usenik, Peter; Bürmen, Miran; Vrtovec, Tomaž; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan

2011-03-01

Despite major improvements in dental healthcare and technology, dental caries remains one of the most prevalent chronic diseases of modern society. The initial stages of dental caries are characterized by demineralization of enamel crystals, commonly known as white spots which are difficult to diagnose. If detected early enough, such demineralization can be arrested and reversed by non-surgical means through well established dental treatments (fluoride therapy, anti-bacterial therapy, low intensity laser irradiation). Near-infrared (NIR) hyper-spectral imaging is a new promising technique for early detection of demineralization based on distinct spectral features of healthy and pathological dental tissues. In this study, we apply NIR hyper-spectral imaging to classify and visualize healthy and pathological dental tissues including enamel, dentin, calculus, dentin caries, enamel caries and demineralized areas. For this purpose, a standardized teeth database was constructed consisting of 12 extracted human teeth with different degrees of natural dental lesions imaged by NIR hyper-spectral system, X-ray and digital color camera. The color and X-ray images of teeth were presented to a clinical expert for localization and classification of the dental tissues, thereby obtaining the gold standard. Principal component analysis was used for multivariate local modeling of healthy and pathological dental tissues. Finally, the dental tissues were classified by employing multiple discriminant analysis. High agreement was observed between the resulting classification and the gold standard with the classification sensitivity and specificity exceeding 85 % and 97 %, respectively. This study demonstrates that NIR hyper-spectral imaging has considerable diagnostic potential for imaging hard dental tissues.

A blood-based proteomic classifier for the molecular characterization of pulmonary nodules.

PubMed

Li, Xiao-jun; Hayward, Clive; Fong, Pui-Yee; Dominguez, Michel; Hunsucker, Stephen W; Lee, Lik Wee; McLean, Matthew; Law, Scott; Butler, Heather; Schirm, Michael; Gingras, Olivier; Lamontagne, Julie; Allard, Rene; Chelsky, Daniel; Price, Nathan D; Lam, Stephen; Massion, Pierre P; Pass, Harvey; Rom, William N; Vachani, Anil; Fang, Kenneth C; Hood, Leroy; Kearney, Paul

2013-10-16

Each year, millions of pulmonary nodules are discovered by computed tomography and subsequently biopsied. Because most of these nodules are benign, many patients undergo unnecessary and costly invasive procedures. We present a 13-protein blood-based classifier that differentiates malignant and benign nodules with high confidence, thereby providing a diagnostic tool to avoid invasive biopsy on benign nodules. Using a systems biology strategy, we identified 371 protein candidates and developed a multiple reaction monitoring (MRM) assay for each. The MRM assays were applied in a three-site discovery study (n = 143) on plasma samples from patients with benign and stage IA lung cancer matched for nodule size, age, gender, and clinical site, producing a 13-protein classifier. The classifier was validated on an independent set of plasma samples (n = 104), exhibiting a negative predictive value (NPV) of 90%. Validation performance on samples from a nondiscovery clinical site showed an NPV of 94%, indicating the general effectiveness of the classifier. A pathway analysis demonstrated that the classifier proteins are likely modulated by a few transcription regulators (NF2L2, AHR, MYC, and FOS) that are associated with lung cancer, lung inflammation, and oxidative stress networks. The classifier score was independent of patient nodule size, smoking history, and age, which are risk factors used for clinical management of pulmonary nodules. Thus, this molecular test provides a potential complementary tool to help physicians in lung cancer diagnosis.

Native and European haplotypes of Phragmites Australis (common reed) in the central Platte River, Nebraska

USGS Publications Warehouse

Larson, D.L.; Galatowitsch, S.M.; Larson, J.L.

2011-01-01

Phragmites australis (common reed) is known to have occurred along the Platte River historically, but recent rapid increases in both distribution and density have begun to impact habitat for migrating sandhill cranes and nesting piping plovers and least terns. Invasiveness in Phragmites has been associated with the incursion of a European genotype (haplotype M) in other areas; determining the genotype of Phragmites along the central Platte River has implications for proper management of the river system. In 2008 we sampled Phragmites patches along the central Platte River from Lexington to Chapman, NE, stratified by bridge segments, to determine the current distribution of haplotype E (native) and haplotype M genotypes. In addition, we did a retrospective analysis of historical Phragmites collections from the central Platte watershed (1902-2006) at the Bessey Herbarium. Fresh tissue from the 2008 survey and dried tissue from the herbarium specimens were classified as haplotype M or E using the restriction fragment length polymorphism procedure. The European haplotype was predominant in the 2008 samples: only 14 Phragmites shoots were identified as native haplotype E; 224 were non-native haplotype M. The retrospective analysis revealed primarily native haplotype individuals. Only collections made in Lancaster County, near Lincoln, NE, were haplotype M, and the earliest of these was collected in 1973. ?? 2011 Copyright by the Center for Great Plains Studies, University of Nebraska-Lincoln.

Computational selection of antibody-drug conjugate targets for breast cancer

PubMed Central

Fauteux, François; Hill, Jennifer J.; Jaramillo, Maria L.; Pan, Youlian; Phan, Sieu; Famili, Fazel; O'Connor-McCourt, Maureen

2016-01-01

The selection of therapeutic targets is a critical aspect of antibody-drug conjugate research and development. In this study, we applied computational methods to select candidate targets overexpressed in three major breast cancer subtypes as compared with a range of vital organs and tissues. Microarray data corresponding to over 8,000 tissue samples were collected from the public domain. Breast cancer samples were classified into molecular subtypes using an iterative ensemble approach combining six classification algorithms and three feature selection techniques, including a novel kernel density-based method. This feature selection method was used in conjunction with differential expression and subcellular localization information to assemble a primary list of targets. A total of 50 cell membrane targets were identified, including one target for which an antibody-drug conjugate is in clinical use, and six targets for which antibody-drug conjugates are in clinical trials for the treatment of breast cancer and other solid tumors. In addition, 50 extracellular proteins were identified as potential targets for non-internalizing strategies and alternative modalities. Candidate targets linked with the epithelial-to-mesenchymal transition were identified by analyzing differential gene expression in epithelial and mesenchymal tumor-derived cell lines. Overall, these results show that mining human gene expression data has the power to select and prioritize breast cancer antibody-drug conjugate targets, and the potential to lead to new and more effective cancer therapeutics. PMID:26700623

Feature genes in metastatic breast cancer identified by MetaDE and SVM classifier methods.

PubMed

Tuo, Youlin; An, Ning; Zhang, Ming

2018-03-01

The aim of the present study was to investigate the feature genes in metastatic breast cancer samples. A total of 5 expression profiles of metastatic breast cancer samples were downloaded from the Gene Expression Omnibus database, which were then analyzed using the MetaQC and MetaDE packages in R language. The feature genes between metastasis and non‑metastasis samples were screened under the threshold of P<0.05. Based on the protein‑protein interactions (PPIs) in the Biological General Repository for Interaction Datasets, Human Protein Reference Database and Biomolecular Interaction Network Database, the PPI network of the feature genes was constructed. The feature genes identified by topological characteristics were then used for support vector machine (SVM) classifier training and verification. The accuracy of the SVM classifier was then evaluated using another independent dataset from The Cancer Genome Atlas database. Finally, function and pathway enrichment analyses for genes in the SVM classifier were performed. A total of 541 feature genes were identified between metastatic and non‑metastatic samples. The top 10 genes with the highest betweenness centrality values in the PPI network of feature genes were Nuclear RNA Export Factor 1, cyclin‑dependent kinase 2 (CDK2), myelocytomatosis proto‑oncogene protein (MYC), Cullin 5, SHC Adaptor Protein 1, Clathrin heavy chain, Nucleolin, WD repeat domain 1, proteasome 26S subunit non‑ATPase 2 and telomeric repeat binding factor 2. The cyclin‑dependent kinase inhibitor 1A (CDKN1A), E2F transcription factor 1 (E2F1), and MYC interacted with CDK2. The SVM classifier constructed by the top 30 feature genes was able to distinguish metastatic samples from non‑metastatic samples [correct rate, specificity, positive predictive value and negative predictive value >0.89; sensitivity >0.84; area under the receiver operating characteristic curve (AUROC) >0.96]. The verification of the SVM classifier in an independent dataset (35 metastatic samples and 143 non‑metastatic samples) revealed an accuracy of 94.38% and AUROC of 0.958. Cell cycle associated functions and pathways were the most significant terms of the 30 feature genes. A SVM classifier was constructed to assess the possibility of breast cancer metastasis, which presented high accuracy in several independent datasets. CDK2, CDKN1A, E2F1 and MYC were indicated as the potential feature genes in metastatic breast cancer.

Stackable differential mobility analyzer for aerosol measurement

DOEpatents

Cheng, Meng-Dawn [Oak Ridge, TN; Chen, Da-Ren [Creve Coeur, MO

2007-05-08

A multi-stage differential mobility analyzer (MDMA) for aerosol measurements includes a first electrode or grid including at least one inlet or injection slit for receiving an aerosol including charged particles for analysis. A second electrode or grid is spaced apart from the first electrode. The second electrode has at least one sampling outlet disposed at a plurality different distances along its length. A volume between the first and the second electrode or grid between the inlet or injection slit and a distal one of the plurality of sampling outlets forms a classifying region, the first and second electrodes for charging to suitable potentials to create an electric field within the classifying region. At least one inlet or injection slit in the second electrode receives a sheath gas flow into an upstream end of the classifying region, wherein each sampling outlet functions as an independent DMA stage and classifies different size ranges of charged particles based on electric mobility simultaneously.

Application of Atomic Dielectric Resonance Spectroscopy for the screening of blood samples from patients with clinical variant and sporadic CJD

PubMed Central

Fagge, Timothy J; Barclay, G Robin; Stove, G Colin; Stove, Gordon; Robinson, Michael J; Head, Mark W; Ironside, James W; Turner, Marc L

2007-01-01

Background Sub-clinical variant Creutzfeldt-Jakob disease (vCJD) infection and reports of vCJD transmission through blood transfusion emphasise the need for blood screening assays to ensure the safety of blood and transplanted tissues. Most assays aim to detect abnormal prion protein (PrPSc), although achieving required sensitivity is a challenge. Methods We have used innovative Atomic Dielectric Resonance Spectroscopy (ADRS), which determines dielectric properties of materials which are established by reflectivity and penetration of radio/micro waves, to analyse blood samples from patients and controls to identify characteristic ADR signatures unique to blood from vCJD and to sCJD patients. Initial sets of blood samples from vCJD, sCJD, non-CJD neurological diseases and normal healthy adults (blood donors) were screened as training samples to determine group-specific ADR characteristics, and provided a basis for classification of blinded sets of samples. Results Blood sample groups from vCJD, sCJD, non-CJD neurological diseases and normal healthy adults (blood donors) screened by ADRS were classified with 100% specificity and sensitivity, discriminating these by a co-variance expert analysis system. Conclusion ADRS appears capable of recognising and discriminating serum samples from vCJD, sCJD, non-CJD neurological diseases, and normal healthy adults, and might be developed to provide a system for primary screening or confirmatory assay complementary to other screening systems. PMID:17760958

Interactive Medical Volume Visualization for Surgical Operations

DTIC Science & Technology

2001-10-25

the preprocessing and processing stages, related medical brain tissues, which are skull, white matter, gray matter and pathology ( tumor ), are segmented ...from 12 or 16 bit data depths. NMR segmentation plays an important role in our work, because, classifying brain tissues from NMR slices requires an...performing segmentation of brain structures. Our segmentation process uses Self Organizing Feature Maps (SOFM) [12]. In SOM, on the contrary to Feedback

Hard and soft tissue surgical complications in dental implantology.

PubMed

Aziz, Shahid R

2015-05-01

This article discusses surgical complications associated with the placement of dental implants, specifically focusing on how they occur (etiology), as well as their management and prevention. Dental implant surgical complications can be classified into those of hard and soft tissues. In general, complications can be avoided with thorough preoperative treatment planning and proper surgical technique. Copyright © 2015 Elsevier Inc. All rights reserved.

Modified classification and single-stage microsurgical repair of posttraumatic infected massive bone defects in lower extremities.

PubMed

Yang, Yun-fa; Xu, Zhong-he; Zhang, Guang-ming; Wang, Jian-wei; Hu, Si-wang; Hou, Zhi-qi; Xu, Da-chuan

2013-11-01

Posttraumatic infected massive bone defects in lower extremities are difficult to repair because they frequently exhibit massive bone and/or soft tissue defects, serious bone infection, and excessive scar proliferation. This study aimed to determine whether these defects could be classified and repaired at a single stage. A total of 51 cases of posttraumatic infected massive bone defect in lower extremity were included in this study. They were classified into four types on the basis of the conditions of the bone defects, soft tissue defects, and injured limb length, including Type A (without soft tissue defects), Type B (with soft tissue defects of 10 × 20 cm or less), Type C (with soft tissue defects of 10 × 20 cm or more), and Type D (with the limb shortening of 3 cm or more). Four types of single-stage microsurgical repair protocols were planned accordingly and implemented respectively. These protocols included the following: Protocol A, where vascularized fibular graft was implemented for Type A; Protocol B, where vascularized fibular osteoseptocutaneous graft was implemented for Type B; Protocol C, where vascularized fibular graft and anterior lateral thigh flap were used for Type C; and Protocol D, where limb lengthening and Protocols A, B, or C were used for Type D. There were 12, 33, 4, and 2 cases of Types A, B, C, and D, respectively, according to this classification. During the surgery, three cases of planned Protocol B had to be shifted into Protocol C; however, all microsurgical repairs were completed. With reference to Johner-Wruhs evaluation method, the total percentage of excellent and good results was 82.35% after 6 to 41 months of follow-up. It was concluded that posttraumatic massive bone defects could be accurately classified into four types on the basis of the conditions of bone defects, soft tissue coverage, and injured limb length, and successfully repaired with the single-stage repair protocols after thorough debridement. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Association between antinuclear antibody titers and connective tissue diseases in a Rheumatology Department.

PubMed

Menor Almagro, Raúl; Rodríguez Gutiérrez, Juan Francisco; Martín-Martínez, María Auxiliadora; Rodríguez Valls, María José; Aranda Valera, Concepción; de la Iglesia Salgado, José Luís

To determine the dilution titles at antinuclear antibodies (ANA) by indirect immunofluorescence observed in cell substrate HEp-2 and its association with the diagnosis of systemic connective tissue disease in ANA test requested by a Rheumatology Unit. Samples of patients attended for the first time in the rheumatology unit, without prior ANA test, between January 2010 and December 2012 were selected. The dilution titers, immunofluorescence patterns and antigen specificity were recorded. In January 2015 the diagnosis of the patients were evaluated and classified in systemic disease connective tissue (systemic lupus erythematosus, Sjögren's syndrome, systemic sclerosis, undifferentiated connective, antiphospholipid syndrome, mixed connective tissue and inflammatory myophaty) or not systemic disease connective tissue. A total of 1282 ANA tests requested by the Rheumatology Unit in subjects without previous study, 293 were positive, predominance of women (81.9%). Patients with systemic connective tissue disease were recorded 105, and 188 without systemic connective tissue disease. For 1/640 dilutions the positive predictive value in the connective was 73.3% compared to 26.6% of non-connective, and for values ≥1/1,280 85% versus 15% respectively. When performing the multivariate analysis we observed a positive association between 1/320 dilution OR 3.069 (95% CI: 1.237-7.614; P=.016), 1/640 OR 12.570 (95% CI: 3.659-43.187; P=.000) and ≥1/1,280 OR 42.136 (95% CI: 8.604-206.345; P=.000). These results show association titles dilution ≥1/320 in ANA's first test requested by a Rheumatology Unit with patients with systemic connective tissue disease. The VPP in these patients was higher than previous studies requested by other medical specialties. This may indicate the importance of application of the test in a targeted way. Copyright © 2016 Elsevier España, S.L.U. and Sociedad Española de Reumatología y Colegio Mexicano de Reumatología. All rights reserved.

A survey of supervised machine learning models for mobile-phone based pathogen identification and classification

NASA Astrophysics Data System (ADS)

Ceylan Koydemir, Hatice; Feng, Steve; Liang, Kyle; Nadkarni, Rohan; Tseng, Derek; Benien, Parul; Ozcan, Aydogan

2017-03-01

Giardia lamblia causes a disease known as giardiasis, which results in diarrhea, abdominal cramps, and bloating. Although conventional pathogen detection methods used in water analysis laboratories offer high sensitivity and specificity, they are time consuming, and need experts to operate bulky equipment and analyze the samples. Here we present a field-portable and cost-effective smartphone-based waterborne pathogen detection platform that can automatically classify Giardia cysts using machine learning. Our platform enables the detection and quantification of Giardia cysts in one hour, including sample collection, labeling, filtration, and automated counting steps. We evaluated the performance of three prototypes using Giardia-spiked water samples from different sources (e.g., reagent-grade, tap, non-potable, and pond water samples). We populated a training database with >30,000 cysts and estimated our detection sensitivity and specificity using 20 different classifier models, including decision trees, nearest neighbor classifiers, support vector machines (SVMs), and ensemble classifiers, and compared their speed of training and classification, as well as predicted accuracies. Among them, cubic SVM, medium Gaussian SVM, and bagged-trees were the most promising classifier types with accuracies of 94.1%, 94.2%, and 95%, respectively; we selected the latter as our preferred classifier for the detection and enumeration of Giardia cysts that are imaged using our mobile-phone fluorescence microscope. Without the need for any experts or microbiologists, this field-portable pathogen detection platform can present a useful tool for water quality monitoring in resource-limited-settings.

Classification of THz pulse signals using two-dimensional cross-correlation feature extraction and non-linear classifiers.

PubMed

Siuly; Yin, Xiaoxia; Hadjiloucas, Sillas; Zhang, Yanchun

2016-04-01

This work provides a performance comparison of four different machine learning classifiers: multinomial logistic regression with ridge estimators (MLR) classifier, k-nearest neighbours (KNN), support vector machine (SVM) and naïve Bayes (NB) as applied to terahertz (THz) transient time domain sequences associated with pixelated images of different powder samples. The six substances considered, although have similar optical properties, their complex insertion loss at the THz part of the spectrum is significantly different because of differences in both their frequency dependent THz extinction coefficient as well as differences in their refractive index and scattering properties. As scattering can be unquantifiable in many spectroscopic experiments, classification solely on differences in complex insertion loss can be inconclusive. The problem is addressed using two-dimensional (2-D) cross-correlations between background and sample interferograms, these ensure good noise suppression of the datasets and provide a range of statistical features that are subsequently used as inputs to the above classifiers. A cross-validation procedure is adopted to assess the performance of the classifiers. Firstly the measurements related to samples that had thicknesses of 2mm were classified, then samples at thicknesses of 4mm, and after that 3mm were classified and the success rate and consistency of each classifier was recorded. In addition, mixtures having thicknesses of 2 and 4mm as well as mixtures of 2, 3 and 4mm were presented simultaneously to all classifiers. This approach provided further cross-validation of the classification consistency of each algorithm. The results confirm the superiority in classification accuracy and robustness of the MLR (least accuracy 88.24%) and KNN (least accuracy 90.19%) algorithms which consistently outperformed the SVM (least accuracy 74.51%) and NB (least accuracy 56.86%) classifiers for the same number of feature vectors across all studies. The work establishes a general methodology for assessing the performance of other hyperspectral dataset classifiers on the basis of 2-D cross-correlations in far-infrared spectroscopy or other parts of the electromagnetic spectrum. It also advances the wider proliferation of automated THz imaging systems across new application areas e.g., biomedical imaging, industrial processing and quality control where interpretation of hyperspectral images is still under development. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Active Self-Paced Learning for Cost-Effective and Progressive Face Identification.

PubMed

Lin, Liang; Wang, Keze; Meng, Deyu; Zuo, Wangmeng; Zhang, Lei

2018-01-01

This paper aims to develop a novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of different individuals. By naturally combining two recently rising techniques: active learning (AL) and self-paced learning (SPL), our framework is capable of automatically annotating new instances and incorporating them into training under weak expert recertification. We first initialize the classifier using a few annotated samples for each individual, and extract image features using the convolutional neural nets. Then, a number of candidates are selected from the unannotated samples for classifier updating, in which we apply the current classifiers ranking the samples by the prediction confidence. In particular, our approach utilizes the high-confidence and low-confidence samples in the self-paced and the active user-query way, respectively. The neural nets are later fine-tuned based on the updated classifiers. Such heuristic implementation is formulated as solving a concise active SPL optimization problem, which also advances the SPL development by supplementing a rational dynamic curriculum constraint. The new model finely accords with the "instructor-student-collaborative" learning mode in human education. The advantages of this proposed framework are two-folds: i) The required number of annotated samples is significantly decreased while the comparable performance is guaranteed. A dramatic reduction of user effort is also achieved over other state-of-the-art active learning techniques. ii) The mixture of SPL and AL effectively improves not only the classifier accuracy compared to existing AL/SPL methods but also the robustness against noisy data. We evaluate our framework on two challenging datasets, which include hundreds of persons under diverse conditions, and demonstrate very promising results. Please find the code of this project at: http://hcp.sysu.edu.cn/projects/aspl/.

Classification and identification of molecules through factor analysis method based on terahertz spectroscopy

NASA Astrophysics Data System (ADS)

Huang, Jianglou; Liu, Jinsong; Wang, Kejia; Yang, Zhengang; Liu, Xiaming

2018-06-01

By means of factor analysis approach, a method of molecule classification is built based on the measured terahertz absorption spectra of the molecules. A data matrix can be obtained by sampling the absorption spectra at different frequency points. The data matrix is then decomposed into the product of two matrices: a weight matrix and a characteristic matrix. By using the K-means clustering to deal with the weight matrix, these molecules can be classified. A group of samples (spirobenzopyran, indole, styrene derivatives and inorganic salts) has been prepared, and measured via a terahertz time-domain spectrometer. These samples are classified with 75% accuracy compared to that directly classified via their molecular formulas.

Quantification of tumor morphology via 3D histology: application to oral cavity cancers

NASA Astrophysics Data System (ADS)

Doyle, Scott; Brandwein-Gensler, Margaret; Tomaszewski, John

2016-03-01

Traditional histopathology quantifies disease through the study of glass slides, i.e. two-dimensional samples that are representative of the overall process. We hypothesize that 3D reconstruction can enhance our understanding of histopathologic interpretations. To test this hypothesis, we perform a pilot study of the risk model for oral cavity cancer (OCC), which stratifies patients into low-, intermediate-, and high-risk for locoregional disease-free survival. Classification is based on study of hematoxylin and eosin (H and E) stained tissues sampled from the resection specimens. In this model, the Worst Pattern of Invasion (WPOI) is assessed, representing specific architectural features at the interface between cancer and non-cancer tissue. Currently, assessment of WPOI is based on 2D sections of tissue, representing complex 3D structures of tumor growth. We believe that by reconstructing a 3D model of tumor growth and quantifying the tumor-host interface, we can obtain important diagnostic information that is difficult to assess in 2D. Therefore, we introduce a pilot study framework for visualizing tissue architecture and morphology in 3D from serial sections of histopathology. This framework can be used to enhance predictive models for diseases where severity is determined by 3D biological structure. In this work we utilize serial H and E-stained OCC resections obtained from 7 patients exhibiting WPOI-3 (low risk of recurrence) through WPOI-5 (high risk of recurrence). A supervised classifier automatically generates a map of tumor regions on each slide, which are then co-registered using an elastic deformation algorithm. A smooth 3D model of the tumor region is generated from the registered maps, which is suitable for quantitative tumor interface morphology feature extraction. We report our preliminary models created with this system and suggest further enhancements to traditional histology scoring mechanisms that take spatial architecture into consideration.

Quantitative photoacoustic assessment of ex-vivo lymph nodes of colorectal cancer patients

NASA Astrophysics Data System (ADS)

Sampathkumar, Ashwin; Mamou, Jonathan; Saegusa-Beercroft, Emi; Chitnis, Parag V.; Machi, Junji; Feleppa, Ernest J.

2015-03-01

Staging of cancers and selection of appropriate treatment requires histological examination of multiple dissected lymph nodes (LNs) per patient, so that a staggering number of nodes require histopathological examination, and the finite resources of pathology facilities create a severe processing bottleneck. Histologically examining the entire 3D volume of every dissected node is not feasible, and therefore, only the central region of each node is examined histologically, which results in severe sampling limitations. In this work, we assess the feasibility of using quantitative photoacoustics (QPA) to overcome the limitations imposed by current procedures and eliminate the resulting under sampling in node assessments. QPA is emerging as a new hybrid modality that assesses tissue properties and classifies tissue type based on multiple estimates derived from spectrum analysis of photoacoustic (PA) radiofrequency (RF) data and from statistical analysis of envelope-signal data derived from the RF signals. Our study seeks to use QPA to distinguish cancerous from non-cancerous regions of dissected LNs and hence serve as a reliable means of imaging and detecting small but clinically significant cancerous foci that would be missed by current methods. Dissected lymph nodes were placed in a water bath and PA signals were generated using a wavelength-tunable (680-950 nm) laser. A 26-MHz, f-2 transducer was used to sense the PA signals. We present an overview of our experimental setup; provide a statistical analysis of multi-wavelength classification parameters (mid-band fit, slope, intercept) obtained from the PA signal spectrum generated in the LNs; and compare QPA performance with our established quantitative ultrasound (QUS) techniques in distinguishing metastatic from non-cancerous tissue in dissected LNs. QPA-QUS methods offer a novel general means of tissue typing and evaluation in a broad range of disease-assessment applications, e.g., cardiac, intravascular, musculoskeletal, endocrine-gland, etc.

Classifier performance prediction for computer-aided diagnosis using a limited dataset.

PubMed

Sahiner, Berkman; Chan, Heang-Ping; Hadjiiski, Lubomir

2008-04-01

In a practical classifier design problem, the true population is generally unknown and the available sample is finite-sized. A common approach is to use a resampling technique to estimate the performance of the classifier that will be trained with the available sample. We conducted a Monte Carlo simulation study to compare the ability of the different resampling techniques in training the classifier and predicting its performance under the constraint of a finite-sized sample. The true population for the two classes was assumed to be multivariate normal distributions with known covariance matrices. Finite sets of sample vectors were drawn from the population. The true performance of the classifier is defined as the area under the receiver operating characteristic curve (AUC) when the classifier designed with the specific sample is applied to the true population. We investigated methods based on the Fukunaga-Hayes and the leave-one-out techniques, as well as three different types of bootstrap methods, namely, the ordinary, 0.632, and 0.632+ bootstrap. The Fisher's linear discriminant analysis was used as the classifier. The dimensionality of the feature space was varied from 3 to 15. The sample size n2 from the positive class was varied between 25 and 60, while the number of cases from the negative class was either equal to n2 or 3n2. Each experiment was performed with an independent dataset randomly drawn from the true population. Using a total of 1000 experiments for each simulation condition, we compared the bias, the variance, and the root-mean-squared error (RMSE) of the AUC estimated using the different resampling techniques relative to the true AUC (obtained from training on a finite dataset and testing on the population). Our results indicated that, under the study conditions, there can be a large difference in the RMSE obtained using different resampling methods, especially when the feature space dimensionality is relatively large and the sample size is small. Under this type of conditions, the 0.632 and 0.632+ bootstrap methods have the lowest RMSE, indicating that the difference between the estimated and the true performances obtained using the 0.632 and 0.632+ bootstrap will be statistically smaller than those obtained using the other three resampling methods. Of the three bootstrap methods, the 0.632+ bootstrap provides the lowest bias. Although this investigation is performed under some specific conditions, it reveals important trends for the problem of classifier performance prediction under the constraint of a limited dataset.

Retrospective MicroRNA Sequencing: Complementary DNA Library Preparation Protocol Using Formalin-fixed Paraffin-embedded RNA Specimens.

PubMed

Loudig, Olivier; Liu, Christina; Rohan, Thomas; Ben-Dov, Iddo Z

2018-05-05

-Archived, clinically classified formalin-fixed paraffin-embedded (FFPE) tissues can provide nucleic acids for retrospective molecular studies of cancer development. By using non-invasive or pre-malignant lesions from patients who later develop invasive disease, gene expression analyses may help identify early molecular alterations that predispose to cancer risk. It has been well described that nucleic acids recovered from FFPE tissues have undergone severe physical damage and chemical modifications, which make their analysis difficult and generally requires adapted assays. MicroRNAs (miRNAs), however, which represent a small class of RNA molecules spanning only up to ~18-24 nucleotides, have been shown to withstand long-term storage and have been successfully analyzed in FFPE samples. Here we present a 3' barcoded complementary DNA (cDNA) library preparation protocol specifically optimized for the analysis of small RNAs extracted from archived tissues, which was recently demonstrated to be robust and highly reproducible when using archived clinical specimens stored for up to 35 years. This library preparation is well adapted to the multiplex analysis of compromised/degraded material where RNA samples (up to 18) are ligated with individual 3' barcoded adapters and then pooled together for subsequent enzymatic and biochemical preparations prior to analysis. All purifications are performed by polyacrylamide gel electrophoresis (PAGE), which allows size-specific selections and enrichments of barcoded small RNA species. This cDNA library preparation is well adapted to minute RNA inputs, as a pilot polymerase chain reaction (PCR) allows determination of a specific amplification cycle to produce optimal amounts of material for next-generation sequencing (NGS). This approach was optimized for the use of degraded FFPE RNA from specimens archived for up to 35 years and provides highly reproducible NGS data.

Results of elemental and stable isotopic measurements, and dietary composition of Arctic grayling (Thymallus arcticus) collected in 2000 and 2001 from the Fortymile River Watershed, Alaska

USGS Publications Warehouse

Crock, J.G.; Seal, R.R.; Gough, L.P.; Weber-Scannell, P.

2003-01-01

We report the results of the elemental and stable isotopic analyses, as well as the composition of stomach contents, of Arctic grayling (Thymallus arcticus), an ecologically important resident freshwater sport and subsistence fish in the Fortymile River Mining District of the Interior Highlands Ecoregion in eastern Alaska. These data are presented here as a data compilation with minimal interpretation or discussion. Further analyses of the data will be presented elsewhere. The study area has been mined for placer gold for over a century and is currently experiencing renewed mineral exploration activity. The results for the analysis of 40 inorganic elements are reported for grayling muscle (fillet) tissue, liver tissue, and stomach contents from 34 individuals caught at 11 sites within the watershed. The 11 sites were classified as occurring within the following lithologies: metavolcanic (7 sites), metasedimentary (3 sites), and granitic intrusion (1 site). This information (along with fish tissue stable isotope data) is critical in the assessment of the influence of regional lithology on the fish chemical composition, especially the trace metal content. We report the nitrogen, carbon, and sulfur stable isotope composition of muscle samples. Nitrogen isotopes appear homogeneous (d15N = 7.6 to 9.7 permil) whereas carbon and sulfur isotope compositions of the same samples span a range from d 13C = ?33.1 to ?25.8 permil, and d 34S = ?8.4 to 8.2 permil. Stomach content material was examined for the occurrence and frequency of macroinvertebrate composition and diversity in three individual fish. Results showed a high degree of diversity with 9 to 15 invertebrate taxa; both aquatic and terrestrial forms were represented.

Single-cell quantitative HER2 measurement identifies heterogeneity and distinct subgroups within traditionally defined HER2-positive patients.

PubMed

Onsum, Matthew D; Geretti, Elena; Paragas, Violette; Kudla, Arthur J; Moulis, Sharon P; Luus, Lia; Wickham, Thomas J; McDonagh, Charlotte F; MacBeath, Gavin; Hendriks, Bart S

2013-11-01

Human epidermal growth factor receptor 2 (HER2) is an important biomarker for breast and gastric cancer prognosis and patient treatment decisions. HER2 positivity, as defined by IHC or fluorescent in situ hybridization testing, remains an imprecise predictor of patient response to HER2-targeted therapies. Challenges to correct HER2 assessment and patient stratification include intratumoral heterogeneity, lack of quantitative and/or objective assays, and differences between measuring HER2 amplification at the protein versus gene level. We developed a novel immunofluorescence method for quantitation of HER2 protein expression at the single-cell level on FFPE patient samples. Our assay uses automated image analysis to identify and classify tumor versus non-tumor cells, as well as quantitate the HER2 staining for each tumor cell. The HER2 staining level is converted to HER2 protein expression using a standard cell pellet array stained in parallel with the tissue sample. This approach allows assessment of HER2 expression and heterogeneity within a tissue section at the single-cell level. By using this assay, we identified distinct subgroups of HER2 heterogeneity within traditional definitions of HER2 positivity in both breast and gastric cancers. Quantitative assessment of intratumoral HER2 heterogeneity may offer an opportunity to improve the identification of patients likely to respond to HER2-targeted therapies. The broad applicability of the assay was demonstrated by measuring HER2 expression profiles on multiple tumor types, and on normal and diseased heart tissues. Copyright © 2013 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.

Comparison of the behavior of normal factor IX and the factor IX Bm variant Hilo in the prothrombin time test using tissue factors from bovine, human, and rabbit sources.

PubMed

Lefkowitz, J B; Monroe, D M; Kasper, C K; Roberts, H R

1993-07-01

A subset of hemophilia B patients have a prolonged bovine-brain prothrombin time. These CRM+ patients are classified as having hemophilia Bm. The prolongation of the prothrombin time has been reported only with bovine brain (referred to as ox brain in some literature) as the source of thromboplastin; prothrombin times determined with thromboplastin from rabbit brain or human brain are not reported to be prolonged. Factor IX from a hemophilia Bm patient (factor IX Hilo) was isolated. The activity of factor IX Hilo was compared to that of normal factor IX in prothrombin time assays when the thromboplastin source was of bovine, rabbit, or human origin. Factor IX, either normal or Hilo, prolonged a prothrombin time regardless of the tissue factor source. However, unless thromboplastin was from a bovine source, this prolongation required high concentrations of factor IX. Further, factor IX normal was as effective as factor IX Hilo in prolonging the prothrombin time when rabbit or human thromboplastin was used. With bovine thromboplastin, factor IX Hilo was significantly better than factor IX normal at prolonging the prothrombin time. The amount of prolongation was dependent on the amount of factor IX Hilo added. In addition, the prolongation was dependent on the concentration of factor X present in the sample. The prothrombin time changed as much as 20 seconds when the factor X concentration was varied from 50% to 150% to normal (fixed concentration of factor IX Hilo). These results demonstrate the difficulty of classifying the severity of a hemophilia Bm patient based on the bovine brain prothrombin time unless both the factor IX and factor X concentrations are known.

An automated technique for potential differentiation of ovarian mature teratomas from other benign tumours using neural networks classification of 2D ultrasound static images: a pilot study

NASA Astrophysics Data System (ADS)

Al-karawi, Dhurgham; Sayasneh, A.; Al-Assam, Hisham; Jassim, Sabah; Page, N.; Timmerman, D.; Bourne, T.; Du, Hongbo

2017-05-01

Ovarian cysts are a common pathology in women of all age groups. It is estimated that 5-10% of women have a surgical intervention to remove an ovarian cyst in their lifetime. Given this frequency rate, characterization of ovarian masses is essential for optimal management of patients. Patients with benign ovarian masses can be managed conservatively if they are asymptomatic. Mature teratomas are common benign ovarian cysts that occur, in most cases, in premenopausal women. These ovarian cysts can contain different types of human tissue including bone, cartilage, fat, hair, or other tissue. If they are causing no symptoms, they can be harmless and may not require surgery. Subjective assessment by ultrasound examiners has a high diagnostic accuracy when characterising mature teratomas from other types of tumours. The aim of this study is to develop a computerised technique with the potential to characterise mature teratomas and distinguish them from other types of benign ovarian tumours. Local Binary Pattern (LBP) was applied to extract texture features that are specific in distinguishing teratomas. Neural Networks (NN) was then used as a classifier for recognising mature teratomas. A pilot sample set of 130 B-mode static ovarian ultrasound images (41 mature teratomas tumours and 89 other types of benign tumours) was used to test the effectiveness of the proposed technique. Test results show an average accuracy rate of 99.4% with a sensitivity of 100%, specificity of 98.8% and positive predictive value of 98.9%. This study demonstrates that the NN and LBP techniques can accurately classify static 2D B-mode ultrasound images of benign ovarian masses into mature teratomas and other types of benign tumours.

Discrimination of trait-based characteristics by trace element bioaccumulation in riverine fishes

USGS Publications Warehouse

Short, T.M.; DeWeese, L.R.; Dubrovsky, N.M.

2008-01-01

Relations between tissue trace element concentrations and species traits were examined for 45 fish species to determine the extent to which trait-based characteristics accounted for relative differences among species in trace element bioaccumulation. Percentages of fish species correctly classified by discriminant analysis according to traits predicted by tissue trace element concentrations ranged from 72% to 87%. Tissue concentrations of copper, mercury, selenium, and zinc appeared to have the greatest overall influence on differentiating species according to trait characteristics. Discrimination of trait characteristics did not appear to be strongly influenced by local sources of trace elements in the streambed sediment. Bioaccumulation was greatest for those species classified as primarily detritivores, having relatively large adult body size, considered nonmigratory with respect to reproductive strategy, occurring mostly in large or variable size streams and rivers, preferring depositional areas within the stream channel, and preferring benthic rather than open-water habitats. Our findings provide evidence of the strong relationship between bioaccumulation of environmental trace elements and trait-based factors that influence contaminant exposure. ?? 2008 NRC.

Planning schistosomiasis control: investigation of alternative sampling strategies for Schistosoma mansoni to target mass drug administration of praziquantel in East Africa.

PubMed

Sturrock, Hugh J W; Gething, Pete W; Ashton, Ruth A; Kolaczinski, Jan H; Kabatereine, Narcis B; Brooker, Simon

2011-09-01

In schistosomiasis control, there is a need to geographically target treatment to populations at high risk of morbidity. This paper evaluates alternative sampling strategies for surveys of Schistosoma mansoni to target mass drug administration in Kenya and Ethiopia. Two main designs are considered: lot quality assurance sampling (LQAS) of children from all schools; and a geostatistical design that samples a subset of schools and uses semi-variogram analysis and spatial interpolation to predict prevalence in the remaining unsurveyed schools. Computerized simulations are used to investigate the performance of sampling strategies in correctly classifying schools according to treatment needs and their cost-effectiveness in identifying high prevalence schools. LQAS performs better than geostatistical sampling in correctly classifying schools, but at a cost with a higher cost per high prevalence school correctly classified. It is suggested that the optimal surveying strategy for S. mansoni needs to take into account the goals of the control programme and the financial and drug resources available.

Method And Apparatus For Examining A Tissue Using The Spectral Wing Emission Therefrom Induced By Visible To Infrared Photoexcitation.

DOEpatents

Alfano, Robert R.; Demos, Stavros G.; Zhang, Gang

2003-12-16

Method and an apparatus for examining a tissue using the spectral wing emission therefrom induced by visible to infrared photoexcitation. In one aspect, the method is used to characterize the condition of a tissue sample and comprises the steps of (a) photoexciting the tissue sample with substantially monochromatic light having a wavelength of at least 600 nm; and (b) using the resultant far red and near infrared spectral wing emission (SW) emitted from the tissue sample to characterize the condition of the tissue sample. In one embodiment, the substantially monochromatic photoexciting light is a continuous beam of light, and the resultant steady-state far red and near infrared SW emission from the tissue sample is used to characterize the condition of the tissue sample. In another embodiment, the substantially monochromatic photoexciting light is a light pulse, and the resultant time-resolved far red and near infrared SW emission emitted from the tissue sample is used to characterize the condition of the tissue sample. In still another embodiment, the substantially monochromatic photoexciting light is a polarized light pulse, and the parallel and perpendicular components of the resultant polarized time-resolved SW emission emitted from the tissue sample are used to characterize the condition of the tissue sample.

TSAPA: identification of tissue-specific alternative polyadenylation sites in plants.

PubMed

Ji, Guoli; Chen, Moliang; Ye, Wenbin; Zhu, Sheng; Ye, Congting; Su, Yaru; Peng, Haonan; Wu, Xiaohui

2018-06-15

Alternative polyadenylation (APA) is now emerging as a widespread mechanism modulated tissue-specifically, which highlights the need to define tissue-specific poly(A) sites for profiling APA dynamics across tissues. We have developed an R package called TSAPA based on the machine learning model for identifying tissue-specific poly(A) sites in plants. A feature space including more than 200 features was assembled to specifically characterize poly(A) sites in plants. The classification model in TSAPA can be customized by selecting desirable features or classifiers. TSAPA is also capable of predicting tissue-specific poly(A) sites in unannotated intergenic regions. TSAPA will be a valuable addition to the community for studying dynamics of APA in plants. https://github.com/BMILAB/TSAPA. Supplementary data are available at Bioinformatics online.

Mining big data sets of plankton images: a zero-shot learning approach to retrieve labels without training data

NASA Astrophysics Data System (ADS)

Orenstein, E. C.; Morgado, P. M.; Peacock, E.; Sosik, H. M.; Jaffe, J. S.

2016-02-01

Technological advances in instrumentation and computing have allowed oceanographers to develop imaging systems capable of collecting extremely large data sets. With the advent of in situ plankton imaging systems, scientists must now commonly deal with "big data" sets containing tens of millions of samples spanning hundreds of classes, making manual classification untenable. Automated annotation methods are now considered to be the bottleneck between collection and interpretation. Typically, such classifiers learn to approximate a function that predicts a predefined set of classes for which a considerable amount of labeled training data is available. The requirement that the training data span all the classes of concern is problematic for plankton imaging systems since they sample such diverse, rapidly changing populations. These data sets may contain relatively rare, sparsely distributed, taxa that will not have associated training data; a classifier trained on a limited set of classes will miss these samples. The computer vision community, leveraging advances in Convolutional Neural Networks (CNNs), has recently attempted to tackle such problems using "zero-shot" object categorization methods. Under a zero-shot framework, a classifier is trained to map samples onto a set of attributes rather than a class label. These attributes can include visual and non-visual information such as what an organism is made out of, where it is distributed globally, or how it reproduces. A second stage classifier is then used to extrapolate a class. In this work, we demonstrate a zero-shot classifier, implemented with a CNN, to retrieve out-of-training-set labels from images. This method is applied to data from two continuously imaging, moored instruments: the Scripps Plankton Camera System (SPCS) and the Imaging FlowCytobot (IFCB). Results from simulated deployment scenarios indicate zero-shot classifiers could be successful at recovering samples of rare taxa in image sets. This capability will allow ecologists to identify trends in the distribution of difficult to sample organisms in their data.

[MicroRNA Target Prediction Based on Support Vector Machine Ensemble Classification Algorithm of Under-sampling Technique].

PubMed

Chen, Zhiru; Hong, Wenxue

2016-02-01

Considering the low accuracy of prediction in the positive samples and poor overall classification effects caused by unbalanced sample data of MicroRNA (miRNA) target, we proposes a support vector machine (SVM)-integration of under-sampling and weight (IUSM) algorithm in this paper, an under-sampling based on the ensemble learning algorithm. The algorithm adopts SVM as learning algorithm and AdaBoost as integration framework, and embeds clustering-based under-sampling into the iterative process, aiming at reducing the degree of unbalanced distribution of positive and negative samples. Meanwhile, in the process of adaptive weight adjustment of the samples, the SVM-IUSM algorithm eliminates the abnormal ones in negative samples with robust sample weights smoothing mechanism so as to avoid over-learning. Finally, the prediction of miRNA target integrated classifier is achieved with the combination of multiple weak classifiers through the voting mechanism. The experiment revealed that the SVM-IUSW, compared with other algorithms on unbalanced dataset collection, could not only improve the accuracy of positive targets and the overall effect of classification, but also enhance the generalization ability of miRNA target classifier.

Physical Mechanisms of Soft Tissue Injury from Penetrating Ballistic Impact

DTIC Science & Technology

2012-11-30

SUPPLEMENTARY NOTES 14. ABSTRACT Most civilian nonfatal gunshot injuries and murders involve handguns . Gunshot wounds are often classified as being due to...high-velocity or low-velocity projectiles, e.g. rifle or handgun rounds. However, this is a historical distinction, and there is overlap in energy...that can be delivered to tissue by modern rifle and handgun rounds. Also, the same diameter (caliber) bullet can have different impact energies

Recurrence quantification as potential bio-markers for diagnosis of pre-cancer

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Sabyasachi; Pratiher, Sawon; Barman, Ritwik; Pratiher, Souvik; Pradhan, Asima; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2017-03-01

In this paper, the spectroscopy signals have been analyzed in recurrence plots (RP), and extract recurrence quantification analysis (RQA) parameters from the RP in order to classify the tissues into normal and different precancerous grades. Three RQA parameters have been quantified in order to extract the important features in the spectroscopy data. These features have been fed to different classifiers for classification. Simulation results validate the efficacy of the recurrence quantification as potential bio-markers for diagnosis of pre-cancer.

Large-Scale Propagation of Ultrasound in a 3-D Breast Model Based on High-Resolution MRI Data

PubMed Central

Tillett, Jason C.; Metlay, Leon A.; Waag, Robert C.

2010-01-01

A 40 × 35 × 25-mm3 specimen of human breast consisting mostly of fat and connective tissue was imaged using a 3-T magnetic resonance scanner. The resolutions in the image plane and in the orthogonal direction were 130 μm and 150 μm, respectively. Initial processing to prepare the data for segmentation consisted of contrast inversion, interpolation, and noise reduction. Noise reduction used a multilevel bidirectional median filter to preserve edges. The volume of data was segmented into regions of fat and connective tissue by using a combination of local and global thresholding. Local thresholding was performed to preserve fine detail, while global thresholding was performed to minimize the interclass variance between voxels classified as background and voxels classified as object. After smoothing the data to avoid aliasing artifacts, the segmented data volume was visualized using iso-surfaces. The isosurfaces were enhanced using transparency, lighting, shading, reflectance, and animation. Computations of pulse propagation through the model illustrate its utility for the study of ultrasound aberration. The results show the feasibility of using the described combination of methods to demonstrate tissue morphology in a form that provides insight about the way ultrasound beams are aberrated in three dimensions by tissue. PMID:20172794

Large-scale propagation of ultrasound in a 3-D breast model based on high-resolution MRI data.

PubMed

Salahura, Gheorghe; Tillett, Jason C; Metlay, Leon A; Waag, Robert C

2010-06-01

A 40 x 35 x 25-mm(3) specimen of human breast consisting mostly of fat and connective tissue was imaged using a 3-T magnetic resonance scanner. The resolutions in the image plane and in the orthogonal direction were 130 microm and 150 microm, respectively. Initial processing to prepare the data for segmentation consisted of contrast inversion, interpolation, and noise reduction. Noise reduction used a multilevel bidirectional median filter to preserve edges. The volume of data was segmented into regions of fat and connective tissue by using a combination of local and global thresholding. Local thresholding was performed to preserve fine detail, while global thresholding was performed to minimize the interclass variance between voxels classified as background and voxels classified as object. After smoothing the data to avoid aliasing artifacts, the segmented data volume was visualized using isosurfaces. The isosurfaces were enhanced using transparency, lighting, shading, reflectance, and animation. Computations of pulse propagation through the model illustrate its utility for the study of ultrasound aberration. The results show the feasibility of using the described combination of methods to demonstrate tissue morphology in a form that provides insight about the way ultrasound beams are aberrated in three dimensions by tissue.

Pediatric sports injuries: an age comparison of children versus adolescents.

PubMed

Stracciolini, Andrea; Casciano, Rebecca; Levey Friedman, Hilary; Meehan, William P; Micheli, Lyle J

2013-08-01

Significant knowledge deficits exist regarding sports injuries in the young child. Children continue to engage in physically demanding, organized sports to a greater extent despite the lack of physical readiness, predisposing themselves to injury. To evaluate sports injuries sustained in very young children (5-12 years) versus their older counterparts (13-17 years) with regard to the type and location of injuries, severity, and diagnosis. Cross-sectional study; Level of evidence, 3. A retrospective chart review was performed on a 5% random probability sample (final N = 2133) of 5- to 17-year-old patients treated for sports injuries in the Division of Sports Medicine at a large, academic pediatric medical center between 2000 and 2009. Using descriptive statistics, correlates of injuries by age group, injury type, and body area are shown. Five- to 12-year-old patients differed in key ways from older patients. Children in this category sustained injuries that were more often traumatic in nature and more commonly of the upper extremity. Older patients (13-17 years) were more likely to be treated for injuries to the chest, hip/pelvis, and spine. A greater proportion of the older children were treated for overuse injuries, as compared with their younger counterparts (54.4% vs. 49.2%, respectively), and a much larger proportion of these injuries were classified as soft tissue injuries as opposed to bony injuries (37.9% vs. 26.1%, respectively). Injury diagnosis differed between the 2 age groups. The 13- to 17-year age group sustained more anterior cruciate ligament injuries, meniscal tears, and spondylolysis, while younger children were diagnosed with fractures, including physeal fractures, apophysitis, and osteochondritis dissecans. The 5- to 12-year-old patients treated for spine injuries were disproportionately female (75.8%); most of these injuries were overuse (78.8%) and bony (60.6%); over one third of the youngest children were diagnosed with spondylolysis. Surgery was required in 40% of the injuries in the full sample. Sports injuries to children differ by age in injury diagnosis, type, and body area. Older children sustain a greater proportion of overuse injuries classified as soft tissue in nature. Children of all ages are sustaining significant sports injuries that require surgical intervention.

The decision tree classifier - Design and potential. [for Landsat-1 data

NASA Technical Reports Server (NTRS)

Hauska, H.; Swain, P. H.

1975-01-01

A new classifier has been developed for the computerized analysis of remote sensor data. The decision tree classifier is essentially a maximum likelihood classifier using multistage decision logic. It is characterized by the fact that an unknown sample can be classified into a class using one or several decision functions in a successive manner. The classifier is applied to the analysis of data sensed by Landsat-1 over Kenosha Pass, Colorado. The classifier is illustrated by a tree diagram which for processing purposes is encoded as a string of symbols such that there is a unique one-to-one relationship between string and decision tree.

Classifying murine glomerulonephritis using optical coherence tomography and optical coherence elastography.

PubMed

Liu, Chih-Hao; Du, Yong; Singh, Manmohan; Wu, Chen; Han, Zhaolong; Li, Jiasong; Chang, Anthony; Mohan, Chandra; Larin, Kirill V

2016-08-01

Acute glomerulonephritis caused by antiglomerular basement membrane marked by high mortality. The primary reason for this is delayed diagnosis via blood examination, urine analysis, tissue biopsy, or ultrasound and X-ray computed tomography imaging. Blood, urine, and tissue-based diagnoses can be time consuming, while ultrasound and CT imaging have relatively low spatial resolution, with reduced sensitivity. Optical coherence tomography is a noninvasive and high-resolution imaging technique that provides superior spatial resolution (micrometer scale) as compared to ultrasound and CT. Changes in tissue properties can be detected based on the optical metrics analyzed from the OCT signals, such as optical attenuation and speckle variance. Furthermore, OCT does not rely on ionizing radiation as with CT imaging. In addition to structural changes, the elasticity of the kidney can significantly change due to nephritis. In this work, OCT has been utilized to quantify the difference in tissue properties between healthy and nephritic murine kidneys. Although OCT imaging could identify the diseased tissue, its classification accuracy is clinically inadequate. By combining optical metrics with elasticity, the classification accuracy improves from 76% to 95%. These results show that OCT combined with OCE can be a powerful tool for identifying and classifying nephritis. Therefore, the OCT/OCE method could potentially be used as a minimally invasive tool for longitudinal studies during the progression and therapy of glomerulonephritis as well as complement and, perhaps, substitute highly invasive tissue biopsies. Elastic-wave propagation in mouse healthy and nephritic kidneys. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Automated gastric cancer diagnosis on H&E-stained sections; ltraining a classifier on a large scale with multiple instance machine learning

NASA Astrophysics Data System (ADS)

Cosatto, Eric; Laquerre, Pierre-Francois; Malon, Christopher; Graf, Hans-Peter; Saito, Akira; Kiyuna, Tomoharu; Marugame, Atsushi; Kamijo, Ken'ichi

2013-03-01

We present a system that detects cancer on slides of gastric tissue sections stained with hematoxylin and eosin (H&E). At its heart is a classi er trained using the semi-supervised multi-instance learning framework (MIL) where each tissue is represented by a set of regions-of-interest (ROI) and a single label. Such labels are readily obtained because pathologists diagnose each tissue independently as part of the normal clinical work ow. From a large dataset of over 26K gastric tissue sections from over 12K patients obtained from a clinical load spanning several months, we train a MIL classi er on a patient-level partition of the dataset (2/3 of the patients) and obtain a very high performance of 96% (AUC), tested on the remaining 1/3 never-seen before patients (over 8K tissues). We show this level of performance to match the more costly supervised approach where individual ROIs need to be labeled manually. The large amount of data used to train this system gives us con dence in its robustness and that it can be safely used in a clinical setting. We demonstrate how it can improve the clinical work ow when used for pre-screening or quality control. For pre-screening, the system can diagnose 47% of the tissues with a very low likelihood (< 1%) of missing cancers, thus halving the clinicians' caseload. For quality control, compared to random rechecking of 33% of the cases, the system achieves a three-fold increase in the likelihood of catching cancers missed by pathologists. The system is currently in regular use at independent pathology labs in Japan where it is used to double-check clinician's diagnoses. At the end of 2012 it will have analyzed over 80,000 slides of gastric and colorectal samples (200,000 tissues).

MO-F-CAMPUS-I-03: Tissue Equivalent Material Phantom to Test and Optimize Coherent Scatter Imaging for Tumor Classification

DOE Office of Scientific and Technical Information (OSTI.GOV)

Albanese, K; Morris, R; Lakshmanan, M

Purpose: To accurately model different breast geometries using a tissue equivalent phantom, and to classify these tissues in a coherent x-ray scatter imaging system. Methods: A breast phantom has been designed to assess the capability of coded aperture coherent x-ray scatter imaging system to classify different types of breast tissue (adipose, fibroglandular, tumor). The tissue-equivalent phantom was modeled as a hollow plastic cylinder containing multiple cylindrical and spherical inserts that can be positioned, rearranged, or removed to model different breast geometries. Each enclosure can be filled with a tissue-equivalent material and excised human tumors. In this study, beef and lard,more » placed inside 2-mm diameter plastic Nalgene containers, were used as surrogates for fibroglandular and adipose tissue, respectively. The phantom was imaged at 125 kVp, 40 mA for 10 seconds each with a 1-mm pencil beam. The raw data were reconstructed using a model-based reconstruction algorithm and yielded the location and form factor, or momentum transfer (q) spectrum of the materials that were imaged. The measured material form factors were then compared to the ground truth measurements acquired by x-ray diffraction (XRD) imaging. Results: The tissue equivalent phantom was found to accurately model different types of breast tissue by qualitatively comparing our measured form factors to those of adipose and fibroglandular tissue from literature. Our imaging system has been able to define the location and composition of the various materials in the phantom. Conclusion: This work introduces a new tissue equivalent phantom for testing and optimization of our coherent scatter imaging system for material classification. In future studies, the phantom will enable the use of a variety of materials including excised human tissue specimens in evaluating and optimizing our imaging system using pencil- and fan-beam geometries. United States Department of Homeland Security Duke University Medical Center - Department of Radiology Carl E Ravin Advanced Imaging Laboratories Duke University Medical Physics Graduate Program.« less

Use of Lot Quality Assurance Sampling to Ascertain Levels of Drug Resistant Tuberculosis in Western Kenya

PubMed Central

Cohen, Ted; Zignol, Matteo; Nyakan, Edwin; Hedt-Gauthier, Bethany L.; Gardner, Adrian; Kamle, Lydia; Injera, Wilfred; Carter, E. Jane

2016-01-01

Objective To classify the prevalence of multi-drug resistant tuberculosis (MDR-TB) in two different geographic settings in western Kenya using the Lot Quality Assurance Sampling (LQAS) methodology. Design The prevalence of drug resistance was classified among treatment-naïve smear positive TB patients in two settings, one rural and one urban. These regions were classified as having high or low prevalence of MDR-TB according to a static, two-way LQAS sampling plan selected to classify high resistance regions at greater than 5% resistance and low resistance regions at less than 1% resistance. Results This study classified both the urban and rural settings as having low levels of TB drug resistance. Out of the 105 patients screened in each setting, two patients were diagnosed with MDR-TB in the urban setting and one patient was diagnosed with MDR-TB in the rural setting. An additional 27 patients were diagnosed with a variety of mono- and poly- resistant strains. Conclusion Further drug resistance surveillance using LQAS may help identify the levels and geographical distribution of drug resistance in Kenya and may have applications in other countries in the African Region facing similar resource constraints. PMID:27167381

Use of Lot Quality Assurance Sampling to Ascertain Levels of Drug Resistant Tuberculosis in Western Kenya.

PubMed

Jezmir, Julia; Cohen, Ted; Zignol, Matteo; Nyakan, Edwin; Hedt-Gauthier, Bethany L; Gardner, Adrian; Kamle, Lydia; Injera, Wilfred; Carter, E Jane

2016-01-01

To classify the prevalence of multi-drug resistant tuberculosis (MDR-TB) in two different geographic settings in western Kenya using the Lot Quality Assurance Sampling (LQAS) methodology. The prevalence of drug resistance was classified among treatment-naïve smear positive TB patients in two settings, one rural and one urban. These regions were classified as having high or low prevalence of MDR-TB according to a static, two-way LQAS sampling plan selected to classify high resistance regions at greater than 5% resistance and low resistance regions at less than 1% resistance. This study classified both the urban and rural settings as having low levels of TB drug resistance. Out of the 105 patients screened in each setting, two patients were diagnosed with MDR-TB in the urban setting and one patient was diagnosed with MDR-TB in the rural setting. An additional 27 patients were diagnosed with a variety of mono- and poly- resistant strains. Further drug resistance surveillance using LQAS may help identify the levels and geographical distribution of drug resistance in Kenya and may have applications in other countries in the African Region facing similar resource constraints.

AUTOCLASSIFICATION OF THE VARIABLE 3XMM SOURCES USING THE RANDOM FOREST MACHINE LEARNING ALGORITHM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Farrell, Sean A.; Murphy, Tara; Lo, Kitty K., E-mail: s.farrell@physics.usyd.edu.au

In the current era of large surveys and massive data sets, autoclassification of astrophysical sources using intelligent algorithms is becoming increasingly important. In this paper we present the catalog of variable sources in the Third XMM-Newton Serendipitous Source catalog (3XMM) autoclassified using the Random Forest machine learning algorithm. We used a sample of manually classified variable sources from the second data release of the XMM-Newton catalogs (2XMMi-DR2) to train the classifier, obtaining an accuracy of ∼92%. We also evaluated the effectiveness of identifying spurious detections using a sample of spurious sources, achieving an accuracy of ∼95%. Manual investigation of amore » random sample of classified sources confirmed these accuracy levels and showed that the Random Forest machine learning algorithm is highly effective at automatically classifying 3XMM sources. Here we present the catalog of classified 3XMM variable sources. We also present three previously unidentified unusual sources that were flagged as outlier sources by the algorithm: a new candidate supergiant fast X-ray transient, a 400 s X-ray pulsar, and an eclipsing 5 hr binary system coincident with a known Cepheid.« less

Gene expression of leptin, resistin, and adiponectin in the white adipose tissue of obese patients with non-alcoholic fatty liver disease and insulin resistance.

PubMed

Baranova, Ancha; Gowder, Shobha J; Schlauch, Karen; Elariny, Hazem; Collantes, Rochelle; Afendy, Arian; Ong, Janus P; Goodman, Zachary; Chandhoke, Vikas; Younossi, Zobair M

2006-09-01

Adipose tissue is an active endocrine organ that secretes a variety of metabolically important substances including adipokines. These factors affect insulin sensitivity and may represent a link between obesity, insulin resistance, type 2 diabetes (DM), and nonalcoholic fatty liver disease (NAFLD). This study uses real-time polymerase chain reaction (PCR) quantification of mRNAs encoding adiponectin, leptin, and resistin on snap-frozen samples of intra-abdominal adipose tissue of morbidly obese patients undergoing bariatric surgery. Morbidly obese patients undergoing bariatric surgery were studied. Patients were classified into two groups: Group A (with insulin resistance) (N=11; glucose 149.84 +/- 40.56 mg/dL; serum insulin 8.28 +/- 3.52 microU/mL), and Group B (without insulin resistance) (N=10; glucose 102.2 +/- 8.43 mg/dL; serum insulin 3.431 +/- 1.162 microU/mL). Adiponectin mRNA in intra-abdominal adipose tissue and serum adiponectin levels were significantly lower in Group A compared to Group B patients (P<0.016 and P<0.03, respectively). Although serum resistin was higher in Group A than in Group B patients (P<0.005), resistin gene expression was not different between the two groups. Finally, for leptin, neither serum level nor gene expression was different between the two groups. Serum adiponectin level was the only predictor of nonalcoholic steatohepatitis (NASH) in this study (P=0.024). Obese patients with insulin resistance have decreased serum adiponectin and increased serum resistin. Additionally, adiponectin gene expression is also decreased in the adipose tissue of these patients. This low level of adiponectin expression may predispose patients to the progressive form of NAFLD or NASH.

Late night activity regarding stroke codes: LuNAR strokes.

PubMed

Tafreshi, Gilda; Raman, Rema; Ernstrom, Karin; Rapp, Karen; Meyer, Brett C

2012-08-01

There is diurnal variation for cardiac arrest and sudden cardiac death. Stroke may show a similar pattern. We assessed whether strokes presenting during a particular time of day or night are more likely of vascular etiology. To compare emergency department stroke codes arriving between 22:00 and 8:00 hours (LuNAR strokes) vs. others (n-LuNAR strokes). The purpose was to determine if late night strokes are more likely to be true strokes or warrant acute tissue plasminogen activator evaluations. We reviewed prospectively collected cases in the University of California, San Diego Stroke Team database gathered over a four-year period. Stroke codes at six emergency departments were classified based on arrival time. Those arriving between 22:00 and 8:00 hours were classified as LuNAR stroke codes, the remainder were classified as 'n-LuNAR'. Patients were further classified as intracerebral hemorrhage, acute ischemic stroke not receiving tissue plasminogen activator, acute ischemic stroke receiving tissue plasminogen activator, transient ischemic attack, and nonstroke. Categorical outcomes were compared using Fisher's Exact test. Continuous outcomes were compared using Wilcoxon's Rank-sum test. A total of 1607 patients were included in our study, of which, 299 (19%) were LuNAR code strokes. The overall median NIHSS was five, higher in the LuNAR group (n-LuNAR 5, LuNAR 7; P=0·022). There was no overall differences in patient diagnoses between LuNAR and n-LuNAR strokes (P=0·169) or diagnosis of acute ischemic stroke receiving tissue plasminogen activator (n-LuNAR 191 (14·6%), LuNAR 42 (14·0%); P=0·86). Mean arrival to computed tomography scan time was longer during LuNAR hours (n-LuNAR 54·9±76·3 min, LuNAR 62·5±87·7 min; P=0·027). There was no significant difference in 90-day mortality (n-LuNAR 15·0%, LuNAR 13·2%; P=0·45). Our stroke center experience showed no difference in diagnosis of acute ischemic stroke between day and night stroke codes. This similarity was further supported in similar rates of tissue plasminogen activator administration. Late night strokes may warrant a more rapid stroke specialist evaluation due to the longer time elapsed from symptom onset and the longer time to computed tomography scan. © 2011 The Authors. International Journal of Stroke © 2011 World Stroke Organization.

Stackable differential mobility analyzer for aerosol measurement

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cheng, Meng-Dawn; Chen, Da-Ren

2007-05-08

A multi-stage differential mobility analyzer (MDMA) for aerosol measurements includes a first electrode or grid including at least one inlet or injection slit for receiving an aerosol including charged particles for analysis. A second electrode or grid is spaced apart from the first electrode. The second electrode has at least one sampling outlet disposed at a plurality different distances along its length. A volume between the first and the second electrode or grid between the inlet or injection slit and a distal one of the plurality of sampling outlets forms a classifying region, the first and second electrodes for chargingmore » to suitable potentials to create an electric field within the classifying region. At least one inlet or injection slit in the second electrode receives a sheath gas flow into an upstream end of the classifying region, wherein each sampling outlet functions as an independent DMA stage and classifies different size ranges of charged particles based on electric mobility simultaneously.« less

Predicting Classifier Performance with Limited Training Data: Applications to Computer-Aided Diagnosis in Breast and Prostate Cancer

PubMed Central

Basavanhally, Ajay; Viswanath, Satish; Madabhushi, Anant

2015-01-01

Clinical trials increasingly employ medical imaging data in conjunction with supervised classifiers, where the latter require large amounts of training data to accurately model the system. Yet, a classifier selected at the start of the trial based on smaller and more accessible datasets may yield inaccurate and unstable classification performance. In this paper, we aim to address two common concerns in classifier selection for clinical trials: (1) predicting expected classifier performance for large datasets based on error rates calculated from smaller datasets and (2) the selection of appropriate classifiers based on expected performance for larger datasets. We present a framework for comparative evaluation of classifiers using only limited amounts of training data by using random repeated sampling (RRS) in conjunction with a cross-validation sampling strategy. Extrapolated error rates are subsequently validated via comparison with leave-one-out cross-validation performed on a larger dataset. The ability to predict error rates as dataset size increases is demonstrated on both synthetic data as well as three different computational imaging tasks: detecting cancerous image regions in prostate histopathology, differentiating high and low grade cancer in breast histopathology, and detecting cancerous metavoxels in prostate magnetic resonance spectroscopy. For each task, the relationships between 3 distinct classifiers (k-nearest neighbor, naive Bayes, Support Vector Machine) are explored. Further quantitative evaluation in terms of interquartile range (IQR) suggests that our approach consistently yields error rates with lower variability (mean IQRs of 0.0070, 0.0127, and 0.0140) than a traditional RRS approach (mean IQRs of 0.0297, 0.0779, and 0.305) that does not employ cross-validation sampling for all three datasets. PMID:25993029

Random forests ensemble classifier trained with data resampling strategy to improve cardiac arrhythmia diagnosis.

PubMed

Ozçift, Akin

2011-05-01

Supervised classification algorithms are commonly used in the designing of computer-aided diagnosis systems. In this study, we present a resampling strategy based Random Forests (RF) ensemble classifier to improve diagnosis of cardiac arrhythmia. Random forests is an ensemble classifier that consists of many decision trees and outputs the class that is the mode of the class's output by individual trees. In this way, an RF ensemble classifier performs better than a single tree from classification performance point of view. In general, multiclass datasets having unbalanced distribution of sample sizes are difficult to analyze in terms of class discrimination. Cardiac arrhythmia is such a dataset that has multiple classes with small sample sizes and it is therefore adequate to test our resampling based training strategy. The dataset contains 452 samples in fourteen types of arrhythmias and eleven of these classes have sample sizes less than 15. Our diagnosis strategy consists of two parts: (i) a correlation based feature selection algorithm is used to select relevant features from cardiac arrhythmia dataset. (ii) RF machine learning algorithm is used to evaluate the performance of selected features with and without simple random sampling to evaluate the efficiency of proposed training strategy. The resultant accuracy of the classifier is found to be 90.0% and this is a quite high diagnosis performance for cardiac arrhythmia. Furthermore, three case studies, i.e., thyroid, cardiotocography and audiology, are used to benchmark the effectiveness of the proposed method. The results of experiments demonstrated the efficiency of random sampling strategy in training RF ensemble classification algorithm. Copyright © 2011 Elsevier Ltd. All rights reserved.

Multicolor microRNA FISH effectively differentiates tumor types

PubMed Central

Renwick, Neil; Cekan, Pavol; Masry, Paul A.; McGeary, Sean E.; Miller, Jason B.; Hafner, Markus; Li, Zhen; Mihailovic, Aleksandra; Morozov, Pavel; Brown, Miguel; Gogakos, Tasos; Mobin, Mehrpouya B.; Snorrason, Einar L.; Feilotter, Harriet E.; Zhang, Xiao; Perlis, Clifford S.; Wu, Hong; Suárez-Fariñas, Mayte; Feng, Huichen; Shuda, Masahiro; Moore, Patrick S.; Tron, Victor A.; Chang, Yuan; Tuschl, Thomas

2013-01-01

MicroRNAs (miRNAs) are excellent tumor biomarkers because of their cell-type specificity and abundance. However, many miRNA detection methods, such as real-time PCR, obliterate valuable visuospatial information in tissue samples. To enable miRNA visualization in formalin-fixed paraffin-embedded (FFPE) tissues, we developed multicolor miRNA FISH. As a proof of concept, we used this method to differentiate two skin tumors, basal cell carcinoma (BCC) and Merkel cell carcinoma (MCC), with overlapping histologic features but distinct cellular origins. Using sequencing-based miRNA profiling and discriminant analysis, we identified the tumor-specific miRNAs miR-205 and miR-375 in BCC and MCC, respectively. We addressed three major shortcomings in miRNA FISH, identifying optimal conditions for miRNA fixation and ribosomal RNA (rRNA) retention using model compounds and high-pressure liquid chromatography (HPLC) analyses, enhancing signal amplification and detection by increasing probe-hapten linker lengths, and improving probe specificity using shortened probes with minimal rRNA sequence complementarity. We validated our method on 4 BCC and 12 MCC tumors. Amplified miR-205 and miR-375 signals were normalized against directly detectable reference rRNA signals. Tumors were classified using predefined cutoff values, and all were correctly identified in blinded analysis. Our study establishes a reliable miRNA FISH technique for parallel visualization of differentially expressed miRNAs in FFPE tumor tissues. PMID:23728175

Long non-coding RNA biomarker for human laryngeal squamous cell carcinoma prognosis.

PubMed

Chen, Jingjing; Shen, Zhisen; Deng, Hongxia; Zhou, Wei; Liao, Qi; Mu, Ying

2018-05-15

Long non-coding RNAs (lncRNA) were discovered in tumors. The regulation of lncRNA in human laryngeal squamous cell carcinoma (LSCC) remains incomplete. Uncovering the potential of lncRNA to stratify the prognosis of LSCC and streamline the vast amount of clinical information will affect medical interventions. The surgical resected LSCC tissues, adjacent non-cancerous tissues (ANCT) and lymph node metastatic tissue (LNM) were collected from 76 patients for lncRNA AC008440.10 expression assay. The stages of LSCC and LNM were classified accordingly. We integrated the epigenetic information with enhanced CT imaging and pathological evaluations to predict the patients' survival by comprehensive statistical algorithms using equal weighting. Significant downregulation of lncRNA AC008440.10 was detected in LSCC tumor and metastatic lymph node in advanced stage of patient samples compared with those in early stage. The pattern of differentially expressed AC008440.10 displayed a clear trend that significantly related to tumor progression. The downregulation of lncRNA AC008440.10 correlates with increasing risk of metastasis, poor prognosis and patient survival. The potential for lncRNA AC008440.10 to be developed as a novel biomarker for stratification of the prognosis was especially promising when clinic parameters were hybridized with equal weight, and using a panel of complementary parameters yielded a more powerful predictability of LSCC prognosis than any single parameter individually. Copyright © 2017. Published by Elsevier B.V.

Prognostic impact of a compartment-specific angiogenic marker profile in patients with pancreatic cancer.

PubMed

Kahlert, Christoph; Fiala, Maria; Musso, Gabriel; Halama, Niels; Keim, Sophia; Mazzone, Massimiliano; Lasitschka, Felix; Pecqueux, Mathieu; Klupp, Fee; Schmidt, Thomas; Rahbari, Nuh; Schölch, Sebastian; Pilarsky, Christian; Ulrich, Alexis; Schneider, Martin; Weitz, Juergen; Koch, Moritz

2014-12-30

Pancreatic cancer consists of a heterogenous bulk of tumor cells and stroma cells which contribute to tumor progression by releasing angiogenic factors. Those factors can be detected as circulating serum factors. We performed a compartment-specific analysis of tumor-derived and stroma-derived angiogenic factors to identify biomarkers and molecular targets for the treatment of pancreatic cancer. Kryo-frozen tissue from primary ductal adenocarcinomas (n = 51) was laser-microdissected to isolate tumor and stroma tissue. Expression of 17 angiogenic factors (angiopoietin-2, follistatin, GCSF, HGF, interleukin-8, leptin, PDGF-BB, PECAM-1, VEGF, matrix metalloproteinase -1, -2, -3, -7, -9, -10, -12, and -13) was analyzed using a multiplex elisa assay for tissue-derived proteins and corresponding serum. Our study reveals a compartment-specific expression profile for several angiogenic factors and matrix metalloproteinases. ROC analysis of corresponding serum samples reveals MMP-7 and MMP-12 as strong classifiers for the diagnosis of patients with pancreatic cancer vs. healthy control donors. High expression of tumor-derived PDGF-BB and MMP-1 correlates with prolonged survival in univariate and multivariate analysis. In conclusion, a distinct expression patterns for angiogenic cytokines and MMPs in pancreatic cancer and surrounding stroma may implicate them as novel targets for cancer treatment. Tumor-derived PDGF-BB and MMP-1 are significant and independent prognostic markers for poor survival.

Multiclass classification of microarray data samples with a reduced number of genes

PubMed Central

2011-01-01

Background Multiclass classification of microarray data samples with a reduced number of genes is a rich and challenging problem in Bioinformatics research. The problem gets harder as the number of classes is increased. In addition, the performance of most classifiers is tightly linked to the effectiveness of mandatory gene selection methods. Critical to gene selection is the availability of estimates about the maximum number of genes that can be handled by any classification algorithm. Lack of such estimates may lead to either computationally demanding explorations of a search space with thousands of dimensions or classification models based on gene sets of unrestricted size. In the former case, unbiased but possibly overfitted classification models may arise. In the latter case, biased classification models unable to support statistically significant findings may be obtained. Results A novel bound on the maximum number of genes that can be handled by binary classifiers in binary mediated multiclass classification algorithms of microarray data samples is presented. The bound suggests that high-dimensional binary output domains might favor the existence of accurate and sparse binary mediated multiclass classifiers for microarray data samples. Conclusions A comprehensive experimental work shows that the bound is indeed useful to induce accurate and sparse multiclass classifiers for microarray data samples. PMID:21342522

Autofluoresence spectroscopy for in-vivo diagnosis of human oral carcinogenesis

NASA Astrophysics Data System (ADS)

Wang, Chih-Yu; Tsai, Tsuimin; Chen, Hsin-Ming; Kuo, Ying-Shiung; Chen, Chin-Tin; Chiang, Chung-Ping

2002-09-01

An in vivo study of human oral cancer diagnosis by using autofluorescence spectroscopy is presented. A Xenon-lamp with a motor-controlled monochromator was adopted as the excitation light source. We chose the excitation wavelength of 330 nm, and the spectral measurement range was from 340 nm to 601 nm. A Y-type fiber bundle was used to guide the excitation light, and collect the autofluorescence of samples. The emitted light was detected by a motor-controlled monochromator and a PMT. After measurement, the measured sites were sectioned and sent for histological examination. In total 15 normal sites, 30 OSF (oral submucosa fibrosis) sites, 26 EH (epithelial hyperkratosis) sites, 13 ED (epithelial dysplasia) sites, and 13 SCC (squamous cell carcinoma) sites were measured. The discriminant algorithm was established by partial-least squares (PLS) method with cross-validation technique. By extracting the first two t-scores of each sample and make scattering plot, we found that the samples of different cancerous stages were in grouped distinct locations, except that samples of ED and EH were mixed together. It means that this algorithm can be used to classify normal, premalignant, and malignant tissues. We conclude that autofluorescence spectroscopy may be useful for in vivo detection of early stage oral cancer.

Investigation of the "true" extraction recovery of analytes from multiple types of tissues and its impact on tissue bioanalysis using two model compounds.

PubMed

Yuan, Long; Ma, Li; Dillon, Lisa; Fancher, R Marcus; Sun, Huadong; Zhu, Mingshe; Lehman-McKeeman, Lois; Aubry, Anne-Françoise; Ji, Qin C

2016-11-16

LC-MS/MS has been widely applied to the quantitative analysis of tissue samples. However, one key remaining issue is that the extraction recovery of analyte from spiked tissue calibration standard and quality control samples (QCs) may not accurately represent the "true" recovery of analyte from incurred tissue samples. This may affect the accuracy of LC-MS/MS tissue bioanalysis. Here, we investigated whether the recovery determined using tissue QCs by LC-MS/MS can accurately represent the "true" recovery from incurred tissue samples using two model compounds: BMS-986104, a S1P 1 receptor modulator drug candidate, and its phosphate metabolite, BMS-986104-P. We first developed a novel acid and surfactant assisted protein precipitation method for the extraction of BMS-986104 and BMS-986104-P from rat tissues, and determined their recoveries using tissue QCs by LC-MS/MS. We then used radioactive incurred samples from rats dosed with 3 H-labeled BMS-986104 to determine the absolute total radioactivity recovery in six different tissues. The recoveries determined using tissue QCs and incurred samples matched with each other very well. The results demonstrated that, in this assay, tissue QCs accurately represented the incurred tissue samples to determine the "true" recovery, and LC-MS/MS assay was accurate for tissue bioanalysis. Another aspect we investigated is how the tissue QCs should be prepared to better represent the incurred tissue samples. We compared two different QC preparation methods (analyte spiked in tissue homogenates or in intact tissues) and demonstrated that the two methods had no significant difference when a good sample preparation was in place. The developed assay showed excellent accuracy and precision, and was successfully applied to the quantitative determination of BMS-986104 and BMS-986104-P in tissues in a rat toxicology study. Copyright © 2016 Elsevier B.V. All rights reserved.

Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding.

PubMed

Alegro, Maryana; Theofilas, Panagiotis; Nguy, Austin; Castruita, Patricia A; Seeley, William; Heinsen, Helmut; Ushizima, Daniela M; Grinberg, Lea T

2017-04-15

Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility. Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set. Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings. We compared our results to four open-access IF-based cell-counting tools available in the literature. Our method showed improved accuracy for all data samples. The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks. Copyright © 2017 Elsevier B.V. All rights reserved.

The pulmonary response to fiberglass dust. Report of the Committee on Environmental Health. American College of Chest Physicians.

PubMed

1976-02-01

Fiberglass inhalation seems to produce a minimal tissue response in the lungs, and the reaction is one of macrophagic mobilization and is characteristic of the pulmonary response to those nonfibrogenic dusts classified as nuisance dusts. In order to merit the designation of a nuisance dust, the pulmonary response must fulfill the following three requisites:31(p5) (1) The alveolar architecture must remain intact. (2) The stromal proliferation is minimal and consists mainly of reticulin. (3) The tissue reaction is potentially reversible. Inasmuch as the pulmonary reaction to the dusts of fiber glass fulfills all of these requirements, it should be classified as a nuisance dust. 31(p5) There is no evidence to indicate that inhaling fiber glass is associated with either permanent respiratory impairment or carcinogenesis; however, the final verdict as far as the latter is concerned must await the findings of long-term mortality studies.

Hybrid Radar Emitter Recognition Based on Rough k-Means Classifier and Relevance Vector Machine

PubMed Central

Yang, Zhutian; Wu, Zhilu; Yin, Zhendong; Quan, Taifan; Sun, Hongjian

2013-01-01

Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for recognizing radar emitter signals. In this paper, a hybrid recognition approach is presented that classifies radar emitter signals by exploiting the different separability of samples. The proposed approach comprises two steps, namely the primary signal recognition and the advanced signal recognition. In the former step, a novel rough k-means classifier, which comprises three regions, i.e., certain area, rough area and uncertain area, is proposed to cluster the samples of radar emitter signals. In the latter step, the samples within the rough boundary are used to train the relevance vector machine (RVM). Then RVM is used to recognize the samples in the uncertain area; therefore, the classification accuracy is improved. Simulation results show that, for recognizing radar emitter signals, the proposed hybrid recognition approach is more accurate, and presents lower computational complexity than traditional approaches. PMID:23344380

Identification of usual interstitial pneumonia pattern using RNA-Seq and machine learning: challenges and solutions.

PubMed

Choi, Yoonha; Liu, Tiffany Ting; Pankratz, Daniel G; Colby, Thomas V; Barth, Neil M; Lynch, David A; Walsh, P Sean; Raghu, Ganesh; Kennedy, Giulia C; Huang, Jing

2018-05-09

We developed a classifier using RNA sequencing data that identifies the usual interstitial pneumonia (UIP) pattern for the diagnosis of idiopathic pulmonary fibrosis. We addressed significant challenges, including limited sample size, biological and technical sample heterogeneity, and reagent and assay batch effects. We identified inter- and intra-patient heterogeneity, particularly within the non-UIP group. The models classified UIP on transbronchial biopsy samples with a receiver-operating characteristic area under the curve of ~ 0.9 in cross-validation. Using in silico mixed samples in training, we prospectively defined a decision boundary to optimize specificity at ≥85%. The penalized logistic regression model showed greater reproducibility across technical replicates and was chosen as the final model. The final model showed sensitivity of 70% and specificity of 88% in the test set. We demonstrated that the suggested methodologies appropriately addressed challenges of the sample size, disease heterogeneity and technical batch effects and developed a highly accurate and robust classifier leveraging RNA sequencing for the classification of UIP.

Molecular Characterization of Hypoderma SPP. in Domestic Ruminants from Turkey and Pakistan.

PubMed

Ahmed, Haroon; Simsek, Sami; Saki, Cem Ecmel; Kesik, Harun Kaya; Kilinc, Seyma Gunyakti

2017-08-01

The aim of this study was to determine the morphological and molecular characterization of Hypoderma spp. in cattle and yak from provinces in Turkey and Pakistan. In total, 78 Hypoderma larvae were collected from slaughtered animals in Turkey and Pakistan from October 2015 to January 2016. Thirty-eight of these 78 Hypoderma larvae were morphologically classified as third instar larvae (L3s) of Hypoderma bovis, 37 were classified as Hypoderma lineatum, and 3 were classified as suspected or unidentified. The restriction enzyme TaqI was used to differentiate the Hypoderma spp. by polymerase chain reaction (PCR)-restriction fragment length polymorphism (RFLP). According to the sequences and the PCR-RFLP results, all larval samples from cattle from Turkey were classified as H. bovis, except for 1 sample classified as H. lineatum. All Hypoderma larvae from Pakistan were classified as H. lineatum from cattle and as Hypoderma sinense from yak. This study provides the first molecular characterization of H. lineatum (cattle) and H. sinense (yak) in Pakistan based on PCR-RFLP and sequencing results.

Differences in microRNA expression during tumor development in the transition and peripheral zones of the prostate

PubMed Central

2013-01-01

Background The prostate is divided into three glandular zones, the peripheral zone (PZ), the transition zone (TZ), and the central zone. Most prostate tumors arise in the peripheral zone (70-75%) and in the transition zone (20-25%) while only 10% arise in the central zone. The aim of this study was to investigate if differences in miRNA expression could be a possible explanation for the difference in propensity of tumors in the zones of the prostate. Methods Patients with prostate cancer were included in the study if they had a tumor with Gleason grade 3 in the PZ, the TZ, or both (n=16). Normal prostate tissue was collected from men undergoing cystoprostatectomy (n=20). The expression of 667 unique miRNAs was investigated using TaqMan low density arrays for miRNAs. Student’s t-test was used in order to identify differentially expressed miRNAs, followed by hierarchical clustering and principal component analysis (PCA) to study the separation of the tissues. The ADtree algorithm was used to identify markers for classification of tissues and a cross-validation procedure was used to test the generality of the identified miRNA-based classifiers. Results The t-tests revealed that the major differences in miRNA expression are found between normal and malignant tissues. Hierarchical clustering and PCA based on differentially expressed miRNAs between normal and malignant tissues showed perfect separation between samples, while the corresponding analyses based on differentially expressed miRNAs between the two zones showed several misplaced samples. A classification and cross-validation procedure confirmed these results and several potential miRNA markers were identified. Conclusions The results of this study indicate that the major differences in the transcription program are those arising during tumor development, rather than during normal tissue development. In addition, tumors arising in the TZ have more unique differentially expressed miRNAs compared to the PZ. The results also indicate that separate miRNA expression signatures for diagnosis might be needed for tumors arising in the different zones. MicroRNA signatures that are specific for PZ and TZ tumors could also lead to more accurate prognoses, since tumors arising in the PZ tend to be more aggressive than tumors arising in the TZ. PMID:23890084

Morphology and growth of polarized tissues.

PubMed

Blanch-Mercader, C; Casademunt, J; Joanny, J F

2014-05-01

We study and classify the time-dependent morphologies of polarized tissues subject to anisotropic but spatially homogeneous growth. Extending previous studies, we model the tissue as a fluid, and discuss the interplay of the active stresses generated by the anisotropic cell division and three types of passive mechanical forces: viscous stresses, friction with the environment and tension at the tissue boundary. The morphology dynamics is formulated as a free-boundary problem, and conformal mapping techniques are used to solve the evolution numerically. We combine analytical and numerical results to elucidate how the different passive forces compete with the active stresses to shape the tissue in different temporal regimes and derive the corresponding scaling laws. We show that in general the aspect ratio of elongated tissues is non-monotonic in time, eventually recovering isotropic shapes in the presence of friction forces, which are asymptotically dominant.

High correlation of double Debye model parameters in skin cancer detection.

PubMed

Truong, Bao C Q; Tuan, H D; Fitzgerald, Anthony J; Wallace, Vincent P; Nguyen, H T

2014-01-01

The double Debye model can be used to capture the dielectric response of human skin in terahertz regime due to high water content in the tissue. The increased water proportion is widely considered as a biomarker of carcinogenesis, which gives rise of using this model in skin cancer detection. Therefore, the goal of this paper is to provide a specific analysis of the double Debye parameters in terms of non-melanoma skin cancer classification. Pearson correlation is applied to investigate the sensitivity of these parameters and their combinations to the variation in tumor percentage of skin samples. The most sensitive parameters are then assessed by using the receiver operating characteristic (ROC) plot to confirm their potential of classifying tumor from normal skin. Our positive outcomes support further steps to clinical application of terahertz imaging in skin cancer delineation.

A rare case of monozygotic iniodymic diprosopiasis in a German Holstein calf.

PubMed

Weber, Jim; Behn, Holger; Freick, Markus

2017-06-01

Craniofacial duplication abnormity is a rare phenomenon in buiatric practice. This report attends to a male German Holstein calf which could be classified as a diprosopic iniodymus. A fetus exhibiting a doubled face was delivered after fetotomy. To our knowledge, this is the first description of diprosopiasis with two cranial cavities as well as two separate encephala in a calf showing the potential extent of duplication. Throughout this work also the question is answered of whether this malformation in a bovine species arose from one embryo or rather, there is a dizygotic background by genotyping of tissue samples from both parts of the diprosopus. Regarding etiology, not only hereditary dispositions including among others a failed function of the signaling molecule Sonic hedgehog mediating regulation of craniofacial morphogenesis, but also incompletely separated monozygotic twins are discussed.

Principles, Techniques, and Applications of Tissue Microfluidics

NASA Technical Reports Server (NTRS)

Wade, Lawrence A.; Kartalov, Emil P.; Shibata, Darryl; Taylor, Clive

2011-01-01

The principle of tissue microfluidics and its resultant techniques has been applied to cell analysis. Building microfluidics to suit a particular tissue sample would allow the rapid, reliable, inexpensive, highly parallelized, selective extraction of chosen regions of tissue for purposes of further biochemical analysis. Furthermore, the applicability of the techniques ranges beyond the described pathology application. For example, they would also allow the posing and successful answering of new sets of questions in many areas of fundamental research. The proposed integration of microfluidic techniques and tissue slice samples is called tissue microfluidics because it molds the microfluidic architectures in accordance with each particular structure of each specific tissue sample. Thus, microfluidics can be built around the tissues, following the tissue structure, or alternatively, the microfluidics can be adapted to the specific geometry of particular tissues. By contrast, the traditional approach is that microfluidic devices are structured in accordance with engineering considerations, while the biological components in applied devices are forced to comply with these engineering presets. The proposed principles represent a paradigm shift in microfluidic technology in three important ways: Microfluidic devices are to be directly integrated with, onto, or around tissue samples, in contrast to the conventional method of off-chip sample extraction followed by sample insertion in microfluidic devices. Architectural and operational principles of microfluidic devices are to be subordinated to suit specific tissue structure and needs, in contrast to the conventional method of building devices according to fluidic function alone and without regard to tissue structure. Sample acquisition from tissue is to be performed on-chip and is to be integrated with the diagnostic measurement within the same device, in contrast to the conventional method of off-chip sample prep and subsequent insertion into a diagnostic device. A more advanced form of tissue integration with microfluidics is tissue encapsulation, wherein the sample is completely encapsulated within a microfluidic device, to allow for full surface access. The immediate applications of these approaches lie with diagnostics of tissue slices and biopsy samples e.g. for cancer but the approaches would also be very useful in comparative genomics and other areas of fundamental research involving heterogeneous tissue samples.

An Exemplar-Based Multi-View Domain Generalization Framework for Visual Recognition.

PubMed

Niu, Li; Li, Wen; Xu, Dong; Cai, Jianfei

2018-02-01

In this paper, we propose a new exemplar-based multi-view domain generalization (EMVDG) framework for visual recognition by learning robust classifier that are able to generalize well to arbitrary target domain based on the training samples with multiple types of features (i.e., multi-view features). In this framework, we aim to address two issues simultaneously. First, the distribution of training samples (i.e., the source domain) is often considerably different from that of testing samples (i.e., the target domain), so the performance of the classifiers learnt on the source domain may drop significantly on the target domain. Moreover, the testing data are often unseen during the training procedure. Second, when the training data are associated with multi-view features, the recognition performance can be further improved by exploiting the relation among multiple types of features. To address the first issue, considering that it has been shown that fusing multiple SVM classifiers can enhance the domain generalization ability, we build our EMVDG framework upon exemplar SVMs (ESVMs), in which a set of ESVM classifiers are learnt with each one trained based on one positive training sample and all the negative training samples. When the source domain contains multiple latent domains, the learnt ESVM classifiers are expected to be grouped into multiple clusters. To address the second issue, we propose two approaches under the EMVDG framework based on the consensus principle and the complementary principle, respectively. Specifically, we propose an EMVDG_CO method by adding a co-regularizer to enforce the cluster structures of ESVM classifiers on different views to be consistent based on the consensus principle. Inspired by multiple kernel learning, we also propose another EMVDG_MK method by fusing the ESVM classifiers from different views based on the complementary principle. In addition, we further extend our EMVDG framework to exemplar-based multi-view domain adaptation (EMVDA) framework when the unlabeled target domain data are available during the training procedure. The effectiveness of our EMVDG and EMVDA frameworks for visual recognition is clearly demonstrated by comprehensive experiments on three benchmark data sets.

Effect of finite sample size on feature selection and classification: a simulation study.

PubMed

Way, Ted W; Sahiner, Berkman; Hadjiiski, Lubomir M; Chan, Heang-Ping

2010-02-01

The small number of samples available for training and testing is often the limiting factor in finding the most effective features and designing an optimal computer-aided diagnosis (CAD) system. Training on a limited set of samples introduces bias and variance in the performance of a CAD system relative to that trained with an infinite sample size. In this work, the authors conducted a simulation study to evaluate the performances of various combinations of classifiers and feature selection techniques and their dependence on the class distribution, dimensionality, and the training sample size. The understanding of these relationships will facilitate development of effective CAD systems under the constraint of limited available samples. Three feature selection techniques, the stepwise feature selection (SFS), sequential floating forward search (SFFS), and principal component analysis (PCA), and two commonly used classifiers, Fisher's linear discriminant analysis (LDA) and support vector machine (SVM), were investigated. Samples were drawn from multidimensional feature spaces of multivariate Gaussian distributions with equal or unequal covariance matrices and unequal means, and with equal covariance matrices and unequal means estimated from a clinical data set. Classifier performance was quantified by the area under the receiver operating characteristic curve Az. The mean Az values obtained by resubstitution and hold-out methods were evaluated for training sample sizes ranging from 15 to 100 per class. The number of simulated features available for selection was chosen to be 50, 100, and 200. It was found that the relative performance of the different combinations of classifier and feature selection method depends on the feature space distributions, the dimensionality, and the available training sample sizes. The LDA and SVM with radial kernel performed similarly for most of the conditions evaluated in this study, although the SVM classifier showed a slightly higher hold-out performance than LDA for some conditions and vice versa for other conditions. PCA was comparable to or better than SFS and SFFS for LDA at small samples sizes, but inferior for SVM with polynomial kernel. For the class distributions simulated from clinical data, PCA did not show advantages over the other two feature selection methods. Under this condition, the SVM with radial kernel performed better than the LDA when few training samples were available, while LDA performed better when a large number of training samples were available. None of the investigated feature selection-classifier combinations provided consistently superior performance under the studied conditions for different sample sizes and feature space distributions. In general, the SFFS method was comparable to the SFS method while PCA may have an advantage for Gaussian feature spaces with unequal covariance matrices. The performance of the SVM with radial kernel was better than, or comparable to, that of the SVM with polynomial kernel under most conditions studied.

HPLC assisted Raman spectroscopic studies on bladder cancer

NASA Astrophysics Data System (ADS)

Zha, W. L.; Cheng, Y.; Yu, W.; Zhang, X. B.; Shen, A. G.; Hu, J. M.

2015-04-01

We applied confocal Raman spectroscopy to investigate 12 normal bladder tissues and 30 tumor tissues, and then depicted the spectral differences between the normal and the tumor tissues and the potential canceration mechanism with the aid of the high-performance liquid chromatographic (HPLC) technique. Normal tissues were demonstrated to contain higher tryptophan, cholesterol and lipid content, while bladder tumor tissues were rich in nucleic acids, collagen and carotenoids. In particular, β-carotene, one of the major types of carotenoids, was found through HPLC analysis of the extract of bladder tissues. The statistical software SPSS was applied to classify the spectra of the two types of tissues according to their differences. The sensitivity and specificity of 96.7 and 66.7% were obtained, respectively. In addition, different layers of the bladder wall including mucosa (lumps), muscle and adipose bladder tissue were analyzed by Raman mapping technique in response to previous Raman studies of bladder tissues. All of these will play an important role as a directive tool for the future diagnosis of bladder cancer in vivo.

Quantitative method to assess caries via fluorescence imaging from the perspective of autofluorescence spectral analysis

NASA Astrophysics Data System (ADS)

Chen, Q. G.; Zhu, H. H.; Xu, Y.; Lin, B.; Chen, H.

2015-08-01

A quantitative method to discriminate caries lesions for a fluorescence imaging system is proposed in this paper. The autofluorescence spectral investigation of 39 teeth samples classified by the International Caries Detection and Assessment System levels was performed at 405 nm excitation. The major differences in the different caries lesions focused on the relative spectral intensity range of 565-750 nm. The spectral parameter, defined as the ratio of wavebands at 565-750 nm to the whole spectral range, was calculated. The image component ratio R/(G + B) of color components was statistically computed by considering the spectral parameters (e.g. autofluorescence, optical filter, and spectral sensitivity) in our fluorescence color imaging system. Results showed that the spectral parameter and image component ratio presented a linear relation. Therefore, the image component ratio was graded as <0.66, 0.66-1.06, 1.06-1.62, and >1.62 to quantitatively classify sound, early decay, established decay, and severe decay tissues, respectively. Finally, the fluorescence images of caries were experimentally obtained, and the corresponding image component ratio distribution was compared with the classification result. A method to determine the numerical grades of caries using a fluorescence imaging system was proposed. This method can be applied to similar imaging systems.

On the anatomy and histology of the pubovisceral muscle enthesis in women.

PubMed

Kim, Jinyong; Ramanah, Rajeev; DeLancey, John O L; Ashton-Miller, James A

2011-09-01

The origin of the pubovisceral muscle (PVM) from the pubic bone is known to be at elevated risk for injury during difficult vaginal births. We examined the anatomy and histology of its enthesial origin to classify its type and see if it differs from appendicular entheses. Parasagittal sections of the pubic bone, PVM enthesis, myotendinous junction, and muscle proper were harvested from five female cadavers (51-98 years). Histological sections were prepared with hematoxylin and eosin, Masson's trichrome, and Verhoeff-Van Gieson stains. The type of enthesis was identified according to a published enthesial classification scheme. Quantitative imaging analysis was performed in sampling bands 2 mm apart along the enthesis to determine its cross-sectional area and composition. The PVM enthesis can be classified as a fibrous enthesis. The PVM muscle fibers terminated in collagenous fibers that insert tangentially onto the periosteum of the pubic bone for the most part. Sharpey's fibers were not observed. In a longitudinal cross-section, the area of the connective tissue and muscle becomes equal approximately 8 mm from the pubic bone. The PVM originates bilaterally from the pubic bone via fibrous entheses whose collagen fibers arise tangentially from the periosteum of the pubic bone. Copyright © 2010 Wiley-Liss, Inc.

On the Anatomy and Histology of the Pubovisceral Muscle Enthesis in Women

PubMed Central

Kim, Jinyong; Ramanah, Rajeev; DeLancey, John O. L.; Ashton-Miller, James A.

2012-01-01

Aims The origin of the pubovisceral muscle (PVM) from the pubic bone is known to be at elevated risk for injury during difficult vaginal births. We examined the anatomy and histology of its enthesial origin to classify its type and see if it differs from appendicular entheses. Methods Parasagittal sections of the pubic bone, PVM enthesis, myotendinous junction and muscle proper were harvested from five female cadavers (51 - 98 years). Histological sections were prepared with hematoxylin and eosin, Masson’s trichrome, and Verhoeff-Van Gieson stains. The type of enthesis was identified according to a published enthesial classification scheme. Quantitative imaging analysis was performed in sampling bands 2 mm apart along the enthesis to determine its cross-sectional area and composition. Results The PVM enthesis can be classified as a fibrous enthesis. The PVM muscle fibers terminated in collagenous fibers that insert tangentially onto the periosteum of the pubic bone for the most part. Sharpey’s fibers were not observed. In a longitudinal cross-section, the area of the connective tissue and muscle becomes equal approximately 8 mm from the pubic bone. Conclusion The PVM originates bilaterally from the pubic bone via fibrous entheses whose collagen fibers arise tangentially from the periosteum of the pubic bone. PMID:21567449

Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers.

PubMed

Mougiakakou, Stavroula G; Valavanis, Ioannis K; Nikita, Alexandra; Nikita, Konstantina S

2007-09-01

The aim of the present study is to define an optimally performing computer-aided diagnosis (CAD) architecture for the classification of liver tissue from non-enhanced computed tomography (CT) images into normal liver (C1), hepatic cyst (C2), hemangioma (C3), and hepatocellular carcinoma (C4). To this end, various CAD architectures, based on texture features and ensembles of classifiers (ECs), are comparatively assessed. Number of regions of interests (ROIs) corresponding to C1-C4 have been defined by experienced radiologists in non-enhanced liver CT images. For each ROI, five distinct sets of texture features were extracted using first order statistics, spatial gray level dependence matrix, gray level difference method, Laws' texture energy measures, and fractal dimension measurements. Two different ECs were constructed and compared. The first one consists of five multilayer perceptron neural networks (NNs), each using as input one of the computed texture feature sets or its reduced version after genetic algorithm-based feature selection. The second EC comprised five different primary classifiers, namely one multilayer perceptron NN, one probabilistic NN, and three k-nearest neighbor classifiers, each fed with the combination of the five texture feature sets or their reduced versions. The final decision of each EC was extracted by using appropriate voting schemes, while bootstrap re-sampling was utilized in order to estimate the generalization ability of the CAD architectures based on the available relatively small-sized data set. The best mean classification accuracy (84.96%) is achieved by the second EC using a fused feature set, and the weighted voting scheme. The fused feature set was obtained after appropriate feature selection applied to specific subsets of the original feature set. The comparative assessment of the various CAD architectures shows that combining three types of classifiers with a voting scheme, fed with identical feature sets obtained after appropriate feature selection and fusion, may result in an accurate system able to assist differential diagnosis of focal liver lesions from non-enhanced CT images.

Combining routine morphology, p16(INK4a) immunohistochemistry, and in situ hybridization for the detection of human papillomavirus infection in penile carcinomas: a tissue microarray study using classifier performance analyses.

PubMed

Chaux, Alcides; Cubilla, Antonio L; Haffner, Michael C; Lecksell, Kristen L; Sharma, Rajni; Burnett, Arthur L; Netto, George J

2014-02-01

Infection by high-risk human papillomavirus (HR-HPV) plays an important role in the pathogenesis of penile cancer in approximately 50% of the patients. The gold standard for human papillomavirus (HPV) detection is the polymerase chain reaction (PCR) assay. However, technical requirements and associated costs preclude the worldwide use of PCR assays on a routine basis. Herein, we evaluated the predictive abilities of tumor morphology, immunohistochemistry for p16(INK4a) expression, and in situ hybridization (ISH) for HR-HPV detection in defining HPV status, as established by PCR. Tissue samples from 48 patients with HPV-positive penile squamous cell carcinoma (SCC) were included in 4 tissue microarrays (TMA). Sensitivities and specificities were as follows: tumor morphology, 70% and 68%; p16(INK4a) immunohistochemistry, 65% and 90%; HR-HPV ISH, 47% and 100%. Regarding combinations of the predictors, the best performance was seen when HR-HPV ISH and p16(INK4a) immunohistochemistry were combined, regardless of the tumor morphology: sensitivity, 88%; specificity, 64%; area under the receiver-operating characteristic (AUC) curve, 0.83. Combinations of tumor morphology with p16(INK4a) immunohistochemistry or with HR-HPV ISH performed similarly well. In penile SCC, both p16(INK4a) immunohistochemistry and ISH for HR-HPV increase the predictive ability of routine morphology in defining HPV status. These tests can be interpreted differentially, depending on the necessity of a higher sensitivity or a higher specificity. For research/screening studies, we recommend combining tumor morphology, p16(INK4a) immunohistochemistry, and HR-HPV ISH. To increase sensitivity, positivity in any of these predictors should be considered as indicative of HPV infection. For routine diagnosis of clinical cases, criteria should be more stringent, and, to achieve the highest specificity in classifying a case as HPV-related, all predictors should be consistently positive. The data generated in the present study could be used in algorithms for defining HPV status in penile carcinomas. Copyright © 2014 Elsevier Inc. All rights reserved.

Learning semantic histopathological representation for basal cell carcinoma classification

NASA Astrophysics Data System (ADS)

Gutiérrez, Ricardo; Rueda, Andrea; Romero, Eduardo

2013-03-01

Diagnosis of a histopathology glass slide is a complex process that involves accurate recognition of several structures, their function in the tissue and their relation with other structures. The way in which the pathologist represents the image content and the relations between those objects yields a better and accurate diagnoses. Therefore, an appropriate semantic representation of the image content will be useful in several analysis tasks such as cancer classification, tissue retrieval and histopahological image analysis, among others. Nevertheless, to automatically recognize those structures and extract their inner semantic meaning are still very challenging tasks. In this paper we introduce a new semantic representation that allows to describe histopathological concepts suitable for classification. The approach herein identify local concepts using a dictionary learning approach, i.e., the algorithm learns the most representative atoms from a set of random sampled patches, and then models the spatial relations among them by counting the co-occurrence between atoms, while penalizing the spatial distance. The proposed approach was compared with a bag-of-features representation in a tissue classification task. For this purpose, 240 histological microscopical fields of view, 24 per tissue class, were collected. Those images fed a Support Vector Machine classifier per class, using 120 images as train set and the remaining ones for testing, maintaining the same proportion of each concept in the train and test sets. The obtained classification results, averaged from 100 random partitions of training and test sets, shows that our approach is more sensitive in average than the bag-of-features representation in almost 6%.

RNA-seq analysis and de novo transcriptome assembly of Jerusalem artichoke (Helianthus tuberosus Linne).

PubMed

Jung, Won Yong; Lee, Sang Sook; Kim, Chul Wook; Kim, Hyun-Soon; Min, Sung Ran; Moon, Jae Sun; Kwon, Suk-Yoon; Jeon, Jae-Heung; Cho, Hye Sun

2014-01-01

Jerusalem artichoke (Helianthus tuberosus L.) has long been cultivated as a vegetable and as a source of fructans (inulin) for pharmaceutical applications in diabetes and obesity prevention. However, transcriptomic and genomic data for Jerusalem artichoke remain scarce. In this study, Illumina RNA sequencing (RNA-Seq) was performed on samples from Jerusalem artichoke leaves, roots, stems and two different tuber tissues (early and late tuber development). Data were used for de novo assembly and characterization of the transcriptome. In total 206,215,632 paired-end reads were generated. These were assembled into 66,322 loci with 272,548 transcripts. Loci were annotated by querying against the NCBI non-redundant, Phytozome and UniProt databases, and 40,215 loci were homologous to existing database sequences. Gene Ontology terms were assigned to 19,848 loci, 15,434 loci were matched to 25 Clusters of Eukaryotic Orthologous Groups classifications, and 11,844 loci were classified into 142 Kyoto Encyclopedia of Genes and Genomes pathways. The assembled loci also contained 10,778 potential simple sequence repeats. The newly assembled transcriptome was used to identify loci with tissue-specific differential expression patterns. In total, 670 loci exhibited tissue-specific expression, and a subset of these were confirmed using RT-PCR and qRT-PCR. Gene expression related to inulin biosynthesis in tuber tissue was also investigated. Exsiting genetic and genomic data for H. tuberosus are scarce. The sequence resources developed in this study will enable the analysis of thousands of transcripts and will thus accelerate marker-assisted breeding studies and studies of inulin biosynthesis in Jerusalem artichoke.

Cost model for biobanks.

PubMed

Gonzalez-Sanchez, M Beatriz; Lopez-Valeiras, Ernesto; Morente, Manuel M; Fernández Lago, Orlando

2013-10-01

Current economic conditions and budget constraints in publicly funded biomedical research have brought about a renewed interest in analyzing the cost and economic viability of research infrastructures. However, there are no proposals for specific cost accounting models for these types of organizations in the international scientific literature. The aim of this paper is to present the basis of a cost analysis model useful for any biobank regardless of the human biological samples that it stores for biomedical research. The development of a unique cost model for biobanks can be a complicated task due to the diversity of the biological samples they store. Different types of samples (DNA, tumor tissues, blood, serum, etc.) require different production processes. Nonetheless, the common basic steps of the production process can be identified. Thus, the costs incurred in each step can be analyzed in detail to provide cost information. Six stages and four cost objects were obtained by taking the production processes of biobanks belonging to the Spanish National Biobank Network as a starting point. Templates and examples are provided to help managers to identify and classify the costs involved in their own biobanks to implement the model. The application of this methodology will provide accurate information on cost objects, along with useful information to give an economic value to the stored samples, to analyze the efficiency of the production process and to evaluate the viability of some sample collections.

Evaluation of Oil-Palm Fungal Disease Infestation with Canopy Hyperspectral Reflectance Data

PubMed Central

Lelong, Camille C. D.; Roger, Jean-Michel; Brégand, Simon; Dubertret, Fabrice; Lanore, Mathieu; Sitorus, Nurul A.; Raharjo, Doni A.; Caliman, Jean-Pierre

2010-01-01

Fungal disease detection in perennial crops is a major issue in estate management and production. However, nowadays such diagnostics are long and difficult when only made from visual symptom observation, and very expensive and damaging when based on root or stem tissue chemical analysis. As an alternative, we propose in this study to evaluate the potential of hyperspectral reflectance data to help detecting the disease efficiently without destruction of tissues. This study focuses on the calibration of a statistical model of discrimination between several stages of Ganoderma attack on oil palm trees, based on field hyperspectral measurements at tree scale. Field protocol and measurements are first described. Then, combinations of pre-processing, partial least square regression and linear discriminant analysis are tested on about hundred samples to prove the efficiency of canopy reflectance in providing information about the plant sanitary status. A robust algorithm is thus derived, allowing classifying oil-palm in a 4-level typology, based on disease severity from healthy to critically sick stages, with a global performance close to 94%. Moreover, this model discriminates sick from healthy trees with a confidence level of almost 98%. Applications and further improvements of this experiment are finally discussed. PMID:22315565

Development of a SYBR Green I-based real-time PCR for detection of elephant endotheliotropic herpesvirus 1 infection in Asian elephants (Elephas maximus).

PubMed

Sariya, Ladawan; Chatsirivech, Jarin; Suksai, Parut; Wiriyarat, Witthawat; Songjaeng, Adisak; Tangsudjai, Siriporn; Kanthasaewee, Oraphan; Maikaew, Umaporn; Chaichoun, Kridsada

2012-10-01

Elephant endotheliotropic herpesvirus 1 (EEHV1) can cause fatal hemorrhagic disease in Asian elephants (Elephas maximus). Several studies have described this virus as a major threat to young Asian elephants. A SYBR Green I-based real-time polymerase chain reaction (PCR) was developed to identify EEHV1 on trunk swabs and necropsied tissues. Two of 29 (6.9%) trunk swab samples from healthy Asian elephants were positive for EEHV1. The viruses were analyzed and classified as EEHV1A based on 231 nucleotides of the terminase gene. Necropsied spleen and heart tissue showed the highest level and second highest levels of DNA virus copy accumulation, respectively. The detection limit of the test was 276 copies/μl of DNA. There was no cross-reaction with other mammalian herpesviruses, such as herpes simplex virus 1 and equine herpesvirus 2. Inter- and intra-assay showed low coefficients of variation values indicating the reproducibility of the test. The results indicated that the test can be practically used for epidemiological study, clinical diagnosis, and management and control of EEHV1. Copyright © 2012 Elsevier B.V. All rights reserved.

Minimum distance classification in remote sensing

NASA Technical Reports Server (NTRS)

Wacker, A. G.; Landgrebe, D. A.

1972-01-01

The utilization of minimum distance classification methods in remote sensing problems, such as crop species identification, is considered. Literature concerning both minimum distance classification problems and distance measures is reviewed. Experimental results are presented for several examples. The objective of these examples is to: (a) compare the sample classification accuracy of a minimum distance classifier, with the vector classification accuracy of a maximum likelihood classifier, and (b) compare the accuracy of a parametric minimum distance classifier with that of a nonparametric one. Results show the minimum distance classifier performance is 5% to 10% better than that of the maximum likelihood classifier. The nonparametric classifier is only slightly better than the parametric version.

A computational study on convolutional feature combination strategies for grade classification in colon cancer using fluorescence microscopy data

NASA Astrophysics Data System (ADS)

Chowdhury, Aritra; Sevinsky, Christopher J.; Santamaria-Pang, Alberto; Yener, Bülent

2017-03-01

The cancer diagnostic workflow is typically performed by highly specialized and trained pathologists, for which analysis is expensive both in terms of time and money. This work focuses on grade classification in colon cancer. The analysis is performed over 3 protein markers; namely E-cadherin, beta actin and colagenIV. In addition, we also use a virtual Hematoxylin and Eosin (HE) stain. This study involves a comparison of various ways in which we can manipulate the information over the 4 different images of the tissue samples and come up with a coherent and unified response based on the data at our disposal. Pre- trained convolutional neural networks (CNNs) is the method of choice for feature extraction. The AlexNet architecture trained on the ImageNet database is used for this purpose. We extract a 4096 dimensional feature vector corresponding to the 6th layer in the network. Linear SVM is used to classify the data. The information from the 4 different images pertaining to a particular tissue sample; are combined using the following techniques: soft voting, hard voting, multiplication, addition, linear combination, concatenation and multi-channel feature extraction. We observe that we obtain better results in general than when we use a linear combination of the feature representations. We use 5-fold cross validation to perform the experiments. The best results are obtained when the various features are linearly combined together resulting in a mean accuracy of 91.27%.

Voxel-based Gaussian naïve Bayes classification of ischemic stroke lesions in individual T1-weighted MRI scans.

PubMed

Griffis, Joseph C; Allendorfer, Jane B; Szaflarski, Jerzy P

2016-01-15

Manual lesion delineation by an expert is the standard for lesion identification in MRI scans, but it is time-consuming and can introduce subjective bias. Alternative methods often require multi-modal MRI data, user interaction, scans from a control population, and/or arbitrary statistical thresholding. We present an approach for automatically identifying stroke lesions in individual T1-weighted MRI scans using naïve Bayes classification. Probabilistic tissue segmentation and image algebra were used to create feature maps encoding information about missing and abnormal tissue. Leave-one-case-out training and cross-validation was used to obtain out-of-sample predictions for each of 30 cases with left hemisphere stroke lesions. Our method correctly predicted lesion locations for 30/30 un-trained cases. Post-processing with smoothing (8mm FWHM) and cluster-extent thresholding (100 voxels) was found to improve performance. Quantitative evaluations of post-processed out-of-sample predictions on 30 cases revealed high spatial overlap (mean Dice similarity coefficient=0.66) and volume agreement (mean percent volume difference=28.91; Pearson's r=0.97) with manual lesion delineations. Our automated approach agrees with manual tracing. It provides an alternative to automated methods that require multi-modal MRI data, additional control scans, or user interaction to achieve optimal performance. Our fully trained classifier has applications in neuroimaging and clinical contexts. Copyright © 2015 Elsevier B.V. All rights reserved.

Biological and Proteolytic Variation in the Venom of Crotalus scutulatus scutulatus from Mexico.

PubMed

Borja, Miguel; Neri-Castro, Edgar; Castañeda-Gaytán, Gamaliel; Strickland, Jason L; Parkinson, Christopher L; Castañeda-Gaytán, Juan; Ponce-López, Roberto; Lomonte, Bruno; Olvera-Rodríguez, Alejandro; Alagón, Alejandro; Pérez-Morales, Rebeca

2018-01-08

Rattlesnake venoms may be classified according to the presence/absence and relative abundance of the neurotoxic phospholipases A 2 s (PLA 2 s), such as Mojave toxin, and snake venom metalloproteinases (SVMPs). In Mexico, studies to determine venom variation in Mojave Rattlesnakes ( Crotalus scutulatus scutulatus ) are limited and little is known about the biological and proteolytic activities in this species. Tissue (34) and venom (29) samples were obtained from C. s. scutulatus from different locations within their distribution in Mexico. Mojave toxin detection was carried out at the genomic (by PCR) and protein (by ELISA) levels for all tissue and venom samples. Biological activity was tested on representative venoms by measuring LD 50 and hemorrhagic activity. To determine the approximate amount of SVMPs, 15 venoms were separated by RP-HPLC and variation in protein profile and proteolytic activity was evaluated by SDS-PAGE ( n = 28) and Hide Powder Azure proteolytic analysis ( n = 27). Three types of venom were identified in Mexico which is comparable to the intraspecific venom diversity observed in the Sonoran Desert of Arizona, USA: Venom Type A (∼Type II), with Mojave toxin, highly toxic, lacking hemorrhagic activity, and with scarce proteolytic activity; Type B (∼Type I), without Mojave toxin, less toxic than Type A, highly hemorrhagic and proteolytic; and Type A + B, containing Mojave toxin, as toxic as venom Type A, variable in hemorrhagic activity and with intermediate proteolytic activity. We also detected a positive correlation between SVMP abundance and hemorrhagic and proteolytic activities. Although more sampling is necessary, our results suggest that venoms containing Mojave toxin and venom lacking this toxin are distributed in the northwest and southeast portions of the distribution in Mexico, respectively, while an intergradation in the middle of both zones is present.

Biological and Proteolytic Variation in the Venom of Crotalus scutulatus scutulatus from Mexico

PubMed Central

Castañeda-Gaytán, Gamaliel; Castañeda-Gaytán, Juan; Ponce-López, Roberto; Olvera-Rodríguez, Alejandro; Alagón, Alejandro; Pérez-Morales, Rebeca

2018-01-01

Rattlesnake venoms may be classified according to the presence/absence and relative abundance of the neurotoxic phospholipases A2s (PLA2s), such as Mojave toxin, and snake venom metalloproteinases (SVMPs). In Mexico, studies to determine venom variation in Mojave Rattlesnakes (Crotalus scutulatus scutulatus) are limited and little is known about the biological and proteolytic activities in this species. Tissue (34) and venom (29) samples were obtained from C. s. scutulatus from different locations within their distribution in Mexico. Mojave toxin detection was carried out at the genomic (by PCR) and protein (by ELISA) levels for all tissue and venom samples. Biological activity was tested on representative venoms by measuring LD50 and hemorrhagic activity. To determine the approximate amount of SVMPs, 15 venoms were separated by RP-HPLC and variation in protein profile and proteolytic activity was evaluated by SDS-PAGE (n = 28) and Hide Powder Azure proteolytic analysis (n = 27). Three types of venom were identified in Mexico which is comparable to the intraspecific venom diversity observed in the Sonoran Desert of Arizona, USA: Venom Type A (∼Type II), with Mojave toxin, highly toxic, lacking hemorrhagic activity, and with scarce proteolytic activity; Type B (∼Type I), without Mojave toxin, less toxic than Type A, highly hemorrhagic and proteolytic; and Type A + B, containing Mojave toxin, as toxic as venom Type A, variable in hemorrhagic activity and with intermediate proteolytic activity. We also detected a positive correlation between SVMP abundance and hemorrhagic and proteolytic activities. Although more sampling is necessary, our results suggest that venoms containing Mojave toxin and venom lacking this toxin are distributed in the northwest and southeast portions of the distribution in Mexico, respectively, while an intergradation in the middle of both zones is present. PMID:29316683

Study design in high-dimensional classification analysis.

PubMed

Sánchez, Brisa N; Wu, Meihua; Song, Peter X K; Wang, Wen

2016-10-01

Advances in high throughput technology have accelerated the use of hundreds to millions of biomarkers to construct classifiers that partition patients into different clinical conditions. Prior to classifier development in actual studies, a critical need is to determine the sample size required to reach a specified classification precision. We develop a systematic approach for sample size determination in high-dimensional (large [Formula: see text] small [Formula: see text]) classification analysis. Our method utilizes the probability of correct classification (PCC) as the optimization objective function and incorporates the higher criticism thresholding procedure for classifier development. Further, we derive the theoretical bound of maximal PCC gain from feature augmentation (e.g. when molecular and clinical predictors are combined in classifier development). Our methods are motivated and illustrated by a study using proteomics markers to classify post-kidney transplantation patients into stable and rejecting classes. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Time-resolved contrast-enhanced MRA (TWIST) with gadofosveset trisodium in the classification of soft-tissue vascular anomalies in the head and neck in children following updated 2014 ISSVA classification: first report on systematic evaluation of MRI and TWIST in a cohort of 47 children.

PubMed

Higgins, L J; Koshy, J; Mitchell, S E; Weiss, C R; Carson, K A; Huisman, T A G M; Tekes, A

2016-01-01

To evaluate the relative accuracy of contrast-enhanced time-resolved angiography with interleaved stochastic trajectories versus conventional contrast-enhanced magnetic resonance imaging (MRI) following International Society for the Study of Vascular Anomalies updated 2014-based classification of soft-tissue vascular anomalies in the head and neck in children. Time-resolved angiography with interleaved stochastic trajectories versus conventional contrast-enhanced MRI of children with diagnosis of soft-tissue vascular anomalies in the head and neck referred for MRI between 2008 and 2014 were retrospectively reviewed. Forty-seven children (0-18 years) were evaluated. Two paediatric neuroradiologists evaluated time-resolved MRA and conventional MRI in two different sessions (30 days apart). Blood-pool endovascular MRI contrast agent gadofosveset trisodium was used. The present cohort had the following diagnoses: infantile haemangioma (n=6), venous malformation (VM; n=23), lymphatic malformation (LM; n=16), arteriovenous malformation (AVM; n=2). Time-resolved MRA alone accurately classified 38/47 (81%) and conventional MRI 42/47 (89%), respectively. Although time-resolved MRA alone is slightly superior to conventional MRI alone for diagnosis of infantile haemangioma, conventional MRI is slightly better for diagnosis of venous and LMs. Neither time-resolved MRA nor conventional MRI was sufficient for accurate diagnosis of AVM in this cohort. Conventional MRI combined with time-resolved MRA accurately classified 44/47 cases (94%). Time-resolved MRA using gadofosveset trisodium can accurately classify soft-tissue vascular anomalies in the head and neck in children. The addition of time-resolved MRA to existing conventional MRI protocols provides haemodynamic information, assisting the diagnosis of vascular anomalies in the paediatric population at one-third of the dose of other MRI contrast agents. Copyright © 2015 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

[Histopathological Diagnosis of Invasive Fungal Infections in Formalin-Fixed and Paraffin-Embedded Tissues in Conjunction with Molecular Methods].

PubMed

Shinozaki, Minoru; Tochigi, Naobumi; Sadamoto, Sota; Yamagata Murayama, Somay; Wakayama, Megumi; Nemoto, Tetsuo

2018-01-01

The main objective of this study was to evaluate the relationship between histopathology, polymerase chain reaction (PCR), and in situ hybridization (ISH) for the identification of causative fungi in formalin-fixed and paraffin-embedded (FFPE) tissue specimens. Since pathogenic fungi in tissue specimens can be difficult to identify morphologically, PCR and ISH have been usually employed as auxiliary procedures. However, little comparison has been made on the sensitivity and specificity of PCR and ISH using FFPE specimens. Therefore, to compare and clarify the reproducibility and usefulness of PCR and ISH as auxiliary procedures for histological identification, we performed histopathological review, PCR assays, and ISH to identify pathogenic fungi in 59 FFPE tissue specimens obtained from 49 autopsies. The following are the main findings for this retrospective review: i) even for cases classified as "mold not otherwise specified" (MNOS), two cases could be identified as Aspergillus species by molecular methods; ii) all cases classified as non-zygomycetes mold (NZM) were Aspergillus species and were not identified by molecular methods as other fungi; iii) all 3 cases classified as zygomycetes mold (ZM) could be identified by molecular methods as Mucorales; iv) except for 1 case identified by molecular methods as Trichosporon spp., 5 cases were originally identified as dimorphic yeast (DY). As a measure of nucleic acid integrity, PCR and ISH successfully detected human and fungal nucleic acids in approximately 60% of the specimens. Detection of Aspergillus DNA by nested PCR assay and by ISH against the A. fumigatus ALP gene were similarly sensitive and significant (p<0.01). Thus, our findings demonstrated the potential risk of error in the classification of fungi based on pathological diagnosis. Combining molecular methods such as ISH and PCR on FFPE specimens with pathological diagnosis should improve diagnostic accuracy of fungal infection.

Frog sound identification using extended k-nearest neighbor classifier

NASA Astrophysics Data System (ADS)

Mukahar, Nordiana; Affendi Rosdi, Bakhtiar; Athiar Ramli, Dzati; Jaafar, Haryati

2017-09-01

Frog sound identification based on the vocalization becomes important for biological research and environmental monitoring. As a result, different types of feature extractions and classifiers have been employed to evaluate the accuracy of frog sound identification. This paper presents a frog sound identification with Extended k-Nearest Neighbor (EKNN) classifier. The EKNN classifier integrates the nearest neighbors and mutual sharing of neighborhood concepts, with the aims of improving the classification performance. It makes a prediction based on who are the nearest neighbors of the testing sample and who consider the testing sample as their nearest neighbors. In order to evaluate the classification performance in frog sound identification, the EKNN classifier is compared with competing classifier, k -Nearest Neighbor (KNN), Fuzzy k -Nearest Neighbor (FKNN) k - General Nearest Neighbor (KGNN)and Mutual k -Nearest Neighbor (MKNN) on the recorded sounds of 15 frog species obtained in Malaysia forest. The recorded sounds have been segmented using Short Time Energy and Short Time Average Zero Crossing Rate (STE+STAZCR), sinusoidal modeling (SM), manual and the combination of Energy (E) and Zero Crossing Rate (ZCR) (E+ZCR) while the features are extracted by Mel Frequency Cepstrum Coefficient (MFCC). The experimental results have shown that the EKNCN classifier exhibits the best performance in terms of accuracy compared to the competing classifiers, KNN, FKNN, GKNN and MKNN for all cases.

An Improvement To The k-Nearest Neighbor Classifier For ECG Database

NASA Astrophysics Data System (ADS)

Jaafar, Haryati; Hidayah Ramli, Nur; Nasir, Aimi Salihah Abdul

2018-03-01

The k nearest neighbor (kNN) is a non-parametric classifier and has been widely used for pattern classification. However, in practice, the performance of kNN often tends to fail due to the lack of information on how the samples are distributed among them. Moreover, kNN is no longer optimal when the training samples are limited. Another problem observed in kNN is regarding the weighting issues in assigning the class label before classification. Thus, to solve these limitations, a new classifier called Mahalanobis fuzzy k-nearest centroid neighbor (MFkNCN) is proposed in this study. Here, a Mahalanobis distance is applied to avoid the imbalance of samples distribition. Then, a surrounding rule is employed to obtain the nearest centroid neighbor based on the distributions of training samples and its distance to the query point. Consequently, the fuzzy membership function is employed to assign the query point to the class label which is frequently represented by the nearest centroid neighbor Experimental studies from electrocardiogram (ECG) signal is applied in this study. The classification performances are evaluated in two experimental steps i.e. different values of k and different sizes of feature dimensions. Subsequently, a comparative study of kNN, kNCN, FkNN and MFkCNN classifier is conducted to evaluate the performances of the proposed classifier. The results show that the performance of MFkNCN consistently exceeds the kNN, kNCN and FkNN with the best classification rates of 96.5%.

Shape based segmentation of MRIs of the bones in the knee using phase and intensity information

NASA Astrophysics Data System (ADS)

Fripp, Jurgen; Bourgeat, Pierrick; Crozier, Stuart; Ourselin, Sébastien

2007-03-01

The segmentation of the bones from MR images is useful for performing subsequent segmentation and quantitative measurements of cartilage tissue. In this paper, we present a shape based segmentation scheme for the bones that uses texture features derived from the phase and intensity information in the complex MR image. The phase can provide additional information about the tissue interfaces, but due to the phase unwrapping problem, this information is usually discarded. By using a Gabor filter bank on the complex MR image, texture features (including phase) can be extracted without requiring phase unwrapping. These texture features are then analyzed using a support vector machine classifier to obtain probability tissue matches. The segmentation of the bone is fully automatic and performed using a 3D active shape model based approach driven using gradient and texture information. The 3D active shape model is automatically initialized using a robust affine registration. The approach is validated using a database of 18 FLASH MR images that are manually segmented, with an average segmentation overlap (Dice similarity coefficient) of 0.92 compared to 0.9 obtained using the classifier only.

Automatic breast tissue density estimation scheme in digital mammography images

NASA Astrophysics Data System (ADS)

Menechelli, Renan C.; Pacheco, Ana Luisa V.; Schiabel, Homero

2017-03-01

Cases of breast cancer have increased substantially each year. However, radiologists are subject to subjectivity and failures of interpretation which may affect the final diagnosis in this examination. The high density features in breast tissue are important factors related to these failures. Thus, among many functions some CADx (Computer-Aided Diagnosis) schemes are classifying breasts according to the predominant density. In order to aid in such a procedure, this work attempts to describe automated software for classification and statistical information on the percentage change in breast tissue density, through analysis of sub regions (ROIs) from the whole mammography image. Once the breast is segmented, the image is divided into regions from which texture features are extracted. Then an artificial neural network MLP was used to categorize ROIs. Experienced radiologists have previously determined the ROIs density classification, which was the reference to the software evaluation. From tests results its average accuracy was 88.7% in ROIs classification, and 83.25% in the classification of the whole breast density in the 4 BI-RADS density classes - taking into account a set of 400 images. Furthermore, when considering only a simplified two classes division (high and low densities) the classifier accuracy reached 93.5%, with AUC = 0.95.

A preliminary result of three-dimensional microarray technology to gene analysis with endoscopic ultrasound-guided fine-needle aspiration specimens and pancreatic juices

PubMed Central

2010-01-01

Background Analysis of gene expression and gene mutation may add information to be different from ordinary pathological tissue diagnosis. Since samples obtained endoscopically are very small, it is desired that more sensitive technology is developed for gene analysis. We investigated whether gene expression and gene mutation analysis by newly developed ultra-sensitive three-dimensional (3D) microarray is possible using small amount samples from endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) specimens and pancreatic juices. Methods Small amount samples from 17 EUS-FNA specimens and 16 pancreatic juices were obtained. After nucleic acid extraction, the samples were amplified with labeling and analyzed by the 3D microarray. Results The analyzable rate with the microarray was 46% (6/13) in EUS-FNA specimens of RNAlater® storage, and RNA degradations were observed in all the samples of frozen storage. In pancreatic juices, the analyzable rate was 67% (4/6) in frozen storage samples and 20% (2/10) in RNAlater® storage. EUS-FNA specimens were classified into cancer and non-cancer by gene expression analysis and K-ras codon 12 mutations were also detected using the 3D microarray. Conclusions Gene analysis from small amount samples obtained endoscopically was possible by newly developed 3D microarray technology. High quality RNA from EUS-FNA samples were obtained and remained in good condition only using RNA stabilizer. In contrast, high quality RNA from pancreatic juice samples were obtained only in frozen storage without RNA stabilizer. PMID:20416107

Classification of breast abnormalities using artificial neural network

NASA Astrophysics Data System (ADS)

Zaman, Nur Atiqah Kamarul; Rahman, Wan Eny Zarina Wan Abdul; Jumaat, Abdul Kadir; Yasiran, Siti Salmah

2015-05-01

Classification is the process of recognition, differentiation and categorizing objects into groups. Breast abnormalities are calcifications which are tumor markers that indicate the presence of cancer in the breast. The aims of this research are to classify the types of breast abnormalities using artificial neural network (ANN) classifier and to evaluate the accuracy performance using receiver operating characteristics (ROC) curve. The methods used in this research are ANN for breast abnormalities classifications and Canny edge detector as a feature extraction method. Previously the ANN classifier provides only the number of benign and malignant cases without providing information for specific cases. However in this research, the type of abnormality for each image can be obtained. The existing MIAS MiniMammographic database classified the mammogram images into three features only namely characteristic of background tissues, class of abnormality and radius of abnormality. However, in this research three other features are added-in. These three features are number of spots, area and shape of abnormalities. Lastly the performance of the ANN classifier is evaluated using ROC curve. It is found that ANN has an accuracy of 97.9% which is considered acceptable.

The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia.

PubMed

Diamond, James; Anderson, Neil H; Bartels, Peter H; Montironi, Rodolfo; Hamilton, Peter W

2004-09-01

Quantitative examination of prostate histology offers clues in the diagnostic classification of lesions and in the prediction of response to treatment and prognosis. To facilitate the collection of quantitative data, the development of machine vision systems is necessary. This study explored the use of imaging for identifying tissue abnormalities in prostate histology. Medium-power histological scenes were recorded from whole-mount radical prostatectomy sections at x 40 objective magnification and assessed by a pathologist as exhibiting stroma, normal tissue (nonneoplastic epithelial component), or prostatic carcinoma (PCa). A machine vision system was developed that divided the scenes into subregions of 100 x 100 pixels and subjected each to image-processing techniques. Analysis of morphological characteristics allowed the identification of normal tissue. Analysis of image texture demonstrated that Haralick feature 4 was the most suitable for discriminating stroma from PCa. Using these morphological and texture measurements, it was possible to define a classification scheme for each subregion. The machine vision system is designed to integrate these classification rules and generate digital maps of tissue composition from the classification of subregions; 79.3% of subregions were correctly classified. Established classification rates have demonstrated the validity of the methodology on small scenes; a logical extension was to apply the methodology to whole slide images via scanning technology. The machine vision system is capable of classifying these images. The machine vision system developed in this project facilitates the exploration of morphological and texture characteristics in quantifying tissue composition. It also illustrates the potential of quantitative methods to provide highly discriminatory information in the automated identification of prostatic lesions using computer vision.

Comparison of disease prevalence in two populations in the presence of misclassification.

PubMed

Tang, Man-Lai; Qiu, Shi-Fang; Poon, Wai-Yin

2012-11-01

Comparing disease prevalence in two groups is an important topic in medical research, and prevalence rates are obtained by classifying subjects according to whether they have the disease. Both high-cost infallible gold-standard classifiers or low-cost fallible classifiers can be used to classify subjects. However, statistical analysis that is based on data sets with misclassifications leads to biased results. As a compromise between the two classification approaches, partially validated sets are often used in which all individuals are classified by fallible classifiers, and some of the individuals are validated by the accurate gold-standard classifiers. In this article, we develop several reliable test procedures and approximate sample size formulas for disease prevalence studies based on the difference between two disease prevalence rates with two independent partially validated series. Empirical studies show that (i) the Score test produces close-to-nominal level and is preferred in practice; and (ii) the sample size formula based on the Score test is also fairly accurate in terms of the empirical power and type I error rate, and is hence recommended. A real example from an aplastic anemia study is used to illustrate the proposed methodologies. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Establishment and function of tissue-resident innate lymphoid cells in the skin.

PubMed

Yang, Jie; Zhao, Luming; Xu, Ming; Xiong, Na

2017-07-01

Innate lymphoid cells (ILCs) are a newly classified family of immune cells of the lymphoid lineage. While they could be found in both lymphoid organs and non-lymphoid tissues, ILCs are preferentially enriched in barrier tissues such as the skin, intestine, and lung where they could play important roles in maintenance of tissue integrity and function and protection against assaults of foreign agents. On the other hand, dysregulated activation of ILCs could contribute to tissue inflammatory diseases. In spite of recent progress towards understanding roles of ILCs in the health and disease, mechanisms regulating specific establishment, activation, and function of ILCs in barrier tissues are still poorly understood. We herein review the up-to-date understanding of tissue-specific relevance of ILCs. Particularly we will focus on resident ILCs of the skin, the outmost barrier tissue critical in protection against various foreign hazardous agents and maintenance of thermal and water balance. In addition, we will discuss remaining outstanding questions yet to be addressed.

Chemometric brand differentiation of commercial spices using direct analysis in real time mass spectrometry.

PubMed

Pavlovich, Matthew J; Dunn, Emily E; Hall, Adam B

2016-05-15

Commercial spices represent an emerging class of fuels for improvised explosives. Being able to classify such spices not only by type but also by brand would represent an important step in developing methods to analytically investigate these explosive compositions. Therefore, a combined ambient mass spectrometric/chemometric approach was developed to quickly and accurately classify commercial spices by brand. Direct analysis in real time mass spectrometry (DART-MS) was used to generate mass spectra for samples of black pepper, cayenne pepper, and turmeric, along with four different brands of cinnamon, all dissolved in methanol. Unsupervised learning techniques showed that the cinnamon samples clustered according to brand. Then, we used supervised machine learning algorithms to build chemometric models with a known training set and classified the brands of an unknown testing set of cinnamon samples. Ten independent runs of five-fold cross-validation showed that the training set error for the best-performing models (i.e., the linear discriminant and neural network models) was lower than 2%. The false-positive percentages for these models were 3% or lower, and the false-negative percentages were lower than 10%. In particular, the linear discriminant model perfectly classified the testing set with 0% error. Repeated iterations of training and testing gave similar results, demonstrating the reproducibility of these models. Chemometric models were able to classify the DART mass spectra of commercial cinnamon samples according to brand, with high specificity and low classification error. This method could easily be generalized to other classes of spices, and it could be applied to authenticating questioned commercial samples of spices or to examining evidence from improvised explosives. Copyright © 2016 John Wiley & Sons, Ltd.

Brain metastasis detection by resonant Raman optical biopsy method

NASA Astrophysics Data System (ADS)

Zhou, Yan; Liu, Cheng-hui; Cheng, Gangge; Zhou, Lixin; Zhang, Chunyuan; Pu, Yang; Li, Zhongwu; Liu, Yulong; Li, Qingbo; Wang, Wei; Alfano, Robert R.

2014-03-01

Resonant Raman (RR) spectroscopy provides an effective way to enhance Raman signal from particular bonds associated with key molecules due to changes on a molecular level. In this study, RR is used for detection of human brain metastases of five kinds of primary organs of lung, breast, kidney, rectal and orbital in ex-vivo. The RR spectra of brain metastases cancerous tissues were measured and compared with those of normal brain tissues and the corresponding primary cancer tissues. The differences of five types of brain metastases tissues in key bio-components of carotene, tryptophan, lactate, alanine and methyl/methylene group were investigated. The SVM-KNN classifier was used to categorize a set of RR spectra data of brain metastasis of lung cancerous tissues from normal brain tissue, yielding diagnostic sensitivity and specificity at 100% and 75%, respectively. The RR spectroscopy may provide new moleculebased optical probe tools for diagnosis and classification of brain metastatic of cancers.

Classification algorithm of ovarian tissue based on co-registered ultrasound and photoacoustic tomography

NASA Astrophysics Data System (ADS)

Li, Hai; Kumavor, Patrick D.; Alqasemi, Umar; Zhu, Quing

2014-03-01

Human ovarian tissue features extracted from photoacoustic spectra data, beam envelopes and co-registered ultrasound and photoacoustic images are used to characterize cancerous vs. normal processes using a support vector machine (SVM) classifier. The centers of suspicious tumor areas are estimated from the Gaussian fitting of the mean Radon transforms of the photoacoustic image along 0 and 90 degrees. Normalized power spectra are calculated using the Fourier transform of the photoacoustic beamformed data across these suspicious areas, where the spectral slope and 0-MHz intercepts are extracted. Image statistics, envelope histogram fitting and maximum output of 6 composite filters of cancerous or normal patterns along with other previously used features are calculated to compose a total of 17 features. These features are extracted from 169 datasets of 19 ex vivo ovaries. Half of the cancerous and normal datasets are randomly chosen to train a SVM classifier with polynomial kernel and the remainder is used for testing. With 50 times data resampling, the SVM classifier, for the training group, gives 100% sensitivity and 100% specificity. For the testing group, it gives 89.68+/- 6.37% sensitivity and 93.16+/- 3.70% specificity. These results are superior to those obtained earlier by our group using features extracted from photoacoustic raw data or image statistics only.

DCS-SVM: a novel semi-automated method for human brain MR image segmentation.

PubMed

Ahmadvand, Ali; Daliri, Mohammad Reza; Hajiali, Mohammadtaghi

2017-11-27

In this paper, a novel method is proposed which appropriately segments magnetic resonance (MR) brain images into three main tissues. This paper proposes an extension of our previous work in which we suggested a combination of multiple classifiers (CMC)-based methods named dynamic classifier selection-dynamic local training local Tanimoto index (DCS-DLTLTI) for MR brain image segmentation into three main cerebral tissues. This idea is used here and a novel method is developed that tries to use more complex and accurate classifiers like support vector machine (SVM) in the ensemble. This work is challenging because the CMC-based methods are time consuming, especially on huge datasets like three-dimensional (3D) brain MR images. Moreover, SVM is a powerful method that is used for modeling datasets with complex feature space, but it also has huge computational cost for big datasets, especially those with strong interclass variability problems and with more than two classes such as 3D brain images; therefore, we cannot use SVM in DCS-DLTLTI. Therefore, we propose a novel approach named "DCS-SVM" to use SVM in DCS-DLTLTI to improve the accuracy of segmentation results. The proposed method is applied on well-known datasets of the Internet Brain Segmentation Repository (IBSR) and promising results are obtained.

Classification of cardiac-related artifacts in dynamic contrast breast MRI

NASA Astrophysics Data System (ADS)

Stegbauer, Keith C.; Smith, Justin P.; Niemeyer, Tanya L.; Wood, Chris

2004-05-01

Dynamic contrast breast MRI is becoming an important adjunct in screening women at high risk for breast cancer, determining extent of disease (staging) and monitoring response to therapy. In dynamic contrast breast MRI, regions of rapid contrast uptake indicate increases in vascularity which can be associated with abnormal tissue, sometimes significant for malignant disease. To show these areas of enhancement, subtractions between the pre and post contrast images and maximum intensity projections (MIPs) are computed. Many projections are obscured by normally enhancing anatomy (heart, aorta, pulmonary vessels). Identification of these structures allows their removal from MIPs, which improves image quality, diagnostic utility and the conspicuity of the enhancing regions. In this study, a fully automated classifier is presented which uses the spatial location of enhancing regions to separate those that occur inside the chest wall from those occurring in the tissue of interest (breast, axilla, chest wall). The classifier was trained on 21 studies each acquired at a different institution (699 clusters of pixels), and tested on 7 studies (231 clusters of pixels) that were not part of the training set. Multiple cost functions for training were examined. The measurements for the peak performance of the classifier were sensitivity 97.0%, specificity 99.4%, PPV 99.9%, NPV 78.8%.

Classifying magnetic resonance image modalities with convolutional neural networks

NASA Astrophysics Data System (ADS)

Remedios, Samuel; Pham, Dzung L.; Butman, John A.; Roy, Snehashis

2018-02-01

Magnetic Resonance (MR) imaging allows the acquisition of images with different contrast properties depending on the acquisition protocol and the magnetic properties of tissues. Many MR brain image processing techniques, such as tissue segmentation, require multiple MR contrasts as inputs, and each contrast is treated differently. Thus it is advantageous to automate the identification of image contrasts for various purposes, such as facilitating image processing pipelines, and managing and maintaining large databases via content-based image retrieval (CBIR). Most automated CBIR techniques focus on a two-step process: extracting features from data and classifying the image based on these features. We present a novel 3D deep convolutional neural network (CNN)- based method for MR image contrast classification. The proposed CNN automatically identifies the MR contrast of an input brain image volume. Specifically, we explored three classification problems: (1) identify T1-weighted (T1-w), T2-weighted (T2-w), and fluid-attenuated inversion recovery (FLAIR) contrasts, (2) identify pre vs postcontrast T1, (3) identify pre vs post-contrast FLAIR. A total of 3418 image volumes acquired from multiple sites and multiple scanners were used. To evaluate each task, the proposed model was trained on 2137 images and tested on the remaining 1281 images. Results showed that image volumes were correctly classified with 97.57% accuracy.

Diagnostic performance of swab PCR as an alternative to tissue culture methods for diagnosing infections associated with fracture fixation devices.

PubMed

Omar, Mohamed; Suero, Eduardo M; Liodakis, Emmanouil; Reichling, Moritz; Guenther, Daniel; Decker, Sebastian; Stiesch, Meike; Krettek, Christian; Eberhard, Jörg

2016-07-01

Molecular procedures could potentially improve diagnoses of orthopaedic implant-related infections, but are not yet clinically implemented. Analysis of sonication fluid shows the highest sensitivity for diagnosing implant infections in cases of revision surgery with implant removal. However, there remains controversy regarding the best method for obtaining specimens in cases of revision surgery with implant retention. Tissue culture is the most common diagnostic method for pathogen identification in such cases. Here we aimed to assess the diagnostic performance of swab PCR analysis compared to tissue culture from patients undergoing revision surgery of fracture fixation devices. We prospectively investigated 62 consecutive subjects who underwent revision surgery of fracture fixation devices during a two-year period. Tissue samples were collected for cultures, and swabs from the implant surface were obtained for 16S rRNA PCR analysis. Subjects were classified as having an implant-related infection if (1) they presented with a sinus tract or open wound in communication with the implant; or (2) purulence was encountered intraoperatively; or (3) two out of three tissue cultures tested positive for the presence of the same pathogen. Tissue culture and swab PCR results from the subjects were used to calculate the sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and area under the ROC curve (AUC) for identifying an orthopaedic implant-related infection. Orthopaedic implant-related infections were detected in 51 subjects. Tissue culture identified infections in 47 cases, and swab PCR in 35 cases. Among the 11 aseptic cases, tissue culture was positive in 2 cases and swab PCR in 4 cases. Tissue culture showed a significantly higher area under the ROC curve for diagnosing infection (AUC=0.89; 95% CI, 0.67-0.96) compared to swab PCR (AUC=0.66; 95% CI, 0.46-0.80) (p=0.033). Compared to swab PCR, tissue culture showed better performance for diagnosing orthopaedic implant-related infection. Although molecular methods are expected to yield higher diagnostic accuracy than cultures, it appears that the method of obtaining specimens plays an important role. Improved methods of specimen collection are required before swab PCR can become a reliable alternative to tissue-consumptive methods. Copyright © 2016 Elsevier Ltd. All rights reserved.

Computer-aided diagnosis of early knee osteoarthritis based on MRI T2 mapping.

PubMed

Wu, Yixiao; Yang, Ran; Jia, Sen; Li, Zhanjun; Zhou, Zhiyang; Lou, Ting

2014-01-01

This work was aimed at studying the method of computer-aided diagnosis of early knee OA (OA: osteoarthritis). Based on the technique of MRI (MRI: Magnetic Resonance Imaging) T2 Mapping, through computer image processing, feature extraction, calculation and analysis via constructing a classifier, an effective computer-aided diagnosis method for knee OA was created to assist doctors in their accurate, timely and convenient detection of potential risk of OA. In order to evaluate this method, a total of 1380 data from the MRI images of 46 samples of knee joints were collected. These data were then modeled through linear regression on an offline general platform by the use of the ImageJ software, and a map of the physical parameter T2 was reconstructed. After the image processing, the T2 values of ten regions in the WORMS (WORMS: Whole-organ Magnetic Resonance Imaging Score) areas of the articular cartilage were extracted to be used as the eigenvalues in data mining. Then,a RBF (RBF: Radical Basis Function) network classifier was built to classify and identify the collected data. The classifier exhibited a final identification accuracy of 75%, indicating a good result of assisting diagnosis. Since the knee OA classifier constituted by a weights-directly-determined RBF neural network didn't require any iteration, our results demonstrated that the optimal weights, appropriate center and variance could be yielded through simple procedures. Furthermore, the accuracy for both the training samples and the testing samples from the normal group could reach 100%. Finally, the classifier was superior both in time efficiency and classification performance to the frequently used classifiers based on iterative learning. Thus it was suitable to be used as an aid to computer-aided diagnosis of early knee OA.

Nanotopography-guided tissue engineering and regenerative medicine☆

PubMed Central

Kim, Hong Nam; Jiao, Alex; Hwang, Nathaniel S.; Kim, Min Sung; Kang, Do Hyun; Kim, Deok-Ho; Suh, Kahp-Yang

2017-01-01

Human tissues are intricate ensembles of multiple cell types embedded in complex and well-defined structures of the extracellular matrix (ECM). The organization of ECM is frequently hierarchical from nano to macro, with many proteins forming large scale structures with feature sizes up to several hundred microns. Inspired from these natural designs of ECM, nanotopography-guided approaches have been increasingly investigated for the last several decades. Results demonstrate that the nanotopography itself can activate tissue-specific function in vitro as well as promote tissue regeneration in vivo upon transplantation. In this review, we provide an extensive analysis of recent efforts to mimic functional nanostructures in vitro for improved tissue engineering and regeneration of injured and damaged tissues. We first characterize the role of various nanostructures in human tissues with respect to each tissue-specific function. Then, we describe various fabrication methods in terms of patterning principles and material characteristics. Finally, we summarize the applications of nanotopography to various tissues, which are classified into four types depending on their functions: protective, mechano-sensitive, electro-active, and shear stress-sensitive tissues. Some limitations and future challenges are briefly discussed at the end. PMID:22921841

Multi-view L2-SVM and its multi-view core vector machine.

PubMed

Huang, Chengquan; Chung, Fu-lai; Wang, Shitong

2016-03-01

In this paper, a novel L2-SVM based classifier Multi-view L2-SVM is proposed to address multi-view classification tasks. The proposed Multi-view L2-SVM classifier does not have any bias in its objective function and hence has the flexibility like μ-SVC in the sense that the number of the yielded support vectors can be controlled by a pre-specified parameter. The proposed Multi-view L2-SVM classifier can make full use of the coherence and the difference of different views through imposing the consensus among multiple views to improve the overall classification performance. Besides, based on the generalized core vector machine GCVM, the proposed Multi-view L2-SVM classifier is extended into its GCVM version MvCVM which can realize its fast training on large scale multi-view datasets, with its asymptotic linear time complexity with the sample size and its space complexity independent of the sample size. Our experimental results demonstrated the effectiveness of the proposed Multi-view L2-SVM classifier for small scale multi-view datasets and the proposed MvCVM classifier for large scale multi-view datasets. Copyright © 2015 Elsevier Ltd. All rights reserved.

Texture classification of normal tissues in computed tomography using Gabor filters

NASA Astrophysics Data System (ADS)

Dettori, Lucia; Bashir, Alia; Hasemann, Julie

2007-03-01

The research presented in this article is aimed at developing an automated imaging system for classification of normal tissues in medical images obtained from Computed Tomography (CT) scans. Texture features based on a bank of Gabor filters are used to classify the following tissues of interests: liver, spleen, kidney, aorta, trabecular bone, lung, muscle, IP fat, and SQ fat. The approach consists of three steps: convolution of the regions of interest with a bank of 32 Gabor filters (4 frequencies and 8 orientations), extraction of two Gabor texture features per filter (mean and standard deviation), and creation of a Classification and Regression Tree-based classifier that automatically identifies the various tissues. The data set used consists of approximately 1000 DIACOM images from normal chest and abdominal CT scans of five patients. The regions of interest were labeled by expert radiologists. Optimal trees were generated using two techniques: 10-fold cross-validation and splitting of the data set into a training and a testing set. In both cases, perfect classification rules were obtained provided enough images were available for training (~65%). All performance measures (sensitivity, specificity, precision, and accuracy) for all regions of interest were at 100%. This significantly improves previous results that used Wavelet, Ridgelet, and Curvelet texture features, yielding accuracy values in the 85%-98% range The Gabor filters' ability to isolate features at different frequencies and orientations allows for a multi-resolution analysis of texture essential when dealing with, at times, very subtle differences in the texture of tissues in CT scans.

Extravasation emergencies: state-of-the-art management and progress in clinical research.

PubMed

Pluschnig, Ursula; Haslik, Werner; Bartsch, Rupert; Mader, Robert M

2016-01-01

In cancer treatment, extravasation is defined as an inadvertent instillation or leakage of cytotoxic drugs into the perivascular space during infusion. As a dreaded complication of chemotherapy, extravasation has gained increasing attention in recent years. Classified according to their subcutaneous toxicity, three types of cytotoxins have been established: vesicants, irritants and nonvesicant drugs. Vesicant cytotoxic drugs may induce tissue damage, ulceration and tissue necrosis. Although we have established measures to manage extravasation emergencies, prevention is of paramount importance. This may be achieved within hospitals through regular training and education, which is best provided by a specialised and experienced task force including all disciplines involved in cancer therapy. Moreover, clinical and translational studies contribute to a better management of chemotherapy-induced extravasation as shown by our group in recent years. We were able to demonstrate that the evaluation of blood flow by indocyanine green angiography in the extravasation area predicts the extent of damage and the need of future surgical intervention. When a Port-a-Cath® extravasation is detected early, a subcutaneous wash-out procedure was found to be beneficial, corroborated by the analytical evaluation of the removed cytotoxic compound epirubicin. In another study, the tissue distribution of platinum was quantified at the anatomic level in cryosections of various tissues. This novel knowledge complements and supports our current efforts to handle extravasations better. On the other hand, a number of new drugs (chemotherapy, monoclonal antibodies, checkpoint inhibitors etc.) with many open issues to reliably classify their tissue toxicity still require our attention.

An evaluation of sampling and full enumeration strategies for Fisher Jenks classification in big data settings

USGS Publications Warehouse

Rey, Sergio J.; Stephens, Philip A.; Laura, Jason R.

2017-01-01

Large data contexts present a number of challenges to optimal choropleth map classifiers. Application of optimal classifiers to a sample of the attribute space is one proposed solution. The properties of alternative sampling-based classification methods are examined through a series of Monte Carlo simulations. The impacts of spatial autocorrelation, number of desired classes, and form of sampling are shown to have significant impacts on the accuracy of map classifications. Tradeoffs between improved speed of the sampling approaches and loss of accuracy are also considered. The results suggest the possibility of guiding the choice of classification scheme as a function of the properties of large data sets.

Automatic brain tumor detection in MRI: methodology and statistical validation

NASA Astrophysics Data System (ADS)

Iftekharuddin, Khan M.; Islam, Mohammad A.; Shaik, Jahangheer; Parra, Carlos; Ogg, Robert

2005-04-01

Automated brain tumor segmentation and detection are immensely important in medical diagnostics because it provides information associated to anatomical structures as well as potential abnormal tissue necessary to delineate appropriate surgical planning. In this work, we propose a novel automated brain tumor segmentation technique based on multiresolution texture information that combines fractal Brownian motion (fBm) and wavelet multiresolution analysis. Our wavelet-fractal technique combines the excellent multiresolution localization property of wavelets to texture extraction of fractal. We prove the efficacy of our technique by successfully segmenting pediatric brain MR images (MRIs) from St. Jude Children"s Research Hospital. We use self-organizing map (SOM) as our clustering tool wherein we exploit both pixel intensity and multiresolution texture features to obtain segmented tumor. Our test results show that our technique successfully segments abnormal brain tissues in a set of T1 images. In the next step, we design a classifier using Feed-Forward (FF) neural network to statistically validate the presence of tumor in MRI using both the multiresolution texture and the pixel intensity features. We estimate the corresponding receiver operating curve (ROC) based on the findings of true positive fractions and false positive fractions estimated from our classifier at different threshold values. An ROC, which can be considered as a gold standard to prove the competence of a classifier, is obtained to ascertain the sensitivity and specificity of our classifier. We observe that at threshold 0.4 we achieve true positive value of 1.0 (100%) sacrificing only 0.16 (16%) false positive value for the set of 50 T1 MRI analyzed in this experiment.

Estimating local scaling properties for the classification of interstitial lung disease patterns

NASA Astrophysics Data System (ADS)

Huber, Markus B.; Nagarajan, Mahesh B.; Leinsinger, Gerda; Ray, Lawrence A.; Wismueller, Axel

2011-03-01

Local scaling properties of texture regions were compared in their ability to classify morphological patterns known as 'honeycombing' that are considered indicative for the presence of fibrotic interstitial lung diseases in high-resolution computed tomography (HRCT) images. For 14 patients with known occurrence of honeycombing, a stack of 70 axial, lung kernel reconstructed images were acquired from HRCT chest exams. 241 regions of interest of both healthy and pathological (89) lung tissue were identified by an experienced radiologist. Texture features were extracted using six properties calculated from gray-level co-occurrence matrices (GLCM), Minkowski Dimensions (MDs), and the estimation of local scaling properties with Scaling Index Method (SIM). A k-nearest-neighbor (k-NN) classifier and a Multilayer Radial Basis Functions Network (RBFN) were optimized in a 10-fold cross-validation for each texture vector, and the classification accuracy was calculated on independent test sets as a quantitative measure of automated tissue characterization. A Wilcoxon signed-rank test was used to compare two accuracy distributions including the Bonferroni correction. The best classification results were obtained by the set of SIM features, which performed significantly better than all the standard GLCM and MD features (p < 0.005) for both classifiers with the highest accuracy (94.1%, 93.7%; for the k-NN and RBFN classifier, respectively). The best standard texture features were the GLCM features 'homogeneity' (91.8%, 87.2%) and 'absolute value' (90.2%, 88.5%). The results indicate that advanced texture features using local scaling properties can provide superior classification performance in computer-assisted diagnosis of interstitial lung diseases when compared to standard texture analysis methods.

Automated breast tissue density assessment using high order regional texture descriptors in mammography

NASA Astrophysics Data System (ADS)

Law, Yan Nei; Lieng, Monica Keiko; Li, Jingmei; Khoo, David Aik-Aun

2014-03-01

Breast cancer is the most common cancer and second leading cause of cancer death among women in the US. The relative survival rate is lower among women with a more advanced stage at diagnosis. Early detection through screening is vital. Mammography is the most widely used and only proven screening method for reliably and effectively detecting abnormal breast tissues. In particular, mammographic density is one of the strongest breast cancer risk factors, after age and gender, and can be used to assess the future risk of disease before individuals become symptomatic. A reliable method for automatic density assessment would be beneficial and could assist radiologists in the evaluation of mammograms. To address this problem, we propose a density classification method which uses statistical features from different parts of the breast. Our method is composed of three parts: breast region identification, feature extraction and building ensemble classifiers for density assessment. It explores the potential of the features extracted from second and higher order statistical information for mammographic density classification. We further investigate the registration of bilateral pairs and time-series of mammograms. The experimental results on 322 mammograms demonstrate that (1) a classifier using features from dense regions has higher discriminative power than a classifier using only features from the whole breast region; (2) these high-order features can be effectively combined to boost the classification accuracy; (3) a classifier using these statistical features from dense regions achieves 75% accuracy, which is a significant improvement from 70% accuracy obtained by the existing approaches.

Target discrimination method for SAR images based on semisupervised co-training

NASA Astrophysics Data System (ADS)

Wang, Yan; Du, Lan; Dai, Hui

2018-01-01

Synthetic aperture radar (SAR) target discrimination is usually performed in a supervised manner. However, supervised methods for SAR target discrimination may need lots of labeled training samples, whose acquirement is costly, time consuming, and sometimes impossible. This paper proposes an SAR target discrimination method based on semisupervised co-training, which utilizes a limited number of labeled samples and an abundant number of unlabeled samples. First, Lincoln features, widely used in SAR target discrimination, are extracted from the training samples and partitioned into two sets according to their physical meanings. Second, two support vector machine classifiers are iteratively co-trained with the extracted two feature sets based on the co-training algorithm. Finally, the trained classifiers are exploited to classify the test data. The experimental results on real SAR images data not only validate the effectiveness of the proposed method compared with the traditional supervised methods, but also demonstrate the superiority of co-training over self-training, which only uses one feature set.

Recent Developments in Vascular Imaging Techniques in Tissue Engineering and Regenerative Medicine

PubMed Central

Upputuri, Paul Kumar; Sivasubramanian, Kathyayini; Mark, Chong Seow Khoon; Pramanik, Manojit

2015-01-01

Adequate vascularisation is key in determining the clinical outcome of stem cells and engineered tissue in regenerative medicine. Numerous imaging modalities have been developed and used for the visualization of vascularisation in tissue engineering. In this review, we briefly discuss the very recent advances aiming at high performance imaging of vasculature. We classify the vascular imaging modalities into three major groups: nonoptical methods (X-ray, magnetic resonance, ultrasound, and positron emission imaging), optical methods (optical coherence, fluorescence, multiphoton, and laser speckle imaging), and hybrid methods (photoacoustic imaging). We then summarize the strengths and challenges of these methods for preclinical and clinical applications. PMID:25821821

A modification of procedures for petrographic analysis of tertiary Indonesian coals

NASA Astrophysics Data System (ADS)

Moore, T. A.; Ferm, J. C.

A study undertaken to characterize the Eocene coals from southeast Kalimantan has shown that standard preparation procedures fail to capture some basic petrographic properties of the coal. Modification of these procedures permits recognition of distinct plant parts and tissues embedded in finer grained matrix components. Plant parts and tissues can be classified on the basis of morphology and degree of degradation. The highest concentration and best preservation of plant parts and tissues occurs in banded coal and is lowest in the non-banded coal. Use of these procedures, which relates megascopic appearance to petrographic character, should allow more precise utilization of the coal.

International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society International Multidisciplinary Classification of Lung Adenocarcinoma

PubMed Central

Travis, William D.; Brambilla, Elisabeth; Noguchi, Masayuki; Nicholson, Andrew G.; Geisinger, Kim R.; Yatabe, Yasushi; Beer, David G.; Powell, Charles A.; Riely, Gregory J.; Van Schil, Paul E.; Garg, Kavita; Austin, John H. M.; Asamura, Hisao; Rusch, Valerie W.; Hirsch, Fred R.; Scagliotti, Giorgio; Mitsudomi, Tetsuya; Huber, Rudolf M.; Ishikawa, Yuichi; Jett, James; Sanchez-Cespedes, Montserrat; Sculier, Jean-Paul; Takahashi, Takashi; Tsuboi, Masahiro; Vansteenkiste, Johan; Wistuba, Ignacio; Yang, Pan-Chyr; Aberle, Denise; Brambilla, Christian; Flieder, Douglas; Franklin, Wilbur; Gazdar, Adi; Gould, Michael; Hasleton, Philip; Henderson, Douglas; Johnson, Bruce; Johnson, David; Kerr, Keith; Kuriyama, Keiko; Lee, Jin Soo; Miller, Vincent A.; Petersen, Iver; Roggli, Victor; Rosell, Rafael; Saijo, Nagahiro; Thunnissen, Erik; Tsao, Ming; Yankelewitz, David

2015-01-01

Introduction Adenocarcinoma is the most common histologic type of lung cancer. To address advances in oncology, molecular biology, pathology, radiology, and surgery of lung adenocarcinoma, an international multidisciplinary classification was sponsored by the International Association for the Study of Lung Cancer, American Thoracic Society, and European Respiratory Society. This new adenocarcinoma classification is needed to provide uniform terminology and diagnostic criteria, especially for bronchioloalveolar carcinoma (BAC), the overall approach to small nonresection cancer specimens, and for multidisciplinary strategic management of tissue for molecular and immunohistochemical studies. Methods An international core panel of experts representing all three societies was formed with oncologists/pulmonologists, pathologists, radiologists, molecular biologists, and thoracic surgeons. A systematic review was performed under the guidance of the American Thoracic Society Documents Development and Implementation Committee. The search strategy identified 11,368 citations of which 312 articles met specified eligibility criteria and were retrieved for full text review. A series of meetings were held to discuss the development of the new classification, to develop the recommendations, and to write the current document. Recommendations for key questions were graded by strength and quality of the evidence according to the Grades of Recommendation, Assessment, Development, and Evaluation approach. Results The classification addresses both resection specimens, and small biopsies and cytology. The terms BAC and mixed subtype adenocarcinoma are no longer used. For resection specimens, new concepts are introduced such as adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) for small solitary adenocarcinomas with either pure lepidic growth (AIS) or predominant lepidic growth with ≤5 mm invasion (MIA) to define patients who, if they undergo complete resection, will have 100% or near 100% disease-specific survival, respectively. AIS and MIA are usually nonmucinous but rarely may be mucinous. Invasive adenocarcinomas are classified by predominant pattern after using comprehensive histologic subtyping with lepidic (formerly most mixed subtype tumors with nonmucinous BAC), acinar, papillary, and solid patterns; micropapillary is added as a new histologic subtype. Variants include invasive mucinous adenocarcinoma (formerly mucinous BAC), colloid, fetal, and enteric adenocarcinoma. This classification provides guidance for small biopsies and cytology specimens, as approximately 70% of lung cancers are diagnosed in such samples. Non-small cell lung carcinomas (NSCLCs), in patients with advanced-stage disease, are to be classified into more specific types such as adenocarcinoma or squamous cell carcinoma, whenever possible for several reasons: (1) adenocarcinoma or NSCLC not otherwise specified should be tested for epidermal growth factor receptor (EGFR) mutations as the presence of these mutations is predictive of responsiveness to EGFR tyrosine kinase inhibitors, (2) adenocarcinoma histology is a strong predictor for improved outcome with pemetrexed therapy compared with squamous cell carcinoma, and (3) potential life-threatening hemorrhage may occur in patients with squamous cell carcinoma who receive bevacizumab. If the tumor cannot be classified based on light microscopy alone, special studies such as immunohistochemistry and/or mucin stains should be applied to classify the tumor further. Use of the term NSCLC not otherwise specified should be minimized. Conclusions This new classification strategy is based on a multidisciplinary approach to diagnosis of lung adenocarcinoma that incorporates clinical, molecular, radiologic, and surgical issues, but it is primarily based on histology. This classification is intended to support clinical practice, and research investigation and clinical trials. As EGFR mutation is a validated predictive marker for response and progression-free survival with EGFR tyrosine kinase inhibitors in advanced lung adenocarcinoma, we recommend that patients with advanced adenocarcinomas be tested for EGFR mutation. This has implications for strategic management of tissue, particularly for small biopsies and cytology samples, to maximize high-quality tissue available for molecular studies. Potential impact for tumor, node, and metastasis staging include adjustment of the size T factor according to only the invasive component (1) pathologically in invasive tumors with lepidic areas or (2) radiologically by measuring the solid component of part-solid nodules. PMID:21252716

International association for the study of lung cancer/american thoracic society/european respiratory society international multidisciplinary classification of lung adenocarcinoma.

PubMed

Travis, William D; Brambilla, Elisabeth; Noguchi, Masayuki; Nicholson, Andrew G; Geisinger, Kim R; Yatabe, Yasushi; Beer, David G; Powell, Charles A; Riely, Gregory J; Van Schil, Paul E; Garg, Kavita; Austin, John H M; Asamura, Hisao; Rusch, Valerie W; Hirsch, Fred R; Scagliotti, Giorgio; Mitsudomi, Tetsuya; Huber, Rudolf M; Ishikawa, Yuichi; Jett, James; Sanchez-Cespedes, Montserrat; Sculier, Jean-Paul; Takahashi, Takashi; Tsuboi, Masahiro; Vansteenkiste, Johan; Wistuba, Ignacio; Yang, Pan-Chyr; Aberle, Denise; Brambilla, Christian; Flieder, Douglas; Franklin, Wilbur; Gazdar, Adi; Gould, Michael; Hasleton, Philip; Henderson, Douglas; Johnson, Bruce; Johnson, David; Kerr, Keith; Kuriyama, Keiko; Lee, Jin Soo; Miller, Vincent A; Petersen, Iver; Roggli, Victor; Rosell, Rafael; Saijo, Nagahiro; Thunnissen, Erik; Tsao, Ming; Yankelewitz, David

2011-02-01

Adenocarcinoma is the most common histologic type of lung cancer. To address advances in oncology, molecular biology, pathology, radiology, and surgery of lung adenocarcinoma, an international multidisciplinary classification was sponsored by the International Association for the Study of Lung Cancer, American Thoracic Society, and European Respiratory Society. This new adenocarcinoma classification is needed to provide uniform terminology and diagnostic criteria, especially for bronchioloalveolar carcinoma (BAC), the overall approach to small nonresection cancer specimens, and for multidisciplinary strategic management of tissue for molecular and immunohistochemical studies. An international core panel of experts representing all three societies was formed with oncologists/pulmonologists, pathologists, radiologists, molecular biologists, and thoracic surgeons. A systematic review was performed under the guidance of the American Thoracic Society Documents Development and Implementation Committee. The search strategy identified 11,368 citations of which 312 articles met specified eligibility criteria and were retrieved for full text review. A series of meetings were held to discuss the development of the new classification, to develop the recommendations, and to write the current document. Recommendations for key questions were graded by strength and quality of the evidence according to the Grades of Recommendation, Assessment, Development, and Evaluation approach. The classification addresses both resection specimens, and small biopsies and cytology. The terms BAC and mixed subtype adenocarcinoma are no longer used. For resection specimens, new concepts are introduced such as adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) for small solitary adenocarcinomas with either pure lepidic growth (AIS) or predominant lepidic growth with ≤ 5 mm invasion (MIA) to define patients who, if they undergo complete resection, will have 100% or near 100% disease-specific survival, respectively. AIS and MIA are usually nonmucinous but rarely may be mucinous. Invasive adenocarcinomas are classified by predominant pattern after using comprehensive histologic subtyping with lepidic (formerly most mixed subtype tumors with nonmucinous BAC), acinar, papillary, and solid patterns; micropapillary is added as a new histologic subtype. Variants include invasive mucinous adenocarcinoma (formerly mucinous BAC), colloid, fetal, and enteric adenocarcinoma. This classification provides guidance for small biopsies and cytology specimens, as approximately 70% of lung cancers are diagnosed in such samples. Non-small cell lung carcinomas (NSCLCs), in patients with advanced-stage disease, are to be classified into more specific types such as adenocarcinoma or squamous cell carcinoma, whenever possible for several reasons: (1) adenocarcinoma or NSCLC not otherwise specified should be tested for epidermal growth factor receptor (EGFR) mutations as the presence of these mutations is predictive of responsiveness to EGFR tyrosine kinase inhibitors, (2) adenocarcinoma histology is a strong predictor for improved outcome with pemetrexed therapy compared with squamous cell carcinoma, and (3) potential life-threatening hemorrhage may occur in patients with squamous cell carcinoma who receive bevacizumab. If the tumor cannot be classified based on light microscopy alone, special studies such as immunohistochemistry and/or mucin stains should be applied to classify the tumor further. Use of the term NSCLC not otherwise specified should be minimized. This new classification strategy is based on a multidisciplinary approach to diagnosis of lung adenocarcinoma that incorporates clinical, molecular, radiologic, and surgical issues, but it is primarily based on histology. This classification is intended to support clinical practice, and research investigation and clinical trials. As EGFR mutation is a validated predictive marker for response and progression-free survival with EGFR tyrosine kinase inhibitors in advanced lung adenocarcinoma, we recommend that patients with advanced adenocarcinomas be tested for EGFR mutation. This has implications for strategic management of tissue, particularly for small biopsies and cytology samples, to maximize high-quality tissue available for molecular studies. Potential impact for tumor, node, and metastasis staging include adjustment of the size T factor according to only the invasive component (1) pathologically in invasive tumors with lepidic areas or (2) radiologically by measuring the solid component of part-solid nodules.

Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples.

PubMed

Turkki, Riku; Linder, Nina; Kovanen, Panu E; Pellinen, Teijo; Lundin, Johan

2016-01-01

Immune cell infiltration in tumor is an emerging prognostic biomarker in breast cancer. The gold standard for quantification of immune cells in tissue sections is visual assessment through a microscope, which is subjective and semi-quantitative. In this study, we propose and evaluate an approach based on antibody-guided annotation and deep learning to quantify immune cell-rich areas in hematoxylin and eosin (H&E) stained samples. Consecutive sections of formalin-fixed parafin-embedded samples obtained from the primary tumor of twenty breast cancer patients were cut and stained with H&E and the pan-leukocyte CD45 antibody. The stained slides were digitally scanned, and a training set of immune cell-rich and cell-poor tissue regions was annotated in H&E whole-slide images using the CD45-expression as a guide. In analysis, the images were divided into small homogenous regions, superpixels, from which features were extracted using a pretrained convolutional neural network (CNN) and classified with a support of vector machine. The CNN approach was compared to texture-based classification and to visual assessments performed by two pathologists. In a set of 123,442 labeled superpixels, the CNN approach achieved an F-score of 0.94 (range: 0.92-0.94) in discrimination of immune cell-rich and cell-poor regions, as compared to an F-score of 0.88 (range: 0.87-0.89) obtained with the texture-based classification. When compared to visual assessment of 200 images, an agreement of 90% (κ = 0.79) to quantify immune infiltration with the CNN approach was achieved while the inter-observer agreement between pathologists was 90% (κ = 0.78). Our findings indicate that deep learning can be applied to quantify immune cell infiltration in breast cancer samples using a basic morphology staining only. A good discrimination of immune cell-rich areas was achieved, well in concordance with both leukocyte antigen expression and pathologists' visual assessment.

Predictive ability of positive clinical culture results and International Classification of Diseases, Ninth Revision, to identify and classify noninvasive Staphylococcus aureus infections: a validation study.

PubMed

Tracy, LaRee A; Furuno, Jon P; Harris, Anthony D; Singer, Mary; Langenberg, Patricia; Roghmann, Mary-Claire

2010-07-01

To develop and validate an algorithm to identify and classify noninvasive infections due to Staphylococcus aureus by using positive clinical culture results and administrative data. Retrospective cohort study. Veterans Affairs Maryland Health Care System. Data were collected retrospectively on all S. aureus clinical culture results from samples obtained from nonsterile body sites during October 1998 through September 2008 and associated administrative claims records. An algorithm was developed to identify noninvasive infections on the basis of a unique S. aureus-positive culture result from a nonsterile site sample with a matching International Classification of Diseases, Ninth Revision (ICD-9-CM), code for infection at time of sampling. Medical records of a subset of cases were reviewed to find the proportion of true noninvasive infections (cases that met the Centers for Disease Control and Prevention National Healthcare Safety Network [NHSN] definition of infection). Positive predictive value (PPV) and negative predictive value (NPV) were calculated for all infections and according to body site of infection. We identified 4,621 unique S. aureus-positive culture results, of which 2,816 (60.9%) results met our algorithm definition of noninvasive S. aureus infection and 1,805 (39.1%) results lacked a matching ICD-9-CM code. Among 96 cases that met our algorithm criteria for noninvasive S. aureus infection, 76 also met the NHSN criteria (PPV, 79.2% [95% confidence interval, 70.0%-86.1%]). Among 98 cases that failed to meet the algorithm criteria, 80 did not meet the NHSN criteria (NPV, 81.6% [95% confidence interval, 72.8%-88.0%]). The PPV of all culture results was 55.4%. The algorithm was most predictive for skin and soft-tissue infections and bone and joint infections. When culture-based surveillance methods are used, the addition of administrative ICD-9-CM codes for infection can increase the PPV of true noninvasive S. aureus infection over the use of positive culture results alone.

The evaluation of alternate methodologies for land cover classification in an urbanizing area

NASA Technical Reports Server (NTRS)

Smekofski, R. M.

1981-01-01

The usefulness of LANDSAT in classifying land cover and in identifying and classifying land use change was investigated using an urbanizing area as the study area. The question of what was the best technique for classification was the primary focus of the study. The many computer-assisted techniques available to analyze LANDSAT data were evaluated. Techniques of statistical training (polygons from CRT, unsupervised clustering, polygons from digitizer and binary masks) were tested with minimum distance to the mean, maximum likelihood and canonical analysis with minimum distance to the mean classifiers. The twelve output images were compared to photointerpreted samples, ground verified samples and a current land use data base. Results indicate that for a reconnaissance inventory, the unsupervised training with canonical analysis-minimum distance classifier is the most efficient. If more detailed ground truth and ground verification is available, the polygons from the digitizer training with the canonical analysis minimum distance is more accurate.

Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations

PubMed Central

Zhang, Yi; Ren, Jinchang; Jiang, Jianmin

2015-01-01

Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions. PMID:26089862

Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations.

PubMed

Zhang, Yi; Ren, Jinchang; Jiang, Jianmin

2015-01-01

Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

Mapping raised bogs with an iterative one-class classification approach

NASA Astrophysics Data System (ADS)

Mack, Benjamin; Roscher, Ribana; Stenzel, Stefanie; Feilhauer, Hannes; Schmidtlein, Sebastian; Waske, Björn

2016-10-01

Land use and land cover maps are one of the most commonly used remote sensing products. In many applications the user only requires a map of one particular class of interest, e.g. a specific vegetation type or an invasive species. One-class classifiers are appealing alternatives to common supervised classifiers because they can be trained with labeled training data of the class of interest only. However, training an accurate one-class classification (OCC) model is challenging, particularly when facing a large image, a small class and few training samples. To tackle these problems we propose an iterative OCC approach. The presented approach uses a biased Support Vector Machine as core classifier. In an iterative pre-classification step a large part of the pixels not belonging to the class of interest is classified. The remaining data is classified by a final classifier with a novel model and threshold selection approach. The specific objective of our study is the classification of raised bogs in a study site in southeast Germany, using multi-seasonal RapidEye data and a small number of training sample. Results demonstrate that the iterative OCC outperforms other state of the art one-class classifiers and approaches for model selection. The study highlights the potential of the proposed approach for an efficient and improved mapping of small classes such as raised bogs. Overall the proposed approach constitutes a feasible approach and useful modification of a regular one-class classifier.

Exome sequencing identifies SUCO mutations in mesial temporal lobe epilepsy.

PubMed

Sha, Zhiqiang; Sha, Longze; Li, Wenting; Dou, Wanchen; Shen, Yan; Wu, Liwen; Xu, Qi

2015-03-30

Mesial temporal lobe epilepsy (mTLE) is the main type and most common medically intractable form of epilepsy. Severity of disease-based stratified samples may help identify new disease-associated mutant genes. We analyzed mRNA expression profiles from patient hippocampal tissue. Three of the seven patients had severe mTLE with generalized-onset convulsions and consciousness loss that occurred over many years. We found that compared with other groups, patients with severe mTLE were classified into a distinct group. Whole-exome sequencing and Sanger sequencing validation in all seven patients identified three novel SUN domain-containing ossification factor (SUCO) mutations in severely affected patients. Furthermore, SUCO knock down significantly reduced dendritic length in vitro. Our results indicate that mTLE defects may affect neuronal development, and suggest that neurons have abnormal development due to lack of SUCO, which may be a generalized-onset epilepsy-related gene. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Soft tissue recurrence of giant cell tumor of the bone: Prevalence and radiographic features.

PubMed

Xu, Leilei; Jin, Jing; Hu, Annan; Xiong, Jin; Wang, Dongmei; Sun, Qi; Wang, Shoufeng

2017-11-01

Recurrence of giant cell tumor of bone (GCTB) in the soft tissue is rarely seen in the clinical practice. This study aims to determine the prevalence of soft tissue recurrence of GCTB, and to characterize its radiographic features. A total of 291 patients treated by intralesional curettage for histologically diagnosed GCTB were reviewed. 6 patients were identified to have the recurrence of GCTB in the soft tissue, all of whom had undergone marginal resection of the lesion. Based on the x-ray, CT and MRI imaging, the radiographic features of soft tissue recurrence were classified into 3 types. Type I was defined as soft tissue recurrence with peripheral ossification, type II was defined as soft tissue recurrence with central ossification, and type III was defined as pure soft tissue recurrence without ossification. Demographic data including period of recurrence and follow-up duration after the second surgery were recorded for these 6 patients. Musculoskeletal Tumor Society (MSTS) scoring system was used to evaluate functional outcomes. The overall recurrence rate was 2.1% (6/291). The mean interval between initial surgery and recurrence was 11.3 ± 4.1 months (range, 5-17). The recurrence lesions were located in the thigh of 2 patients, in the forearm of 2 patients and in the leg of the other 2 patients. According to the classification system mentioned above, 2 patients were classified with type I, 1 as type II and 3 as type III. After the marginal excision surgery, all patients were consistently followed up for a mean period of 13.4 ± 5.3 months (range, 6-19), with no recurrence observed at the final visit. All the patients were satisfied with the surgical outcome. According to the MSTS scale, the mean postoperative functional score was 28.0 ± 1.2 (range, 26-29). The classification of soft tissue recurrence of GCTB may be helpful for the surgeon to select the appropriate imaging procedure to detect the recurrence. In addition, the marginal resection can produce a favorable outcome for the patients.

Generic Learning-Based Ensemble Framework for Small Sample Size Face Recognition in Multi-Camera Networks.

PubMed

Zhang, Cuicui; Liang, Xuefeng; Matsuyama, Takashi

2014-12-08

Multi-camera networks have gained great interest in video-based surveillance systems for security monitoring, access control, etc. Person re-identification is an essential and challenging task in multi-camera networks, which aims to determine if a given individual has already appeared over the camera network. Individual recognition often uses faces as a trial and requires a large number of samples during the training phrase. This is difficult to fulfill due to the limitation of the camera hardware system and the unconstrained image capturing conditions. Conventional face recognition algorithms often encounter the "small sample size" (SSS) problem arising from the small number of training samples compared to the high dimensionality of the sample space. To overcome this problem, interest in the combination of multiple base classifiers has sparked research efforts in ensemble methods. However, existing ensemble methods still open two questions: (1) how to define diverse base classifiers from the small data; (2) how to avoid the diversity/accuracy dilemma occurring during ensemble. To address these problems, this paper proposes a novel generic learning-based ensemble framework, which augments the small data by generating new samples based on a generic distribution and introduces a tailored 0-1 knapsack algorithm to alleviate the diversity/accuracy dilemma. More diverse base classifiers can be generated from the expanded face space, and more appropriate base classifiers are selected for ensemble. Extensive experimental results on four benchmarks demonstrate the higher ability of our system to cope with the SSS problem compared to the state-of-the-art system.

Generic Learning-Based Ensemble Framework for Small Sample Size Face Recognition in Multi-Camera Networks

PubMed Central

Zhang, Cuicui; Liang, Xuefeng; Matsuyama, Takashi

2014-01-01

Multi-camera networks have gained great interest in video-based surveillance systems for security monitoring, access control, etc. Person re-identification is an essential and challenging task in multi-camera networks, which aims to determine if a given individual has already appeared over the camera network. Individual recognition often uses faces as a trial and requires a large number of samples during the training phrase. This is difficult to fulfill due to the limitation of the camera hardware system and the unconstrained image capturing conditions. Conventional face recognition algorithms often encounter the “small sample size” (SSS) problem arising from the small number of training samples compared to the high dimensionality of the sample space. To overcome this problem, interest in the combination of multiple base classifiers has sparked research efforts in ensemble methods. However, existing ensemble methods still open two questions: (1) how to define diverse base classifiers from the small data; (2) how to avoid the diversity/accuracy dilemma occurring during ensemble. To address these problems, this paper proposes a novel generic learning-based ensemble framework, which augments the small data by generating new samples based on a generic distribution and introduces a tailored 0–1 knapsack algorithm to alleviate the diversity/accuracy dilemma. More diverse base classifiers can be generated from the expanded face space, and more appropriate base classifiers are selected for ensemble. Extensive experimental results on four benchmarks demonstrate the higher ability of our system to cope with the SSS problem compared to the state-of-the-art system. PMID:25494350

ANALYSIS OF SAMPLING TECHNIQUES FOR IMBALANCED DATA: AN N=648 ADNI STUDY

PubMed Central

Dubey, Rashmi; Zhou, Jiayu; Wang, Yalin; Thompson, Paul M.; Ye, Jieping

2013-01-01

Many neuroimaging applications deal with imbalanced imaging data. For example, in Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, the mild cognitive impairment (MCI) cases eligible for the study are nearly two times the Alzheimer’s disease (AD) patients for structural magnetic resonance imaging (MRI) modality and six times the control cases for proteomics modality. Constructing an accurate classifier from imbalanced data is a challenging task. Traditional classifiers that aim to maximize the overall prediction accuracy tend to classify all data into the majority class. In this paper, we study an ensemble system of feature selection and data sampling for the class imbalance problem. We systematically analyze various sampling techniques by examining the efficacy of different rates and types of undersampling, oversampling, and a combination of over and under sampling approaches. We thoroughly examine six widely used feature selection algorithms to identify significant biomarkers and thereby reduce the complexity of the data. The efficacy of the ensemble techniques is evaluated using two different classifiers including Random Forest and Support Vector Machines based on classification accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity measures. Our extensive experimental results show that for various problem settings in ADNI, (1). a balanced training set obtained with K-Medoids technique based undersampling gives the best overall performance among different data sampling techniques and no sampling approach; and (2). sparse logistic regression with stability selection achieves competitive performance among various feature selection algorithms. Comprehensive experiments with various settings show that our proposed ensemble model of multiple undersampled datasets yields stable and promising results. PMID:24176869

Automating Cell Detection and Classification in Human Brain Fluorescent Microscopy Images Using Dictionary Learning and Sparse Coding

PubMed Central

Alegro, Maryana; Theofilas, Panagiotis; Nguy, Austin; Castruita, Patricia A.; Seeley, William; Heinsen, Helmut; Ushizima, Daniela M.

2017-01-01

Background Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility. New method Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set. Results Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings. Comparison with existing methods We compared our results to four open-access IF-based cell-counting tools available in the literature. Our method showed improved accuracy for all data samples. Conclusion The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks. PMID:28267565

Classification based upon gene expression data: bias and precision of error rates.

PubMed

Wood, Ian A; Visscher, Peter M; Mengersen, Kerrie L

2007-06-01

Gene expression data offer a large number of potentially useful predictors for the classification of tissue samples into classes, such as diseased and non-diseased. The predictive error rate of classifiers can be estimated using methods such as cross-validation. We have investigated issues of interpretation and potential bias in the reporting of error rate estimates. The issues considered here are optimization and selection biases, sampling effects, measures of misclassification rate, baseline error rates, two-level external cross-validation and a novel proposal for detection of bias using the permutation mean. Reporting an optimal estimated error rate incurs an optimization bias. Downward bias of 3-5% was found in an existing study of classification based on gene expression data and may be endemic in similar studies. Using a simulated non-informative dataset and two example datasets from existing studies, we show how bias can be detected through the use of label permutations and avoided using two-level external cross-validation. Some studies avoid optimization bias by using single-level cross-validation and a test set, but error rates can be more accurately estimated via two-level cross-validation. In addition to estimating the simple overall error rate, we recommend reporting class error rates plus where possible the conditional risk incorporating prior class probabilities and a misclassification cost matrix. We also describe baseline error rates derived from three trivial classifiers which ignore the predictors. R code which implements two-level external cross-validation with the PAMR package, experiment code, dataset details and additional figures are freely available for non-commercial use from http://www.maths.qut.edu.au/profiles/wood/permr.jsp

Multi-scale textural feature extraction and particle swarm optimization based model selection for false positive reduction in mammography.

PubMed

Zyout, Imad; Czajkowska, Joanna; Grzegorzek, Marcin

2015-12-01

The high number of false positives and the resulting number of avoidable breast biopsies are the major problems faced by current mammography Computer Aided Detection (CAD) systems. False positive reduction is not only a requirement for mass but also for calcification CAD systems which are currently deployed for clinical use. This paper tackles two problems related to reducing the number of false positives in the detection of all lesions and masses, respectively. Firstly, textural patterns of breast tissue have been analyzed using several multi-scale textural descriptors based on wavelet and gray level co-occurrence matrix. The second problem addressed in this paper is the parameter selection and performance optimization. For this, we adopt a model selection procedure based on Particle Swarm Optimization (PSO) for selecting the most discriminative textural features and for strengthening the generalization capacity of the supervised learning stage based on a Support Vector Machine (SVM) classifier. For evaluating the proposed methods, two sets of suspicious mammogram regions have been used. The first one, obtained from Digital Database for Screening Mammography (DDSM), contains 1494 regions (1000 normal and 494 abnormal samples). The second set of suspicious regions was obtained from database of Mammographic Image Analysis Society (mini-MIAS) and contains 315 (207 normal and 108 abnormal) samples. Results from both datasets demonstrate the efficiency of using PSO based model selection for optimizing both classifier hyper-parameters and parameters, respectively. Furthermore, the obtained results indicate the promising performance of the proposed textural features and more specifically, those based on co-occurrence matrix of wavelet image representation technique. Copyright © 2015 Elsevier Ltd. All rights reserved.

Differentiation of Candida albicans, Candida glabrata, and Candida krusei by FT-IR and chemometrics by CHROMagar™ Candida.

PubMed

Wohlmeister, Denise; Vianna, Débora Renz Barreto; Helfer, Virginia Etges; Calil, Luciane Noal; Buffon, Andréia; Fuentefria, Alexandre Meneghello; Corbellini, Valeriano Antonio; Pilger, Diogo André

2017-10-01

Pathogenic Candida species are detected in clinical infections. CHROMagar™ is a phenotypical method used to identify Candida species, although it has limitations, which indicates the need for more sensitive and specific techniques. Infrared Spectroscopy (FT-IR) is an analytical vibrational technique used to identify patterns of metabolic fingerprint of biological matrixes, particularly whole microbial cell systems as Candida sp. in association of classificatory chemometrics algorithms. On the other hand, Soft Independent Modeling by Class Analogy (SIMCA) is one of the typical algorithms still little employed in microbiological classification. This study demonstrates the applicability of the FT-IR-technique by specular reflectance associated with SIMCA to discriminate Candida species isolated from vaginal discharges and grown on CHROMagar™. The differences in spectra of C. albicans, C. glabrata and C. krusei were suitable for use in the discrimination of these species, which was observed by PCA. Then, a SIMCA model was constructed with standard samples of three species and using the spectral region of 1792-1561cm -1 . All samples (n=48) were properly classified based on the chromogenic method using CHROMagar™ Candida. In total, 93.4% (n=45) of the samples were correctly and unambiguously classified (Class I). Two samples of C. albicans were classified correctly, though these could have been C. glabrata (Class II). Also, one C. glabrata sample could have been classified as C. krusei (Class II). Concerning these three samples, one triplicate of each was included in Class II and two in Class I. Therefore, FT-IR associated with SIMCA can be used to identify samples of C. albicans, C. glabrata, and C. krusei grown in CHROMagar™ Candida aiming to improve clinical applications of this technique. Copyright © 2017 Elsevier B.V. All rights reserved.

Quasi-Supervised Scoring of Human Sleep in Polysomnograms Using Augmented Input Variables

PubMed Central

Yaghouby, Farid; Sunderam, Sridhar

2015-01-01

The limitations of manual sleep scoring make computerized methods highly desirable. Scoring errors can arise from human rater uncertainty or inter-rater variability. Sleep scoring algorithms either come as supervised classifiers that need scored samples of each state to be trained, or as unsupervised classifiers that use heuristics or structural clues in unscored data to define states. We propose a quasi-supervised classifier that models observations in an unsupervised manner but mimics a human rater wherever training scores are available. EEG, EMG, and EOG features were extracted in 30s epochs from human-scored polysomnograms recorded from 42 healthy human subjects (18 to 79 years) and archived in an anonymized, publicly accessible database. Hypnograms were modified so that: 1. Some states are scored but not others; 2. Samples of all states are scored but not for transitional epochs; and 3. Two raters with 67% agreement are simulated. A framework for quasi-supervised classification was devised in which unsupervised statistical models—specifically Gaussian mixtures and hidden Markov models—are estimated from unlabeled training data, but the training samples are augmented with variables whose values depend on available scores. Classifiers were fitted to signal features incorporating partial scores, and used to predict scores for complete recordings. Performance was assessed using Cohen's K statistic. The quasi-supervised classifier performed significantly better than an unsupervised model and sometimes as well as a completely supervised model despite receiving only partial scores. The quasi-supervised algorithm addresses the need for classifiers that mimic scoring patterns of human raters while compensating for their limitations. PMID:25679475

Quasi-supervised scoring of human sleep in polysomnograms using augmented input variables.

PubMed

Yaghouby, Farid; Sunderam, Sridhar

2015-04-01

The limitations of manual sleep scoring make computerized methods highly desirable. Scoring errors can arise from human rater uncertainty or inter-rater variability. Sleep scoring algorithms either come as supervised classifiers that need scored samples of each state to be trained, or as unsupervised classifiers that use heuristics or structural clues in unscored data to define states. We propose a quasi-supervised classifier that models observations in an unsupervised manner but mimics a human rater wherever training scores are available. EEG, EMG, and EOG features were extracted in 30s epochs from human-scored polysomnograms recorded from 42 healthy human subjects (18-79 years) and archived in an anonymized, publicly accessible database. Hypnograms were modified so that: 1. Some states are scored but not others; 2. Samples of all states are scored but not for transitional epochs; and 3. Two raters with 67% agreement are simulated. A framework for quasi-supervised classification was devised in which unsupervised statistical models-specifically Gaussian mixtures and hidden Markov models--are estimated from unlabeled training data, but the training samples are augmented with variables whose values depend on available scores. Classifiers were fitted to signal features incorporating partial scores, and used to predict scores for complete recordings. Performance was assessed using Cohen's Κ statistic. The quasi-supervised classifier performed significantly better than an unsupervised model and sometimes as well as a completely supervised model despite receiving only partial scores. The quasi-supervised algorithm addresses the need for classifiers that mimic scoring patterns of human raters while compensating for their limitations. Copyright © 2015 Elsevier Ltd. All rights reserved.

Trace-Element Concentrations in Tissues of Aquatic Organisms from Rivers and Streams of the United States, 1992-1999

USGS Publications Warehouse

DeWeese, Lawrence R.; Stephens, Verlin C.; Short, Terry M.; Dubrovsky, Neil M.

2007-01-01

The U.S. Geological Survey National Water-Quality Assessment Program collected tissue samples from a variety of aquatic organisms during 1992-1999 within 47 study units across the United States. These tissue samples were collected to determine the occurrence and distribution of 20 major and minor trace elements in aquatic organisms. This report presents the tissue trace-element concentration data, sample summaries, and concentration statistics for 1,457 tissue samples representing 76 species or groups of fish, aquatic invertebrates, and plants were collected at 824 sampling sites.

A Predictive Model for Toxicity Effects Assessment of Biotransformed Hepatic Drugs Using Iterative Sampling Method.

PubMed

Tharwat, Alaa; Moemen, Yasmine S; Hassanien, Aboul Ella

2016-12-09

Measuring toxicity is one of the main steps in drug development. Hence, there is a high demand for computational models to predict the toxicity effects of the potential drugs. In this study, we used a dataset, which consists of four toxicity effects:mutagenic, tumorigenic, irritant and reproductive effects. The proposed model consists of three phases. In the first phase, rough set-based methods are used to select the most discriminative features for reducing the classification time and improving the classification performance. Due to the imbalanced class distribution, in the second phase, different sampling methods such as Random Under-Sampling, Random Over-Sampling and Synthetic Minority Oversampling Technique are used to solve the problem of imbalanced datasets. ITerative Sampling (ITS) method is proposed to avoid the limitations of those methods. ITS method has two steps. The first step (sampling step) iteratively modifies the prior distribution of the minority and majority classes. In the second step, a data cleaning method is used to remove the overlapping that is produced from the first step. In the third phase, Bagging classifier is used to classify an unknown drug into toxic or non-toxic. The experimental results proved that the proposed model performed well in classifying the unknown samples according to all toxic effects in the imbalanced datasets.

Blood Based Biomarkers of Early Onset Breast Cancer

DTIC Science & Technology

2016-12-01

discretizes the data, and also using logistic elastic net – a form of linear regression - we were unable to build a classifier that could accurately...classifier for differentiating cases from controls off discretized data. The first pass analysis demonstrated a 35 gene signature that differentiated...to the discretized data for mRNA gene signature, the samples used to “train” were also included in the final samples used to “test” the algorithm

Self-similarity Clustering Event Detection Based on Triggers Guidance

NASA Astrophysics Data System (ADS)

Zhang, Xianfei; Li, Bicheng; Tian, Yuxuan

Traditional method of Event Detection and Characterization (EDC) regards event detection task as classification problem. It makes words as samples to train classifier, which can lead to positive and negative samples of classifier imbalance. Meanwhile, there is data sparseness problem of this method when the corpus is small. This paper doesn't classify event using word as samples, but cluster event in judging event types. It adopts self-similarity to convergence the value of K in K-means algorithm by the guidance of event triggers, and optimizes clustering algorithm. Then, combining with named entity and its comparative position information, the new method further make sure the pinpoint type of event. The new method avoids depending on template of event in tradition methods, and its result of event detection can well be used in automatic text summarization, text retrieval, and topic detection and tracking.

Spectral classifier design with ensemble classifiers and misclassification-rejection: application to elastic-scattering spectroscopy for detection of colonic neoplasia.

PubMed

Rodriguez-Diaz, Eladio; Castanon, David A; Singh, Satish K; Bigio, Irving J

2011-06-01

Optical spectroscopy has shown potential as a real-time, in vivo, diagnostic tool for identifying neoplasia during endoscopy. We present the development of a diagnostic algorithm to classify elastic-scattering spectroscopy (ESS) spectra as either neoplastic or non-neoplastic. The algorithm is based on pattern recognition methods, including ensemble classifiers, in which members of the ensemble are trained on different regions of the ESS spectrum, and misclassification-rejection, where the algorithm identifies and refrains from classifying samples that are at higher risk of being misclassified. These "rejected" samples can be reexamined by simply repositioning the probe to obtain additional optical readings or ultimately by sending the polyp for histopathological assessment, as per standard practice. Prospective validation using separate training and testing sets result in a baseline performance of sensitivity = .83, specificity = .79, using the standard framework of feature extraction (principal component analysis) followed by classification (with linear support vector machines). With the developed algorithm, performance improves to Se ∼ 0.90, Sp ∼ 0.90, at a cost of rejecting 20-33% of the samples. These results are on par with a panel of expert pathologists. For colonoscopic prevention of colorectal cancer, our system could reduce biopsy risk and cost, obviate retrieval of non-neoplastic polyps, decrease procedure time, and improve assessment of cancer risk.

Spectral classifier design with ensemble classifiers and misclassification-rejection: application to elastic-scattering spectroscopy for detection of colonic neoplasia

PubMed Central

Rodriguez-Diaz, Eladio; Castanon, David A.; Singh, Satish K.; Bigio, Irving J.

2011-01-01

Optical spectroscopy has shown potential as a real-time, in vivo, diagnostic tool for identifying neoplasia during endoscopy. We present the development of a diagnostic algorithm to classify elastic-scattering spectroscopy (ESS) spectra as either neoplastic or non-neoplastic. The algorithm is based on pattern recognition methods, including ensemble classifiers, in which members of the ensemble are trained on different regions of the ESS spectrum, and misclassification-rejection, where the algorithm identifies and refrains from classifying samples that are at higher risk of being misclassified. These “rejected” samples can be reexamined by simply repositioning the probe to obtain additional optical readings or ultimately by sending the polyp for histopathological assessment, as per standard practice. Prospective validation using separate training and testing sets result in a baseline performance of sensitivity = .83, specificity = .79, using the standard framework of feature extraction (principal component analysis) followed by classification (with linear support vector machines). With the developed algorithm, performance improves to Se ∼ 0.90, Sp ∼ 0.90, at a cost of rejecting 20–33% of the samples. These results are on par with a panel of expert pathologists. For colonoscopic prevention of colorectal cancer, our system could reduce biopsy risk and cost, obviate retrieval of non-neoplastic polyps, decrease procedure time, and improve assessment of cancer risk. PMID:21721830

Classifying Imbalanced Data Streams via Dynamic Feature Group Weighting with Importance Sampling.

PubMed

Wu, Ke; Edwards, Andrea; Fan, Wei; Gao, Jing; Zhang, Kun

2014-04-01

Data stream classification and imbalanced data learning are two important areas of data mining research. Each has been well studied to date with many interesting algorithms developed. However, only a few approaches reported in literature address the intersection of these two fields due to their complex interplay. In this work, we proposed an importance sampling driven, dynamic feature group weighting framework (DFGW-IS) for classifying data streams of imbalanced distribution. Two components are tightly incorporated into the proposed approach to address the intrinsic characteristics of concept-drifting, imbalanced streaming data. Specifically, the ever-evolving concepts are tackled by a weighted ensemble trained on a set of feature groups with each sub-classifier (i.e. a single classifier or an ensemble) weighed by its discriminative power and stable level. The un-even class distribution, on the other hand, is typically battled by the sub-classifier built in a specific feature group with the underlying distribution rebalanced by the importance sampling technique. We derived the theoretical upper bound for the generalization error of the proposed algorithm. We also studied the empirical performance of our method on a set of benchmark synthetic and real world data, and significant improvement has been achieved over the competing algorithms in terms of standard evaluation metrics and parallel running time. Algorithm implementations and datasets are available upon request.

The epidural needle guidance with an intelligent and automatic identification system for epidural anesthesia

NASA Astrophysics Data System (ADS)

Kao, Meng-Chun; Ting, Chien-Kun; Kuo, Wen-Chuan

2018-02-01

Incorrect placement of the needle causes medical complications in the epidural block, such as dural puncture or spinal cord injury. This study proposes a system which combines an optical coherence tomography (OCT) imaging probe with an automatic identification (AI) system to objectively identify the position of the epidural needle tip. The automatic identification system uses three features as image parameters to distinguish the different tissue by three classifiers. Finally, we found that the support vector machine (SVM) classifier has highest accuracy, specificity, and sensitivity, which reached to 95%, 98%, and 92%, respectively.

Classifying Microorganisms.

ERIC Educational Resources Information Center

Baker, William P.; Leyva, Kathryn J.; Lang, Michael; Goodmanis, Ben

2002-01-01

Focuses on an activity in which students sample air at school and generate ideas about how to classify the microorganisms they observe. The results are used to compare air quality among schools via the Internet. Supports the development of scientific inquiry and technology skills. (DDR)

An ensemble predictive modeling framework for breast cancer classification.

PubMed

Nagarajan, Radhakrishnan; Upreti, Meenakshi

2017-12-01

Molecular changes often precede clinical presentation of diseases and can be useful surrogates with potential to assist in informed clinical decision making. Recent studies have demonstrated the usefulness of modeling approaches such as classification that can predict the clinical outcomes from molecular expression profiles. While useful, a majority of these approaches implicitly use all molecular markers as features in the classification process often resulting in sparse high-dimensional projection of the samples often comparable to that of the sample size. In this study, a variant of the recently proposed ensemble classification approach is used for predicting good and poor-prognosis breast cancer samples from their molecular expression profiles. In contrast to traditional single and ensemble classifiers, the proposed approach uses multiple base classifiers with varying feature sets obtained from two-dimensional projection of the samples in conjunction with a majority voting strategy for predicting the class labels. In contrast to our earlier implementation, base classifiers in the ensembles are chosen based on maximal sensitivity and minimal redundancy by choosing only those with low average cosine distance. The resulting ensemble sets are subsequently modeled as undirected graphs. Performance of four different classification algorithms is shown to be better within the proposed ensemble framework in contrast to using them as traditional single classifier systems. Significance of a subset of genes with high-degree centrality in the network abstractions across the poor-prognosis samples is also discussed. Copyright © 2017 Elsevier Inc. All rights reserved.

MO-DE-207B-03: Improved Cancer Classification Using Patient-Specific Biological Pathway Information Via Gene Expression Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Young, M; Craft, D

Purpose: To develop an efficient, pathway-based classification system using network biology statistics to assist in patient-specific response predictions to radiation and drug therapies across multiple cancer types. Methods: We developed PICS (Pathway Informed Classification System), a novel two-step cancer classification algorithm. In PICS, a matrix m of mRNA expression values for a patient cohort is collapsed into a matrix p of biological pathways. The entries of p, which we term pathway scores, are obtained from either principal component analysis (PCA), normal tissue centroid (NTC), or gene expression deviation (GED). The pathway score matrix is clustered using both k-means and hierarchicalmore » clustering, and a clustering is judged by how well it groups patients into distinct survival classes. The most effective pathway scoring/clustering combination, per clustering p-value, thus generates various ‘signatures’ for conventional and functional cancer classification. Results: PICS successfully regularized large dimension gene data, separated normal and cancerous tissues, and clustered a large patient cohort spanning six cancer types. Furthermore, PICS clustered patient cohorts into distinct, statistically-significant survival groups. For a suboptimally-debulked ovarian cancer set, the pathway-classified Kaplan-Meier survival curve (p = .00127) showed significant improvement over that of a prior gene expression-classified study (p = .0179). For a pancreatic cancer set, the pathway-classified Kaplan-Meier survival curve (p = .00141) showed significant improvement over that of a prior gene expression-classified study (p = .04). Pathway-based classification confirmed biomarkers for the pyrimidine, WNT-signaling, glycerophosphoglycerol, beta-alanine, and panthothenic acid pathways for ovarian cancer. Despite its robust nature, PICS requires significantly less run time than current pathway scoring methods. Conclusion: This work validates the PICS method to improve cancer classification using biological pathways. Patients are classified with greater specificity and physiological relevance as compared to current gene-specific approaches. Focus now moves to utilizing PICS for pan-cancer patient-specific treatment response prediction.« less

Quantification of HCV RNA in Liver Tissue by bDNA Assay.

PubMed

Dailey, P J; Collins, M L; Urdea, M S; Wilber, J C

1999-01-01

With this statement, Sherlock and Dooley have described two of the three major challenges involved in quantitatively measuring any analyte in tissue samples: the distribution of the analyte in the tissue; and the standard of reference, or denominator, with which to make comparisons between tissue samples. The third challenge for quantitative measurement of an analyte in tissue is to ensure reproducible and quantitative recovery of the analyte on extraction from tissue samples. This chapter describes a method that can be used to measure HCV RNA quantitatively in liver biopsy and tissue samples using the bDNA assay. All three of these challenges-distribution, denominator, and recovery-apply to the measurement of HCV RNA in liver biopsies.

NanoString, a novel digital color-coded barcode technology: current and future applications in molecular diagnostics.

PubMed

Tsang, Hin-Fung; Xue, Vivian Weiwen; Koh, Su-Pin; Chiu, Ya-Ming; Ng, Lawrence Po-Wah; Wong, Sze-Chuen Cesar

2017-01-01

Formalin-fixed, paraffin-embedded (FFPE) tissue sample is a gold mine of resources for molecular diagnosis and retrospective clinical studies. Although molecular technologies have expanded the range of mutations identified in FFPE samples, the applications of existing technologies are limited by the low nucleic acids yield and poor extraction quality. As a result, the routine clinical applications of molecular diagnosis using FFPE samples has been associated with many practical challenges. NanoString technologies utilize a novel digital color-coded barcode technology based on direct multiplexed measurement of gene expression and offer high levels of precision and sensitivity. Each color-coded barcode is attached to a single target-specific probe corresponding to a single gene which can be individually counted without amplification. Therefore, NanoString is especially useful for measuring gene expression in degraded clinical specimens. Areas covered: This article describes the applications of NanoString technologies in molecular diagnostics and challenges associated with its applications and the future development. Expert commentary: Although NanoString technology is still in the early stages of clinical use, it is expected that NanoString-based cancer expression panels would play more important roles in the future in classifying cancer patients and in predicting the response to therapy for better personal therapeutic care.

Characterisation of the aerobic bacterial flora of boid snakes: application of MALDI-TOF mass spectrometry.

PubMed

Plenz, Bastian; Schmidt, Volker; Grosse-Herrenthey, Anke; Krüger, Monika; Pees, Michael

2015-03-14

The aim of this study was to identify aerobic bacterial isolates from the respiratory tract of boids with matrix-assisted laser desorption ionisation-time-of-flight mass spectrometry (MALDI-TOF MS). From 47 boid snakes, swabs from the oral cavity, tracheal wash samples and, in cases in which postmortem examination was performed, pulmonary tissue samples were taken. Each snake was classified as having inflammation of the respiratory tract and/or oral cavity, or without evidence of inflammation based on combination of clinical, cytological and histopathological findings. Samples collected from the respiratory tract and oral cavity were inoculated onto routine media and bacteria were cultured aerobically. All morphologically distinct individual colonies obtained were analysed using MALDI-TOF MS. Unidentified isolates detected in more than three snakes were selected for further 16S rDNA PCR and sequencing. Among all examined isolates (n=243), 49 per cent (n=119) could be sufficiently speciated using MALDI-TOF MS. Molecular biology revealed several bacterial species that have not been previously described in reptiles. With an average of 6.3 different isolates from the respiratory tract and/or oral cavity, boids with inflammatory disease harboured significantly more bacterial species than boids without inflammatory disease (average 2.8 isolates). British Veterinary Association.

Metals detected by ICP/MS in wound tissue of war injuries without fragments in Gaza

PubMed Central

2010-01-01

Background The amount and identity of metals incorporated into "weapons without fragments" remain undisclosed to health personnel. This poses a long-term risk of assumption and contributes to additional hazards for victims because of increased difficulties with clinical management. We assessed if there was evidence that metals are embedded in "wounds without fragments" of victims of the Israeli military operations in Gaza in 2006 and 2009. Methods Biopsies of "wounds without fragments" from clinically classified injuries, amputation (A), charred (C), burns (B), multiple piercing wounds by White Phosphorus (WP) (M), were analyzed by ICP/MS for content in 32 metals. Results Toxic and carcinogenic metals were detected in folds over control tissues in wound tissues from all injuries: in A and C wounds (Al, Ti, Cu, Sr, Ba, Co, Hg, V, Cs and Sn), in M wounds (Al, Ti, Cu, Sr, Ba, Co and Hg) and in B wounds (Co, Hg, Cs, and Sn); Pb and U in wounds of all classes; B, As, Mn, Rb, Cd, Cr, Zn in wounds of all classes, but M; Ni was in wounds of class A. Kind and amounts of metals correlate with clinical classification of injuries, exposing a specific metal signature, similar for 2006 and 2009 samples. Conclusions The presence of toxic and carcinogenic metals in wound tissue is indicative of the presence in weapon inducing the injury. Metal contamination of wounds carries unknown long term risks for survivors, and can imply effects on populations from environmental contamination. We discuss remediation strategies, and believe that these data suggest the need for epidemiological and environmental surveys. PMID:20579349

Laser photobiomodulation in pressure ulcer healing of human diabetic patients: gene expression analysis of inflammatory biochemical markers.

PubMed

Ruh, Anelice Calixto; Frigo, Lúcio; Cavalcanti, Marcos Fernando Xisto Braga; Svidnicki, Paulo; Vicari, Viviane Nogaroto; Lopes-Martins, Rodrigo Alvaro Brandão; Leal Junior, Ernesto Cesar Pinto; De Isla, Natalia; Diomede, Francesca; Trubiani, Oriana; Favero, Giovani Marino

2018-01-01

Pressure ulcers (PU) are wounds located mainly on bone surfaces where the tissue under pressure suffers ischemia leading to cellular lesion and necrosis , its causes and the healing process depend on several factors. The aim of this study was evaluating the gene expression of inflammatory/reparative factors: IL6, TNF, VEGF, and TGF, which take part in the tissue healing process under effects of low-level laser therapy (LLLT). In order to perform lesion area analysis, PUs were photographed and computer analyzed. Biochemical analysis was performed sa.mpling ulcer border tissue obtained through biopsy before and after laser therapy and quantitative real-time PCR (qRT-PCR) analysis. The study comprised eight individuals, mean age sixty-two years old, and sacroiliac and calcaneous PU, classified as degree III and IV according to the National Pressure Ulcer Advisory Panel (NPUAP). PUs were irradiated with low-level laser (InGaAIP, 100 mW, 660 nm), energy density 2 J/cm 2 , once a day, with intervals of 24 h, totaling 12 applications. The lesion area analysis revealed averaged improvement of the granulation tissue size up to 50% from pre- to post-treatment. qRT-PCR analysis revealed that IL6 values were not significantly different before and after treatment, TNF gene expression was reduced, and VEFG and TGF-β gene expression increased after treatment. After LLLT, wounds presented improvement in gross appearance, with increase in factors VEFG and TGF-β, and reduction of TNF; despite our promising results, they have to be analyzed carefully as this study did not have a control group.

Cutaneous and Subcutaneous Soft Tissue Tumours in Snakes: A Retrospective Study of 33 Cases.

PubMed

Dietz, J; Heckers, K O; Aupperle, H; Pees, M

2016-07-01

Cutaneous and subcutaneous soft tissue tumours have been rarely described in detail in snakes. Several malignant entities show strikingly similar histological patterns and therefore the term soft tissue sarcoma (STS) has become a standard histopathological diagnosis. The present study characterizes soft tissue tumours in 33 snakes. Samples included 29 surgically excised masses and four carcasses. Additionally, six animals were humanely destroyed and submitted for necropsy examination following tumour recurrence. Benign neoplasms (n = 8) were described as lipomas of varying differentiation. Recurrence was observed in two of five snakes in which the clinical course was recorded. Malignant neoplasms (n = 25) were diagnosed as STS and graded according to a three-point system previously applied to canine STS. Five (20%) of the primary tumours were classified as grade 1, eleven (44%) as grade 2 and nine (36%) as grade 3 sarcomas. Clinically, recurrence of STS was observed in 11 of 17 cases with available follow-up information. Pathologically, multiple cutaneous metastases were found in one grass snake (Natrix natrix), while visceral metastases were observed in one carpet python (Morelia spilota) and two corn snakes (Pantherophis guttatus). Metastatic risk appears to increase with histological grade. Surgical excision generally represents the current therapy of choice for STS. This study includes the first reports of conventional lipomas in a ribbon snake (Thamnophis radix), angiolipomas in a black-headed python (Aspidites melanocephalus) and a corn snake as well as of STS in a Jamaican boa (Epicrates subflavus), emerald tree boa (Corallus caninus), grass snake (N. natrix), African house snake (Lamprophis fuliginosus), California kingsnake (Lampropeltis getula californiae) and common garter snake (Thamnophis sirtalis). Copyright © 2016 Elsevier Ltd. All rights reserved.

A fuzzy classifier system for process control

NASA Technical Reports Server (NTRS)

Karr, C. L.; Phillips, J. C.

1994-01-01

A fuzzy classifier system that discovers rules for controlling a mathematical model of a pH titration system was developed by researchers at the U.S. Bureau of Mines (USBM). Fuzzy classifier systems successfully combine the strengths of learning classifier systems and fuzzy logic controllers. Learning classifier systems resemble familiar production rule-based systems, but they represent their IF-THEN rules by strings of characters rather than in the traditional linguistic terms. Fuzzy logic is a tool that allows for the incorporation of abstract concepts into rule based-systems, thereby allowing the rules to resemble the familiar 'rules-of-thumb' commonly used by humans when solving difficult process control and reasoning problems. Like learning classifier systems, fuzzy classifier systems employ a genetic algorithm to explore and sample new rules for manipulating the problem environment. Like fuzzy logic controllers, fuzzy classifier systems encapsulate knowledge in the form of production rules. The results presented in this paper demonstrate the ability of fuzzy classifier systems to generate a fuzzy logic-based process control system.

Investigating the biochemical progression of liver disease through fibrosis, cirrhosis, dysplasia, and hepatocellular carcinoma using Fourier transform infrared spectroscopic imaging

NASA Astrophysics Data System (ADS)

Sreedhar, Hari; Pant, Mamta; Ronquillo, Nemencio R.; Davidson, Bennett; Nguyen, Peter; Chennuri, Rohini; Choi, Jacqueline; Herrera, Joaquin A.; Hinojosa, Ana C.; Jin, Ming; Kajdacsy-Balla, Andre; Guzman, Grace; Walsh, Michael J.

2014-03-01

Hepatocellular carcinoma (HCC) is the most common form of primary hepatic carcinoma. HCC ranks the fourth most prevalent malignant tumor and the third leading cause of cancer related death in the world. Hepatocellular carcinoma develops in the context of chronic liver disease and its evolution is characterized by progression through intermediate stages to advanced disease and possibly even death. The primary sequence of hepatocarcinogenesis includes the development of cirrhosis, followed by dysplasia, and hepatocellular carcinoma.1 We addressed the utility of Fourier Transform Infrared (FT-IR) spectroscopic imaging, both as a diagnostic tool of the different stages of the disease and to gain insight into the biochemical process associated with disease progression. Tissue microarrays were obtained from the University of Illinois at Chicago tissue bank consisting of liver explants from 12 transplant patients. Tissue core biopsies were obtained from each explant targeting regions of normal, liver cell dysplasia including large cell change and small cell change, and hepatocellular carcinoma. We obtained FT-IR images of these tissues using a modified FT-IR system with high definition capabilities. Firstly, a supervised spectral classifier was built to discriminate between normal and cancerous hepatocytes. Secondly, an expanded classifier was built to discriminate small cell and large cell changes in liver disease. With the emerging advances in FT-IR instrumentation and computation there is a strong drive to develop this technology as a powerful adjunct to current histopathology approaches to improve disease diagnosis and prognosis.

Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization.

PubMed

Kainz, Philipp; Pfeiffer, Michael; Urschler, Martin

2017-01-01

Segmentation of histopathology sections is a necessary preprocessing step for digital pathology. Due to the large variability of biological tissue, machine learning techniques have shown superior performance over conventional image processing methods. Here we present our deep neural network-based approach for segmentation and classification of glands in tissue of benign and malignant colorectal cancer, which was developed to participate in the GlaS@MICCAI2015 colon gland segmentation challenge. We use two distinct deep convolutional neural networks (CNN) for pixel-wise classification of Hematoxylin-Eosin stained images. While the first classifier separates glands from background, the second classifier identifies gland-separating structures. In a subsequent step, a figure-ground segmentation based on weighted total variation produces the final segmentation result by regularizing the CNN predictions. We present both quantitative and qualitative segmentation results on the recently released and publicly available Warwick-QU colon adenocarcinoma dataset associated with the GlaS@MICCAI2015 challenge and compare our approach to the simultaneously developed other approaches that participated in the same challenge. On two test sets, we demonstrate our segmentation performance and show that we achieve a tissue classification accuracy of 98% and 95%, making use of the inherent capability of our system to distinguish between benign and malignant tissue. Our results show that deep learning approaches can yield highly accurate and reproducible results for biomedical image analysis, with the potential to significantly improve the quality and speed of medical diagnoses.

Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization

PubMed Central

Kainz, Philipp; Pfeiffer, Michael

2017-01-01

Segmentation of histopathology sections is a necessary preprocessing step for digital pathology. Due to the large variability of biological tissue, machine learning techniques have shown superior performance over conventional image processing methods. Here we present our deep neural network-based approach for segmentation and classification of glands in tissue of benign and malignant colorectal cancer, which was developed to participate in the GlaS@MICCAI2015 colon gland segmentation challenge. We use two distinct deep convolutional neural networks (CNN) for pixel-wise classification of Hematoxylin-Eosin stained images. While the first classifier separates glands from background, the second classifier identifies gland-separating structures. In a subsequent step, a figure-ground segmentation based on weighted total variation produces the final segmentation result by regularizing the CNN predictions. We present both quantitative and qualitative segmentation results on the recently released and publicly available Warwick-QU colon adenocarcinoma dataset associated with the GlaS@MICCAI2015 challenge and compare our approach to the simultaneously developed other approaches that participated in the same challenge. On two test sets, we demonstrate our segmentation performance and show that we achieve a tissue classification accuracy of 98% and 95%, making use of the inherent capability of our system to distinguish between benign and malignant tissue. Our results show that deep learning approaches can yield highly accurate and reproducible results for biomedical image analysis, with the potential to significantly improve the quality and speed of medical diagnoses. PMID:29018612

Fiber optic FTIR instrument for in vivo detection of colonic neoplasia

NASA Astrophysics Data System (ADS)

Van Nortwick, Matthew; Hargrove, John; Wolters, Rolf; Crawford, James M.; Arroyo, May; Mackanos, Mark; Contag, Christopher H.; Wang, Thomas D.

2009-02-01

We demonstrate the proof of concept for use of a fiber optic FTIR instrument to perform in vivo detection of colonic neoplasia as an adjunct to medical endoscopy. FTIR is sensitive to the molecular composition of tissue, and can be used as a guide for biopsy by identifying pre-malignant tissue (dysplasia). First, we demonstrate the use of a silver halide optical fiber to collect mid-infrared absorption spectra in the 950 to 1800 cm-1 regime with high signal-to-noise from biopsy specimens of colonic mucosa tissue ex vivo. We observed subtle differences in wavenumber and magnitude of the absorbance peaks over this regime. We then show that optimal sub-ranges can be defined within this spectral regime and that spectral pre-processing can be performed to classify the tissue as normal, hyperplasia, or dysplasia with high levels of performance. We used a partial least squares discriminant analysis and a leave-one-subject-out crossvalidation strategy to classify the spectra. The results were compared with histology, and the optimal thresholds resulted in an overall sensitivity, specificity, accuracy, and positive predictive value of 96%, 92%, 93%, and 82%, respectively for this technique. We demonstrate that mid-infrared absorption spectra can be collected remotely with an optical fiber and used to identify colonic dysplasia with high accuracy. We are now developing an endoscope compatible optical fiber to use this technique clinically for the early detection of cancer.

Detection of cervical intraepithelial neoplasias and cancers in cervical tissue by in vivo light scattering

PubMed Central

Mourant, Judith R.; Bocklage, Thérese J.; Powers, Tamara M.; Greene, Heather M.; Dorin, Maxine H.; Waxman, Alan G.; Zsemlye, Meggan M.; Smith, Harriet O.

2009-01-01

Objective To examine the utility of in vivo elastic light scattering measurements to identify cervical intraepithelial neoplasias (CIN) 2/3 and cancers in women undergoing colposcopy and to determine the effects of patient characteristics such as menstrual status on the elastic light scattering spectroscopic measurements. Materials and Methods A fiber optic probe was used to measure light transport in the cervical epithelium of patients undergoing colposcopy. Spectroscopic results from 151 patients were compared with histopathology of the measured and biopsied sites. A method of classifying the measured sites into two clinically relevant categories was developed and tested using five-fold cross-validation. Results Statistically significant effects by age at diagnosis, menopausal status, timing of the menstrual cycle, and oral contraceptive use were identified, and adjustments based upon these measurements were incorporated in the classification algorithm. A sensitivity of 77±5% and a specificity of 62±2% were obtained for separating CIN 2/3 and cancer from other pathologies and normal tissue. Conclusions The effects of both menstrual status and age should be taken into account in the algorithm for classifying tissue sites based on elastic light scattering spectroscopy. When this is done, elastic light scattering spectroscopy shows good potential for real-time diagnosis of cervical tissue at colposcopy. Guiding biopsy location is one potential near-term clinical application area, while facilitating ”see and treat” protocols is a longer term goal. Improvements in accuracy are essential. PMID:20694193

AVNM: A Voting based Novel Mathematical Rule for Image Classification.

PubMed

Vidyarthi, Ankit; Mittal, Namita

2016-12-01

In machine learning, the accuracy of the system depends upon classification result. Classification accuracy plays an imperative role in various domains. Non-parametric classifier like K-Nearest Neighbor (KNN) is the most widely used classifier for pattern analysis. Besides its easiness, simplicity and effectiveness characteristics, the main problem associated with KNN classifier is the selection of a number of nearest neighbors i.e. "k" for computation. At present, it is hard to find the optimal value of "k" using any statistical algorithm, which gives perfect accuracy in terms of low misclassification error rate. Motivated by the prescribed problem, a new sample space reduction weighted voting mathematical rule (AVNM) is proposed for classification in machine learning. The proposed AVNM rule is also non-parametric in nature like KNN. AVNM uses the weighted voting mechanism with sample space reduction to learn and examine the predicted class label for unidentified sample. AVNM is free from any initial selection of predefined variable and neighbor selection as found in KNN algorithm. The proposed classifier also reduces the effect of outliers. To verify the performance of the proposed AVNM classifier, experiments are made on 10 standard datasets taken from UCI database and one manually created dataset. The experimental result shows that the proposed AVNM rule outperforms the KNN classifier and its variants. Experimentation results based on confusion matrix accuracy parameter proves higher accuracy value with AVNM rule. The proposed AVNM rule is based on sample space reduction mechanism for identification of an optimal number of nearest neighbor selections. AVNM results in better classification accuracy and minimum error rate as compared with the state-of-art algorithm, KNN, and its variants. The proposed rule automates the selection of nearest neighbor selection and improves classification rate for UCI dataset and manually created dataset. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Optimal number of features as a function of sample size for various classification rules.

PubMed

Hua, Jianping; Xiong, Zixiang; Lowey, James; Suh, Edward; Dougherty, Edward R

2005-04-15

Given the joint feature-label distribution, increasing the number of features always results in decreased classification error; however, this is not the case when a classifier is designed via a classification rule from sample data. Typically (but not always), for fixed sample size, the error of a designed classifier decreases and then increases as the number of features grows. The potential downside of using too many features is most critical for small samples, which are commonplace for gene-expression-based classifiers for phenotype discrimination. For fixed sample size and feature-label distribution, the issue is to find an optimal number of features. Since only in rare cases is there a known distribution of the error as a function of the number of features and sample size, this study employs simulation for various feature-label distributions and classification rules, and across a wide range of sample and feature-set sizes. To achieve the desired end, finding the optimal number of features as a function of sample size, it employs massively parallel computation. Seven classifiers are treated: 3-nearest-neighbor, Gaussian kernel, linear support vector machine, polynomial support vector machine, perceptron, regular histogram and linear discriminant analysis. Three Gaussian-based models are considered: linear, nonlinear and bimodal. In addition, real patient data from a large breast-cancer study is considered. To mitigate the combinatorial search for finding optimal feature sets, and to model the situation in which subsets of genes are co-regulated and correlation is internal to these subsets, we assume that the covariance matrix of the features is blocked, with each block corresponding to a group of correlated features. Altogether there are a large number of error surfaces for the many cases. These are provided in full on a companion website, which is meant to serve as resource for those working with small-sample classification. For the companion website, please visit http://public.tgen.org/tamu/ofs/ e-dougherty@ee.tamu.edu.

Development and validation of a multiplex conventional PCR assay for simultaneous detection and grouping of porcine bocaviruses.

PubMed

Zheng, Xiaowen; Liu, Gaopeng; Opriessnig, Tanja; Wang, Zining; Yang, Zongqi; Jiang, Yonghou

2016-10-01

Porcine bocavirus (PBoV), a newly described porcine parvovirus, has received attention because it can be commonly identified in clinically affected pigs including pigs with post-weaning multisystemic wasting syndrome (PWMS) and pigs with diarrhea. In recent years, novel PBoVs have been identified and were classified into three genogroups, but the ability to detect and classify these novel PBoVs is not comprehensive to date. In this study, a multiplex conventional PCR assay for simultaneous detection and grouping of PBoVs was developed by screening combinations of mixed primer pairs followed by optimization of the PCR conditions. This method exclusively amplifies targeted fragments of 531bp from the VP1 gene of PBoV G1, 291bp from the NP1 gene of PBoV G2, and 384bp from the NP1/VP1 gene of PBoV G3. The assay has a detection limit of 1.0×10(3)copies/μL for PBoV G1 4.5×10(3) for PBoV G2 and 3.8×10(3) for PBoV G3 based on testing mixed purified plasmid constructs containing the specific viral target fragments. The performance of the multiplex PCR assay was comparable to that of the single PCRs which used the same primer pairs. Using the newly established multiplex PCR assay, 227 field samples including faeces, serum and tissue samples from pigs were investigated. All three PBoV genogroups were detected in the clinical samples with a detection rate of 1.3%, 2.6% and 12.3%, respectively for PBoV G1, G2 and G3. Additionally, coinfections with two or more PBoV were detected in 1.7% of the samples investigated. These results indicate the multiplex PCR assay is specific, sensitive and rapid, and can be used for the detection and differentiation of single and multiple infections of the three PBoV genogroups in pigs. Copyright © 2016 Elsevier B.V. All rights reserved.

Combined Bisulfite Restriction Analysis for brain tissue identification.

PubMed

Samsuwan, Jarunya; Muangsub, Tachapol; Yanatatsaneejit, Pattamawadee; Mutirangura, Apiwat; Kitkumthorn, Nakarin

2018-05-01

According to the tissue-specific methylation database (doi: 10.1016/j.gene.2014.09.060), methylation at CpG locus cg03096975 in EML2 has been preliminarily proven to be specific to brain tissue. In this study, we enlarged sample size and developed a technique for identifying brain tissue in aged samples. Combined Bisulfite Restriction Analysis-for EML2 (COBRA-EML2) technique was established and validated in various organ samples obtained from 108 autopsies. In addition, this technique was also tested for its reliability, minimal DNA concentration detected, and use in aged samples and in samples obtained from specific brain compartments and spinal cord. COBRA-EML2 displayed 100% sensitivity and specificity for distinguishing brain tissue from other tissues, showed high reliability, was capable of detecting minimal DNA concentration (0.015ng/μl), could be used for identifying brain tissue in aged samples. In summary, COBRA-EML2 is a technique to identify brain tissue. This analysis is useful in criminal cases since it can identify the vital organ tissues from small samples acquired from criminal scenes. The results from this analysis can be counted as a medical and forensic marker supporting criminal investigations, and as one of the evidences in court rulings. Copyright © 2018 Elsevier B.V. All rights reserved.

Spatial and spectral analysis of corneal epithelium injury using hyperspectral images

NASA Astrophysics Data System (ADS)

Md Noor, Siti Salwa; Michael, Kaleena; Marshall, Stephen; Ren, Jinchang

2017-12-01

Eye assessment is essential in preventing blindness. Currently, the existing methods to assess corneal epithelium injury are complex and require expert knowledge. Hence, we have introduced a non-invasive technique using hyperspectral imaging (HSI) and an image analysis algorithm of corneal epithelium injury. Three groups of images were compared and analyzed, namely healthy eyes, injured eyes, and injured eyes with stain. Dimensionality reduction using principal component analysis (PCA) was applied to reduce massive data and redundancies. The first 10 principal components (PCs) were selected for further processing. The mean vector of 10 PCs with 45 pairs of all combinations was computed and sent to two classifiers. A quadratic Bayes normal classifier (QDC) and a support vector classifier (SVC) were used in this study to discriminate the eleven eyes into three groups. As a result, the combined classifier of QDC and SVC showed optimal performance with 2D PCA features (2DPCA-QDSVC) and was utilized to classify normal and abnormal tissues, using color image segmentation. The result was compared with human segmentation. The outcome showed that the proposed algorithm produced extremely promising results to assist the clinician in quantifying a cornea injury.

Combined data mining/NIR spectroscopy for purity assessment of lime juice

NASA Astrophysics Data System (ADS)

Shafiee, Sahameh; Minaei, Saeid

2018-06-01

This paper reports the data mining study on the NIR spectrum of lime juice samples to determine their purity (natural or synthetic). NIR spectra for 72 pure and synthetic lime juice samples were recorded in reflectance mode. Sample outliers were removed using PCA analysis. Different data mining techniques for feature selection (Genetic Algorithm (GA)) and classification (including the radial basis function (RBF) network, Support Vector Machine (SVM), and Random Forest (RF) tree) were employed. Based on the results, SVM proved to be the most accurate classifier as it achieved the highest accuracy (97%) using the raw spectrum information. The classifier accuracy dropped to 93% when selected feature vector by GA search method was applied as classifier input. It can be concluded that some relevant features which produce good performance with the SVM classifier are removed by feature selection. Also, reduced spectra using PCA do not show acceptable performance (total accuracy of 66% by RBFNN), which indicates that dimensional reduction methods such as PCA do not always lead to more accurate results. These findings demonstrate the potential of data mining combination with near-infrared spectroscopy for monitoring lime juice quality in terms of natural or synthetic nature.

Fuzzy Nonlinear Proximal Support Vector Machine for Land Extraction Based on Remote Sensing Image

PubMed Central

Zhong, Xiaomei; Li, Jianping; Dou, Huacheng; Deng, Shijun; Wang, Guofei; Jiang, Yu; Wang, Yongjie; Zhou, Zebing; Wang, Li; Yan, Fei

2013-01-01

Currently, remote sensing technologies were widely employed in the dynamic monitoring of the land. This paper presented an algorithm named fuzzy nonlinear proximal support vector machine (FNPSVM) by basing on ETM+ remote sensing image. This algorithm is applied to extract various types of lands of the city Da’an in northern China. Two multi-category strategies, namely “one-against-one” and “one-against-rest” for this algorithm were described in detail and then compared. A fuzzy membership function was presented to reduce the effects of noises or outliers on the data samples. The approaches of feature extraction, feature selection, and several key parameter settings were also given. Numerous experiments were carried out to evaluate its performances including various accuracies (overall accuracies and kappa coefficient), stability, training speed, and classification speed. The FNPSVM classifier was compared to the other three classifiers including the maximum likelihood classifier (MLC), back propagation neural network (BPN), and the proximal support vector machine (PSVM) under different training conditions. The impacts of the selection of training samples, testing samples and features on the four classifiers were also evaluated in these experiments. PMID:23936016

Tissue spray ionization mass spectrometry for rapid recognition of human lung squamous cell carcinoma

NASA Astrophysics Data System (ADS)

Wei, Yiping; Chen, Liru; Zhou, Wei; Chingin, Konstantin; Ouyang, Yongzhong; Zhu, Tenggao; Wen, Hua; Ding, Jianhua; Xu, Jianjun; Chen, Huanwen

2015-05-01

Tissue spray ionization mass spectrometry (TSI-MS) directly on small tissue samples has been shown to provide highly specific molecular information. In this study, we apply this method to the analysis of 38 pairs of human lung squamous cell carcinoma tissue (cancer) and adjacent normal lung tissue (normal). The main components of pulmonary surfactants, dipalmitoyl phosphatidylcholine (DPPC, m/z 757.47), phosphatidylcholine (POPC, m/z 782.52), oleoyl phosphatidylcholine (DOPC, m/z 808.49), and arachidonic acid stearoyl phosphatidylcholine (SAPC, m/z 832.43), were identified using high-resolution tandem mass spectrometry. Monte Carlo sampling partial least squares linear discriminant analysis (PLS-LDA) was used to distinguish full-mass-range mass spectra of cancer samples from the mass spectra of normal tissues. With 5 principal components and 30 - 40 Monte Carlo samplings, the accuracy of cancer identification in matched tissue samples reached 94.42%. Classification of a tissue sample required less than 1 min, which is much faster than the analysis of frozen sections. The rapid, in situ diagnosis with minimal sample consumption provided by TSI-MS is advantageous for surgeons. TSI-MS allows them to make more informed decisions during surgery.

Application of ribonucleoside vanadyl complex (RVC) for developing a multifunctional tissue preservative solution

PubMed Central

Yu, Cheng-Chia; Chen, Chin-Chuan

2018-01-01

The quality of biological samples greatly affects the accuracy of scientific results. However, RNA in cryopreserved tissues gradually degrades during storage, leading to errors in the results of subsequent experiments. A suitable sample preservative solution can prolong storage and enhance the research value of samples. Here, we developed a sample preservative solution using the properties of the ribonucleoside vanadyl complex (RVC) and compared its effects on RNA and DNA quality, protein activity, and tissue morphology with the commercially available and widely used RNAlater® Stabilization Solution. The results showed that both the RVC-based preservative solution and RNAlater can effectively delay RNA degradation in tissue samples stored at 4°C or −80°C compared with samples stored without any preservative solution. In contrast to RNAlater, the RVC-based preservative solution did not result in damage to the tissue morphology or a loss of protein activity. Additionally, the RVC-based preservative solution did not affect the RNA and genomic DNA contents of the tissue samples or the results of subsequent experimental analyses. An RVC-based reagent can be used as a multifunctional yet relatively inexpensive tissue preservative solution to provide a comprehensive and cost-effective method for preserving samples for tissue banks. PMID:29538436

Generative Models for Similarity-based Classification

DTIC Science & Technology

2007-01-01

NC), local nearest centroid (local NC), k-nearest neighbors ( kNN ), and condensed nearest neighbors (CNN) are all similarity-based classifiers which...vector machine to the k nearest neighbors of the test sample [80]. The SVM- KNN method was developed to address the robustness and dimensionality...concerns that afflict nearest neighbors and SVMs. Similarly to the nearest-means classifier, the SVM- KNN is a hybrid local and global classifier developed

Walking Objectively Measured: Classifying Accelerometer Data with GPS and Travel Diaries

PubMed Central

Kang, Bumjoon; Moudon, Anne V.; Hurvitz, Philip M.; Reichley, Lucas; Saelens, Brian E.

2013-01-01

Purpose This study developed and tested an algorithm to classify accelerometer data as walking or non-walking using either GPS or travel diary data within a large sample of adults under free-living conditions. Methods Participants wore an accelerometer and a GPS unit, and concurrently completed a travel diary for 7 consecutive days. Physical activity (PA) bouts were identified using accelerometry count sequences. PA bouts were then classified as walking or non-walking based on a decision-tree algorithm consisting of 7 classification scenarios. Algorithm reliability was examined relative to two independent analysts’ classification of a 100-bout verification sample. The algorithm was then applied to the entire set of PA bouts. Results The 706 participants’ (mean age 51 years, 62% female, 80% non-Hispanic white, 70% college graduate or higher) yielded 4,702 person-days of data and had a total of 13,971 PA bouts. The algorithm showed a mean agreement of 95% with the independent analysts. It classified physical activity into 8,170 (58.5 %) walking bouts and 5,337 (38.2%) non-walking bouts; 464 (3.3%) bouts were not classified for lack of GPS and diary data. Nearly 70% of the walking bouts and 68% of the non-walking bouts were classified using only the objective accelerometer and GPS data. Travel diary data helped classify 30% of all bouts with no GPS data. The mean duration of PA bouts classified as walking was 15.2 min (SD=12.9). On average, participants had 1.7 walking bouts and 25.4 total walking minutes per day. Conclusions GPS and travel diary information can be helpful in classifying most accelerometer-derived PA bouts into walking or non-walking behavior. PMID:23439414

Automatic seed selection for segmentation of liver cirrhosis in laparoscopic sequences

NASA Astrophysics Data System (ADS)

Sinha, Rahul; Marcinczak, Jan Marek; Grigat, Rolf-Rainer

2014-03-01

For computer aided diagnosis based on laparoscopic sequences, image segmentation is one of the basic steps which define the success of all further processing. However, many image segmentation algorithms require prior knowledge which is given by interaction with the clinician. We propose an automatic seed selection algorithm for segmentation of liver cirrhosis in laparoscopic sequences which assigns each pixel a probability of being cirrhotic liver tissue or background tissue. Our approach is based on a trained classifier using SIFT and RGB features with PCA. Due to the unique illumination conditions in laparoscopic sequences of the liver, a very low dimensional feature space can be used for classification via logistic regression. The methodology is evaluated on 718 cirrhotic liver and background patches that are taken from laparoscopic sequences of 7 patients. Using a linear classifier we achieve a precision of 91% in a leave-one-patient-out cross-validation. Furthermore, we demonstrate that with logistic probability estimates, seeds with high certainty of being cirrhotic liver tissue can be obtained. For example, our precision of liver seeds increases to 98.5% if only seeds with more than 95% probability of being liver are used. Finally, these automatically selected seeds can be used as priors in Graph Cuts which is demonstrated in this paper.

Second harmonic generation microscopy analysis of extracellular matrix changes in human idiopathic pulmonary fibrosis

PubMed Central

Tilbury, Karissa; Hocker, James; Wen, Bruce L.; Sandbo, Nathan; Singh, Vikas; Campagnola, Paul J.

2014-01-01

Abstract. Patients with idiopathic fibrosis (IPF) have poor long-term survival as there are limited diagnostic/prognostic tools or successful therapies. Remodeling of the extracellular matrix (ECM) has been implicated in IPF progression; however, the structural consequences on the collagen architecture have not received considerable attention. Here, we demonstrate that second harmonic generation (SHG) and multiphoton fluorescence microscopy can quantitatively differentiate normal and IPF human tissues. For SHG analysis, we developed a classifier based on wavelet transforms, principle component analysis, and a K-nearest-neighbor algorithm to classify the specific alterations of the collagen structure observed in IPF tissues. The resulting ROC curves obtained by varying the numbers of principal components and nearest neighbors yielded accuracies of >95%. In contrast, simpler metrics based on SHG intensity and collagen coverage in the image provided little or no discrimination. We also characterized the change in the elastin/collagen balance by simultaneously measuring the elastin autofluorescence and SHG intensities and found that the IPF tissues were less elastic relative to collagen. This is consistent with known mechanical consequences of the disease. Understanding ECM remodeling in IPF via nonlinear optical microscopy may enhance our ability to differentiate patients with rapid and slow progression and, thus, provide better prognostic information. PMID:25134793

Vitamin C–enriched gelatin supplementation before intermittent activity augments collagen synthesis12

PubMed Central

Shaw, Gregory; Lee-Barthel, Ann; Ross, Megan LR; Wang, Bing; Baar, Keith

2017-01-01

Background: Musculoskeletal injuries are the most common complaint in active populations. More than 50% of all injuries in sports can be classified as sprains, strains, ruptures, or breaks of musculoskeletal tissues. Nutritional and/or exercise interventions that increase collagen synthesis and strengthen these tissues could have an important effect on injury rates. Objective: This study was designed to determine whether gelatin supplementation could increase collagen synthesis. Design: Eight healthy male subjects completed a randomized, double-blinded, crossover-design study in which they consumed either 5 or 15 g of vitamin C–enriched gelatin or a placebo control. After the initial drink, blood was taken every 30 min to determine amino acid content in the blood. A larger blood sample was taken before and 1 h after consumption of gelatin for treatment of engineered ligaments. One hour after the initial supplement, the subjects completed 6 min of rope-skipping to stimulate collagen synthesis. This pattern of supplementation was repeated 3 times/d with ≥6 h between exercise bouts for 3 d. Blood was drawn before and 4, 24, 48, and 72 h after the first exercise bout for determination of amino-terminal propeptide of collagen I content. Results: Supplementation with increasing amounts of gelatin increased circulating glycine, proline, hydroxyproline, and hydroxylysine, peaking 1 h after the supplement was given. Engineered ligaments treated for 6 d with serum from samples collected before or 1 h after subjects consumed a placebo or 5 or 15 g gelatin showed increased collagen content and improved mechanics. Subjects who took 15 g gelatin 1 h before exercise showed double the amino-terminal propeptide of collagen I in their blood, indicating increased collagen synthesis. Conclusion: These data suggest that adding gelatin to an intermittent exercise program improves collagen synthesis and could play a beneficial role in injury prevention and tissue repair. This trial was registered at the Australian New Zealand Clinical Trials Registry as ACTRN12616001092482. PMID:27852613

Discrimination of lichen genera and species using element concentrations

USGS Publications Warehouse

Bennett, James P.

2008-01-01

The importance of organic chemistry in the classification of lichens is well established, but inorganic chemistry has been largely overlooked. Six lichen species were studied over a period of 23 years that were growing in 11 protected areas of the northern Great Lakes ecoregion, which were not greatly influenced by anthropogenic particulates or gaseous air pollutants. The elemental data from these studies were aggregated in order to test the hypothesis that differences among species in tissue element concentrations were large enough to discriminate between taxa faithfully. Concentrations of 16 chemical elements that were found in tissue samples from Cladonia rangiferina, Evernia mesomorpha, Flavopunctelia flaventior, Hypogymnia physodes, Parmelia sulcata, and Punctelia rudecta were analyzed statistically using multivariate discriminant functions and CART analyses, as well as t-tests. Genera and species were clearly separated in element space, and elemental discriminant functions were able to classify 91-100 of the samples correctly into species. At the broadest level, a Zn concentration of 51 ppm in tissues of four of the lichen species effectively discriminated foliose from fruticose species. Similarly, a S concentration of 680 ppm discriminated C. rangiferina and E. mesomorpha, and a Ca concentration of 10 436 ppm discriminated H. physodes from P. sulcata. For the three parmelioid species, a Ca concentration >32 837 ppm discriminated Punctelia rudecta from the other two species, while a Zn concentration of 56 ppm discriminated Parmelia sulcata from F. flaventior. Foliose species also had higher concentrations than did fruticose species of all elements except Na. Elemental signatures for each of the six species were developed using standardized means. Twenty-four mechanisms explaining the differences among species are summarized. Finally, the relationships of four species based on element concentrations, using additive-trees clustering of a Euclidean-distance matrix, produced identical relationships as did analyses based on secondary product chemistry that used additive-trees clustering of a Jaccard similarity matrix. At least for these six species, element composition has taxonomic significance, and may be useful for discriminating other taxa.

Vitamin C-enriched gelatin supplementation before intermittent activity augments collagen synthesis.

PubMed

Shaw, Gregory; Lee-Barthel, Ann; Ross, Megan Lr; Wang, Bing; Baar, Keith

2017-01-01

Musculoskeletal injuries are the most common complaint in active populations. More than 50% of all injuries in sports can be classified as sprains, strains, ruptures, or breaks of musculoskeletal tissues. Nutritional and/or exercise interventions that increase collagen synthesis and strengthen these tissues could have an important effect on injury rates. This study was designed to determine whether gelatin supplementation could increase collagen synthesis. Eight healthy male subjects completed a randomized, double-blinded, crossover-design study in which they consumed either 5 or 15 g of vitamin C-enriched gelatin or a placebo control. After the initial drink, blood was taken every 30 min to determine amino acid content in the blood. A larger blood sample was taken before and 1 h after consumption of gelatin for treatment of engineered ligaments. One hour after the initial supplement, the subjects completed 6 min of rope-skipping to stimulate collagen synthesis. This pattern of supplementation was repeated 3 times/d with ≥6 h between exercise bouts for 3 d. Blood was drawn before and 4, 24, 48, and 72 h after the first exercise bout for determination of amino-terminal propeptide of collagen I content. Supplementation with increasing amounts of gelatin increased circulating glycine, proline, hydroxyproline, and hydroxylysine, peaking 1 h after the supplement was given. Engineered ligaments treated for 6 d with serum from samples collected before or 1 h after subjects consumed a placebo or 5 or 15 g gelatin showed increased collagen content and improved mechanics. Subjects who took 15 g gelatin 1 h before exercise showed double the amino-terminal propeptide of collagen I in their blood, indicating increased collagen synthesis. These data suggest that adding gelatin to an intermittent exercise program improves collagen synthesis and could play a beneficial role in injury prevention and tissue repair. This trial was registered at the Australian New Zealand Clinical Trials Registry as ACTRN12616001092482. © 2017 American Society for Nutrition.

Assessment of gene expression profiles in peripheral occlusive arterial disease.

PubMed

Bubenek, Serban; Nastase, Anca; Niculescu, Ana Maria; Baila, Sorin; Herlea, Vlad; Lazar, Vadimir; Paslaru, Liliana; Botezatu, Anca; Tomescu, Dana; Popescu, Irinel; Dima, Simona

2012-01-01

Molecular events responsible for the onset and progression of peripheral occlusive arterial disease (POAD) are incompletely understood. Gene expression profiling may point out relevant features of the disease. Tissue samples were collected as operatory waste from a total of 36 patients with (n = 18) and without (n = 18) POAD. The tissues were histologically evaluated, and the patients with POAD were classified according to Leriche-Fontaine (LF) classification: 11% with stage IIB, 22% with stage III, and 67% with stage IV. Total RNA was isolated from all samples and hybridized onto Agilent 4×44K Oligo microarray slides. The bioinformatic analysis identified genes differentially expressed between control and pathologic tissues. Ten genes with a fold change ≥ 2 (1 with a fold change ≥ 1.8) were selected for quantitative polymerase chain reaction validation (GPC3, CFD, GDF10, ITLN1, TSPAN8, MMP28, NNMT, SERPINA5, LUM, and FDXR). C-reactive protein (CRP) was assessed with a specific assay, while nicotinamide N-methyltransferase (NNMT) was evaluated in the patient serum by enzyme-linked immunosorbent assay. A multiple regression analysis showed that the level of CRP in the serum is correlated with the POAD LF stages (r(2) = 0.22, P = 0.046) and that serum NNMT is higher in IV LF POAD patients (P = 0.005). The mRNA gene expression of LUM is correlated with the LF stage (r(2) = 0.45, P = 0.009), and the mRNA level of ITLN1 is correlated with the ankle-brachial index (r(2) = 0.42, P = 0.008). Our analysis shows that NNMT, ITLN1, LUM, CFD, and TSPAN8 in combination with other known markers, such as CRP, could be evaluated as a panel of biomarkers of POAD. Copyright © 2012 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.

Limited amplification of chronic wasting disease prions in the peripheral tissues of intracerebrally inoculated cattle

USDA-ARS?s Scientific Manuscript database

Chronic wasting disease (CWD) is a fatal neurodegenerative disease, classified as a prion disease or transmissible spongiform encephalopathy (TSE) similar to bovine spongiform encephalopathy (BSE). Cervids affected by CWD accumulate an abnormal protease resistant prion protein throughout the central...