Affinity States of Striatal Dopamine D2 Receptors in Antipsychotic-Free Patients with Schizophrenia

PubMed Central

Kubota, Manabu; Nagashima, Tomohisa; Takano, Harumasa; Kodaka, Fumitoshi; Fujiwara, Hironobu; Takahata, Keisuke; Moriguchi, Sho; Higuchi, Makoto; Okubo, Yoshiro; Takahashi, Hidehiko; Ito, Hiroshi

2017-01-01

Abstract Background Dopamine D2 receptors are reported to have high-affinity (D2High) and low-affinity (D2Low) states. Although an increased proportion of D2High has been demonstrated in animal models of schizophrenia, few clinical studies have investigated this alteration of D2High in schizophrenia in vivo. Methods Eleven patients with schizophrenia, including 10 antipsychotic-naive and 1 antipsychotic-free individuals, and 17 healthy controls were investigated. Psychopathology was assessed by Positive and Negative Syndrome Scale, and a 5-factor model was used. Two radioligands, [11C]raclopride and [11C]MNPA, were employed to quantify total dopamine D2 receptor and D2High, respectively, in the striatum by measuring their binding potentials. Binding potential values of [11C]raclopride and [11C]MNPA and the binding potential ratio of [11C]MNPA to [11C]raclopride in the striatal subregions were statistically compared between the 2 diagnostic groups using multivariate analysis of covariance controlling for age, gender, and smoking. Correlations between binding potential and Positive and Negative Syndrome Scale scores were also examined. Results Multivariate analysis of covariance demonstrated a significant effect of diagnosis (schizophrenia and control) on the binding potential ratio (P=.018), although the effects of diagnosis on binding potential values obtained with either [11C]raclopride or [11C]MNPA were nonsignificant. Posthoc test showed that the binding potential ratio was significantly higher in the putamen of patients (P=.017). The Positive and Negative Syndrome Scale “depressed” factor in patients was positively correlated with binding potential values of both ligands in the caudate. Conclusions The present study indicates the possibilities of: (1) a higher proportion of D2High in the putamen despite unaltered amounts of total dopamine D2 receptors; and (2) associations between depressive symptoms and amounts of caudate dopamine D2 receptors in patients with schizophrenia. PMID:29016872

Assessment of Platelet Function in Traumatic Brain Injury-A Retrospective Observational Study in the Neuro-Critical Care Setting.

PubMed

Lindblad, Caroline; Thelin, Eric Peter; Nekludov, Michael; Frostell, Arvid; Nelson, David W; Svensson, Mikael; Bellander, Bo-Michael

2018-01-01

Despite seemingly functional coagulation, hemorrhagic lesion progression is a common and devastating condition following traumatic brain injury (TBI), stressing the need for new diagnostic techniques. Multiple electrode aggregometry (MEA) measures platelet function and could aid in coagulopathy assessment following TBI. The aims of this study were to evaluate MEA temporal dynamics, influence of concomitant therapy, and its capabilities to predict lesion progression and clinical outcome in a TBI cohort. Adult TBI patients in a neurointensive care unit that underwent MEA sampling were retrospectively included. MEA was sampled if the patient was treated with antiplatelet therapy, bled heavily during surgery, or had abnormal baseline coagulation values. We assessed platelet activation pathways involving the arachidonic acid receptor (ASPI), P2Y 12 receptor, and thrombin receptor (TRAP). ASPI was the primary focus of analysis. If several samples were obtained, they were included. Retrospective data were extracted from hospital charts. Outcome variables were radiologic hemorrhagic progression and Glasgow Outcome Scale assessed prospectively at 12 months posttrauma. MEA levels were compared between patients on antiplatelet therapy. Linear mixed effect models and uni-/multivariable regression models were used to study longitudinal dynamics, hemorrhagic progression and outcome, respectively. In total, 178 patients were included (48% unfavorable outcome). ASPI levels increased from initially low values in a time-dependent fashion ( p  < 0.001). Patients on cyclooxygenase inhibitors demonstrated low ASPI levels ( p  < 0.001), while platelet transfusion increased them ( p  < 0.001). The first ASPI ( p  = 0.039) and TRAP ( p  = 0.009) were significant predictors of outcome, but not lesion progression, in univariate analyses. In multivariable analysis, MEA values were not independently correlated with outcome. A general longitudinal trend of MEA is identified in this TBI cohort, even in patients without known antiplatelet therapies. Values appear also affected by platelet inhibitory treatment and by platelet transfusions. While significant in univariate models to predict outcome, MEA values did not independently correlate to outcome or lesion progression in multivariable analyses. Further prospective studies to monitor coagulation in TBI patients are warranted, in particular the interpretation of pathological MEA values in patients without antiplatelet therapies.

Increased Eps15 homology domain 1 and RAB11FIP3 expression regulate breast cancer progression via promoting epithelial growth factor receptor recycling.

PubMed

Tong, Dandan; Liang, Ya-Nan; Stepanova, A A; Liu, Yu; Li, Xiaobo; Wang, Letian; Zhang, Fengmin; Vasilyeva, N V

2017-02-01

Recent research indicates that the C-terminal Eps15 homology domain 1 is associated with epithelial growth factor receptor-mediated endocytosis recycling in non-small-cell lung cancer. The aim of this study was to determine the clinical significance of Eps15 homology domain 1 gene expression in relation to phosphorylation of epithelial growth factor receptor expression in patients with breast cancer. Primary breast cancer samples from 306 patients were analyzed for Eps15 homology domain 1, RAB11FIP3, and phosphorylation of epithelial growth factor receptor expression via immunohistochemistry. The clinical significance was assessed via a multivariate Cox regression analysis, Kaplan-Meier curves, and the log-rank test. Eps15 homology domain 1 and phosphorylation of epithelial growth factor receptor were upregulated in 60.46% (185/306) and 53.92% (165/306) of tumor tissues, respectively, as assessed by immunohistochemistry. The statistical correlation analysis indicated that Eps15 homology domain 1 overexpression was positively correlated with the increases in phosphorylation of epithelial growth factor receptor ( r = 0.242, p < 0.001) and RAB11FIP3 ( r = 0.165, p = 0.005) expression. The multivariate Cox proportional hazard model analysis demonstrated that the expression of Eps15 homology domain 1 alone is a significant prognostic marker of breast cancer for the overall survival in the total, chemotherapy, and human epidermal growth factor receptor 2 (-) groups. However, the use of combined expression of Eps15 homology domain 1 and phosphorylation of epithelial growth factor receptor markers is more effective for the disease-free survival in the overall population, chemotherapy, and human epidermal growth factor receptor 2 (-) groups. Moreover, the combined markers are also significant prognostic markers of breast cancer in the human epidermal growth factor receptor 2 (+), estrogen receptor (+), and estrogen receptor (-) groups. Eps15 homology domain 1 has a tumor suppressor function, and the combined marker of Eps15 homology domain 1/phosphorylation of epithelial growth factor receptor expression was identified as a better prognostic marker in breast cancer diagnosis. Furthermore, RAB11FIP3 combines with Eps15 homology domain 1 to promote the endocytosis recycling of phosphorylation of epithelial growth factor receptor.

Epistatic interaction between haplotypes of the ghrelin ligand and receptor genes influence susceptibility to myocardial infarction and coronary artery disease.

PubMed

Baessler, Andrea; Fischer, Marcus; Mayer, Bjoern; Koehler, Martina; Wiedmann, Silke; Stark, Klaus; Doering, Angela; Erdmann, Jeanette; Riegger, Guenter; Schunkert, Heribert; Kwitek, Anne E; Hengstenberg, Christian

2007-04-15

Data from both experimental models and humans provide evidence that ghrelin and its receptor, the growth hormone secretagogue receptor (ghrelin receptor, GHSR), possess a variety of cardiovascular effects. Thus, we hypothesized that genetic variants within the ghrelin system (ligand ghrelin and its receptor GHSR) are associated with susceptibility to myocardial infarction (MI) and coronary artery disease (CAD). Seven single nucleotide polymorphisms (SNPs) covering the GHSR region as well as eight SNPs across the ghrelin gene (GHRL) region were genotyped in index MI patients (864 Caucasians, 'index MI cases') from the German MI family study and in matched controls without evidence of CAD (864 Caucasians, 'controls', MONICA Augsburg). In addition, siblings of these MI patients with documented severe CAD (826 'affected sibs') were matched likewise with controls (n = 826 Caucasian 'controls') and used for verification. The effect of interactions between genetic variants of both genes of the ghrelin system was explored by conditional classification tree models. We found association of several GHSR SNPs with MI [best SNP odds ratio (OR) 1.7 (1.2-2.5); P = 0.002] using a recessive model. Moreover, we identified a common GHSR haplotype which significantly increases the risk for MI [multivariate adjusted OR for homozygous carriers 1.6 (1.1-2.5) and CAD OR 1.6 (1.1-2.5)]. In contrast, no relationship between genetic variants and the disease could be revealed for GHRL. However, the increase in MI/CAD frequency related to the susceptible GHSR haplotype was abolished when it coincided with a common GHRL haplotype. Multivariate adjustments as well as permutation-based methods conveyed the same results. These data are the first to demonstrate an association of SNPs and haplotypes within important genes of the ghrelin system and the susceptibility to MI, whereas association with MI/CAD could be identified for genetic variants across GHSR, no relationship could be revealed for GHRL itself. However, we found an effect of GHRL dependent upon the presence of a common, MI and CAD susceptible haplotype of GHSR. Thus, our data suggest that specific haplotypes of the ghrelin ligand and its receptor act epistatically to affect susceptibility or tolerance to MI and/or CAD.

Factors Predictive of Sentinel Lymph Node Involvement in Primary Breast Cancer.

PubMed

Malter, Wolfram; Hellmich, Martin; Badian, Mayhar; Kirn, Verena; Mallmann, Peter; Krämer, Stefan

2018-06-01

Sentinel lymph node biopsy (SLNB) has replaced axillary lymph node dissection (ALND) for axillary staging in patients with early-stage breast cancer. The need for therapeutic ALND is the subject of ongoing debate especially after the publication of the ACOSOG Z0011 trial. In a retrospective trial with univariate and multivariate analyses, factors predictive of sentinel lymph node involvement should be analyzed in order to define tumor characteristics of breast cancer patients, where SLNB should not be spared to receive important indicators for adjuvant treatment decisions (e.g. thoracic wall irradiation after mastectomy with or without reconstruction). Between 2006 and 2010, 1,360 patients with primary breast cancer underwent SLNB with/without ALND with evaluation of tumor localization, multicentricity and multifocality, histological subtype, tumor size, grading, lymphovascular invasion (LVI), and estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 status. These characteristics were retrospectively analyzed in univariate and multivariate logistic regression models to define significant predictive factors for sentinel lymph node involvement. The multivariate analysis demonstrated that tumor size and LVI (p<0.001) were independent predictive factors for metastatic sentinel lymph node involvement in patients with early-stage breast cancer. Because of the increased risk for metastatic involvement of axillary sentinel nodes in cases with larger breast cancer or diagnosis of LVI, patients with these breast cancer characteristics should not be spared from SLNB in a clinically node-negative situation in order to avoid false-negative results with a high potential for wrong indication of primary breast reconstruction or wrong non-indication of necessary post-mastectomy radiation therapy. The prognostic impact of avoidance of axillary staging with SLNB is analyzed in the ongoing prospective INSEMA trial. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Application of receptor models on water quality data in source apportionment in Kuantan River Basin

PubMed Central

2012-01-01

Recent techniques in the management of surface river water have been expanding the demand on the method that can provide more representative of multivariate data set. A proper technique of the architecture of artificial neural network (ANN) model and multiple linear regression (MLR) provides an advance tool for surface water modeling and forecasting. The development of receptor model was applied in order to determine the major sources of pollutants at Kuantan River Basin, Malaysia. Thirteen water quality parameters were used in principal component analysis (PCA) and new variables of fertilizer waste, surface runoff, anthropogenic input, chemical and mineral changes and erosion are successfully developed for modeling purposes. Two models were compared in terms of efficiency and goodness-of-fit for water quality index (WQI) prediction. The results show that APCS-ANN model gives better performance with high R2 value (0.9680) and small root mean square error (RMSE) value (2.6409) compared to APCS-MLR model. Meanwhile from the sensitivity analysis, fertilizer waste acts as the dominant pollutant contributor (59.82%) to the basin studied followed by anthropogenic input (22.48%), surface runoff (13.42%), erosion (2.33%) and lastly chemical and mineral changes (1.95%). Thus, this study concluded that receptor modeling of APCS-ANN can be used to solve various constraints in environmental problem that exist between water distribution variables toward appropriate water quality management. PMID:23369363

Reported emissions of organic gases are not consistent with observations

PubMed Central

Henry, Ronald C.; Spiegelman, Clifford H.; Collins, John F.; Park, EunSug

1997-01-01

Regulatory agencies and photochemical models of ozone rely on self-reported industrial emission rates of organic gases. Incorrect self-reported emissions can severely impact on air quality models and regulatory decisions. We compared self-reported emissions of organic gases in Houston, Texas, to measurements at a receptor site near the Houston ship channel, a major petrochemical complex. We analyzed hourly observations of total nonmethane organic carbon and 54 hydrocarbon compounds from C-2 to C-9 for the period June through November, 1993. We were able to demonstrate severe inconsistencies between reported emissions and major sources as derived from the data using a multivariate receptor model. The composition and the location of the sources as deduced from the data are not consistent with the reported industrial emissions. On the other hand, our observationally based methods did correctly identify the location and composition of a relatively small nearby chemical plant. This paper provides strong empirical evidence that regulatory agencies and photochemical models are making predictions based on inaccurate industrial emissions. PMID:11038551

Central arterial stiffness is associated with systemic inflammation among Asians with type 2 diabetes.

PubMed

Zhang, Xiao; Liu, Jian Jun; Fang Sum, Chee; Ying, Yeoh Lee; Tavintharan, Subramaniam; Ng, Xiao Wei; Su, Chang; Low, Serena; Lee, Simon Bm; Tang, Wern Ee; Lim, Su Chi

2016-07-01

To examine the relationship between inflammation and central arterial stiffness in a type 2 diabetes Asian cohort. Central arterial stiffness was estimated by carotid-femoral pulse wave velocity and augmentation index. Linear regression model was used to evaluate the association of high-sensitivity C-reactive protein and soluble receptor for advanced glycation end products with pulse wave velocity and augmentation index. High-sensitivity C-reactive protein was analysed as a continuous variable and categories (<1, 1-3, and >3 mg/L). There is no association between high-sensitivity C-reactive protein and pulse wave velocity. Augmentation index increased with high-sensitivity C-reactive protein as a continuous variable (β = 0.328, p = 0.049) and categories (β = 1.474, p = 0.008 for high-sensitivity C-reactive protein: 1-3 mg/L and β = 1.323, p = 0.019 for high-sensitivity C-reactive protein: >3 mg/L) after multivariable adjustment. No association was observed between augmentation index and soluble receptor for advanced glycation end products. Each unit increase in natural log-transformed soluble receptor for advanced glycation end products was associated with 0.328 m/s decrease in pulse wave velocity after multivariable adjustment (p = 0.007). Elevated high-sensitivity C-reactive protein and decreased soluble receptor for advanced glycation end products are associated with augmentation index and pulse wave velocity, respectively, suggesting the potential role of systemic inflammation in the pathogenesis of central arterial stiffness in type 2 diabetes. © The Author(s) 2016.

SUGGESTIONS FOR OPTIMIZED PLANNING OF MULTIVARIATE MONITORING OF ATMOSPHERIC POLLUTION

EPA Science Inventory

Recent work in factor analysis of multivariate data sets has shown that variables with little signal should not be included in the factor analysis. Work also shows that rotational ambiguity is reduced if sources impacting a receptor have both large and small contributions. Thes...

Is pre-transplant sensitization against angiotensin II type 1 receptor still a risk factor of graft and patient outcome in kidney transplantation in the anti-HLA Luminex era? A retrospective study.

PubMed

Deltombe, Clement; Gillaizeau, Florence; Anglicheau, Daniel; Morelon, Emmanuel; Trébern-Launay, Katy; Le Borgne, Florent; Rimbert, Marie; Guérif, Pierrick; Malard-Castagnet, Stéphanie; Foucher, Yohann; Giral, Magali

2017-11-01

We aimed to assess the correlation of anti-angiotensin II type 1 receptor antibodies (anti-AT1R-Abs) before transplantation on a multicentric cohort of kidney transplant recipients (2008-2012), under tacrolimus and mycophenolate mofetil (MMF), screened by Luminex technology for anti-HLA immunization. Anti-AT1R antibody levels were measured by ELISA in pretransplantation sera of 940 kidney recipients from three French centers of the DIVAT cohort. Multivariable Cox models estimated the association between pretransplant anti-angiotensin II type 1 receptor antibodies and time to acute rejection episodes (ARE) or time to graft failure. Within our cohort, 387 patients (41.2%) had pretransplant AT1R-Abs higher than 10 U/ml and only 8% (72/970) greater than 17 U/ml. The cumulative probability of clinically relevant (cr)-ARE was 22.5% at 1 year post-transplantation [95% CI (19.9-25.4%)]. The cumulative probability of graft failure and patient death were 10.6% [95% CI (8.4-13.3%)] and 5.7% [95% CI (4.0-8.1%)] at 3 years post-transplantation, respectively. Multivariate Cox models indicated that pretransplant anti-AT1R antibody levels higher than 10 U/ml were not significantly independently associated with higher risks of acute rejection episodes [HR = 1.04, 95% CI (0.80-1.35)] nor with risk of graft failure [HR = 0.86, 95% CI (0.56-1.33)]. Our study did not confirm an association between pretransplant anti-AT1R antibody levels and kidney transplant outcomes. © 2017 Steunstichting ESOT.

Combined caveolin-1 and epidermal growth factor receptor expression as a prognostic marker for breast cancer.

PubMed

Liang, Ya-Nan; Liu, Yu; Wang, Letian; Yao, Guodong; Li, Xiaobo; Meng, Xiangning; Wang, Fan; Li, Ming; Tong, Dandan; Geng, Jingshu

2018-06-01

Previous studies have indicated that caveolin-1 (Cav-1) is able to bind the signal transduction factor epidermal growth factor receptor (EGFR) to regulate its tyrosine kinase activity. The aim of the present study was to evaluate the clinical significance of Cav-1 gene expression in association with the expression of EGFR in patients with breast cancer. Primary breast cancer samples from 306 patients were analyzed for Cav-1 and EGFR expression using immunohistochemistry, and clinical significance was assessed using multivariate Cox regression analysis, Kaplan-Meier estimator curves and the log-rank test. Stromal Cav-1 was downregulated in 38.56% (118/306) of tumor tissues, whereas cytoplasmic EGFR and Cav-1 were overexpressed in 53.92% (165/306) and 44.12% (135/306) of breast cancer tissues, respectively. EGFR expression was positively associated with cytoplasmic Cav-1 and not associated with stromal Cav-1 expression in breast cancer samples; however, low expression of stromal Cav-1 was negatively associated with cytoplasmic Cav-1 expression in total tumor tissues, and analogous results were identified in the chemotherapy group. Multivariate Cox's proportional hazards model analysis revealed that, for patients in the estrogen receptor (ER)(+) group, the expression of stromal Cav-1 alone was a significant prognostic marker of breast cancer. However, in the chemotherapy, human epidermal growth factor receptor 2 (HER-2)(-), HER-2(+) and ER(-) groups, the use of combined markers was more effective prognostic marker. Stromal Cav-1 has a tumor suppressor function, and the combined marker stromal Cav-1/EGFR expression was identified as an improved prognostic marker in the diagnosis of breast cancer. Parenchymal expression of Cav-1 is able to promote EGFR signaling in breast cancer, potentially being required for EGFR-mediated initiation of mitosis.

Men and women show similar survival outcome in stage IV breast cancer.

PubMed

Wu, San-Gang; Zhang, Wen-Wen; Liao, Xu-Lin; Sun, Jia-Yuan; Li, Feng-Yan; Su, Jing-Jun; He, Zhen-Yu

2017-08-01

To evaluate the clinicopathological features, patterns of distant metastases, and survival outcome between stage IV male breast cancer (MBC) and female breast cancer (FBC). Patients diagnosed with stage IV MBC and FBC between 2010 and 2013 were included using the Surveillance, Epidemiology, and End Results program. Univariate and multivariate Cox regression analyses were used to analyze risk factors for overall survival (OS). A total of 4997 patients were identified, including 60 MBC and 4937 FBC. Compared with FBC, patients with MBC were associated with a significantly higher rate of estrogen receptor-positive, progesterone receptor-positive, unmarried, lung metastases, and a lower frequency of liver metastases. Univariate and multivariate analyses showed no significant difference in OS between MBC and FBC. In the propensity score-matched population, there was also no difference in survival between MBC and FBC. Multivariate analysis of MBC showed that OS was longer for patients aged 50-69 years and with estrogen receptor-positive disease. There was no significant difference in survival outcome between stage IV MBC and FBC, but significant differences in clinicopathological features and patterns of metastases between the genders. Copyright © 2017 Elsevier Ltd. All rights reserved.

Clinicopathological significance of chemokine receptor (CCR1, CCR3, CCR4, CCR5, CCR7 and CXCR4) expression in head and neck squamous cell carcinomas.

PubMed

González-Arriagada, Wilfredo A; Lozano-Burgos, Carlos; Zúñiga-Moreta, Rodrigo; González-Díaz, Paulina; Coletta, Ricardo D

2018-05-24

Head and neck squamous cell carcinoma shows high prevalence of lymph node metastasis at diagnosis, and despite the advances in treatment, the overall 5-year survival is still under 50%. Chemokine receptors have a role in the development and progression of cancer, but their effect in head and neck carcinoma remains poorly characterised. This study aimed to assess the prognostic value of CCR1, CCR3, CCR4, CCR5, CCR7 and CXCR4 in head and neck squamous cell carcinomas. Immunohistochemical expression of chemokine receptors was evaluated in a retrospective cohort of 76 cases of head and neck squamous cell carcinoma. Clinicopathological associations were analysed using the chi-square test, survival curves were analysed according to the Kaplan-Meier method, and the Cox proportional hazard model was applied for multivariate survival analysis. The chemokine receptors were highly expressed in primary carcinomas, except for CCR1 and CCR3. Significant associations were detected, including the associations between CCR5 expression and lymph node metastasis (N stage, P = .03), advanced clinical stage (P = .003), poor differentiation of tumours (P = .05) and recurrence (P = .01). The high expression of CCR5 was also associated with shortened disease-free survival (HR: 2.85, 95% CI: 1.09-8.14, P = .05), but the association did not withstand the Cox multivariate survival analysis. At univariate analysis, high expression of CCR7 was associated with disease-free survival and low levels of CXCR4 were significantly associated with both disease-specific and disease-free survival. These findings show that chemokine receptors may have an important role in head and neck squamous cell carcinoma progression, regional lymph node metastasis and recurrence. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

The effect of distant metastases sites on survival in de novo stage-IV breast cancer: A SEER database analysis.

PubMed

Wu, San-Gang; Li, Hui; Tang, Li-Ying; Sun, Jia-Yuan; Zhang, Wen-Wen; Li, Feng-Yan; Chen, Yong-Xiong; He, Zhen-Yu

2017-06-01

To investigate the effect of distant metastases sites on survival in patients with de novo stage-IV breast cancer. From 2010 to 2013, patients with a diagnosis of de novo stage-IV breast cancer were identified using the Surveillance, Epidemiology, and End Results database. Univariate and multivariate Cox regression analyses were performed to analyze the effect of distant metastases sites on breast cancer-specific survival and overall survival. A total of 7575 patients were identified. The most common metastatic sites were bone, followed by lung, liver, and brain. Patients with hormone receptor+/human epidermal growth factor receptor 2- and hormone receptor+/human epidermal growth factor receptor 2+ status were more prone to bone metastases. Lung and brain metastases were common in hormone receptor-/human epidermal growth factor receptor 2+ and hormone receptor-/human epidermal growth factor receptor 2- subtypes, and patients with hormone receptor+/ human epidermal growth factor receptor 2+ and hormone receptor-/human epidermal growth factor receptor 2+ subtypes were more prone to liver metastases. Patients with liver and brain metastases had unfavorable prognosis for breast cancer-specific survival and overall survival, whereas bone and lung metastases had no effect on patient survival in multivariate analyses. The hormone receptor-/human epidermal growth factor receptor 2- subtype conferred a significantly poorer outcome in terms of breast cancer-specific survival and overall survival. hormone receptor+/human epidermal growth factor receptor 2+ disease was associated with the best prognosis in terms of breast cancer-specific survival and overall survival. Patients with liver and brain metastases were more likely to experience poor prognosis for breast cancer-specific survival and overall survival by various breast cancer subtypes. Distant metastases sites have differential impact on clinical outcomes in stage-IV breast cancer. Follow-up screening for brain and liver metastases might be effective in improving breast cancer-specific survival and overall survival.

EFFECT OF SYSTEMIC BETA-BLOCKERS, ACE INHIBITORS, AND ANGIOTENSIN RECEPTOR BLOCKERS ON DEVELOPMENT OF CHOROIDAL NEOVASCULARIZATION IN PATIENTS WITH AGE-RELATED MACULAR DEGENERATION.

PubMed

Thomas, Akshay S; Redd, Travis; Hwang, Thomas

2015-10-01

Recent studies have suggested that the use of systemic beta-blockers, angiotensin-converting enzyme inhibitors, and angiotensin receptor blockers can induce regression of choroidal neovascularization in rodent models. The purpose of this study is to evaluate if these agents have a protective effect against the development of choroidal neovascularization in patients with age-related macular degeneration. In this single-center retrospective case-control study, the charts of 250 patients with neovascular age-related macular degeneration were compared with those of 250 controls with dry age-related macular degeneration. Charts were reviewed for current and past use of beta-blockers, angiotensin-converting enzyme inhibitors, and angiotensin receptor blockers. Frequency tables were generated, and associations were examined using chi-square tests, t-tests, and multivariate logistic regression. There was no statistically significant difference between rates of beta-blocker use (P = 0.57), angiotensin-converting enzyme inhibitors use (P = 0.20), or angiotensin receptor blockers use (P = 0.61) between the 2 groups. Additionally, there was no statistically significant difference between rates of use of combinations of the above drugs between the two groups. Although there is growing evidence that beta-blockers, angiotensin-converting enzyme inhibitors, and angiotensin receptor blockers can induce regression of choroidal neovascularization in rodent models, these medications do not seem to confer a protective effect against the development of choroidal neovascularization in patients with age-related macular degeneration.

Seasonal and spatial variation in reactive oxygen species activity of quasi-ultrafine particles (PM0.25) in the Los Angeles metropolitan area and its association with chemical composition

NASA Astrophysics Data System (ADS)

Saffari, Arian; Daher, Nancy; Shafer, Martin M.; Schauer, James J.; Sioutas, Constantinos

2013-11-01

Seasonal and spatial variation in redox activity of quasi-ultrafine particles (PM0.25) and its association with chemical species was investigated at 9 distinct sampling sites across the Los Angeles metropolitan area. Biologically reactive oxygen species (ROS) assay (generation of ROS in rat alveolar macrophage cells) was employed in order to assess the redox activity of PM0.25 samples. Seasonally, fall and summer displayed higher volume-based ROS activity (i.e. ROS activity per unit volume of air) compared to spring and winter. ROS levels were generally higher at near source and urban background sites compared to rural receptor locations, except for summer when comparable ROS activity was observed at the rural receptor sites. Univariate linear regression analysis indicated association (R > 0.7) between ROS activity and organic carbon (OC), water soluble organic carbon (WSOC) and water soluble transition metals (including Fe, V, Cr, Cd, Ni, Zn, Mn, Pb and Cu). A multivariate regression method was also used to obtain a model to predict the ROS activity of PM0.25, based on its water-soluble components. The most important species associated with ROS were Cu and La at the source site of Long Beach, and Fe and V at urban Los Angeles sites. These metals are tracers of road dust enriched with vehicular emissions (Fe and Cu) and residual oil combustion (V and La). At Riverside, a rural receptor location, WSOC and Ni (tracers of secondary organic aerosol and metal plating, respectively) were the dominant species driving the ROS activity. At Long Beach, the multivariate model was able to reconstruct the ROS activity with a high coefficient of determination (R2 = 0.82). For Los Angeles and Riverside, however, the regression models could only explain 63% and 68% of the ROS activity, respectively. The unexplained portion of the measured ROS activity is likely attributed to the nature of organic species not captured in the organic carbon (OC) measurement as well as non-linear effects, which were not included in our linear model.

Multiple site receptor modeling with a minimal spanning tree combined with a Kohonen neural network

NASA Astrophysics Data System (ADS)

Hopke, Philip K.

1999-12-01

A combination of two pattern recognition methods has been developed that allows the generation of geographical emission maps form multivariate environmental data. In such a projection into a visually interpretable subspace by a Kohonen Self-Organizing Feature Map, the topology of the higher dimensional variables space can be preserved, but parts of the information about the correct neighborhood among the sample vectors will be lost. This can partly be compensated for by an additional projection of Prim's Minimal Spanning Tree into the trained neural network. This new environmental receptor modeling technique has been adapted for multiple sampling sites. The behavior of the method has been studied using simulated data. Subsequently, the method has been applied to mapping data sets from the Southern California Air Quality Study. The projection of a 17 chemical variables measured at up to 8 sampling sites provided a 2D, visually interpretable, geometrically reasonable arrangement of air pollution source sin the South Coast Air Basin.

Emission sources estimation of size-segregated suburban aerosols measured in continental part of Balkan region using PMF5.0 multivariate receptor model

NASA Astrophysics Data System (ADS)

Petrovic, Srdjan; Đuričić-Milanković, Jelena; Anđelković, Ivan; Pantelić, Ana; Gambaro, Andrea; Đorđević, Dragana

2017-04-01

Using Low-Pressure Cascade Impactors by Dr Berner size segregated particulate matter in the size ranges: 0.27 ≤ Dp ≤ 0.53 μm, 0.53 ≤ Dp ≤ 1.06 μm, 1.06 ≤ Dp ≤ 2.09 μm, 2.09 ≤ Dp ≤ 4.11 μm, 4.11 ≤ Dp ≤ 8.11 μm and 8.11 ≤ Dp ≤ 16 μm were collected. Forty-eight-hour size segregated particulate matter samples from atmospheric aerosols in the sub-urban site of Belgrade were measured during two years (in 2012th to 2013in). ICP-MS was used to quantify next elements: Ag, Al, As, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, K, Hg, Na, Ni, Mg, Mn, Mo, Pb, Se, Sb, Ti, Tl, V and Zn. In order to examine the number of sources and their fingerprints, EPA PMF 5.0 multivariate receptor tool was used. Error estimation methods (bootstrap, displacement, and bootstrap enhanced by displacement) in the analysis of the obtained solutions have enabled proper detection of the number and types of sources. This analysis of the results indicated the existence of four main sources that contribute to air pollution in the suburban area of Belgrade.

Hepatitis B virus infection in Taiwan: The role of NTCP rs2296651 variant in relation to sex.

PubMed

Nfor, O N; Wu, M-F; Debnath, T; Lee, C-T; Lee, W; Liu, W-H; Tantoh, D M; Hsu, S-Y; Liaw, Y-P

2018-04-16

Sodium taurocholate cotransporting polypeptide (NTCP) is a functional receptor for hepatitis B virus (HBV) infection. NTCP rs2296651 is believed to be an Asian-specific variant responsible for HBV susceptibility. We investigated the relationship between rs2296651 and HBV infection in Taiwan based on stratification by gender and menopausal status. We recruited 10 017 Taiwan Biobank participants aged 30-70 years with complete genetic data and sociodemographic information. Gender-stratified multivariate logistic regression models were used to determine the relationship between NTCP variant and HBV infection. Among individuals with HBV infection, the genotype frequencies of GG, AG and AA in women were 0.85, 0.15 and 0 while those in men were 0.82, 0.18 and 0, respectively. The multivariate-adjusted odds ratios (OR) of HBV infection were 0.77 (95% CI 0.59-0.10) in women and 0.98 (95% CI 0.79-1.20) in men. The adjusted OR was 0.87 (CI 0.63-1.19) in premenopausal and 0.59 (0.36-0.97) in postmenopausal women. We found that genetic variation in the HBV receptor gene (NTCP) was significantly associated with a decreased risk of HBV infection in Taiwanese women. © 2018 The Authors. Journal of Viral Hepatitis Published by John Wiley & Sons Ltd.

Fruit and vegetable intake and risk of breast cancer by hormone receptor status.

PubMed

Jung, Seungyoun; Spiegelman, Donna; Baglietto, Laura; Bernstein, Leslie; Boggs, Deborah A; van den Brandt, Piet A; Buring, Julie E; Cerhan, James R; Gaudet, Mia M; Giles, Graham G; Goodman, Gary; Hakansson, Niclas; Hankinson, Susan E; Helzlsouer, Kathy; Horn-Ross, Pamela L; Inoue, Manami; Krogh, Vittorio; Lof, Marie; McCullough, Marjorie L; Miller, Anthony B; Neuhouser, Marian L; Palmer, Julie R; Park, Yikyung; Robien, Kim; Rohan, Thomas E; Scarmo, Stephanie; Schairer, Catherine; Schouten, Leo J; Shikany, James M; Sieri, Sabina; Tsugane, Schoichiro; Visvanathan, Kala; Weiderpass, Elisabete; Willett, Walter C; Wolk, Alicja; Zeleniuch-Jacquotte, Anne; Zhang, Shumin M; Zhang, Xuehong; Ziegler, Regina G; Smith-Warner, Stephanie A

2013-02-06

Estrogen receptor-negative (ER(-)) breast cancer has few known or modifiable risk factors. Because ER(-) tumors account for only 15% to 20% of breast cancers, large pooled analyses are necessary to evaluate precisely the suspected inverse association between fruit and vegetable intake and risk of ER(-) breast cancer. Among 993 466 women followed for 11 to 20 years in 20 cohort studies, we documented 19 869 estrogen receptor positive (ER(+)) and 4821 ER(-) breast cancers. We calculated study-specific multivariable relative risks (RRs) and 95% confidence intervals (CIs) using Cox proportional hazards regression analyses and then combined them using a random-effects model. All statistical tests were two-sided. Total fruit and vegetable intake was statistically significantly inversely associated with risk of ER(-) breast cancer but not with risk of breast cancer overall or of ER(+) tumors. The inverse association for ER(-) tumors was observed primarily for vegetable consumption. The pooled relative risks comparing the highest vs lowest quintile of total vegetable consumption were 0.82 (95% CI = 0.74 to 0.90) for ER(-) breast cancer and 1.04 (95% CI = 0.97 to 1.11) for ER(+) breast cancer (P (common-effects) by ER status < .001). Total fruit consumption was non-statistically significantly associated with risk of ER(-) breast cancer (pooled multivariable RR comparing the highest vs lowest quintile = 0.94, 95% CI = 0.85 to 1.04). We observed no association between total fruit and vegetable intake and risk of overall breast cancer. However, vegetable consumption was inversely associated with risk of ER(-) breast cancer in our large pooled analyses.

A stochastic storm surge generator for the German North Sea and the multivariate statistical assessment of the simulation results

NASA Astrophysics Data System (ADS)

Wahl, Thomas; Jensen, Jürgen; Mudersbach, Christoph

2010-05-01

Storm surges along the German North Sea coastline led to major damages in the past and the risk of inundation is expected to increase in the course of an ongoing climate change. The knowledge of the characteristics of possible storm surges is essential for the performance of integrated risk analyses, e.g. based on the source-pathway-receptor concept. The latter includes the storm surge simulation/analyses (source), modelling of dike/dune breach scenarios (pathway) and the quantification of potential losses (receptor). In subproject 1b of the German joint research project XtremRisK (www.xtremrisk.de), a stochastic storm surge generator for the south-eastern North Sea area is developed. The input data for the multivariate model are high resolution sea level observations from tide gauges during extreme events. Based on 25 parameters (19 sea level parameters and 6 time parameters) observed storm surge hydrographs consisting of three tides are parameterised. Followed by the adaption of common parametric probability distributions and a large number of Monte-Carlo-Simulations, the final reconstruction leads to a set of 100.000 (default) synthetic storm surge events with a one-minute resolution. Such a data set can potentially serve as the basis for a large number of applications. For risk analyses, storm surges with peak water levels exceeding the design water levels are of special interest. The occurrence probabilities of the simulated extreme events are estimated based on multivariate statistics, considering the parameters "peak water level" and "fullness/intensity". In the past, most studies considered only the peak water levels during extreme events, which might not be the most important parameter in any cases. Here, a 2D-Archimedian copula model is used for the estimation of the joint probabilities of the selected parameters, accounting for the structures of dependence overlooking the margins. In coordination with subproject 1a, the results will be used as the input for the XtremRisK subprojects 2 to 4. The project is funded by the German Federal Ministry of Education and Research (BMBF) (Project No. 03 F 0483 B).

Dietary glycemic index and glycemic load and breast cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC).

PubMed

Romieu, Isabelle; Ferrari, Pietro; Rinaldi, Sabina; Slimani, Nadia; Jenab, Mazda; Olsen, Anja; Tjonneland, Anne; Overvad, Kim; Boutron-Ruault, Marie-Christine; Lajous, Martin; Kaaks, Rudolf; Teucher, Birgit; Boeing, Heiner; Trichopoulou, Antonia; Naska, Androniki; Vasilopoulo, Effie; Sacerdote, Carlotta; Tumino, Rosario; Masala, Giovanna; Sieri, Sabina; Panico, Salvatore; Bueno-de-Mesquita, H Bas; Van-der-A, Daphne; van Gils, Carla H; Peeters, Petra H M; Lund, Eiliv; Skeie, Guri; Asli, Lene Angell; Rodriguez, Laudina; Navarro, Carmen; Amiano, Pilar; Sanchez, Maria-José; Barricarte, Aurelio; Buckland, Genevieve; Sonestedt, Emily; Wirfält, Elisabet; Hallmans, Göran; Johansson, Ingegerd; Key, Timothy J; Allen, Naomi E; Khaw, Kay-Tee; Wareham, Nicholas J; Norat, Teresa; Riboli, Elio; Clavel-Chapelon, Françoise

2012-08-01

The glycemic potential of a diet is associated with chronically elevated insulin concentrations, which may augment breast cancer (BC) risk by stimulating insulin receptor or by affecting insulin-like growth factor I (IGF-I)-mediated mitogenesis. It is unclear whether this effect differs by BC phenotype. The objective was to investigate the relation between glycemic index (GI), glycemic load (GL), and total carbohydrate intake with BC by using data from the European Prospective Investigation into Cancer and Nutrition (EPIC). We identified 11,576 women with invasive BC among 334,849 EPIC women aged 34-66 y (5th to 95th percentiles) at baseline over a median follow-up of 11.5 y. Dietary GI and GL were calculated from country-specific dietary questionnaires. We used multivariable Cox proportional hazards models to quantify the association between GI, GL, and carbohydrate intake and BC risk. BC tumors were classified by receptor status. Overall GI, GL, and carbohydrates were not related to BC. Among postmenopausal women, GL and carbohydate intake were significantly associated with an increased risk of estrogen receptor-negative (ER(-)) BC when extreme quintiles (Q) were compared [multivariable HR(Q5-Q1) (95% CI) = 1.36 (1.02, 1.82; P-trend = 0.010) and HR(Q5-Q1) = 1.41 (1.05, 1.89; P-trend = 0.009), respectively]. Further stratification by progesterone receptor (PR) status showed slightly stronger associations with ER(-)/PR(-) BC [HR(Q5-Q1) (95% CI) = 1.48 (1.07, 2.05; P-trend = 0.010) for GL and HR(Q5-Q1) = 1.62 (1.15, 2.30; P-trend = 0.005) for carbohydrates]. No significant association with ER-positive BC was observed. Our results indicate that a diet with a high GL and carbohydrate intake is positively associated with an increased risk of developing ER(-) and ER(-)/PR(-) BC among postmenopausal women.

Functional Dissociation of Group III Metabotropic Glutamate Receptors Revealed by Direct Comparison between the Behavioral Profiles of Knockout Mouse Lines.

PubMed

Goddyn, Hannelore; Callaerts-Vegh, Zsuzsanna; D'Hooge, Rudi

2015-05-21

Group III metabotropic glutamate receptors (mGlu4, mGlu7, mGlu8) display differential brain distribution, which suggests different behavioral functions. However, comparison across the available animal studies remains methodologically hazardous and controversial. The present report directly compares knockouts for each group III receptor subtype using a single behavioral test battery and multivariate analysis. The behavioral phenotypes of C57BL/6J mice lacking mGlu4, mGlu7, or mGlu8 and their respective littermates were examined using a multimetric test battery, which included elements of neuromotor performance, exploratory behavior, and learning and memory. Multivariate statistical methods were used to identify subtype-specific behavioral profiles and variables that distinguished between these mouse lines. It generally appears that mGlu7 plays a significant role in hippocampus-dependent spatial learning and in some fear-related behaviors, whereas mGlu4 is most clearly involved in startle and motivational processes. Excepting its influence on body weight, the effect of mGlu8 deletion on behavior appears more subtle than that of the other group III receptors. These receptors have been proposed as potential drug targets for a variety of psychopathological conditions. On the basis of these controlled comparisons, we presently conclude that the different group III receptors indeed have quite distinct behavioral functions. © The Author 2015. Published by Oxford University Press on behalf of CINP.

Incorporating Single-nucleotide Polymorphisms Into the Lyman Model to Improve Prediction of Radiation Pneumonitis

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

Tucker, Susan L., E-mail: sltucker@mdanderson.org; Li Minghuan; Xu Ting

2013-01-01

Purpose: To determine whether single-nucleotide polymorphisms (SNPs) in genes associated with DNA repair, cell cycle, transforming growth factor-{beta}, tumor necrosis factor and receptor, folic acid metabolism, and angiogenesis can significantly improve the fit of the Lyman-Kutcher-Burman (LKB) normal-tissue complication probability (NTCP) model of radiation pneumonitis (RP) risk among patients with non-small cell lung cancer (NSCLC). Methods and Materials: Sixteen SNPs from 10 different genes (XRCC1, XRCC3, APEX1, MDM2, TGF{beta}, TNF{alpha}, TNFR, MTHFR, MTRR, and VEGF) were genotyped in 141 NSCLC patients treated with definitive radiation therapy, with or without chemotherapy. The LKB model was used to estimate the risk ofmore » severe (grade {>=}3) RP as a function of mean lung dose (MLD), with SNPs and patient smoking status incorporated into the model as dose-modifying factors. Multivariate analyses were performed by adding significant factors to the MLD model in a forward stepwise procedure, with significance assessed using the likelihood-ratio test. Bootstrap analyses were used to assess the reproducibility of results under variations in the data. Results: Five SNPs were selected for inclusion in the multivariate NTCP model based on MLD alone. SNPs associated with an increased risk of severe RP were in genes for TGF{beta}, VEGF, TNF{alpha}, XRCC1 and APEX1. With smoking status included in the multivariate model, the SNPs significantly associated with increased risk of RP were in genes for TGF{beta}, VEGF, and XRCC3. Bootstrap analyses selected a median of 4 SNPs per model fit, with the 6 genes listed above selected most often. Conclusions: This study provides evidence that SNPs can significantly improve the predictive ability of the Lyman MLD model. With a small number of SNPs, it was possible to distinguish cohorts with >50% risk vs <10% risk of RP when they were exposed to high MLDs.« less

Prognostic value of periostin in early-stage breast cancer treated with conserving surgery and radiotherapy.

PubMed

Li, Changyou; Xu, Jing; Wang, Qi; Geng, Shaoqing; Yan, Zheng; You, Jin; Li, Zhenfeng; Zou, Xiao

2018-05-01

The present study was performed to explore the prognostic significance of periostin expression in a cohort of patients with early-stage breast cancer treated with breast conserving surgery following radiotherapy. A tissue microarray of tumor samples from 259 patients with early-stage breast cancer was assayed for periostin, estrogen receptor (ER), progesterone receptor (PR), ErbB2 receptor tyrosine kinase 2 and Ki-67 expression by immunohistochemistry. The association of periostin with other clinicopathological parameters and clinical outcomes, including local recurrence free survival (RFS), distant metastasis free survival (DFS) and overall survival (OS), were assessed through log-rank tests and univariate and multivariate analysis. Periostin expression was identified in 91 of the 259 tissue samples (35%). The periostin status was significantly associated with histological grade (P=0.001), nodal status (P=0.023), molecular subtype (P<0.01), ER status (P<0.01), PR status (P<0.01) and Ki-67 expression (P=0.011). Furthermore, periostin expression was associated with an increased risk of five-year local recurrence (95.8% vs. 89.0%; P=0.017) and distant metastasis (92.3% vs. 79.1%; P=0.001) in patients with early stage breast cancer. Multivariate analysis using Cox's proportional hazards model demonstrated that periostin expression was an independent predictor of all clinical outcomes in breast cancer (RFS, P=0.018; DFS, P=0.025; OS, P=0.047). Therefore, it was concluded that periostin is associated with an increased risk of local relapse and distant metastasis in early-stage breast cancer treated with conserving surgery and radiotherapy. This association should be further investigated in larger cohorts to validate the clinical significance of periostin expression.

HR-MAS MR Spectroscopy of Breast Cancer Tissue Obtained with Core Needle Biopsy: Correlation with Prognostic Factors

PubMed Central

Choi, Ji Soo; Baek, Hyeon-Man; Kim, Suhkmann; Kim, Min Jung; Youk, Ji Hyun; Moon, Hee Jung; Kim, Eun-Kyung; Han, Kyung Hwa; Kim, Dong-hyun; Kim, Seung Il; Koo, Ja Seung

2012-01-01

The purpose of this study was to examine the correlation between high-resolution magic angle spinning (HR-MAS) magnetic resonance (MR) spectroscopy using core needle biopsy (CNB) specimens and histologic prognostic factors currently used in breast cancer patients. After institutional review board approval and informed consent were obtained for this study, CNB specimens were collected from 36 malignant lesions in 34 patients. Concentrations and metabolic ratios of various choline metabolites were estimated by HR-MAS MR spectroscopy using CNB specimens. HR-MAS spectroscopic values were compared according to histopathologic variables [tumor size, lymph node metastasis, histologic grade, status of estrogens receptor (ER), progesterone receptor (PR), HER2 (a receptor for human epidermal growth factor), and Ki-67, and triple negativity]. Multivariate analysis was performed with Orthogonal Projections to Latent Structure-Discriminant Analysis (OPLS-DA). HR-MAS MR spectroscopy quantified and discriminated choline metabolites in all CNB specimens of the 36 breast cancers. Several metabolite markers [free choline (Cho), phosphocholine (PC), creatine (Cr), taurine, myo-inositol, scyllo-inositol, total choline (tCho), glycine, Cho/Cr, tCho/Cr, PC/Cr] on HR-MAS MR spectroscopy were found to correlate with histologic prognostic factors [ER, PR, HER2, histologic grade, triple negativity, Ki-67, poor prognosis]. OPLS-DA multivariate models were generally able to discriminate the status of histologic prognostic factors (ER, PR, HER2, Ki-67) and prognosis groups. Our study suggests that HR-MAS MR spectroscopy using CNB specimens can predict tumor aggressiveness prior to surgery in breast cancer patients. In addition, it may be helpful in the detection of reliable markers for breast cancer characterization. PMID:23272149

Magnetic resonance metabolic profiling of estrogen receptor-positive breast cancer: correlation with currently used molecular markers

PubMed Central

Koo, Ja Seung; Kim, Siwon; Park, Vivian Youngjean; Kim, Eun-Kyung; Kim, Suhkmann; Kim, Min Jung

2017-01-01

Estrogen receptor (ER)-positive breast cancers overall have a good prognosis, however, some patients suffer relapses and do not respond to endocrine therapy. The purpose of this study was to determine whether there are any correlations between high-resolution magic angle spinning (HR-MAS) magnetic resonance spectroscopy (MRS) metabolic profiles of core needle biopsy (CNB) specimens and the molecular markers currently used in patients with ER-positive breast cancers. The metabolic profiling of CNB samples from 62 ER-positive cancers was performed by HR-MAS MRS. Metabolic profiles were compared according to human epidermal growth factor receptor 2 (HER2) and Ki-67 status, and luminal type, using the Mann-Whitney test. Multivariate analysis was performed with orthogonal projections to latent structure-discriminant analysis (OPLS-DA). In univariate analysis, the HER2-positive group was shown to have higher levels of glycine and glutamate, compared to the HER2-negative group (P<0.01, and P <0.01, respectively). The high Ki-67 group showed higher levels of glutamate than the low Ki-67 group without statistical significance. Luminal B cancers showed higher levels of glycine (P=0.01) than luminal A cancers. In multivariate analysis, the OPLS-DA models built with HR-MAS MR metabolic profiles showed visible discrimination between the subgroups according to HER2 and Ki-67 status, and luminal type. This study showed that the metabolic profiles of CNB samples assessed by HR-MAS MRS can be used to detect potential prognostic biomarkers as well as to understand the difference in metabolic mechanism among subtypes of ER-positive breast cancer. PMID:28969000

Tamoxifen therapy improves overall survival in luminal A subtype of ductal carcinoma in situ: a study based on nationwide Korean Breast Cancer Registry database.

PubMed

Hwang, Ki-Tae; Kim, Eun-Kyu; Jung, Sung Hoo; Lee, Eun Sook; Kim, Seung Il; Lee, Seokwon; Park, Heung Kyu; Kim, Jongjin; Oh, Sohee; Kim, Young A

2018-06-01

To determine the prognostic role of tamoxifen therapy for patients with ductal carcinoma in situ (DCIS) according to molecular subtypes. Data of 14,944 patients with DCIS were analyzed. Molecular subtypes were classified into four categories based on expression of estrogen receptor (ER)/progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2). Kaplan-Meier estimator was used for overall survival analysis while Cox proportional hazards model was used for univariate and multivariate analyses. Luminal A subtype (ER/PR+, HER2-) showed higher (P = .009) survival rate than triple-negative (TN) subtype. Tamoxifen therapy group showed superior (P < .001) survival than no-tamoxifen therapy group. It had survival benefit only for luminal A subtype (P = .001). Tamoxifen therapy resulted in higher survival rate in subgroups with positive ER (P = .006), positive PR (P = .009), and negative HER2 (P < .001). In luminal A subtype, tamoxifen therapy showed lower hazard ratio (HR) compared to no-tamoxifen therapy (HR, 0.420; 95% CI 0.250-0.705; P = .001). Tamoxifen therapy was a significant independent factor by multivariate analysis (HR, 0.538; 95% CI 0.306-0.946; P = .031) as well as univariate analysis. Tamoxifen therapy group showed superior prognosis than the no-tamoxifen therapy group. Its prognostic influence was only effective for luminal A subtype. Patients with luminal A subtype showed higher survival rate than those with TN subtype. Active tamoxifen therapy is recommended for DCIS patients with luminal A subtype, and routine tests for ER, PR, and HER2 should be considered for DCIS.

Relationship between specific adverse events and efficacy of exemestane therapy in early postmenopausal breast cancer patients.

PubMed

Fontein, D B Y; Houtsma, D; Hille, E T M; Seynaeve, C; Putter, H; Meershoek-Klein Kranenbarg, E; Guchelaar, H J; Gelderblom, H; Dirix, L Y; Paridaens, R; Bartlett, J M S; Nortier, J W R; van de Velde, C J H

2012-12-01

Many adverse events (AEs) associated with aromatase inhibitors (AIs) involve symptoms related to the depletion of circulating estrogens, and may be related to efficacy. We assessed the relationship between specific AEs [hot flashes (HF) and musculoskeletal AEs (MSAE)] and survival outcomes in Dutch and Belgian patients treated with exemestane (EXE) in the Tamoxifen Exemestane Adjuvant Multinational (TEAM) trial. Additionally, the relationship between hormone receptor expression and AEs was assessed. Efficacy end points were relapse-free survival (RFS), overall survival (OS) and breast cancer-specific mortality (BCSM), starting at 6 months after starting EXE treatment. AEs reported in the first 6 months of treatment were included. Specific AEs comprised HF and/or MSAE. Landmark analyses and Cox proportional hazards models assessed survival differences up to 5 years. A total of 1485 EXE patients were included. Patients with HF had a better RFS than patients without HF [multivariate hazard ratio (HR) 0.393, 95% confidence interval (CI) 0.19-0.813; P = 0.012]. The occurrence of MSAE versus no MSAE did not relate to better RFS (multivariate HR 0.677, 95% CI 0.392-1.169; P = 0.162). Trends were maintained for OS and BCSM. Quantitative hormone receptor expression was not associated with specific AEs. Some AEs associated with estrogen depletion are related to better outcomes and may be valuable biomarkers in AI treatment.

Predictive factors of pathologic complete response of HER2-positive breast cancer after preoperative chemotherapy with trastuzumab: development of a specific predictor and study of its utilities using decision curve analysis.

PubMed

Jankowski, Clémentine; Guiu, S; Cortet, M; Charon-Barra, C; Desmoulins, I; Lorgis, V; Arnould, L; Fumoleau, P; Coudert, B; Rouzier, R; Coutant, C; Reyal, F

2017-01-01

The aim of this study was to assess the Institut Gustave Roussy/M.D. Anderson Cancer Center (IGR/MDACC) nomogram in predicting pathologic complete response (pCR) to preoperative chemotherapy in a cohort of human epidermal growth factor receptor 2 (HER2)-positive tumors treated with preoperative chemotherapy with trastuzumab. We then combine clinical and pathological variables associated with pCR into a new nomogram specific to HER2-positive tumors treated by preoperative chemotherapy with trastuzumab. Data from 270 patients with HER2-positive tumors treated with preoperative chemotherapy with trastuzumab at the Institut Curie and at the Georges François Leclerc Cancer Center were used to assess the IGR/MDACC nomogram and to subsequently develop a new nomogram for pCR based on multivariate logistic regression. Model performance was quantified in terms of calibration and discrimination. We studied the utility of the new nomogram using decision curve analysis. The IGR/MDACC nomogram was not accurate for the prediction of pCR in HER2-positive tumors treated by preoperative chemotherapy with trastuzumab, with poor discrimination (AUC = 0.54, 95% CI 0.51-0.58) and poor calibration (p = 0.01). After uni- and multivariate analysis, a new pCR nomogram was built based on T stage (TNM), hormone receptor status, and Ki67 (%). The model had good discrimination with an area under the curve (AUC) at 0.74 (95% CI 0.70-0.79) and adequate calibration (p = 0.93). By decision curve analysis, the model was shown to be relevant between thresholds of 0.3 and 0.7. To the best of our knowledge, ours is the first nomogram to predict pCR in HER2-positive tumors treated by preoperative chemotherapy with trastuzumab. To ensure generalizability, this model needs to be externally validated.

Investigation of the Anxiolytic and Antidepressant Effects of Curcumin, a Compound From Turmeric (Curcuma longa), in the Adult Male Sprague-Dawley Rat.

PubMed

Ceremuga, Tomás Eduardo; Helmrick, Katie; Kufahl, Zachary; Kelley, Jesse; Keller, Brian; Philippe, Fabiola; Golder, James; Padrón, Gina

As the use of herbal medications continues to increase in America, the potential interaction between herbal and prescription medications necessitates the discovery of their mechanisms of action. The purpose of this study was to investigate the anxiolytic and antidepressant effects of curcumin, a compound from turmeric (Curcuma longa), and its effects on the benzodiazepine site of the γ-aminobutyric acid receptor A (GABAA) receptor. Utilizing a prospective, between-subjects group design, 55 male Sprague-Dawley rats were randomly assigned to 1 of the 5 intraperitoneally injected treatment groups: vehicle, curcumin, curcumin + flumazenil, midazolam, and midazolam + curcumin. Behavioral testing was performed using the elevated plus maze, open field test, and forced swim test. A 2-tailed multivariate analysis of variance and least significant difference post hoc tests were used for data analysis. In our models, curcumin did not demonstrate anxiolytic effects or changes in behavioral despair. An interaction of curcumin at the benzodiazepine site of the GABAA receptor was also not observed. Additional studies are recommended that examine the anxiolytic and antidepressant effects of curcumin through alternate dosing regimens, modulation of other subunits on the GABAA receptor, and interactions with other central nervous system neurotransmitter systems.

Assessment of source-specific health effects associated with an unknown number of major sources of multiple air pollutants: a unified Bayesian approach.

PubMed

Park, Eun Sug; Hopke, Philip K; Oh, Man-Suk; Symanski, Elaine; Han, Daikwon; Spiegelman, Clifford H

2014-07-01

There has been increasing interest in assessing health effects associated with multiple air pollutants emitted by specific sources. A major difficulty with achieving this goal is that the pollution source profiles are unknown and source-specific exposures cannot be measured directly; rather, they need to be estimated by decomposing ambient measurements of multiple air pollutants. This estimation process, called multivariate receptor modeling, is challenging because of the unknown number of sources and unknown identifiability conditions (model uncertainty). The uncertainty in source-specific exposures (source contributions) as well as uncertainty in the number of major pollution sources and identifiability conditions have been largely ignored in previous studies. A multipollutant approach that can deal with model uncertainty in multivariate receptor models while simultaneously accounting for parameter uncertainty in estimated source-specific exposures in assessment of source-specific health effects is presented in this paper. The methods are applied to daily ambient air measurements of the chemical composition of fine particulate matter ([Formula: see text]), weather data, and counts of cardiovascular deaths from 1995 to 1997 for Phoenix, AZ, USA. Our approach for evaluating source-specific health effects yields not only estimates of source contributions along with their uncertainties and associated health effects estimates but also estimates of model uncertainty (posterior model probabilities) that have been ignored in previous studies. The results from our methods agreed in general with those from the previously conducted workshop/studies on the source apportionment of PM health effects in terms of number of major contributing sources, estimated source profiles, and contributions. However, some of the adverse source-specific health effects identified in the previous studies were not statistically significant in our analysis, which probably resulted because we incorporated parameter uncertainty in estimated source contributions that has been ignored in the previous studies into the estimation of health effects parameters. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Linear models of coregionalization for multivariate lattice data: Order-dependent and order-free cMCARs.

PubMed

MacNab, Ying C

2016-08-01

This paper concerns with multivariate conditional autoregressive models defined by linear combination of independent or correlated underlying spatial processes. Known as linear models of coregionalization, the method offers a systematic and unified approach for formulating multivariate extensions to a broad range of univariate conditional autoregressive models. The resulting multivariate spatial models represent classes of coregionalized multivariate conditional autoregressive models that enable flexible modelling of multivariate spatial interactions, yielding coregionalization models with symmetric or asymmetric cross-covariances of different spatial variation and smoothness. In the context of multivariate disease mapping, for example, they facilitate borrowing strength both over space and cross variables, allowing for more flexible multivariate spatial smoothing. Specifically, we present a broadened coregionalization framework to include order-dependent, order-free, and order-robust multivariate models; a new class of order-free coregionalized multivariate conditional autoregressives is introduced. We tackle computational challenges and present solutions that are integral for Bayesian analysis of these models. We also discuss two ways of computing deviance information criterion for comparison among competing hierarchical models with or without unidentifiable prior parameters. The models and related methodology are developed in the broad context of modelling multivariate data on spatial lattice and illustrated in the context of multivariate disease mapping. The coregionalization framework and related methods also present a general approach for building spatially structured cross-covariance functions for multivariate geostatistics. © The Author(s) 2016.

Renin-Angiotensin Inhibitors Decrease Recurrence after Transurethral Resection of Bladder Tumor in Patients with Nonmuscle Invasive Bladder Cancer.

PubMed

Blute, Michael L; Rushmer, Timothy J; Shi, Fangfang; Fuller, Benjamin J; Abel, E Jason; Jarrard, David F; Downs, Tracy M

2015-11-01

Prior reports suggest that renin-angiotensin system inhibition may decrease nonmuscle invasive bladder cancer recurrence. We evaluated whether angiotensin converting enzyme inhibitor or angiotensin receptor blocker treatment at initial surgery was associated with decreased recurrence or progression in patients with nonmuscle invasive bladder cancer. Using an institutional bladder cancer database we identified 340 patients with data available on initial transurethral resection of bladder tumor. Progression was defined as an increase to stage T2. Cox proportional hazards models were used to evaluate associations with recurrence-free and progression-free survival. Median patient age was 69.6 years. During a median followup of 3 years (IQR 1.3-6.1) 200 patients (59%) had recurrence and 14 (4.1%) had stage progression. Of those patients 143 were receiving angiotensin converting enzyme inhibitor/angiotensin receptor blockers at the time of the first transurethral resection. On univariate analysis factors associated with improved recurrence-free survival included carcinoma in situ (p = 0.040), bacillus Calmette-Guérin therapy (p = 0.003) and angiotensin converting enzyme inhibitor/angiotensin receptor blocker therapy (p = 0.009). Multivariate analysis demonstrated that patients treated with bacillus Calmette-Guérin therapy (HR 0.68, 95% CI 0.47-0.87, p = 0.002) or angiotensin converting enzyme inhibitor/angiotensin receptor blocker therapy (HR 0.61, 95% CI 0.45-0.84, p = 0.005) were less likely to experience tumor recurrence. The 5-year recurrence-free survival rate was 45.6% for patients treated with angiotensin converting enzyme inhibitor/angiotensin receptor blockers and 28.1% in those not treated with angiotensin converting enzyme inhibitor/angiotensin receptor blockers (p = 0.009). Subgroup analysis was performed to evaluate nonmuscle invasive bladder cancer pathology (Ta, T1 and carcinoma in situ) in 85 patients on bacillus Calmette-Guérin therapy alone and in 52 in whom it was combined with angiotensin converting enzyme inhibitor/angiotensin receptor blocker. Multivariate analysis revealed that patients treated with bacillus Calmette-Guérin alone (HR 2.19, 95% CI 1.01-4.77, p = 0.04) showed worse recurrence-free survival compared to patients treated with bacillus Calmette-Guérin and angiotensin converting enzyme inhibitor/angiotensin receptor blocker (stage Ta HR 0.45, 95% CI 0.21-0.98, p = 0.04). Pharmacological inhibition of the renin-angiotensin system is associated with improved outcomes in patients with bladder cancer. Renin-angiotensin system inhibitor administration in nonmuscle invasive bladder cancer cases should be studied in a prospective randomized trial. Copyright © 2015 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Application of 3D-QSAR in the rational design of receptor ligands and enzyme inhibitors.

PubMed

Mor, Marco; Rivara, Silvia; Lodola, Alessio; Lorenzi, Simone; Bordi, Fabrizio; Plazzi, Pier Vincenzo; Spadoni, Gilberto; Bedini, Annalida; Duranti, Andrea; Tontini, Andrea; Tarzia, Giorgio

2005-11-01

Quantitative structure-activity relationships (QSARs) are frequently employed in medicinal chemistry projects, both to rationalize structure-activity relationships (SAR) for known series of compounds and to help in the design of innovative structures endowed with desired pharmacological actions. As a difference from the so-called structure-based drug design tools, they do not require the knowledge of the biological target structure, but are based on the comparison of drug structural features, thus being defined ligand-based drug design tools. In the 3D-QSAR approach, structural descriptors are calculated from molecular models of the ligands, as interaction fields within a three-dimensional (3D) lattice of points surrounding the ligand structure. These descriptors are collected in a large X matrix, which is submitted to multivariate analysis to look for correlations with biological activity. Like for other QSARs, the reliability and usefulness of the correlation models depends on the validity of the assumptions and on the quality of the data. A careful selection of compounds and pharmacological data can improve the application of 3D-QSAR analysis in drug design. Some examples of the application of CoMFA and CoMSIA approaches to the SAR study and design of receptor or enzyme ligands is described, pointing the attention to the fields of melatonin receptor ligands and FAAH inhibitors.

Reduced retinoids and retinoid receptors' expression in pancreatic cancer: A link to patient survival.

PubMed

Bleul, Tim; Rühl, Ralph; Bulashevska, Svetlana; Karakhanova, Svetlana; Werner, Jens; Bazhin, Alexandr V

2015-09-01

Pancreatic ductal adenocarcinoma (PDAC) represents one of the deadliest cancers in the world. All-trans retinoic acid (ATRA) is the major physiologically active form of vitamin A, regulating expression of many genes. Disturbances of vitamin A metabolism are prevalent in some cancer cells. The main aim of this work was to investigate deeply the components of retinoid signaling in PDAC compared to in the normal pancreas and to prove the clinical importance of retinoid receptor expression. For the study, human tumor tissues obtained from PDAC patients and murine tumors from the orthotopic Panc02 model were used for the analysis of retinoids, using high performance liquid chromatography mass spectrometry and real-time RT-PCR gene expression analysis. Survival probabilities in univariate analysis were estimated using the Kaplan-Meier method and the Cox proportional hazards model was used for the multivariate analysis. In this work, we showed for the first time that the ATRA and all-trans retinol concentration is reduced in PDAC tissue compared to their normal counterparts. The expression of RARα and β as well as RXRα and β are down-regulated in PDAC tissue. This reduced expression of retinoid receptors correlates with the expression of some markers of differentiation and epithelial-to-mesenchymal transition as well as of cancer stem cell markers. Importantly, the expression of RARα and RXRβ is associated with better overall survival of PDAC patients. Thus, reduction of retinoids and their receptors is an important feature of PDAC and is associated with worse patient survival outcomes. © 2014 Wiley Periodicals, Inc.

Estradiol and inflammatory markers in older men.

PubMed

Maggio, Marcello; Ceda, Gian Paolo; Lauretani, Fulvio; Bandinelli, Stefania; Metter, E Jeffrey; Artoni, Andrea; Gatti, Elisa; Ruggiero, Carmelinda; Guralnik, Jack M; Valenti, Giorgio; Ling, Shari M; Basaria, Shehzad; Ferrucci, Luigi

2009-02-01

Aging is characterized by a mild proinflammatory state. In older men, low testosterone levels have been associated with increasing levels of proinflammatory cytokines. It is still unclear whether estradiol (E2), which generally has biological activities complementary to testosterone, affects inflammation. We analyzed data obtained from 399 men aged 65-95 yr enrolled in the Invecchiare in Chianti study with complete data on body mass index (BMI), serum E2, testosterone, IL-6, soluble IL-6 receptor, TNF-alpha, IL-1 receptor antagonist, and C-reactive protein. The relationship between E2 and inflammatory markers was examined using multivariate linear models adjusted for age, BMI, smoking, physical activity, chronic disease, and total testosterone. In age-adjusted analysis, log (E2) was positively associated with log (IL-6) (r = 0.19; P = 0.047), and the relationship was statistically significant (P = 0.032) after adjustments for age, BMI, smoking, physical activity, chronic disease, and serum testosterone levels. Log (E2) was not significantly associated with log (C-reactive protein), log (soluble IL-6 receptor), or log (TNF-alpha) in both age-adjusted and fully adjusted analyses. In older men, E2 is weakly positively associated with IL-6, independent of testosterone and other confounders including BMI.

Analyzing Multiple Outcomes in Clinical Research Using Multivariate Multilevel Models

PubMed Central

Baldwin, Scott A.; Imel, Zac E.; Braithwaite, Scott R.; Atkins, David C.

2014-01-01

Objective Multilevel models have become a standard data analysis approach in intervention research. Although the vast majority of intervention studies involve multiple outcome measures, few studies use multivariate analysis methods. The authors discuss multivariate extensions to the multilevel model that can be used by psychotherapy researchers. Method and Results Using simulated longitudinal treatment data, the authors show how multivariate models extend common univariate growth models and how the multivariate model can be used to examine multivariate hypotheses involving fixed effects (e.g., does the size of the treatment effect differ across outcomes?) and random effects (e.g., is change in one outcome related to change in the other?). An online supplemental appendix provides annotated computer code and simulated example data for implementing a multivariate model. Conclusions Multivariate multilevel models are flexible, powerful models that can enhance clinical research. PMID:24491071

Impact of Diabetes, Insulin, and Metformin Use on the Outcome of Patients With Human Epidermal Growth Factor Receptor 2-Positive Primary Breast Cancer: Analysis From the ALTTO Phase III Randomized Trial.

PubMed

Sonnenblick, Amir; Agbor-Tarh, Dominique; Bradbury, Ian; Di Cosimo, Serena; Azim, Hatem A; Fumagalli, Debora; Sarp, Severine; Wolff, Antonio C; Andersson, Michael; Kroep, Judith; Cufer, Tanja; Simon, Sergio D; Salman, Pamela; Toi, Masakazu; Harris, Lyndsay; Gralow, Julie; Keane, Maccon; Moreno-Aspitia, Alvaro; Piccart-Gebhart, Martine; de Azambuja, Evandro

2017-05-01

Purpose Previous studies have suggested an association between metformin use and improved outcome in patients with diabetes and breast cancer. In the current study, we aimed to explore this association in human epidermal growth factor receptor 2 (HER2 ) -positive primary breast cancer in the context of a large, phase III adjuvant trial. Patients and Methods The ALTTO trial randomly assigned patients with HER2-positive breast cancer to receive 1 year of either trastuzumab alone, lapatinib alone, their sequence, or their combination. In this substudy, we evaluated whether patients with diabetes at study entry-with or without metformin treatment-were associated with different disease-free survival (DFS), distant disease-free survival (DDFS), and overall survival (OS) compared with patients without diabetes. Results A total of 8,381 patients were included in the current analysis: 7,935 patients (94.7%) had no history of diabetes at diagnosis, 186 patients (2.2%) had diabetes with no metformin treatment, and 260 patients (3.1%) were diabetic and had been treated with metformin. Median follow-up was 4.5 years (0.16 to 6.31 years), at which 1,205 (14.38%), 929 (11.08%), and 528 (6.3%) patients experienced DFS, DDFS, and OS events, respectively. Patients with diabetes who had not been treated with metformin experienced worse DFS (multivariable hazard ratio [HR], 1.40; 95% CI, 1.01 to 1.94; P = .043), DDFS (multivariable HR, 1.56; 95% CI, 1.10 to 2.22; P = .013), and OS (multivariable HR, 1.87; 95% CI, 1.23 to 2.85; P = .004). This effect was limited to hormone receptor-positive patients. Whereas insulin treatment was associated with a detrimental effect, metformin had a salutary effect in patients with diabetes who had HER2-positive and hormone receptor-positive breast cancer. Conclusion Metformin may improve the worse prognosis that is associated with diabetes and insulin treatment, mainly in patients with primary HER2-positive and hormone receptor-positive breast cancer.

HER-2/neu Overexpression as a Predictor for the Transition from In situ to Invasive Breast Cancer

PubMed Central

Roses, Robert E.; Paulson, E. Carter; Sharma, Anupama; Schueller, Jeanne E.; Nisenbaum, Harvey; Weinstein, Susan; Fox, Kevin R.; Zhang, Paul J.; Czerniecki, Brian J.

2009-01-01

The clinical implications of HER-2/neu (HER2) expression in ductal carcinoma in situ (DCIS) lesions have yet to be clearly elucidated; this despite the more frequent expression of HER2 in high-grade DCIS lesions compared with invasive cancers. We hypothesized that HER2 overexpression in DCIS is associated with more rapid progression to invasive disease. Immunohistochemical staining for estrogen receptor, progesterone receptor, and HER2 was done on DCIS specimens. Univariate analysis and a multivariate logistic regression were done to determine whether estrogen receptor, progesterone receptor, or HER2 status, comedo necrosis, nuclear grade, lesion size, or patient age predicted the presence of associated invasive disease in patients with DCIS. Invasive foci were found in association with HER2 overexpressing DCIS at a higher frequency than with DCIS that did not overexpress HER2. Although high nuclear grade, large lesion size, and HER2 overexpression were all associated with the presence of invasive disease on univariate analysis, HER2 was the only significant predictor for the presence of invasive disease after multivariate adjustment (odds ratio, 6.4; P = 0.01). These data indicate that HER2 overexpression in DCIS lesions predicts the presence of invasive foci in patients with DCIS and suggest that targeting of HER2 in an early disease setting may forestall or prevent disease progression. PMID:19383888

Hormone receptors status: a strong determinant of the kinetics of brain metastases occurrence compared with HER2 status in breast cancer.

PubMed

Darlix, Amélie; Griguolo, Gaia; Thezenas, Simon; Kantelhardt, Eva; Thomssen, Christoph; Dieci, Maria Vittoria; Miglietta, Federica; Conte, PierFranco; Braccini, Antoine Laurent; Ferrero, Jean Marc; Bailleux, Caroline; Jacot, William; Guarneri, Valentina

2018-06-01

Breast cancer (BC) metastatic behavior varies according to the hormone receptors (HR) and HER2 statuses. Indeed, patients with triple-negative (TN) and HER2+ tumors are at higher risk of brain metastases (BM). The objective of this multinational cohort was to evaluate BM kinetics depending on the BC subtype. We retrospectively analyzed a series of BC patients with BM diagnosed in four European institutions (1996-2016). The delay between BC and BM diagnoses (BM-free survival) according to tumor biology was estimated with the Kaplan-Meier method. A multivariate analysis was performed using the Cox proportional hazards regression model. 649 women were included: 32.0% HER2-/HR+, 24.8% TN, 22.2% HER2+/HR- and 21.0% HER2+/HR+ tumors. Median age at BM diagnosis was 56 (25-85). In univariate analysis, BM-free survival differed depending on tumor biology: HER2-/HR+ 5.3 years (95% CI 4.6-5.9), HER2+/HR+ 4.4 years (95% CI 3.4-5.2), HER2+/HR- 2.6 years (95% CI 2.2-3.1) and TN 2.2 years (95% CI 1.9-2.7) (p < 0.001). It was significantly different between HR+ and HR- tumors (5.0 vs. 2.5 years, p < 0.001), and between HER2+ and HER2- tumors (3.2 vs. 3.8 years, p = 0.039). In multivariate analysis, estrogen-receptors (ER) and progesterone-receptors (PR) negativity, but not HER2 status, were independently associated with BM-free survival (hazard ratio = 1.36 for ER, p = 0.013, 1.31 for PR, p = 0.021, and 1.01 for HER2+ vs. HER2- tumors, p = 0.880). HR- and HER2+ tumors are overrepresented in BC patients with BM, supporting a higher risk of BM in these biological subtypes. HR status, but not HER2 status, impacts the kinetics of BM occurrence.

Characterization of VOC sources in an urban area based on PTR-MS measurements and receptor modelling.

PubMed

Stojić, A; Stojić, S Stanišić; Šoštarić, A; Ilić, L; Mijić, Z; Rajšić, S

2015-09-01

In this study, the concentrations of volatile organic compounds were measured by the use of proton transfer reaction mass spectrometry, together with NO x , NO, NO2, SO2, CO and PM10 and meteorological parameters in an urban area of Belgrade during winter 2014. The multivariate receptor model US EPA Unmix was applied to the obtained dataset resolving six source profiles, which can be attributed to traffic-related emissions, gasoline evaporation/oil refineries, petrochemical industry/biogenic emissions, aged plumes, solid-fuel burning and local laboratories. Besides the vehicle exhaust, accounting for 27.6 % of the total mixing ratios, industrial emissions, which are present in three out of six resolved profiles, exert a significant impact on air quality in the urban area. The major contribution of regional and long-range transport was determined for source profiles associated with petrochemical industry/biogenic emissions (40 %) and gasoline evaporation/oil refineries (29 %) using trajectory sector analysis. The concentration-weighted trajectory model was applied with the aim of resolving the spatial distribution of potential distant sources, and the results indicated that emission sources from neighbouring countries, as well as from Slovakia, Greece, Poland and Scandinavian countries, significantly contribute to the observed concentrations.

Response of dissolved trace metals to land use/land cover and their source apportionment using a receptor model in a subtropic river, China.

PubMed

Li, Siyue; Zhang, Quanfa

2011-06-15

Water samples were collected for determination of dissolved trace metals in 56 sampling sites throughout the upper Han River, China. Multivariate statistical analyses including correlation analysis, stepwise multiple linear regression models, and principal component and factor analysis (PCA/FA) were employed to examine the land use influences on trace metals, and a receptor model of factor analysis-multiple linear regression (FA-MLR) was used for source identification/apportionment of anthropogenic heavy metals in the surface water of the River. Our results revealed that land use was an important factor in water metals in the snow melt flow period and land use in the riparian zone was not a better predictor of metals than land use away from the river. Urbanization in a watershed and vegetation along river networks could better explain metals, and agriculture, regardless of its relative location, however slightly explained metal variables in the upper Han River. FA-MLR analysis identified five source types of metals, and mining, fossil fuel combustion, and vehicle exhaust were the dominant pollutions in the surface waters. The results demonstrated great impacts of human activities on metal concentrations in the subtropical river of China. Copyright © 2011 Elsevier B.V. All rights reserved.

Iron Status in Chronic Heart Failure: Impact on Symptoms, Functional Class and Submaximal Exercise Capacity.

PubMed

Enjuanes, Cristina; Bruguera, Jordi; Grau, María; Cladellas, Mercé; Gonzalez, Gina; Meroño, Oona; Moliner-Borja, Pedro; Verdú, José M; Farré, Nuria; Comín-Colet, Josep

2016-03-01

To evaluate the effect of iron deficiency and anemia on submaximal exercise capacity in patients with chronic heart failure. We undertook a single-center cross-sectional study in a group of stable patients with chronic heart failure. At recruitment, patients provided baseline information and completed a 6-minute walk test to evaluate submaximal exercise capacity and exercise-induced symptoms. At the same time, blood samples were taken for serological evaluation. Iron deficiency was defined as ferritin < 100 ng/mL or transferrin saturation < 20% when ferritin is < 800 ng/mL. Additional markers of iron status were also measured. A total of 538 heart failure patients were eligible for inclusion, with an average age of 71 years and 33% were in New York Heart Association class III/IV. The mean distance walked in the test was 285 ± 101 meters among those with impaired iron status, vs 322 ± 113 meters (P=.002). Symptoms during the test were more frequent in iron deficiency patients (35% vs 27%; P=.028) and the most common symptom reported was fatigue. Multivariate logistic regression analyses showed that increased levels of soluble transferrin receptor indicating abnormal iron status were independently associated with advanced New York Heart Association class (P < .05). Multivariable analysis using generalized additive models, soluble transferrin receptor and ferritin index, both biomarkers measuring iron status, showed a significant, independent and linear association with submaximal exercise capacity (P=.03 for both). In contrast, hemoglobin levels were not significantly associated with 6-minute walk test distance in the multivariable analysis. In patients with chronic heart failure, iron deficiency but not anemia was associated with impaired submaximal exercise capacity and symptomatic functional limitation. Copyright © 2015 Sociedad Española de Cardiología. Published by Elsevier España, S.L.U. All rights reserved.

A Nested Case Control Study of Plasma ICAM-1, E-Selectin and TNF Receptor 2 Levels, and Incident Primary Open-Angle Glaucoma

PubMed Central

Kang, Jae H.; Wiggs, Janey L.; Pasquale, Louis R.

2013-01-01

Purpose. To evaluate prediagnostic markers of endothelial dysfunction and inflammatory processes in primary open-angle glaucoma (POAG). Methods. Blood samples were collected from 1989 to 1990 in the Nurses' Health Study (women) and from 1993 to 1995 in the Health Professionals Follow-up Study (men), and medical-record confirmed incident POAG cases were identified (women: 229 cases and 455 controls; men: 116 cases and 228 controls). Controls were matched on cohort, age, race, ethnicity, cancer status, and date of blood collection. Plasma concentrations of ICAM-1, E-selectin, and soluble TNF receptor 2 (sTNF-R2), a marker related to TNF-α, were measured with ELISA assays. Cohort-specific multivariable conditional logistic regression model results were meta-analyzed. Results. We observed no associations with ICAM-1 or E-selectin. For sTNF-R2, the mean (SD) plasma levels (pg/mL) in cases and controls were 2888 (997) and 2993 (913), respectively, in women; and 2622 (664) and 2569 (688), respectively, in men. Pooled multivariable results showed no relation between sTNF-R2 levels and POAG. However, compared with the lowest tertile of sTNF-R2, the highest tertile showed a significant decreased risk of POAG in women (multivariable odds ratio [OR] = 0.58, 95% confidence interval [CI] = 0.36–0.93; Ptrend = 0.03) but not in men (Ptrend = 0.21; P for heterogeneity by sex = 0.03). Also, among women, the inverse association with sTNF-R2 was stronger with normal-tension glaucoma (NTG; maximum intraocular pressure <21 mm Hg at diagnosis): highest versus lowest tertile comparison OR = 0.29 (95% CI = 0.12–0.71; Ptrend = 0.007). Conclusions. In women, but not in men, higher sTNF-R2 levels at 6 to 8 years before diagnosis were inversely associated with POAG, but more strongly for NTG. PMID:23412091

Bayesian inference on risk differences: an application to multivariate meta-analysis of adverse events in clinical trials.

PubMed

Chen, Yong; Luo, Sheng; Chu, Haitao; Wei, Peng

2013-05-01

Multivariate meta-analysis is useful in combining evidence from independent studies which involve several comparisons among groups based on a single outcome. For binary outcomes, the commonly used statistical models for multivariate meta-analysis are multivariate generalized linear mixed effects models which assume risks, after some transformation, follow a multivariate normal distribution with possible correlations. In this article, we consider an alternative model for multivariate meta-analysis where the risks are modeled by the multivariate beta distribution proposed by Sarmanov (1966). This model have several attractive features compared to the conventional multivariate generalized linear mixed effects models, including simplicity of likelihood function, no need to specify a link function, and has a closed-form expression of distribution functions for study-specific risk differences. We investigate the finite sample performance of this model by simulation studies and illustrate its use with an application to multivariate meta-analysis of adverse events of tricyclic antidepressants treatment in clinical trials.

Receptor for advanced glycation end-products and World Trade Center particulate induced lung function loss: A case-cohort study and murine model of acute particulate exposure

PubMed Central

Haider, Syed H.; Crowley, George; Lee, Audrey; Ebrahim, Minah; Zhang, Liqun; Chen, Lung-Chi; Gordon, Terry; Liu, Mengling; Prezant, David J.; Schmidt, Ann Marie

2017-01-01

World Trade Center-particulate matter(WTC-PM) exposure and metabolic-risk are associated with WTC-Lung Injury(WTC-LI). The receptor for advanced glycation end-products (RAGE) is most highly expressed in the lung, mediates metabolic risk, and single-nucleotide polymorphisms at the AGER-locus predict forced expiratory volume(FEV). Our objectives were to test the hypotheses that RAGE is a biomarker of WTC-LI in the FDNY-cohort and that loss of RAGE in a murine model would protect against acute PM-induced lung disease. We know from previous work that early intense exposure at the time of the WTC collapse was most predictive of WTC-LI therefore we utilized a murine model of intense acute PM-exposure to determine if loss of RAGE is protective and to identify signaling/cytokine intermediates. This study builds on a continuing effort to identify serum biomarkers that predict the development of WTC-LI. A case-cohort design was used to analyze a focused cohort of male never-smokers with normal pre-9/11 lung function. Odds of developing WTC-LI increased by 1.2, 1.8 and 1.0 in firefighters with soluble RAGE (sRAGE)≥97pg/mL, CRP≥2.4mg/L, and MMP-9≤397ng/mL, respectively, assessed in a multivariate logistic regression model (ROCAUC of 0.72). Wild type(WT) and RAGE-deficient(Ager-/-) mice were exposed to PM or PBS-control by oropharyngeal aspiration. Lung function, airway hyperreactivity, bronchoalveolar lavage, histology, transcription factors and plasma/BAL cytokines were quantified. WT-PM mice had decreased FEV and compliance, and increased airway resistance and methacholine reactivity after 24-hours. Decreased IFN-γ and increased LPA were observed in WT-PM mice; similar findings have been reported for firefighters who eventually develop WTC-LI. In the murine model, lack of RAGE was protective from loss of lung function and airway hyperreactivity and was associated with modulation of MAP kinases. We conclude that in a multivariate adjusted model increased sRAGE is associated with WTC-LI. In our murine model, absence of RAGE mitigated acute deleterious effects of PM and may be a biologically plausible mediator of PM-related lung disease. PMID:28926576

Divergent effects of insulin-like growth factor-1 receptor expression on prognosis of estrogen receptor positive versus triple negative invasive ductal breast carcinoma.

PubMed

Hartog, Hermien; Horlings, Hugo M; van der Vegt, Bert; Kreike, Bas; Ajouaou, Abderrahim; van de Vijver, Marc J; Marike Boezen, H; de Bock, Geertruida H; van der Graaf, Winette T A; Wesseling, Jelle

2011-10-01

The insulin-like growth factor type 1 receptor (IGF1R) is involved in progression of breast cancer and resistance to systemic treatment. Targeting IGF1R signaling may, therefore, be beneficial in systemic treatment. We report the effect of IGF1R expression on prognosis in invasive ductal breast carcinoma (IDC), the most common type of breast cancer. Immunohistochemistry was performed on tumor tissue of a consecutive cohort of 429 female patients treated for operable primary IDC. Associations between IGF1R expression with clinicopathological parameters, disease free survival (DFS) and breast cancer specific survival (BCSS) were evaluated by multivariate analyses focusing on ER-positive and triple negative IDC (TN-IDC). To enlarge the TN-IDCs cohort, we analyzed a combined dataset of 51 TN-IDC tumors from our series with 64 TN-IDCs with similar clinicopathological parameters. Patients with tumors expressing cytoplasmic IGF1R have a longer DFS and BCSS (DFS: HR 0.46, 95% CI 0.27-0.49, P = 0.005, BCSS: HR 0.38, 95% CI 0.19-0.74, P = 0.005). This effect was most prominent in ER-positive tumors. However, in a combined series of 105 TN-IDCs cytoplasmic IGF1R expression was associated with a shorter DFS (HR = 2.29, 95% CI 1.08-4.84, P = 0.03), also when combined in a multivariate model, including well-known prognostic factors (HR 2.06; 95% CI 0.95-4.47; P = 0.07). IGF1R expression in ER-positive IDC is strongly related to a favorable DFS and BCSS, but to a shorter DFS in TN-IDC tumors. This divergent effect of IGF1R expression in subgroups of IDC may affect selection of patients for IGF1R targeted therapy.

Soluble Urokinase-Type Plasminogen Activator Receptor Improves Risk Prediction in Patients With Chronic Heart Failure.

PubMed

Koller, Lorenz; Stojkovic, Stefan; Richter, Bernhard; Sulzgruber, Patrick; Potolidis, Christos; Liebhart, Florian; Mörtl, Deddo; Berger, Rudolf; Goliasch, Georg; Wojta, Johann; Hülsmann, Martin; Niessner, Alexander

2017-04-01

This study investigated the predictive value of soluble urokinase-type plasminogen activator receptor (suPAR) in patients with chronic heart failure (CHF). SuPAR originates from proteolytic cleavage of the membrane-bound receptor from activated immune and endothelial cells and reflects the level of immune activation. As inflammation plays a crucial role in the complex pathophysiology of CHF, we hypothesized that suPAR might be a suitable prognostic biomarker in patients with CHF. SuPAR levels were determined in 319 patients with CHF admitted to our outpatient department for heart failure and in a second cohort consisting of 346 patients with CHF, for validation. During a median follow-up time of 3.2 years, 119 patients (37.3%) died. SuPAR was a strong predictor of mortality with a crude hazard ratio (HR) per increase of 1 SD (HR per 1 SD) of 1.96 (95% confidence interval [CI]: 1.63 to 2.35; p < 0.001) in univariate analysis and remained significant after comprehensive multivariate adjustment with an adjusted HR per 1 SD of 1.38 (95% CI: 1.04 to 1.83; p = 0.026). SuPAR added prognostic value beyond the multivariate model indicated by improvements in C-statistics (area under the curve: 0.72 vs 0.74, respectively; p = 0.02), the category-free net reclassification index (24.9%; p = 0.032), and the integrated discrimination improvement (0.011; p = 0.05). Validation in the second cohort yielded consistent results. SuPAR is a strong and independent predictor of mortality in patients with CHF, potentially suitable to refine risk assessment in this vulnerable group of patients. Our results emphasize the impact of immune activation on survival in patients with CHF. Copyright © 2017 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer.

PubMed

Tamez-Peña, Jose-Gerardo; Rodriguez-Rojas, Juan-Andrés; Gomez-Rueda, Hugo; Celaya-Padilla, Jose-Maria; Rivera-Prieto, Roxana-Alicia; Palacios-Corona, Rebeca; Garza-Montemayor, Margarita; Cardona-Huerta, Servando; Treviño, Victor

2018-01-01

In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures.

Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer

PubMed Central

Tamez-Peña, Jose-Gerardo; Rodriguez-Rojas, Juan-Andrés; Gomez-Rueda, Hugo; Celaya-Padilla, Jose-Maria; Rivera-Prieto, Roxana-Alicia; Palacios-Corona, Rebeca; Garza-Montemayor, Margarita; Cardona-Huerta, Servando

2018-01-01

In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures. PMID:29596496

A new multivariate zero-adjusted Poisson model with applications to biomedicine.

PubMed

Liu, Yin; Tian, Guo-Liang; Tang, Man-Lai; Yuen, Kam Chuen

2018-05-25

Recently, although advances were made on modeling multivariate count data, existing models really has several limitations: (i) The multivariate Poisson log-normal model (Aitchison and Ho, ) cannot be used to fit multivariate count data with excess zero-vectors; (ii) The multivariate zero-inflated Poisson (ZIP) distribution (Li et al., 1999) cannot be used to model zero-truncated/deflated count data and it is difficult to apply to high-dimensional cases; (iii) The Type I multivariate zero-adjusted Poisson (ZAP) distribution (Tian et al., 2017) could only model multivariate count data with a special correlation structure for random components that are all positive or negative. In this paper, we first introduce a new multivariate ZAP distribution, based on a multivariate Poisson distribution, which allows the correlations between components with a more flexible dependency structure, that is some of the correlation coefficients could be positive while others could be negative. We then develop its important distributional properties, and provide efficient statistical inference methods for multivariate ZAP model with or without covariates. Two real data examples in biomedicine are used to illustrate the proposed methods. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Model for breast cancer survival: relative prognostic roles of axillary nodal status, TNM stage, estrogen receptor concentration, and tumor necrosis.

PubMed

Shek, L L; Godolphin, W

1988-10-01

The independent prognostic effects of certain clinical and pathological variables measured at the time of primary diagnosis were assessed with Cox multivariate regression analysis. The 859 patients with primary breast cancer, on which the proportional hazards model was based, had a median follow-up of 60 months. Axillary nodal status (categorized as N0, N1-3 or N4+) was the most significant and independent factor in overall survival, but inclusion of TNM stage, estrogen receptor (ER) concentration and tumor necrosis significantly improved survival predictions. Predictions made with the model showed striking subset survival differences within stage: 5-year survival from 36% (N4+, loge[ER] = 0, marked necrosis) to 96% (N0, loge[ER] = 6, no necrosis) in TNM I, and from 0 to 70% for the same categories in TNM IV. Results of the model were used to classify patients into four distinct risk groups according to a derived hazard index. An 8-fold variation in survival was seen with the highest (greater than 3) to lowest index values (less than 1). Each hazard index level included patients with varied combinations of the above factors, but could be considered to denote the same degree of risk of breast cancer mortality. A model with ER concentration, nodal status, and tumor necrosis was found to best predict survival after disease recurrence in 369 patients, thus confirming the enduring biological significance of these factors.

Source apportionment of atmospheric bulk deposition in the Belgrade urban area using Positive Matrix factorization

NASA Astrophysics Data System (ADS)

Tasić, M.; Mijić, Z.; Rajšić, S.; Stojić, A.; Radenković, M.; Joksić, J.

2009-04-01

The primary objective of the present study was to assess anthropogenic impacts of heavy metals to the environment by determination of total atmospheric deposition of heavy metals. Atmospheric depositions (wet + dry) were collected monthly, from June 2002 to December 2006, at three urban locations in Belgrade, using bulk deposition samplers. Concentrations of Fe, Al, Pb, Zn, Cu, Ni, Mn, Cr, V, As and Cd were analyzed using atomic absorption spectrometry. Based upon these results, the study attempted to examine elemental associations in atmospheric deposition and to elucidate the potential sources of heavy metal contaminants in the region by the use of multivariate receptor model Positive Matrix Factorization (PMF).

Impact of smoking history on the outcomes of women with early-stage breast cancer: a secondary analysis of a randomized study.

PubMed

Abdel-Rahman, Omar; Cheung, Winson Y

2018-04-11

To assess the impact of smoking history on the outcomes of early-stage breast cancer patients treated with sequential anthracyclines-taxanes in a randomized study. This is a secondary analysis of patient-level data of 1242 breast cancer patients referred for adjuvant chemotherapy in the BCIRG005 clinical trial. Overall survival was assessed according to smoking history through Kaplan-Meier analysis. Univariate and multivariate Cox regression analyses of factors affecting overall and relapse-free survival were subsequently conducted. Factors that were evaluated included: age, performance status, number of chemotherapy cycles, T stage, lymph node ratio, estrogen receptor status, adjuvant radiotherapy and smoking history. Kaplan-Meier analysis of overall survival according to smoking status (ever smoker vs. never smoker) was conducted. There was a trend toward a better overall survival among never smokers compared to ever smokers; however, it was not statistically significant (P = 0.098). The following factors were associated with better overall survival in multivariate analysis: older age (P = 0.011), complete chemotherapy course (P = 0.002), lower T stage (P < 0.0001), lower lymph node ratio (P < 0.0001) and positive estrogen receptor status (P = 0.006). Otherwise, the following factors were associated with better relapse-free survival in multivariate analysis: older age (P = 0.001), never smoking status (P = 0.021), lower T stage (P = 0.028), lower lymph node ratio (P < 0.0001) and positive estrogen receptor status (P < 0.0001). Early-stage breast cancer patients with a positive smoking history experienced worse relapse-free survival compared to never smokers. Physicians managing breast cancer patients should prioritize discussion about the benefits of smoking cessation when counseling their patients.

Transforming growth factor-β and toll-like receptor-4 polymorphisms are not associated with fibrosis in haemochromatosis

PubMed Central

Wood, Marnie J; Powell, Lawrie W; Dixon, Jeannette L; Subramaniam, V Nathan; Ramm, Grant A

2013-01-01

AIM: To investigate the role of genetic polymorphisms in the progression of hepatic fibrosis in hereditary haemochromatosis. METHODS: A cohort of 245 well-characterised C282Y homozygous patients with haemochromatosis was studied, with all subjects having liver biopsy data and DNA available for testing. This study assessed the association of eight single nucleotide polymorphisms (SNPs) in a total of six genes including toll-like receptor 4 (TLR4), transforming growth factor-beta (TGF-β), oxoguanine DNA glycosylase, monocyte chemoattractant protein 1, chemokine C-C motif receptor 2 and interleukin-10 with liver disease severity. Genotyping was performed using high resolution melt analysis and sequencing. The results were analysed in relation to the stage of hepatic fibrosis in multivariate analysis incorporating other cofactors including alcohol consumption and hepatic iron concentration. RESULTS: There were significant associations between the cofactors of male gender (P = 0.0001), increasing age (P = 0.006), alcohol consumption (P = 0.0001), steatosis (P = 0.03), hepatic iron concentration (P < 0.0001) and the presence of hepatic fibrosis. Of the candidate gene polymorphisms studied, none showed a significant association with hepatic fibrosis in univariate or multivariate analysis incorporating cofactors. We also specifically studied patients with hepatic iron loading above threshold levels for cirrhosis and compared the genetic polymorphisms between those with no fibrosis vs cirrhosis however there was no significant effect from any of the candidate genes studied. Importantly, in this large, well characterised cohort of patients there was no association between SNPs for TGF-β or TLR4 and the presence of fibrosis, cirrhosis or increasing fibrosis stage in multivariate analysis. CONCLUSION: In our large, well characterised group of haemochromatosis subjects we did not demonstrate any relationship between candidate gene polymorphisms and hepatic fibrosis or cirrhosis. PMID:24409064

Transforming growth factor-β and toll-like receptor-4 polymorphisms are not associated with fibrosis in haemochromatosis.

PubMed

Wood, Marnie J; Powell, Lawrie W; Dixon, Jeannette L; Subramaniam, V Nathan; Ramm, Grant A

2013-12-28

To investigate the role of genetic polymorphisms in the progression of hepatic fibrosis in hereditary haemochromatosis. A cohort of 245 well-characterised C282Y homozygous patients with haemochromatosis was studied, with all subjects having liver biopsy data and DNA available for testing. This study assessed the association of eight single nucleotide polymorphisms (SNPs) in a total of six genes including toll-like receptor 4 (TLR4), transforming growth factor-beta (TGF-β), oxoguanine DNA glycosylase, monocyte chemoattractant protein 1, chemokine C-C motif receptor 2 and interleukin-10 with liver disease severity. Genotyping was performed using high resolution melt analysis and sequencing. The results were analysed in relation to the stage of hepatic fibrosis in multivariate analysis incorporating other cofactors including alcohol consumption and hepatic iron concentration. There were significant associations between the cofactors of male gender (P = 0.0001), increasing age (P = 0.006), alcohol consumption (P = 0.0001), steatosis (P = 0.03), hepatic iron concentration (P < 0.0001) and the presence of hepatic fibrosis. Of the candidate gene polymorphisms studied, none showed a significant association with hepatic fibrosis in univariate or multivariate analysis incorporating cofactors. We also specifically studied patients with hepatic iron loading above threshold levels for cirrhosis and compared the genetic polymorphisms between those with no fibrosis vs cirrhosis however there was no significant effect from any of the candidate genes studied. Importantly, in this large, well characterised cohort of patients there was no association between SNPs for TGF-β or TLR4 and the presence of fibrosis, cirrhosis or increasing fibrosis stage in multivariate analysis. In our large, well characterised group of haemochromatosis subjects we did not demonstrate any relationship between candidate gene polymorphisms and hepatic fibrosis or cirrhosis.

Withholding versus Continuing Angiotensin-converting Enzyme Inhibitors or Angiotensin II Receptor Blockers before Noncardiac Surgery: An Analysis of the Vascular events In noncardiac Surgery patIents cOhort evaluatioN Prospective Cohort.

PubMed

Roshanov, Pavel S; Rochwerg, Bram; Patel, Ameen; Salehian, Omid; Duceppe, Emmanuelle; Belley-Côté, Emilie P; Guyatt, Gordon H; Sessler, Daniel I; Le Manach, Yannick; Borges, Flavia K; Tandon, Vikas; Worster, Andrew; Thompson, Alexandra; Koshy, Mithin; Devereaux, Breagh; Spencer, Frederick A; Sanders, Robert D; Sloan, Erin N; Morley, Erin E; Paul, James; Raymer, Karen E; Punthakee, Zubin; Devereaux, P J

2017-01-01

The effect on cardiovascular outcomes of withholding angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers in chronic users before noncardiac surgery is unknown. In this international prospective cohort study, the authors analyzed data from 14,687 patients (including 4,802 angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker users) at least 45 yr old who had in-patient noncardiac surgery from 2007 to 2011. Using multivariable regression models, the authors studied the relationship between withholding angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers and a primary composite outcome of all-cause death, stroke, or myocardial injury after noncardiac surgery at 30 days, with intraoperative and postoperative clinically important hypotension as secondary outcomes. Compared to patients who continued their angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers, the 1,245 (26%) angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker users who withheld their angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers in the 24 h before surgery were less likely to suffer the primary composite outcome of all-cause death, stroke, or myocardial injury (150/1,245 [12.0%] vs. 459/3,557 [12.9%]; adjusted relative risk, 0.82; 95% CI, 0.70 to 0.96; P = 0.01) and intraoperative hypotension (adjusted relative risk, 0.80; 95% CI, 0.72 to 0.93; P < 0.001). The risk of postoperative hypotension was similar between the two groups (adjusted relative risk, 0.92; 95% CI, 0.77 to 1.10; P = 0.36). Results were consistent across the range of preoperative blood pressures. The practice of withholding angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers was only modestly correlated with patient characteristics and the type and timing of surgery. Withholding angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers before major noncardiac surgery was associated with a lower risk of death and postoperative vascular events. A large randomized trial is needed to confirm this finding. In the interim, clinicians should consider recommending that patients withhold angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers 24 h before surgery.

Prognostic value of sex-hormone receptor expression in non-muscle-invasive bladder cancer.

PubMed

Nam, Jong Kil; Park, Sung Woo; Lee, Sang Don; Chung, Moon Kee

2014-09-01

We investigated sex-hormone receptor expression as predicting factor of recurrence and progression in patients with non-muscle invasive bladder cancer. We retrospectively evaluated tumor specimens from patients treated for transitional cell carcinoma of the bladder at our institution between January 2006 and January 2011. Performing immunohistochemistry using a monoclonal androgen receptor antibody and monoclonal estrogen receptor-beta antibody on paraffin-embedded tissue sections, we assessed the relationship of immunohistochemistry results and prognostic factors such as recurrence and progression. A total of 169 patients with bladder cancer were evaluated in this study. Sixty-threepatients had expressed androgen receptors and 52 patients had estrogen receptor beta. On univariable analysis, androgen receptor expression was significant lower in recurrence rates (p=0.001), and estrogen receptor beta expression was significant higher in progression rates (p=0.004). On multivariable analysis, significant association was found between androgen receptor expression and lower recurrence rates (hazard ratio=0.500; 95% confidence interval, 0.294 to 0.852; p=0.011), but estrogen receptor beta expression was not significantly associated with progression rates. We concluded that the possibility of recurrence was low when the androgen receptor was expressed in the bladder cancer specimen and it could be the predicting factor of the stage, number of tumors, carcinoma in situ lesion and recurrence.

Exposure to hazardous air pollutants and risk of incident breast cancer in the nurses' health study II.

PubMed

Hart, Jaime E; Bertrand, Kimberly A; DuPre, Natalie; James, Peter; Vieira, Verónica M; VoPham, Trang; Mittleman, Maggie R; Tamimi, Rulla M; Laden, Francine

2018-03-27

Findings from a recent prospective cohort study in California suggested increased risk of breast cancer associated with higher exposure to certain carcinogenic and estrogen-disrupting hazardous air pollutants (HAPs). However, to date, no nationwide studies have evaluated these possible associations. Our objective was to examine the impacts of mammary carcinogen and estrogen disrupting HAPs on risk of invasive breast cancer in a nationwide cohort. We assigned HAPs from the US Environmental Protection Agency's 2002 National Air Toxics Assessment to 109,239 members of the nationwide, prospective Nurses' Health Study II (NHSII). Risk of overall invasive, estrogen receptor (ER)-positive (ER+), and ER-negative (ER-) breast cancer with increasing quartiles of exposure were assessed in time-varying multivariable proportional hazards models, adjusted for traditional breast cancer risk factors. A total of 3321 invasive cases occurred (2160 ER+, 558 ER-) during follow-up 1989-2011. Overall, there was no consistent pattern of elevated risk of the HAPs with risk of breast cancer. Suggestive elevations were only seen with increasing 1,2-dibromo-3-chloropropane exposures (multivariable adjusted HR of overall breast cancer = 1.12, 95% CI: 0.98-1.29; ER+ breast cancer HR = 1.09; 95% CI: 0.92, 1.30; ER- breast cancer HR = 1.14; 95% CI: 0.81, 1.61; each in the top exposure quartile compared to the lowest). Exposures to HAPs during adulthood were not consistently associated with an increased risk of overall or estrogen-receptor subtypes of invasive breast cancer in this nationwide cohort of women.

Breast Cancer Subtype is Associated With Axillary Lymph Node Metastasis

PubMed Central

He, Zhen-Yu; Wu, San-Gang; Yang, Qi; Sun, Jia-Yuan; Li, Feng-Yan; Lin, Qin; Lin, Huan-Xin

2015-01-01

Abstract The purpose of this study was to assess whether breast cancer subtype (BCS) as determined by estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 can predict the axillary lymph node metastasis in breast cancer. Patients who received breast conserving surgery or mastectomy and axillary lymph node dissection were identified from 2 cancer centers. The associations between clinicopathological variables and axillary lymph node involvement were evaluated in univariate and multivariate regression analyses. A total of 3471 patients met the inclusion criteria, and 53.0% had axillary lymph node metastases at diagnosis. Patients with hormone receptor (HR)−/human epidermal growth factor receptor 2 (HER2)− subtype had a higher grade disease and the lowest rate of lymphovascular invasion. Univariate and multivariable logistic regression analyses showed that BCS was significantly associated with lymph node involvement. Patients with the HR−/HER2− subtype had the lowest odds of having nodal positivity than those with other BCSs. HR+/HER2− (odds ratio [OR] 1.651, 95% confidence interval [CI]: 1.349–2.021, P < 0.001), HR+/HER2+ (OR 1.958, 95%CI 1.542–2.486, P < 0.001), and HR−/HER2+ (OR 1.525, 95%CI 1.181–1.970, P < 0.001) tumors had higher risk of nodal positivity than the HR−/HER2− subtype. The other independent predictors of nodal metastases included tumor size, tumor grade, and lymphovascular invasion. Breast cancer subtype can predict the presence of axillary lymph node metastasis in breast cancer. HR−/HER2− is associated with a reduced risk of axillary lymph node metastasis compared to other BCSs. Our findings may play an important role in guiding axillary treatment considerations if further confirmed in larger sample size studies. PMID:26632910

Decoy receptor 3 is a prognostic factor in renal cell cancer.

PubMed

Macher-Goeppinger, Stephan; Aulmann, Sebastian; Wagener, Nina; Funke, Benjamin; Tagscherer, Katrin E; Haferkamp, Axel; Hohenfellner, Markus; Kim, Sunghee; Autschbach, Frank; Schirmacher, Peter; Roth, Wilfried

2008-10-01

Decoy receptor 3 (DcR3) is a soluble protein that binds to and inactivates the death ligand CD95L. Here, we studied a possible association between DcR3 expression and prognosis in patients with renal cell carcinomas (RCCs). A tissue microarray containing RCC tumor tissue samples and corresponding normal tissue samples was generated. Decoy receptor 3 expression in tumors of 560 patients was examined by immunohistochemistry. The effect of DcR3 expression on disease-specific survival and progression-free survival was assessed using univariate analysis and multivariate Cox regression analysis. Decoy receptor 3 serum levels were determined by ELISA. High DcR3 expression was associated with high-grade (P = .005) and high-stage (P = .048) RCCs. The incidence of distant metastasis (P = .03) and lymph node metastasis (P = .002) was significantly higher in the group with high DcR3 expression. Decoy receptor 3 expression correlated negatively with disease-specific survival (P < .001) and progression-free survival (P < .001) in univariate analyses. A multivariate Cox regression analysis retained DcR3 expression as an independent prognostic factor that outperformed the Karnofsky performance status. In patients with high-stage RCCs expressing DcR3, the 2-year survival probability was 25%, whereas in patients with DcR3-negative tumors, the survival probability was 65% (P < .001). Moreover, DcR3 serum levels were significantly higher in patients with high-stage localized disease (P = .007) and metastatic disease (P = .001). DcR3 expression is an independent prognostic factor of RCC progression and mortality. Therefore, the assessment of DcR3 expression levels offers valuable prognostic information that could be used to select patients for adjuvant therapy studies.

Bim may be a poor prognostic biomarker in breast cancer patients especially in those with luminal A tumors.

PubMed

Maimaiti, Yusufu; Dong, Lingling; Aili, Aikebaier; Maimaitiaili, Maimaitiaili; Huang, Tao; Abudureyimu, Kelimu

2017-07-04

Bcl-2 interacting mediator of cell death (Bim) appears to have contradictory roles in cancer. It is uncertain whether Bim show prognostic significance in patients with breast cancer. To investigate the correlation between Bim expression and clinicopathological characteristics of breast cancer and to evaluate Bim's effect on overall survival (OS). We used immunohistochemistry (IHC) technique to detect the expression of Bim via tissue microarray in 275 breast cancer samples, Kaplan-Meier analysis to perform survival analysis, and Cox proportional hazards regression model to explore the risk factors of breast cancer. The results revealed that Bim expression was significantly correlated with age, estrogen receptor (ER) and/or progesterone receptor (PR), human epidermal growth factor receptor (HER2) and Ki67 expression (P< 0.05). Bim expression was significantly different in the four molecular subtypes (P= 0.000). Survival analysis showed that Bim positive expression contributed to a shorter OS (P= 0.034), especially in patients with luminal A tumors (P= 0.039). Univariate and multivariate regression analysis showed that Bim was an independent prognostic factor for breast cancer (P< 0.05). Bim may serve as an effective predictive factor for lower OS in breast cancer patients, especially in those with luminal A tumors.

Estradiol and Inflammatory Markers in Older Men

PubMed Central

Maggio, Marcello; Ceda, Gian Paolo; Lauretani, Fulvio; Bandinelli, Stefania; Metter, E. Jeffrey; Artoni, Andrea; Gatti, Elisa; Ruggiero, Carmelinda; Guralnik, Jack M.; Valenti, Giorgio; Ling, Shari M.; Basaria, Shehzad; Ferrucci, Luigi

2009-01-01

Background: Aging is characterized by a mild proinflammatory state. In older men, low testosterone levels have been associated with increasing levels of proinflammatory cytokines. It is still unclear whether estradiol (E2), which generally has biological activities complementary to testosterone, affects inflammation. Methods: We analyzed data obtained from 399 men aged 65–95 yr enrolled in the Invecchiare in Chianti study with complete data on body mass index (BMI), serum E2, testosterone, IL-6, soluble IL-6 receptor, TNF-α, IL-1 receptor antagonist, and C-reactive protein. The relationship between E2 and inflammatory markers was examined using multivariate linear models adjusted for age, BMI, smoking, physical activity, chronic disease, and total testosterone. Results: In age-adjusted analysis, log (E2) was positively associated with log (IL-6) (r = 0.19; P = 0.047), and the relationship was statistically significant (P = 0.032) after adjustments for age, BMI, smoking, physical activity, chronic disease, and serum testosterone levels. Log (E2) was not significantly associated with log (C-reactive protein), log (soluble IL-6 receptor), or log (TNF-α) in both age-adjusted and fully adjusted analyses. Conclusions: In older men, E2 is weakly positively associated with IL-6, independent of testosterone and other confounders including BMI. PMID:19050054

Multivariate Strategies in Functional Magnetic Resonance Imaging

ERIC Educational Resources Information Center

Hansen, Lars Kai

2007-01-01

We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a "mind reading" predictive multivariate fMRI model.

Investigating College and Graduate Students' Multivariable Reasoning in Computational Modeling

ERIC Educational Resources Information Center

Wu, Hsin-Kai; Wu, Pai-Hsing; Zhang, Wen-Xin; Hsu, Ying-Shao

2013-01-01

Drawing upon the literature in computational modeling, multivariable reasoning, and causal attribution, this study aims at characterizing multivariable reasoning practices in computational modeling and revealing the nature of understanding about multivariable causality. We recruited two freshmen, two sophomores, two juniors, two seniors, four…

Effect of tumor size on breast cancer-specific survival stratified by joint hormone receptor status in a SEER population-based study

PubMed Central

Zheng, Yi-Zi; Wang, Lei; Hu, Xin; Shao, Zhi-Ming

2015-01-01

Background & Aims The prognostic value of tumor size is variable. We aimed to characterize the interaction between tumor size and hormone receptor (HoR) status to determine breast cancer-specific mortality (BCSM). Methods We used the Surveillance, Epidemiology and End Results (SEER) registry to identify 328, 870 female patients diagnosed with invasive breast cancer from 1990 through 2010. Primary study variables included tumor size, joint HoR status and their corresponding relationship. Kaplan-Meier and adjusted Cox proportional hazards models with interaction terms were utilized. Results The multivariable analysis revealed a significant interaction between tumor size and HoR status (P < 0.001). Using tumors 61–70 mm in size as the reference for estrogen receptor-negative (ER−) and progesterone receptor-negative (PR−) disease, the hazard ratio (HR) for BCSM increased with increasing tumor size across nearly all categories. In the ER-positive (ER+) and PR-positive (PR+) group, however, patients with tumors > 50 mm had nearly identical BCSM rates (P = 0.127, P = 0.099 and P = 0.370 for 51–60 mm, 71–80 mm and > 80 mm tumors, respectively), whereas BCSM was positively correlated with tumors < 51 mm. Conclusions The observation of identical HRs for BCSM among patients with ER+ and PR+ tumors >50 mm underscores the importance of individualized treatment. Our findings may contribute to a better understanding of breast cancer biology. PMID:26036636

A Multivariate Model for the Study of Parental Acceptance-Rejection and Child Abuse.

ERIC Educational Resources Information Center

Rohner, Ronald P.; Rohner, Evelyn C.

This paper proposes a multivariate strategy for the study of parental acceptance-rejection and child abuse and describes a research study on parental rejection and child abuse which illustrates the advantages of using a multivariate, (rather than a simple-model) approach. The multivariate model is a combination of three simple models used to study…

A reassessment of soluble urokinase-type plasminogen activator receptor in glomerular disease

PubMed Central

Spinale, Joann M.; Mariani, Laura H.; Kapoor, Shiv; Zhang, Jidong; Weyant, Robert; Song, Peter X.; Wong, Hetty N.; Troost, Jonathan P.; Gadegbeku, Crystal A.; Gipson, Debbie S.; Kretzler, Matthias; Nihalani, Deepak; Holzman, Lawrence B.

2014-01-01

It has been suggested that soluble urokinase receptor (suPAR) is a causative circulating factor for and a biomarker of focal and segmental glomerulosclerosis (FSGS). Here we undertook validation of these assumptions in both mouse and human models. Injection of recombinant suPAR in wild-type mice did not induce proteinuria within 24 hours. Moreover, a disease phenotype was not seen in an inducible transgenic mouse model that maintained elevated suPAR concentrations for 6 weeks. Plasma and urine suPAR concentrations were evaluated as clinical biomarkers in 241 patients with glomerular disease from the prospective, longitudinal multi-center observational NEPTUNE cohort. The serum suPAR concentration at baseline inversely correlated with estimated glomerular filtration rate (eGFR) and the urine suPAR/creatinine ratio positively correlated with the urine protein/creatinine ratio. After adjusting for eGFR and urine protein, neither the serum nor urine suPAR level was an independent predictor of FSGS histopathology. A multivariable mixed-effects model of longitudinal data evaluated the association between the change in serum suPAR concentration from baseline with eGFR. After adjusting for baseline suPAR concentration, age, gender, proteinuria and time, the change in suPAR from baseline was associated with eGFR, but this association was not different for patients with FSGS as compared to other diagnoses. Thus, these results do not support a pathological role for suPAR in FSGS. PMID:25354239

A reassessment of soluble urokinase-type plasminogen activator receptor in glomerular disease.

PubMed

Spinale, Joann M; Mariani, Laura H; Kapoor, Shiv; Zhang, Jidong; Weyant, Robert; Song, Peter X; Wong, Hetty N; Troost, Jonathan P; Gadegbeku, Crystal A; Gipson, Debbie S; Kretzler, Matthias; Nihalani, Deepak; Holzman, Lawrence B

2015-03-01

It has been suggested that soluble urokinase receptor (suPAR) is a causative circulating factor for and a biomarker of focal and segmental glomerulosclerosis (FSGS). Here we undertook validation of these assumptions in both mouse and human models. Injection of recombinant suPAR in wild-type mice did not induce proteinuria within 24 h. Moreover, a disease phenotype was not seen in an inducible transgenic mouse model that maintained elevated suPAR concentrations for 6 weeks. Plasma and urine suPAR concentrations were evaluated as clinical biomarkers in 241 patients with glomerular disease from the prospective, longitudinal multicenter observational NEPTUNE cohort. The serum suPAR concentration at baseline inversely correlated with estimated glomerular filtration rate (eGFR) and the urine suPAR/creatinine ratio positively correlated with the urine protein/creatinine ratio. After adjusting for eGFR and urine protein, neither the serum nor urine suPAR level was an independent predictor of FSGS histopathology. A multivariable mixed-effects model of longitudinal data evaluated the association between the change in serum suPAR concentration from baseline with eGFR. After adjusting for baseline suPAR concentration, age, gender, proteinuria, and time, the change in suPAR from baseline was associated with eGFR, but this association was not different for patients with FSGS as compared with other diagnoses. Thus these results do not support a pathological role for suPAR in FSGS.

Vorapaxar: The Current Role and Future Directions of a Novel Protease-Activated Receptor Antagonist for Risk Reduction in Atherosclerotic Disease.

PubMed

Gryka, Rebecca J; Buckley, Leo F; Anderson, Sarah M

2017-03-01

Despite the current standard of care, patients with cardiovascular disease remain at a high risk for recurrent events. Inhibition of thrombin-mediated platelet activation through protease-activated receptor-1 antagonism may provide reductions in atherosclerotic disease beyond those achievable with the current standard of care. Our primary objective is to evaluate the clinical literature regarding the role of vorapaxar (Zontivity™) in the reduction of cardiovascular events in patients with a history of myocardial infarction and peripheral artery disease. In particular, we focus on the potential future directions for protease-activating receptor antagonists in the treatment of a broad range of atherosclerotic diseases. A literature search of PubMed and EBSCO was conducted to identify randomized clinical trials from August 2005 to June 2016 using the search terms: 'vorapaxar', 'SCH 530348', 'protease-activated receptor-1 antagonist', and 'Zontivity™'. Bibliographies were searched and additional resources were obtained. Vorapaxar is a first-in-class, protease-activated receptor-1 antagonist. The Thrombin Receptor Antagonist for Clinical Event Reduction (TRACER) trial did not demonstrate a significant reduction in a broad primary composite endpoint. However, the Thrombin-Receptor Antagonist in Secondary Prevention of Atherothrombotic Ischemic Events (TRA 2°P-TIMI 50) trial examined a more traditional composite endpoint and found a significant benefit with vorapaxar. Vorapaxar significantly increased bleeding compared with standard care. Ongoing trials will help define the role of vorapaxar in patients with peripheral arterial disease, patients with diabetes mellitus, and other important subgroups. The use of multivariate modeling may enable the identification of subgroups with maximal benefit and minimal harm from vorapaxar. Vorapaxar provides clinicians with a novel mechanism of action to further reduce the burden of ischemic heart disease. Identification of patients with a high ischemic risk and low bleeding risk would enable clinicians to maximize the utility of this unique agent.

Association of chemokine receptor gene (CCR2-CCR5) haplotypes with acquisition and control of HIV-1 infection in Zambians

PubMed Central

2011-01-01

Background Polymorphisms in chemokine (C-C motif) receptors 2 and 5 genes (CCR2 and CCR5) have been associated with HIV-1 infection and disease progression. We investigated the impact of CCR2-CCR5 haplotypes on HIV-1 viral load (VL) and heterosexual transmission in an African cohort. Between 1995 and 2006, cohabiting Zambian couples discordant for HIV-1 (index seropositive and HIV-1 exposed seronegative {HESN}) were monitored prospectively to determine the role of host genetic factors in HIV-1 control and heterosexual transmission. Genotyping for eight CCR2 and CCR5 variants resolved nine previously recognized haplotypes. By regression and survival analytic techniques, controlling for non-genetic factors, we estimated the effects of these haplotypic variants on a) index partner VL, b) seroconverter VL, c) HIV-1 transmission by index partners, d) HIV-1 acquisition by HESN partners. Results Among 567 couples, 240 virologically linked transmission events had occurred through 2006. HHF*2 homozygosity was associated with significantly lower VL in seroconverters (mean beta = -0.58, log10 P = 0.027) and the HHD/HHE diplotype was associated with significantly higher VL in the seroconverters (mean beta = 0.54, log10 P = 0.014) adjusted for age and gender in multivariable model. HHD/HHE was associated with more rapid acquisition of infection by the HESNs (HR = 2.0, 95% CI = 1.20-3.43, P = 0.008), after adjustments for index partner VL and the presence of genital ulcer or inflammation in either partner in Cox multivariable models. The HHD/HHE effect was stronger in exposed females (HR = 2.1, 95% CI = 1.14-3.95, P = 0.018). Conclusions Among Zambian discordant couples, HIV-1 coreceptor gene haplotypes and diplotypes appear to modulate HIV-1 VL in seroconverters and alter the rate of HIV-1 acquisition by HESNs. These associations replicate or resemble findings reported in other African and European populations. PMID:21429204

Extensions to Multivariate Space Time Mixture Modeling of Small Area Cancer Data.

PubMed

Carroll, Rachel; Lawson, Andrew B; Faes, Christel; Kirby, Russell S; Aregay, Mehreteab; Watjou, Kevin

2017-05-09

Oral cavity and pharynx cancer, even when considered together, is a fairly rare disease. Implementation of multivariate modeling with lung and bronchus cancer, as well as melanoma cancer of the skin, could lead to better inference for oral cavity and pharynx cancer. The multivariate structure of these models is accomplished via the use of shared random effects, as well as other multivariate prior distributions. The results in this paper indicate that care should be taken when executing these types of models, and that multivariate mixture models may not always be the ideal option, depending on the data of interest.

Correlates of Triple Negative Breast Cancer and Chemotherapy Patterns in Black and White Women With Breast Cancer.

PubMed

Sheppard, Vanessa B; Cavalli, Luciane R; Dash, Chiranjeev; Kanaan, Yasmine M; Dilawari, Asma A; Horton, Sara; Makambi, Kepher H

2017-06-01

Triple negative breast cancer (TNBC) tumors are estrogen receptor-negative, progesterone receptor-negative, and human epidermal growth factor-negative. TNBC is responsive to chemotherapy, but chemotherapy might be underused in some patient subgroups. The goal of the present study was to characterize the patterns of chemotherapy use (uptake and completion) in TNBC patients. Women with primary invasive, nonmetastatic breast cancer were recruited in Washington, DC, and Detroit. Data were collected using a standardized telephone survey that captured sociocultural and health care process factors. Clinical data were abstracted from the medical records. We used χ 2 tests to access the association between the receipt of chemotherapy use (initiation and completion) and categorical variables, and t tests were used for continuous variables. Logistic regression models were used to evaluate the factors associated with chemotherapy uptake. Women with TNBC (16% of sample) were more likely to be black than white (68% vs. 32%; P < .05). Among women with TNBC, 60% underwent chemotherapy. Chemotherapy uptake was greater for black than for white women (48.3% vs. 11.7%; P = .01) and in women without (vs. with) healthcare discrimination (35% vs. 25%; P = .04). In multivariable models, only race was associated with the receipt of chemotherapy. Black women were more likely to receive chemotherapy than were white women. The odds ratio of receiving chemotherapy by race was 4.1 (95% confidence interval, 1.3-13.1). Each 1-year increase in age was associated with a lower likelihood of chemotherapy completion (odds ratio, 0.9; 95% confidence interval, 0.826-0.981; P = .02). Women with at least some college were less likely to complete chemotherapy than were those with other education levels (P = .02). A substantial number of TNBC patients failed to receive and/or complete chemotherapy. Differences in chemotherapy uptake by race and sociocultural factors diminished in multivariable models but age and stage remained significant. Suboptimal treatment among women with TNBC could contribute to adverse outcomes. Future investigations are necessary to assess whether the noninitiation and/or noncompletion of chemotherapy is clinically warranted. Copyright © 2017 Elsevier Inc. All rights reserved.

Validation of the IHC4 Breast Cancer Prognostic Algorithm Using Multiple Approaches on the Multinational TEAM Clinical Trial.

PubMed

Bartlett, John M S; Christiansen, Jason; Gustavson, Mark; Rimm, David L; Piper, Tammy; van de Velde, Cornelis J H; Hasenburg, Annette; Kieback, Dirk G; Putter, Hein; Markopoulos, Christos J; Dirix, Luc Y; Seynaeve, Caroline; Rea, Daniel W

2016-01-01

Hormone receptors HER2/neu and Ki-67 are markers of residual risk in early breast cancer. An algorithm (IHC4) combining these markers may provide additional information on residual risk of recurrence in patients treated with hormone therapy. To independently validate the IHC4 algorithm in the multinational Tamoxifen Versus Exemestane Adjuvant Multicenter Trial (TEAM) cohort, originally developed on the trans-ATAC (Arimidex, Tamoxifen, Alone or in Combination Trial) cohort, by comparing 2 methodologies. The IHC4 biomarker expression was quantified on TEAM cohort samples (n = 2919) by using 2 independent methodologies (conventional 3,3'-diaminobezidine [DAB] immunohistochemistry with image analysis and standardized quantitative immunofluorescence [QIF] by AQUA technology). The IHC4 scores were calculated by using the same previously established coefficients and then compared with recurrence-free and distant recurrence-free survival, using multivariate Cox proportional hazards modeling. The QIF model was highly significant for prediction of residual risk (P < .001), with continuous model scores showing a hazard ratio (HR) of 1.012 (95% confidence interval [95% CI]: 1.010-1.014), which was significantly higher than that for the DAB model (HR: 1.008, 95% CI: 1.006-1.009); P < .001). Each model added significant prognostic value in addition to recognized clinical prognostic factors, including nodal status, in multivariate analyses. Quantitative immunofluorescence, however, showed more accuracy with respect to overall residual risk assessment than the DAB model. The use of the IHC4 algorithm was validated on the TEAM trial for predicting residual risk in patients with breast cancer. These data support the use of the IHC4 algorithm clinically, but quantitative and standardized approaches need to be used.

The interaction between vitamin D receptor polymorphisms and sun exposure around time of diagnosis influences melanoma survival.

PubMed

Orlow, Irene; Shi, Yang; Kanetsky, Peter A; Thomas, Nancy E; Luo, Li; Corrales-Guerrero, Sergio; Cust, Anne E; Sacchetto, Lidia; Zanetti, Roberto; Rosso, Stefano; Armstrong, Bruce K; Dwyer, Terence; Venn, Alison; Gallagher, Richard P; Gruber, Stephen B; Marrett, Loraine D; Anton-Culver, Hoda; Busam, Klaus; Begg, Colin B; Berwick, Marianne

2018-03-01

Evidence on the relationship between the vitamin D pathway and outcomes in melanoma is growing, although it is not always clear. We investigated the impact of measured levels of sun exposure at diagnosis on associations of vitamin D receptor gene (VDR) polymorphisms and melanoma death in 3336 incident primary melanoma cases. Interactions between six SNPs and a common 3'-end haplotype were significant (p < .05). These SNPs, and a haplotype, had a statistically significant association with survival among subjects exposed to high UVB in multivariable regression models and exerted their effect in the opposite direction among those with low UVB. SNPs rs1544410/BsmI and rs731236/TaqI remained significant after adjustment for multiple testing. These results suggest that the association between VDR and melanoma-specific survival is modified by sun exposure around diagnosis, and require validation in an independent study. Whether the observed effects are dependent or independent of vitamin D activation remains to be determined. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Prenatal and early-life exposures alter expression of innate immunity genes: the PASTURE cohort study.

PubMed

Loss, Georg; Bitter, Sondhja; Wohlgensinger, Johanna; Frei, Remo; Roduit, Caroline; Genuneit, Jon; Pekkanen, Juha; Roponen, Marjut; Hirvonen, Maija-Riitta; Dalphin, Jean-Charles; Dalphin, Marie-Laure; Riedler, Josef; von Mutius, Erika; Weber, Juliane; Kabesch, Michael; Michel, Sven; Braun-Fahrländer, Charlotte; Lauener, Roger

2012-08-01

There is evidence that gene expression of innate immunity receptors is upregulated by farming-related exposures. We sought to determine environmental and nutritional exposures associated with the gene expression of innate immunity receptors during pregnancy and the first year of a child's life. For the Protection Against Allergy: Study in Rural Environments (PASTURE) birth cohort study, 1133 pregnant women were recruited in rural areas of Austria, Finland, France, Germany, and Switzerland. mRNA expression of the Toll-like receptor (TLR) 1 through TLR9 and CD14 was assessed in blood samples at birth (n= 938) and year 1 (n= 752). Environmental exposures, as assessed by using questionnaires and a diary kept during year 1, and polymorphisms in innate receptor genes were related to gene expression of innate immunity receptors by using ANOVA and multivariate regression analysis. Gene expression of innate immunity receptors in cord blood was overall higher in neonates of farmers (P for multifactorial multivariate ANOVA= .041), significantly so for TLR7 (adjusted geometric means ratio [aGMR], 1.15; 95% CI, 1.02-1.30) and TLR8 (aGMR, 1.15; 95% CI, 1.04-1.26). Unboiled farm milk consumption during the first year of life showed the strongest association with mRNA expression at year 1, taking the diversity of other foods introduced during that period into account: TLR4 (aGMR, 1.22; 95% CI, 1.03-1.45), TLR5 (aGMR, 1.19; 95% CI, 1.01-1.41), and TLR6 (aGMR, 1.20; 95% CI, 1.04-1.38). A previously described modification of the association between farm milk consumption and CD14 gene expression by the single nucleotide polymorphism CD14/C-1721T was not found. Farming-related exposures, such as raw farm milk consumption, that were previously reported to decrease the risk for allergic outcomes were associated with a change in gene expression of innate immunity receptors in early life. Copyright © 2012 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.

Risk of locoregional recurrence by receptor status in breast cancer patients receiving modern systemic therapy and post-mastectomy radiation.

PubMed

Panoff, J E; Hurley, J; Takita, C; Reis, I M; Zhao, W; Sujoy, V; Gomez, C R; Jorda, M; Koniaris, L; Wright, J L

2011-08-01

We assessed differences in locoregional outcome based on receptor status combinations in a cohort of stage II-III breast cancer patients treated with modern trimodality therapy. Medical records of 582 consecutively treated patients receiving post-mastectomy radiation (PMRT) between 1/1999 and 12/2009 were reviewed. Rate of local regional recurrence (LRR) was estimated by the method of cumulative incidence allowing for competing risks. The effect of prognostic factors was examined by Gray's test and by Fine and Gray's modeling approach. Median follow-up was 44.7 months. Five-year progression-free survival (PFS) was 73.9% and overall survival (OS) was 84%. The cumulative 5-year incidence of LRR as first site of failure was 6.2% (95% CI 4.2-8.7). Five-year cumulative incidence of LRR was 8.6 versus 4.4% for estrogen receptor (ER) negative versus ER positive (P = 0.017), 8.5 versus 3.4% for progesterone receptor (PR) negative versus PR positive (P = 0.011), and 1.7 versus 7.5% for HER2 positive (86% received trastuzamab) versus HER2 negative (P = 0.032). Five-year cumulative incidence of LRR was 11.8% for the triple negative subtype and 3.9% for other receptor combinations (P < 0.001). Among patients whose disease is ER positive, 5-year LRR rate was 7.8 versus 3.4% for PR negative versus PR positive (P = 0.130). The prognostic value of the triple negative and HER2 negative subtypes was maintained on multivariate analysis. In the era of HER-2 targeted therapy, tumors that are HER-2 over expressing and are treated with trastuzumab have a very low rate of LRR. ER negative, PR negative, and triple negative status are associated with increased risk of LRR.

The effect of early trauma exposure on serotonin type 1B receptor expression revealed by reduced selective radioligand binding.

PubMed

Murrough, James W; Czermak, Christoph; Henry, Shannan; Nabulsi, Nabeel; Gallezot, Jean-Dominique; Gueorguieva, Ralitza; Planeta-Wilson, Beata; Krystal, John H; Neumaier, John F; Huang, Yiyun; Ding, Yu-Shin; Carson, Richard E; Neumeister, Alexander

2011-09-01

Serotonergic dysfunction is implicated in the pathogenesis of posttraumatic stress disorder (PTSD), and recent animal models suggest that disturbances in serotonin type 1B receptor function, in particular, may contribute to chronic anxiety. However, the specific role of the serotonin type 1B receptor has not been studied in patients with PTSD. To investigate in vivo serotonin type 1B receptor expression in individuals with PTSD, trauma-exposed control participants without PTSD (TC), and healthy (non-trauma-exposed) control participants (HC) using positron emission tomography and the recently developed serotonin type 1B receptor selective radiotracer [(11)C]P943. Cross-sectional positron emission tomography study under resting conditions. Academic and Veterans Affairs medical centers. Ninety-six individuals in 3 study groups: PTSD (n = 49), TC (n = 20), and HC (n = 27). Main Outcome Measure  Regional [(11)C]P943 binding potential (BP(ND)) values in an a priori-defined limbic corticostriatal circuit investigated using multivariate analysis of variance and multiple regression analysis. A history of severe trauma exposure in the PTSD and TC groups was associated with marked reductions in [(11)C]P943 BP(ND) in the caudate, the amygdala, and the anterior cingulate cortex. Participant age at first trauma exposure was strongly associated with low [(11)C]P943 BP(ND). Developmentally earlier trauma exposure also was associated with greater PTSD symptom severity and major depression comorbidity. These data suggest an enduring effect of trauma history on brain function and the phenotype of PTSD. The association of early age at first trauma and more pronounced neurobiological and behavioral alterations in PTSD suggests a developmental component in the cause of PTSD.

Biomarkers of folate and vitamin B12 and breast cancer risk: report from the EPIC cohort.

PubMed

Matejcic, M; de Batlle, J; Ricci, C; Biessy, C; Perrier, F; Huybrechts, I; Weiderpass, E; Boutron-Ruault, M C; Cadeau, C; His, M; Cox, D G; Boeing, H; Fortner, R T; Kaaks, R; Lagiou, P; Trichopoulou, A; Benetou, V; Tumino, R; Panico, S; Sieri, S; Palli, D; Ricceri, F; Bueno-de-Mesquita, H B As; Skeie, G; Amiano, P; Sánchez, M J; Chirlaque, M D; Barricarte, A; Quirós, J R; Buckland, G; van Gils, C H; Peeters, P H; Key, T J; Riboli, E; Gylling, B; Zeleniuch-Jacquotte, A; Gunter, M J; Romieu, I; Chajès, V

2017-03-15

Epidemiological studies have reported inconsistent findings for the association between B vitamins and breast cancer (BC) risk. We investigated the relationship between biomarkers of folate and vitamin B12 and the risk of BC in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Plasma concentrations of folate and vitamin B12 were determined in 2,491 BC cases individually matched to 2,521 controls among women who provided baseline blood samples. Multivariable logistic regression models were used to estimate odds ratios by quartiles of either plasma B vitamin. Subgroup analyses by menopausal status, hormone receptor status of breast tumors (estrogen receptor [ER], progesterone receptor [PR] and human epidermal growth factor receptor 2 [HER2]), alcohol intake and MTHFR polymorphisms (677C > T and 1298A > C) were also performed. Plasma levels of folate and vitamin B12 were not significantly associated with the overall risk of BC or by hormone receptor status. A marginally positive association was found between vitamin B12 status and BC risk in women consuming above the median level of alcohol (OR Q4-Q1  = 1.26; 95% CI 1.00-1.58; P trend  = 0.05). Vitamin B12 status was also positively associated with BC risk in women with plasma folate levels below the median value (OR Q4-Q1  = 1.29; 95% CI 1.02-1.62; P trend  = 0.03). Overall, folate and vitamin B12 status was not clearly associated with BC risk in this prospective cohort study. However, potential interactions between vitamin B12 and alcohol or folate on the risk of BC deserve further investigation. © 2016 UICC.

Prognostic Impact of Loop Diuretics in Patients With Chronic Heart Failure　- Effects of Addition of Renin-Angiotensin-Aldosterone System Inhibitors and β-Blockers.

PubMed

Miura, Masanobu; Sugimura, Koichiro; Sakata, Yasuhiko; Miyata, Satoshi; Tadaki, Soichiro; Yamauchi, Takeshi; Onose, Takeo; Tsuji, Kanako; Abe, Ruri; Oikawa, Takuya; Kasahara, Shintaro; Nochioka, Kotaro; Takahashi, Jun; Shimokawa, Hiroaki

2016-05-25

It remains to be elucidated whether addition of renin-angiotensin-aldosterone system (RAAS) inhibitors and/or β-blockers to loop diuretics has a beneficial prognostic impact on chronic heart failure (CHF) patients. From the Chronic Heart failure Analysis and Registry in the Tohoku district 2 (CHART-2) Study (n=10,219), we enrolled 4,134 consecutive patients with symptomatic stage C/D CHF (mean age, 69.3 years, 67.7% male). We constructed Cox models for composite of death, myocardial infarction, stroke and HF admission. On multivariate inverse probability of treatment weighted (IPTW) Cox modeling, loop diuretics use was associated with worse prognosis with hazard ratio (HR) 1.28 (P<0001). Furthermore, on IPTW multivariate Cox modeling for multiple treatments, both low-dose (<40 mg/day) and high-dose (≥40 mg/day) loop diuretics were associated with worse prognosis with HR 1.32 and 1.56, respectively (both P<0.001). Triple blockade with RAS inhibitor(s), mineral corticoid (aldosterone) receptor antagonist(s) (MRA), and β-blocker(s) was significantly associated with better prognosis in those on low-dose but not on high-dose loop diuretics. Chronic use of loop diuretics is significantly associated with worse prognosis in CHF patients in a dose-dependent manner, whereas the triple combination of RAAS inhibitor(s), MRA, and β-blocker(s) is associated with better prognosis when combined with low-dose loop diuretics. (Circ J 2016; 80: 1396-1403).

Brain galanin system genes interact with life stresses in depression-related phenotypes

PubMed Central

Juhasz, Gabriella; Hullam, Gabor; Eszlari, Nora; Gonda, Xenia; Antal, Peter; Anderson, Ian Muir; Hökfelt, Tomas G. M.; Deakin, J. F. William; Bagdy, Gyorgy

2014-01-01

Galanin is a stress-inducible neuropeptide and cotransmitter in serotonin and norepinephrine neurons with a possible role in stress-related disorders. Here we report that variants in genes for galanin (GAL) and its receptors (GALR1, GALR2, GALR3), despite their disparate genomic loci, conferred increased risk of depression and anxiety in people who experienced childhood adversity or recent negative life events in a European white population cohort totaling 2,361 from Manchester, United Kingdom and Budapest, Hungary. Bayesian multivariate analysis revealed a greater relevance of galanin system genes in highly stressed subjects compared with subjects with moderate or low life stress. Using the same method, the effect of the galanin system genes was stronger than the effect of the well-studied 5-HTTLPR polymorphism in the serotonin transporter gene (SLC6A4). Conventional multivariate analysis using general linear models demonstrated that interaction of galanin system genes with life stressors explained more variance (1.7%, P = 0.005) than the life stress-only model. This effect replicated in independent analysis of the Manchester and Budapest subpopulations, and in males and females. The results suggest that the galanin pathway plays an important role in the pathogenesis of depression in humans by increasing the vulnerability to early and recent psychosocial stress. Correcting abnormal galanin function in depression could prove to be a novel target for drug development. The findings further emphasize the importance of modeling environmental interaction in finding new genes for depression. PMID:24706871

Occurrence and outcome of de novo metastatic breast cancer by subtype in a large, diverse population.

PubMed

Tao, Li; Chu, Laura; Wang, Lisa I; Moy, Lisa; Brammer, Melissa; Song, Chunyan; Green, Marjorie; Kurian, Allison W; Gomez, Scarlett L; Clarke, Christina A

2016-09-01

To examine the occurrence and outcomes of de novo metastatic (Stage IV) breast cancer, particularly with respect to tumor HER2 expression. We studied all 6,268 de novo metastatic breast cancer cases diagnosed from 1 January 2005 to 31 December 2011 and reported to the California Cancer Registry. Molecular subtypes were classified according to HER2 and hormone receptor (HR, including estrogen and/or progesterone receptor) expression. Multivariable logistic regression was used to estimate odds ratios (ORs) and 95 % confidence intervals (CIs) of Stage IV versus Stage I-III breast cancer; Cox proportional hazards regression was used to assess relative hazard (RH) of mortality. Five percent of invasive breast cancer was metastatic at diagnosis. Compared to patients with earlier stage disease, patients with de novo metastatic disease were significantly more likely to have HER2+ tumors (HR+/HER2+: OR 1.29, 95 % CI 1.17-1.42; HR-/HER2+: OR 1.40, 95 %CI 1.25-1.57, vs. HR+/HER2-). Median survival improved over time, but varied substantially across race/ethnicity (Asians: 34 months; African Americans: 6 months), neighborhood socioeconomic status (SES) (highest: 34 months, lowest: 20 months), and molecular subtype (HR+/HER2+: 45 months; triple negative: 12 months). In a multivariable model, triple negative (RH 2.85, 95 % CI 2.50-3.24) and HR-/HER2+ (RH 1.60, 95 % CI 1.37-1.87) had worse, while HR+/HER2+ had similar, risk of all-cause death compared to HR+/HER2- breast cancer. De novo metastatic breast cancer was more likely to be HER2+. Among metastatic tumors, those that were HER2+ had better survival than other subtypes.

Correlation of degree of hypothyroidism with survival outcomes in patients with metastatic renal cell carcinoma receiving vascular endothelial growth factor receptor tyrosine kinase inhibitors.

PubMed

Bailey, Erin B; Tantravahi, Srinivas K; Poole, Austin; Agarwal, Archana M; Straubhar, Alli M; Batten, Julia A; Patel, Shiven B; Wells, Chesley E; Stenehjem, David D; Agarwal, Neeraj

2015-06-01

Hypothyroidism is a common adverse effect of vascular endothelial growth factor receptor tyrosine kinase inhibitor (VEGFR-TKI) therapy in patients with metastatic renal cell carcinoma (mRCC). Some studies have shown an association with improved survival. However, hypothyroidism severity has not been correlated with survival outcomes. We report the incidence and severity of VEGFR-TKI therapy-associated hypothyroidism in correlation with the survival outcomes of patients with mRCC. A retrospective analysis of patients with mRCC who received VEGFR-TKIs (2004 through 2013) was conducted from a single institutional database. Hypothyroidism, progression-free survival (PFS), and overall survival (OS) were assessed. Univariate and multivariate analyses were performed using the Kaplan-Meier method and Cox proportional hazard models. Of 125 patients with mRCC, 65 were eligible. Their median age was 59 years (range, 45-79 years), and 46 (70.8%) were male. Hypothyroidism occurred in 25 patients (38.5%), of whom 13 had a peak thyroid-stimulating hormone (TSH) level > 10 mIU/L during treatment. The median OS was significantly longer in patients with a peak TSH > 10 mIU/L than in patients with a peak TSH of ≤ 10 mIU/L (not reached vs. 21.4 months, P = .005). On multivariate analysis, risk criteria, number of previous therapies, and severe hypothyroidism (TSH > 10 mIU/L) during VEGFR-TKI therapy remained significant for improvements in PFS and OS. The severity of VEGFR-TKI therapy-associated hypothyroidism (TSH > 10 mIU/L) was associated with improved survival outcomes in patients with mRCC and should not necessitate a dose reduction or therapy discontinuation. Copyright © 2015 Elsevier Inc. All rights reserved.

Prognostic Significance of the Number of Positive Lymph Nodes in Women With T1-2N1 Breast Cancer Treated With Mastectomy: Should Patients With 1, 2, and 3 Positive Lymph Nodes Be Grouped Together?

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

Dai Kubicky, Charlotte, E-mail: charlottedai@gmail.com; Mongoue-Tchokote, Solange

2013-04-01

Purpose: To determine whether patients with 1, 2, or 3 positive lymph nodes (LNs) have similar survival outcomes. Methods and Materials: We analyzed the Surveillance, Epidemiology, and End Results registry of breast cancer patients diagnosed between 1990 and 2003. We identified 10,415 women with T1-2N1M0 breast cancer who were treated with mastectomy with no adjuvant radiation, with at least 10 LNs examined and 6 months of follow-up. The Kaplan-Meier method and log–rank test were used for survival analysis. Multivariate analysis was performed using the Cox proportional hazard model. Results: Median follow-up was 92 months. Ten-year overall survival (OS) and cause-specificmore » survival (CSS) were progressively worse with increasing number of positive LNs. Survival rates were 70%, 64%, and 60% (OS), and 82%, 76%, and 72% (CSS) for 1, 2, and 3 positive LNs, respectively. Pairwise log–rank test P values were <.001 (1 vs 2 positive LNs), <.001 (1 vs 3 positive LNs), and .002 (2 vs 3 positive LNs). Multivariate analysis showed that number of positive LNs was a significant predictor of OS and CSS. Hazard ratios increased with the number of positive LNs. In addition, age, primary tumor size, grade, estrogen receptor and progesterone receptor status, race, and year of diagnosis were significant prognostic factors. Conclusions: Our study suggests that patients with 1, 2, and 3 positive LNs have distinct survival outcomes, with increasing number of positive LNs associated with worse OS and CSS. The conventional grouping of 1-3 positive LNs needs to be reconsidered.« less

Tumor expression of calcium sensing receptor and colorectal cancer survival: Results from the nurses' health study and health professionals follow-up study.

PubMed

Momen-Heravi, Fatemeh; Masugi, Yohei; Qian, Zhi Rong; Nishihara, Reiko; Liu, Li; Smith-Warner, Stephanie A; Keum, NaNa; Zhang, Lanjing; Tchrakian, Nairi; Nowak, Jonathan A; Yang, Wanshui; Ma, Yanan; Bowden, Michaela; da Silva, Annacarolina; Wang, Molin; Fuchs, Charles S; Meyerhardt, Jeffrey A; Ng, Kimmie; Wu, Kana; Giovannucci, Edward; Ogino, Shuji; Zhang, Xuehong

2017-12-15

Although experimental evidence suggests calcium-sensing receptor (CASR) as a tumor-suppressor, the prognostic role of tumor CASR expression in colorectal carcinoma remains unclear. We hypothesized that higher tumor CASR expression might be associated with improved survival among colorectal cancer patients. We evaluated tumor expression levels of CASR by immunohistochemistry in 809 incident colorectal cancer patients within the Nurses' Health Study and the Health Professionals Follow-up Study. We used Cox proportional hazards regression models to estimate multivariable hazard ratio (HR) for the association of tumor CASR expression with colorectal cancer-specific and all-cause mortality. We adjusted for potential confounders including tumor biomarkers such as microsatellite instability, CpG island methylator phenotype, LINE-1 methylation level, expressions of PTGS2, VDR and CTNNB1 and mutations of KRAS, BRAF and PIK3CA. There were 240 colorectal cancer-specific deaths and 427 all-cause deaths. The median follow-up of censored patients was 10.8 years (interquartile range: 7.2, 15.1). Compared with patients with no or weak expression of CASR, the multivariable HRs for colorectal cancer-specific mortality were 0.80 [95% confidence interval (CI): 0.55-1.16] in patients with moderate CASR expression and 0.50 (95% CI: 0.32-0.79) in patients with intense CASR expression (p-trend = 0.003). The corresponding HRs for overall mortality were 0.85 (0.64-1.13) and 0.81 (0.58-1.12), respectively. Higher tumor CASR expression was associated with a lower risk of colorectal cancer-specific mortality. This finding needs further confirmation and if confirmed, may lead to better understanding of the role of CASR in colorectal cancer progression. © 2017 UICC.

A multivariate time series approach to modeling and forecasting demand in the emergency department.

PubMed

Jones, Spencer S; Evans, R Scott; Allen, Todd L; Thomas, Alun; Haug, Peter J; Welch, Shari J; Snow, Gregory L

2009-02-01

The goals of this investigation were to study the temporal relationships between the demands for key resources in the emergency department (ED) and the inpatient hospital, and to develop multivariate forecasting models. Hourly data were collected from three diverse hospitals for the year 2006. Descriptive analysis and model fitting were carried out using graphical and multivariate time series methods. Multivariate models were compared to a univariate benchmark model in terms of their ability to provide out-of-sample forecasts of ED census and the demands for diagnostic resources. Descriptive analyses revealed little temporal interaction between the demand for inpatient resources and the demand for ED resources at the facilities considered. Multivariate models provided more accurate forecasts of ED census and of the demands for diagnostic resources. Our results suggest that multivariate time series models can be used to reliably forecast ED patient census; however, forecasts of the demands for diagnostic resources were not sufficiently reliable to be useful in the clinical setting.

The serologic decoy receptor 3 (DcR3) levels are associated with slower disease progression in HIV-1/AIDS patients.

PubMed

Lin, Yu-Ting; Yen, Chia-Hung; Chen, Heng-Li; Liao, Yi-Jen; Lin, I-Feng; Chen, Marcelo; Lan, Yu-Ching; Chuang, Shao-Yuan; Hsieh, Shie-Liang; Chen, Yi-Ming Arthur

2015-06-01

The decoy receptor 3 (DcR3) is a member of the tumor necrosis factor receptor (TNFR) super-family. It counteracts the biological effects of Fas ligands and inhibits apoptosis. The goals of this study were to understand the associations between serologic DcR3 (sDcR3) levels and different human immunodeficiency virus type 1 (HIV-1) subtypes, as well as the AIDS disease progression. Serum samples from 61 HIV/AIDS patients, who had been followed up every 6 months for 3 years, were collected. sDcR3 levels were quantified using an enzyme immunoassay (EIA). The sDcR3 levels in patients with HIV-1 subtype B were significantly higher than those in patients infected with subtype CRF01_AE (p < 0.001). In addition, multivariable linear mixed model analysis demonstrated that HIV-1 subtype B and slow disease progression were associated with higher levels of sDcR3, adjusting for potential predictors (p = 0.0008 and 0.0455, respectively). HIV-1-infected cells may gain a survival advantage by activating DcR3, which prevents infected cell detection by the host immune system. These data indicate that the sDcR3 level is a biomarker for AIDS disease progression. Copyright © 2013. Published by Elsevier B.V.

Comparison of Multidimensional Item Response Models: Multivariate Normal Ability Distributions versus Multivariate Polytomous Ability Distributions. Research Report. ETS RR-08-45

ERIC Educational Resources Information Center

Haberman, Shelby J.; von Davier, Matthias; Lee, Yi-Hsuan

2008-01-01

Multidimensional item response models can be based on multivariate normal ability distributions or on multivariate polytomous ability distributions. For the case of simple structure in which each item corresponds to a unique dimension of the ability vector, some applications of the two-parameter logistic model to empirical data are employed to…

Molecular risk assessment of BIG 1-98 participants by expression profiling using RNA from archival tissue

PubMed Central

2010-01-01

Background The purpose of the work reported here is to test reliable molecular profiles using routinely processed formalin-fixed paraffin-embedded (FFPE) tissues from participants of the clinical trial BIG 1-98 with a median follow-up of 60 months. Methods RNA from fresh frozen (FF) and FFPE tumor samples of 82 patients were used for quality control, and independent FFPE tissues of 342 postmenopausal participants of BIG 1-98 with ER-positive cancer were analyzed by measuring prospectively selected genes and computing scores representing the functions of the estrogen receptor (eight genes, ER_8), the progesterone receptor (five genes, PGR_5), Her2 (two genes, HER2_2), and proliferation (ten genes, PRO_10) by quantitative reverse transcription PCR (qRT-PCR) on TaqMan Low Density Arrays. Molecular scores were computed for each category and ER_8, PGR_5, HER2_2, and PRO_10 scores were combined into a RISK_25 score. Results Pearson correlation coefficients between FF- and FFPE-derived scores were at least 0.94 and high concordance was observed between molecular scores and immunohistochemical data. The HER2_2, PGR_5, PRO_10 and RISK_25 scores were significant predictors of disease free-survival (DFS) in univariate Cox proportional hazard regression. PRO_10 and RISK_25 scores predicted DFS in patients with histological grade II breast cancer and in lymph node positive disease. The PRO_10 and PGR_5 scores were independent predictors of DFS in multivariate Cox regression models incorporating clinical risk indicators; PRO_10 outperformed Ki-67 labeling index in multivariate Cox proportional hazard analyses. Conclusions Scores representing the endocrine responsiveness and proliferation status of breast cancers were developed from gene expression analyses based on RNA derived from FFPE tissues. The validation of the molecular scores with tumor samples of participants of the BIG 1-98 trial demonstrates that such scores can serve as independent prognostic factors to estimate disease free survival (DFS) in postmenopausal patients with estrogen receptor positive breast cancer. Trial Registration Current Controlled Trials: NCT00004205 PMID:20144231

The Impact of Residual Disease After Preoperative Systemic Therapy on Clinical Outcomes in Patients with Inflammatory Breast Cancer.

PubMed

Nakhlis, Faina; Regan, Meredith M; Warren, Laura E; Bellon, Jennifer R; Hirshfield-Bartek, Judith; Duggan, Margaret M; Dominici, Laura S; Golshan, Mehra; Jacene, Heather A; Yeh, Eren D; Mullaney, Erin E; Overmoyer, Beth

2017-09-01

Inflammatory breast cancer (IBC) is a rare and aggressive disease treated with multimodality therapy: preoperative systemic therapy (PST) followed by modified radical mastectomy (MRM), chest wall and regional nodal radiotherapy, and adjuvant biologic therapy and/or endocrine therapy when appropriate. In non-IBC, the degree of pathologic response to PST has been shown to correlate with time to recurrence (TTR) and overall survival (OS). We sought to determine if pathologic response correlates with oncologic outcomes of IBC patients. Following review of IBC patients' records (1997-2014), we identified 258 stage III IBC patients; 181 received PST followed by MRM and radiotherapy and were subsequently analyzed. Pathologic complete response (pCR) to PST, hormone receptor and human epidermal growth factor receptor 2 (HER2) status, grade, and histology were evaluated as predictors of TTR and OS by Cox model. Overall, 95/181 (52%) patients experienced recurrence; 93/95 (98%) were distant metastases (median TTR 3.2 years). Seventy-three patients (40%) died (median OS 6.9 years). pCR was associated with improved TTR (hazard ratio [HR] 0.20, 95% confidence interval [CI] 0.09-0.46, p < 0.01, univariate; HR 0.17, 95% CI 0.07-0.41, p < 0.0001, multivariate) and improved OS (HR 0.26, 95% CI 0.11-0.65, p < 0.01, univariate). In patients with pCR, grade III (HR 1.91, 95% CI 1.16-3.13, p = 0.01), and triple-negative phenotype (HR 3.54, 95% CI 1.79-6.98, p = 0.0003) were associated with shorter TTR, while residual ductal carcinoma in situ was not (HR 0.85, 95% CI 0.53-1.35, p = 0.48, multivariate). In stage III IBC, pCR was associated with prognosis, further influenced by grade, hormone receptor, and HER2 status. Investigating mechanisms that contribute to better response to PST could help improve oncologic outcomes in IBC.

Stochastic modelling of temperatures affecting the in situ performance of a solar-assisted heat pump: The multivariate approach and physical interpretation

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

Loveday, D.L.; Craggs, C.

Box-Jenkins-based multivariate stochastic modeling is carried out using data recorded from a domestic heating system. The system comprises an air-source heat pump sited in the roof space of a house, solar assistance being provided by the conventional tile roof acting as a radiation absorber. Multivariate models are presented which illustrate the time-dependent relationships between three air temperatures - at external ambient, at entry to, and at exit from, the heat pump evaporator. Using a deterministic modeling approach, physical interpretations are placed on the results of the multivariate technique. It is concluded that the multivariate Box-Jenkins approach is a suitable techniquemore » for building thermal analysis. Application to multivariate Box-Jenkins approach is a suitable technique for building thermal analysis. Application to multivariate model-based control is discussed, with particular reference to building energy management systems. It is further concluded that stochastic modeling of data drawn from a short monitoring period offers a means of retrofitting an advanced model-based control system in existing buildings, which could be used to optimize energy savings. An approach to system simulation is suggested.« less

Association of Toll-Like Receptor 4 Polymorphisms with Diabetic Foot Ulcers and Application of Artificial Neural Network in DFU Risk Assessment in Type 2 Diabetes Patients

PubMed Central

Singh, Kanhaiya; Agrawal, Neeraj K.; Gupta, Sanjeev K.

2013-01-01

The Toll-Like receptor 4 (TLR4) plays an important role in immunity, tissue repair, and regeneration. The objective of the present work was to evaluate the association of TLR4 single nucleotide polymorphisms (SNPs) rs4986790, rs4986791, rs11536858 (merged into rs10759931), rs1927911, and rs1927914 with increased diabetic foot ulcer (DFU) risk in patients with type 2 diabetes mellitus (T2DM). PCR-RFLP was used for genotyping TLR4 SNPs in 125 T2DM patients with DFU and 130 controls. The haplotypes and linkage disequilibrium between the SNPs were determined using Haploview software. Multivariate linear regression (MLR) and artificial neural network (ANN) modeling was done to observe their predictability for the risk of DFU in T2DM patients. Risk genotypes of all SNPs except rs1927914 were significantly associated with DFU. Haplotype ACATC (P value = 9.3E − 5) showed strong association with DFU risk. Two haplotypes ATATC (P value = 0.0119) and ATGTT (P value = 0.0087) were found to be protective against DFU. In conclusion TLR4 SNPs and their haplotypes may increase the risk of impairment of wound healing in T2DM patients. ANN model (83%) is found to be better than the MLR model (76%) and can be used as a tool for the DFU risk assessment in T2DM patients. PMID:23936790

Mitochondrial assembly receptor expression is an independent prognosticator for patients with oral tongue squamous cell carcinoma.

PubMed

Su, Yan-Ye; Chen, Chang-Han; Chien, Chih-Yen; Lin, Wei-Che; Huang, Wan-Ting; Li, Shau-Hsuan

2017-01-01

Recent evidence suggests that the local renin-angiotensin system has been implicated in various malignancies. The mitochondrial assembly receptor is a newly identified receptor for angiotensin peptides, angiotensin-(1-7), and has an important role in the renin-angiotensin system. However, the role of the mitochondrial assembly receptor in the prognosis of cancer patients remains unclear. The aim of this study was to evaluate the significance of mitochondrial assembly receptor signaling in the prognosis of oral tongue squamous cell carcinoma. Mitochondrial assembly receptor immunohistochemistry was examined in 151 oral tongue squamous cell carcinoma patients and was correlated with treatment outcome. The functional relevance of the mitochondrial assembly receptor in oral tongue squamous cell carcinoma cell lines was evaluated by 3-(4,5-dimethylthiazole-2-yl)-2,5-diphenyltetrazolium bromide reduction and bromodeoxyuridine incorporation assays. Mitochondrial assembly receptor overexpression was significantly correlated with early pathological T classification ( p=0.029) and the absence of extracapsular spread ( p=0.039). Univariate analyses demonstrated that mitochondrial assembly receptor overexpression was significantly associated with superior overall survival ( p=0.012). In multivariate comparison, mitochondrial assembly receptor overexpression remained independently associated with superior overall survival ( p=0.008, hazard ratio=1.862). In vitro, angiotensin-(1-7) suppressed the cell growth in oral tongue squamous cell carcinoma cells, and this response was reversed by the mitochondrial assembly receptor antagonist, A779. Mitochondrial assembly receptor expression is independently associated with the prognosis of oral tongue squamous cell carcinoma patients. These findings suggest that mitochondrial assembly receptor signaling may be a promising novel target for oral tongue squamous cell carcinoma.

Characterizing multivariate decoding models based on correlated EEG spectral features

PubMed Central

McFarland, Dennis J.

2013-01-01

Objective Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Methods Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). Results The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Conclusions Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. Significance While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. PMID:23466267

Effect of socioeconomic status as measured by education level on survival in breast cancer clinical trials.

PubMed

Herndon, James E; Kornblith, Alice B; Holland, Jimmie C; Paskett, Electra D

2013-02-01

This paper aims to investigate the effect of socioeconomic status, as measured by education, on the survival of breast cancer patients treated on 10 studies conducted by the Cancer and Leukemia Group B. Sociodemographic data, including education, were reported by the patient at trial enrollment. Cox proportional hazards model stratified by treatment arm/study was used to examine the effect of education on survival among patients with early stage and metastatic breast cancer, after adjustment for known prognostic factors. The patient population included 1020 patients with metastatic disease and 5146 patients with early stage disease. Among metastatic patients, factors associated with poorer survival in the final multivariable model included African American race, never married, negative estrogen receptor status, prior hormonal therapy, visceral involvement, and bone involvement. Among early stage patients, significant factors associated with poorer survival included African American race, separated/widowed, post/perimenopausal, negative/unknown estrogen receptor status, negative progesterone receptor status, >4 positive nodes, tumor diameter >2 cm, and education. Having not completed high school was associated with poorer survival among early stage patients. Among metastatic patients, non-African American women who lacked a high school degree had poorer survival than other non-African American women, and African American women who lacked a high school education had better survival than educated African American women. Having less than a high school education is a risk factor for death among patients with early stage breast cancer who participated in a clinical trial, with its impact among metastatic patients being less clear. Post-trial survivorship plans need to focus on women with low social status, as measured by education. Copyright © 2011 John Wiley & Sons, Ltd.

Transmitter receptors reveal segregation of cortical areas in the human superior parietal cortex: relations to visual and somatosensory regions.

PubMed

Scheperjans, Filip; Palomero-Gallagher, Nicola; Grefkes, Christian; Schleicher, Axel; Zilles, Karl

2005-11-01

Regional distributions of ligand binding sites of 12 different neurotransmitter receptors (glutamatergic: AMPA, kainate, NMDA; GABAergic: GABA(A), GABA(B); cholinergic: muscarinic M2, nicotinic; adrenergic: alpha1, alpha2; serotonergic: 5-HT1A, 5-HT2; dopaminergic: D1) were studied in human postmortem brains by means of quantitative receptor autoradiography. Binding site densities were measured in the superior parietal lobule (SPL) (areas 5L, 5M, 5Ci, and different locations within Brodmann's area (BA) 7), somatosensory (BA 2), and visual cortical areas (BA 17, and different locations within BAs 18 and 19). Similarities of receptor distribution between cortical areas were analyzed by cluster analysis, uni- and multivariate statistics of mean receptor densities (averaged over all cortical layers), and profiles representing the laminar distribution patterns of receptors. A considerable heterogeneity of regional receptor densities and laminar patterns between the sites was found in the SPL and the visual cortex. The most prominent regional differences were found for M2 receptors. In the SPL, rostrocaudally oriented changes of receptor densities were more pronounced than those in mediolateral direction. The receptor distribution in the rostral SPL was more similar to that of the somatosensory cortex, whereas caudal SPL resembled the receptor patterns of the dorsolateral extrastriate visual areas. These results suggest a segregation of the different SPL areas based on receptor distribution features typical for somatosensory or visual areas, which fits to the dual functional role of this cortical region, i.e., the involvement of the human SPL in visuomotor and somatosensory motor transformations.

Multivariate Longitudinal Analysis with Bivariate Correlation Test.

PubMed

Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

2016-01-01

In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model's parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated.

Reduced striatal D2 receptor binding in myoclonus-dystonia.

PubMed

Beukers, R J; Booij, J; Weisscher, N; Zijlstra, F; van Amelsvoort, T A M J; Tijssen, M A J

2009-02-01

To study striatal dopamine D(2) receptor availability in DYT11 mutation carriers of the autosomal dominantly inherited disorder myoclonus-dystonia (M-D). Fifteen DYT11 mutation carriers (11 clinically affected) and 15 age- and sex-matched controls were studied using (123)I-IBZM SPECT. Specific striatal binding ratios were calculated using standard templates for striatum and occipital areas. Multivariate analysis with corrections for ageing and smoking showed significantly lower specific striatal to occipital IBZM uptake ratios (SORs) both in the left and right striatum in clinically affected patients and also in all DYT11 mutation carriers compared to control subjects. Our findings are consistent with the theory of reduced dopamine D(2) receptor (D2R) availability in dystonia, although the possibility of increased endogenous dopamine, and consequently, competitive D2R occupancy cannot be ruled out.

Source apportionment of ambient non-methane hydrocarbons in Hong Kong: application of a principal component analysis/absolute principal component scores (PCA/APCS) receptor model.

PubMed

Guo, H; Wang, T; Louie, P K K

2004-06-01

Receptor-oriented source apportionment models are often used to identify sources of ambient air pollutants and to estimate source contributions to air pollutant concentrations. In this study, a PCA/APCS model was applied to the data on non-methane hydrocarbons (NMHCs) measured from January to December 2001 at two sampling sites: Tsuen Wan (TW) and Central & Western (CW) Toxic Air Pollutants Monitoring Stations in Hong Kong. This multivariate method enables the identification of major air pollution sources along with the quantitative apportionment of each source to pollutant species. The PCA analysis identified four major pollution sources at TW site and five major sources at CW site. The extracted pollution sources included vehicular internal engine combustion with unburned fuel emissions, use of solvent particularly paints, liquefied petroleum gas (LPG) or natural gas leakage, and industrial, commercial and domestic sources such as solvents, decoration, fuel combustion, chemical factories and power plants. The results of APCS receptor model indicated that 39% and 48% of the total NMHCs mass concentrations measured at CW and TW were originated from vehicle emissions, respectively. 32% and 36.4% of the total NMHCs were emitted from the use of solvent and 11% and 19.4% were apportioned to the LPG or natural gas leakage, respectively. 5.2% and 9% of the total NMHCs mass concentrations were attributed to other industrial, commercial and domestic sources, respectively. It was also found that vehicle emissions and LPG or natural gas leakage were the main sources of C(3)-C(5) alkanes and C(3)-C(5) alkenes while aromatics were predominantly released from paints. Comparison of source contributions to ambient NMHCs at the two sites indicated that the contribution of LPG or natural gas at CW site was almost twice that at TW site. High correlation coefficients (R(2) > 0.8) between the measured and predicted values suggested that the PCA/APCS model was applicable for estimation of sources of NMHCs in ambient air.

Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

ERIC Educational Resources Information Center

Price, Larry R.

2012-01-01

The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…

Characterizing multivariate decoding models based on correlated EEG spectral features.

PubMed

McFarland, Dennis J

2013-07-01

Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

The two-state dimer receptor model: a general model for receptor dimers.

PubMed

Franco, Rafael; Casadó, Vicent; Mallol, Josefa; Ferrada, Carla; Ferré, Sergi; Fuxe, Kjell; Cortés, Antoni; Ciruela, Francisco; Lluis, Carmen; Canela, Enric I

2006-06-01

Nonlinear Scatchard plots are often found for agonist binding to G-protein-coupled receptors. Because there is clear evidence of receptor dimerization, these nonlinear Scatchard plots can reflect cooperativity on agonist binding to the two binding sites in the dimer. According to this, the "two-state dimer receptor model" has been recently derived. In this article, the performance of the model has been analyzed in fitting data of agonist binding to A(1) adenosine receptors, which are an example of receptor displaying concave downward Scatchard plots. Analysis of agonist/antagonist competition data for dopamine D(1) receptors using the two-state dimer receptor model has also been performed. Although fitting to the two-state dimer receptor model was similar to the fitting to the "two-independent-site receptor model", the former is simpler, and a discrimination test selects the two-state dimer receptor model as the best. This model was also very robust in fitting data of estrogen binding to the estrogen receptor, for which Scatchard plots are concave upward. On the one hand, the model would predict the already demonstrated existence of estrogen receptor dimers. On the other hand, the model would predict that concave upward Scatchard plots reflect positive cooperativity, which can be neither predicted nor explained by assuming the existence of two different affinity states. In summary, the two-state dimer receptor model is good for fitting data of binding to dimeric receptors displaying either linear, concave upward, or concave downward Scatchard plots.

Brain neurotransmitter transporter/receptor genomics and efavirenz central nervous system adverse events.

PubMed

Haas, David W; Bradford, Yuki; Verma, Anurag; Verma, Shefali S; Eron, Joseph J; Gulick, Roy M; Riddler, Sharon A; Sax, Paul E; Daar, Eric S; Morse, Gene D; Acosta, Edward P; Ritchie, Marylyn D

2018-05-29

We characterized associations between central nervous system (CNS) adverse events and brain neurotransmitter transporter/receptor genomics among participants randomized to efavirenz-containing regimens in AIDS Clinical Trials Group studies in the USA. Four clinical trials randomly assigned treatment-naive participants to efavirenz-containing regimens. Genome-wide genotype and PrediXcan were used to infer gene expression levels in tissues including 10 brain regions. Multivariable regression models stratified by race/ethnicity were adjusted for CYP2B6/CYP2A6 genotypes that predict plasma efavirenz exposure, age, and sex. Combined analyses also adjusted for genetic ancestry. Analyses included 167 cases with grade 2 or greater efavirenz-consistent CNS adverse events within 48 weeks of study entry, and 653 efavirenz-tolerant controls. CYP2B6/CYP2A6 genotype level was independently associated with CNS adverse events (odds ratio: 1.07; P=0.044). Predicted expression of six genes postulated to mediate efavirenz CNS side effects (SLC6A2, SLC6A3, PGR, HTR2A, HTR2B, HTR6) were not associated with CNS adverse events after correcting for multiple testing, the lowest P value being for PGR in hippocampus (P=0.012), nor were polymorphisms in these genes or AR and HTR2C, the lowest P value being for rs12393326 in HTR2C (P=6.7×10). As a positive control, baseline plasma bilirubin concentration was associated with predicted liver UGT1A1 expression level (P=1.9×10). Efavirenz-related CNS adverse events were not associated with predicted neurotransmitter transporter/receptor gene expression levels in brain or with polymorphisms in these genes. Variable susceptibility to efavirenz-related CNS adverse events may not be explained by brain neurotransmitter transporter/receptor genomics.

Dose-dependent effect of mammographic breast density on the risk of contralateral breast cancer.

PubMed

Chowdhury, Marzana; Euhus, David; O'Donnell, Maureen; Onega, Tracy; Choudhary, Pankaj K; Biswas, Swati

2018-07-01

Increased mammographic breast density is a significant risk factor for breast cancer. It is not clear if it is also a risk factor for the development of contralateral breast cancer. The data were obtained from Breast Cancer Surveillance Consortium and included women diagnosed with invasive breast cancer or ductal carcinoma in situ between ages 18 and 88 and years 1995 and 2009. Each case of contralateral breast cancer was matched with three controls based on year of first breast cancer diagnosis, race, and length of follow-up. A total of 847 cases and 2541 controls were included. The risk factors included in the study were mammographic breast density, age of first breast cancer diagnosis, family history of breast cancer, anti-estrogen treatment, hormone replacement therapy, menopausal status, and estrogen receptor status, all from the time of first breast cancer diagnosis. Both univariate analysis and multivariate conditional logistic regression analysis were performed. In the final multivariate model, breast density, family history of breast cancer, and anti-estrogen treatment remained significant with p values less than 0.01. Increasing breast density had a dose-dependent effect on the risk of contralateral breast cancer. Relative to 'almost entirely fat' category of breast density, the adjusted odds ratios (and p values) in the multivariate analysis for 'scattered density,' 'heterogeneously dense,' and 'extremely dense' categories were 1.65 (0.036), 2.10 (0.002), and 2.32 (0.001), respectively. Breast density is an independent and significant risk factor for development of contralateral breast cancer. This risk factor should contribute to clinical decision making.

Multivariate Models of Parent-Late Adolescent Gender Dyads: The Importance of Parenting Processes in Predicting Adjustment

ERIC Educational Resources Information Center

McKinney, Cliff; Renk, Kimberly

2008-01-01

Although parent-adolescent interactions have been examined, relevant variables have not been integrated into a multivariate model. As a result, this study examined a multivariate model of parent-late adolescent gender dyads in an attempt to capture important predictors in late adolescents' important and unique transition to adulthood. The sample…

Multilocus analysis reveals three candidate genes for Chinese migraine susceptibility.

PubMed

An, X-K; Fang, J; Yu, Z-Z; Lin, Q; Lu, C-X; Qu, H-L; Ma, Q-L

2017-08-01

Several genome-wide association studies (GWASs) in Caucasian populations have identified 12 loci that are significantly associated with migraine. More evidence suggests that serotonin receptors are also involved in migraine pathophysiology. In the present study, a case-control study was conducted in a cohort of 581 migraine cases and 533 ethnically matched controls among a Chinese population. Eighteen polymorphisms from serotonin receptors and GWASs were selected, and genotyping was performed using a Sequenom MALDI-TOF mass spectrometry iPLEX platform. The genotypic and allelic distributions of MEF2D rs2274316 and ASTN2 rs6478241 were significantly different between migraine patients and controls. Univariate and multivariate analysis revealed significant associations of polymorphisms in the MEF2D and ASTN2 genes with migraine susceptibility. MEF2D, PRDM16 and ASTN2 were also found to be associated with migraine without aura (MO) and migraine with family history. And, MEF2D and ASTN2 also served as genetic risk factors for the migraine without family history. The generalized multifactor dimensionality reduction analysis identified that MEF2D and HTR2E constituted the two-factor interaction model. Our study suggests that the MEF2D, PRDM16 and ASTN2 genes from GWAS are associated with migraine susceptibility, especially MO, among Chinese patients. It appears that there is no association with serotonin receptor related genes. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Dietary fiber intake and risk of breast cancer defined by estrogen and progesterone receptor status: the Japan Public Health Center-based Prospective Study.

PubMed

Narita, Saki; Inoue, Manami; Saito, Eiko; Abe, Sarah K; Sawada, Norie; Ishihara, Junko; Iwasaki, Motoki; Yamaji, Taiki; Shimazu, Taichi; Sasazuki, Shizuka; Shibuya, Kenji; Tsugane, Shoichiro

2017-06-01

Epidemiological studies have suggested a protective effect of dietary fiber intake on breast cancer risk while the results have been inconsistent. Our study aimed to investigate the association between dietary fiber intake and breast cancer risk and to explore whether this association is modified by reproductive factors and hormone receptor status of the tumor. A total of 44,444 women aged 45 to 74 years from the Japan Public Health Center-based Prospective Study were included in analyses. Dietary intake assessment was performed using a validated 138-item food frequency questionnaire (FFQ). Hazard ratios (HRs) and 95% confidence intervals (CIs) for breast cancer incidence were calculated by multivariate Cox proportional hazards regression models. During 624,423 person-years of follow-up period, 681 breast cancer cases were identified. After adjusting for major confounders for breast cancer risk, inverse trends were observed but statistically non-significant. Extremely high intake of fiber was associated with decreased risk of breast cancer but this should be interpreted with caution due to limited statistical power. In stratified analyses by menopausal and hormone receptor status, null associations were observed except for ER-PR- status. Our findings suggest that extreme high fiber intake may be associated with decreased risk of breast cancer but the level of dietary fiber intake among Japanese population might not be sufficient to examine the association between dietary fiber intake and breast cancer risk.

Combined image and genomic analysis of high-grade serous ovarian cancer reveals PTEN loss as a common driver event and prognostic classifier.

PubMed

Martins, Filipe C; Santiago, Ines de; Trinh, Anne; Xian, Jian; Guo, Anne; Sayal, Karen; Jimenez-Linan, Mercedes; Deen, Suha; Driver, Kristy; Mack, Marie; Aslop, Jennifer; Pharoah, Paul D; Markowetz, Florian; Brenton, James D

2014-12-17

TP53 and BRCA1/2 mutations are the main drivers in high-grade serous ovarian carcinoma (HGSOC). We hypothesise that combining tissue phenotypes from image analysis of tumour sections with genomic profiles could reveal other significant driver events. Automatic estimates of stromal content combined with genomic analysis of TCGA HGSOC tumours show that stroma strongly biases estimates of PTEN expression. Tumour-specific PTEN expression was tested in two independent cohorts using tissue microarrays containing 521 cases of HGSOC. PTEN loss or downregulation occurred in 77% of the first cohort by immunofluorescence and 52% of the validation group by immunohistochemistry, and is associated with worse survival in a multivariate Cox-regression model adjusted for study site, age, stage and grade. Reanalysis of TCGA data shows that hemizygous loss of PTEN is common (36%) and expression of PTEN and expression of androgen receptor are positively associated. Low androgen receptor expression was associated with reduced survival in data from TCGA and immunohistochemical analysis of the first cohort. PTEN loss is a common event in HGSOC and defines a subgroup with significantly worse prognosis, suggesting the rational use of drugs to target PI3K and androgen receptor pathways for HGSOC. This work shows that integrative approaches combining tissue phenotypes from images with genomic analysis can resolve confounding effects of tissue heterogeneity and should be used to identify new drivers in other cancers.

A multivariate model and statistical method for validating tree grade lumber yield equations

Treesearch

Donald W. Seegrist

1975-01-01

Lumber yields within lumber grades can be described by a multivariate linear model. A method for validating lumber yield prediction equations when there are several tree grades is presented. The method is based on multivariate simultaneous test procedures.

Multivariate Boosting for Integrative Analysis of High-Dimensional Cancer Genomic Data

PubMed Central

Xiong, Lie; Kuan, Pei-Fen; Tian, Jianan; Keles, Sunduz; Wang, Sijian

2015-01-01

In this paper, we propose a novel multivariate component-wise boosting method for fitting multivariate response regression models under the high-dimension, low sample size setting. Our method is motivated by modeling the association among different biological molecules based on multiple types of high-dimensional genomic data. Particularly, we are interested in two applications: studying the influence of DNA copy number alterations on RNA transcript levels and investigating the association between DNA methylation and gene expression. For this purpose, we model the dependence of the RNA expression levels on DNA copy number alterations and the dependence of gene expression on DNA methylation through multivariate regression models and utilize boosting-type method to handle the high dimensionality as well as model the possible nonlinear associations. The performance of the proposed method is demonstrated through simulation studies. Finally, our multivariate boosting method is applied to two breast cancer studies. PMID:26609213

Soy Food Intake and Circulating Levels of Inflammatory Markers in Chinese Women

PubMed Central

Wu, Sheng Hui; Shu, Xiao Ou; Chow, Wong-Ho; Xiang, Yong-Bing; Zhang, Xianglan; Li, Hong-Lan; Cai, Qiuyin; Ji, Bu-Tian; Cai, Hui; Rothman, Nathaniel; Gao, Yu-Tang; Zheng, Wei; Yang, Gong

2013-01-01

Background Soy and some of its constituents, such as isoflavones, have been shown to affect the inflammatory process in animal studies. The association between soy food intake and inflammatory markers has not been evaluated adequately in humans. Objective Our aim was to evaluate whether higher intake of soy foods was inversely associated with inflammatory markers in 1,005 middle-aged Chinese women. Design In this cross-sectional study, dietary intake of soy foods was assessed by a validated food frequency questionnaire and by a 24-hour recall when biospecimens were procured. A general linear model was used to estimate the geometric means of selected inflammatory markers, including interleukin-6 (IL-6), IL-1β, tumor necrosis factor-α (TNFα), soluble IL-6 receptor, soluble GP130, soluble TNF receptors 1 and 2, and C-reactive protein, across categories of soy food intake after adjusting for age, lifestyle and dietary factors, and history of infectious or inflammation-related diseases. Results We found that multivariable-adjusted geometric mean concentrations of IL-6 and TNFα were inversely associated with quintiles of soy food intake, with a difference between the highest and lowest quintiles of 25.5% for IL-6 (P for trend = 0.008) and 14% for TNFα (P for trend = 0.04). Similar inverse associations were found for TNFα (P for trend = 0.003), soluble TNF receptor 1 (P for trend=0.01), soluble TNF receptor 2 (P for trend=0.02), IL-1β (P for trend=0.05), and IL-6 (P for trend=0.04) when soy food consumption was assessed by the frequency of consumption in the preceding 24 hours. No significant associations were found for other markers studied. Conclusions This study suggests that soy food consumption is related to lower circulating levels of IL-6, TNFα, and soluble TNF receptors 1 and 2 in Chinese women. PMID:22889631

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis

PubMed Central

Galván-Tejada, Carlos E.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Gamboa-Rosales, Hamurabi; Garza-Veloz, Idalia; Martinez-Fierro, Margarita L.

2017-01-01

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions. PMID:28216571

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis.

PubMed

Galván-Tejada, Carlos E; Zanella-Calzada, Laura A; Galván-Tejada, Jorge I; Celaya-Padilla, José M; Gamboa-Rosales, Hamurabi; Garza-Veloz, Idalia; Martinez-Fierro, Margarita L

2017-02-14

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions.

Relationship between substances in seminal plasma and Acrobeads Test results.

PubMed

Komori, Kazuhiko; Tsujimura, Akira; Okamoto, Yoshio; Matsuoka, Yasuhiro; Takao, Tetsuya; Miyagawa, Yasushi; Takada, Shingo; Nonomura, Norio; Okuyama, Akihiko

2009-01-01

To asses the effects of seminal plasma on sperm function. Retrospective case-control study. University hospital. One hundred fourteen infertile men. Acrobeads Test scores (0-4) and measurement of interleukin (IL)-6, soluble IL-6 receptor, epidermal growth factor, insulin-like growth factor-I (IGF-I), transforming growth factor-beta I, superoxide dismutase, calcitonin, and macrophage migration inhibitory factor (MIF) levels in seminal plasma. Kruskal-Wallis test to compare the concentrations of substances as a nonparametric test for differences among Acrobeads Test scores and a multivariable logistic regression model to find independent risk factors associated with abnormal Acrobeads Test results. The Acrobeads Test score was 0 for 7 samples, 1 for 20 samples, 2 for 18 samples, 3 for 28 samples, and 4 for 41 samples. Age, abstinence period, and semen parameters, except for sperm motility and percentage of sperm with abnormal morphology, had no effect on the Acrobeads Test results. Concentrations of IGF-I and MIF were significantly higher in patients with abnormal Acrobeads Test results. Multivariate analysis indicated that MIF and IGF-I were significantly associated with abnormal Acrobeads Test results (scores 0 to 1). Although further studies are needed, IGF-I and MIF in seminal plasma may have negative effects on sperm function.

Cerebral metastases in metastatic breast cancer: disease-specific risk factors and survival.

PubMed

Heitz, F; Rochon, J; Harter, P; Lueck, H-J; Fisseler-Eckhoff, A; Barinoff, J; Traut, A; Lorenz-Salehi, F; du Bois, A

2011-07-01

Survival of patients suffering from cerebral metastases (CM) is limited. Identification of patients with a high risk for CM is warranted to adjust follow-up care and to evaluate preventive strategies. Exploratory analysis of disease-specific parameter in patients with metastatic breast cancer (MBC) treated between 1998 and 2008 using cumulative incidences and Fine and Grays' multivariable regression analyses. After a median follow-up of 4.0 years, 66 patients (10.5%) developed CM. The estimated probability for CM was 5%, 12% and 15% at 1, 5 and 10 years; in contrast, the probability of death without CM was 21%, 61% and 76%, respectively. A small tumor size, ER status, ductal histology, lung and lymph node metastases, human epidermal growth factor receptor 2 positive (HER2+) tumors, younger age and M0 were associated with CM in univariate analyses, the latter three being risk factors in the multivariable model. Survival was shortened in patient developing CM (24.0 months) compared with patients with no CM (33.6 months) in the course of MBC. Young patients, primary with non-metastatic disease and HER2+ tumors, have a high risk to develop CM in MBC. Survival of patients developing CM in the course of MBC is impaired compared with patients without CM.

Intakes of caffeine, coffee and tea and risk of amyotrophic lateral sclerosis: Results from five cohort studies.

PubMed

Fondell, Elinor; O'Reilly, É Ilis J; Fitzgerald, Kathryn C; Falcone, Guido J; Kolonel, Laurence N; Park, Yikyung; Gapstur, Susan M; Ascherio, Alberto

2015-01-01

Caffeine is thought to be neuroprotective by antagonizing the adenosine A2A receptors in the brain and thereby protecting motor neurons from excitotoxicity. We examined the association between consumption of caffeine, coffee and tea and risk of amyotrophic lateral sclerosis (ALS). Longitudinal analyses based on over 1,010,000 males and females in five large cohort studies (the Nurses' Health Study, the Health Professionals Follow-up Study, the Cancer Prevention Study II Nutrition Cohort, the Multiethnic Cohort Study, and the National Institutes of Health-AARP Diet and Health Study). Cohort-specific multivariable-adjusted risk ratios (RR) and 95% confidence intervals (CI) estimates of ALS incidence or death were estimated by Cox proportional hazards regression and pooled using random-effects models. Results showed that a total of 1279 cases of ALS were documented during a mean of 18 years of follow-up. Caffeine intake was not associated with ALS risk; the pooled multivariable-adjusted RR comparing the highest to the lowest quintile of intake was 0.96 (95% CI 0.81-1.16). Similarly, neither coffee nor tea was associated with ALS risk. In conclusion, the results of this large study do not support associations of caffeine or caffeinated beverages with ALS risk.

Intakes of caffeine, coffee and tea and risk of Amyotrophic Lateral Sclerosis: Results from five cohort studies

PubMed Central

Fondell, Elinor; O'Reilly, Éilis J.; Fitzgerald, Kathryn C.; Falcone, Guido J.; Kolonel, Laurence N.; Park, Yikyung; Gapstur, Susan M.; Ascherio, Alberto

2015-01-01

Objective Caffeine is thought to be neuroprotective by antagonizing the adenosine A2A receptors in the brain and thereby protecting motor neurons from excitotoxicity. We examined the association between consumption of caffeine, coffee and tea and risk of Amyotrophic Lateral Sclerosis (ALS). Methods Longitudinal analyses based on over 1 010 000 men and women in 5 large cohort studies [the Nurses’ Health Study, the Health Professionals Follow-up Study, the Cancer Prevention Study II Nutrition Cohort, the Multiethnic Cohort Study, and the National Institutes of Health – AARP Diet and Health Study]. Cohort-specific multivariable-adjusted risk ratios (RR) and 95% confidence intervals (CI) estimates of ALS incidence or death was estimated by Cox proportional hazards regression and pooled using random-effects models. Results A total of 1279 cases of ALS were documented during a mean of 18 years of follow-up. Caffeine intake was not associated with ALS risk; the pooled multivariable-adjusted RR comparing the highest to the lowest quintile of intake was 0.96 (95% CI 0.81-1.16). Similarly, neither coffee nor tea was associated with ALS risk. Conclusion The results of this large study do not support associations of caffeine or caffeinated beverages with ALS risk. PMID:25822002

Multivariate Longitudinal Analysis with Bivariate Correlation Test

PubMed Central

Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

2016-01-01

In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model’s parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated. PMID:27537692

p75 neurotrophin receptor and pro-BDNF promote cell survival and migration in clear cell renal cell carcinoma

PubMed Central

Sánchez-Prieto, Ricardo; Saada, Sofiane; Naves, Thomas; Guillaudeau, Angélique; Perraud, Aurélie; Sindou, Philippe; Lacroix, Aurélie; Descazeaud, Aurélien; Lalloué, Fabrice; Jauberteau, Marie-Odile

2016-01-01

p75NTR, a member of TNF receptor family, is the low affinity receptor common to several mature neurotrophins and the high affinity receptor for pro-neurotrophins. Brain-Derived Neurotrophic Factor (BDNF), a member of neurotrophin family has been described to play an important role in development and progression of several cancers, through its binding to a high affinity tyrosine kinase receptor B (TrkB) and/or p75NTR. However, the functions of these two receptors in renal cell carcinoma (RCC) have never been investigated. An overexpression of p75NTR, pro-BDNF, and to a lesser extent for TrkB and sortilin, was detected by immunohistochemistry in a cohort of 83 clear cell RCC tumors. p75NTR, mainly expressed in tumor tissues, was significantly associated with higher Fuhrman grade in multivariate analysis. In two derived-RCC lines, 786-O and ACHN cells, we demonstrated that pro-BDNF induced cell survival and migration, through p75NTR as provided by p75NTR RNA silencing or blocking anti-p75NTR antibody. This mechanism is independent of TrkB activation as demonstrated by k252a, a tyrosine kinase inhibitor for Trk neurotrophin receptors. Taken together, these data highlight for the first time an important role for p75NTR in renal cancer and indicate a putative novel target therapy in RCC. PMID:27120782

Multivariate spatial models of excess crash frequency at area level: case of Costa Rica.

PubMed

Aguero-Valverde, Jonathan

2013-10-01

Recently, areal models of crash frequency have being used in the analysis of various area-wide factors affecting road crashes. On the other hand, disease mapping methods are commonly used in epidemiology to assess the relative risk of the population at different spatial units. A natural next step is to combine these two approaches to estimate the excess crash frequency at area level as a measure of absolute crash risk. Furthermore, multivariate spatial models of crash severity are explored in order to account for both frequency and severity of crashes and control for the spatial correlation frequently found in crash data. This paper aims to extent the concept of safety performance functions to be used in areal models of crash frequency. A multivariate spatial model is used for that purpose and compared to its univariate counterpart. Full Bayes hierarchical approach is used to estimate the models of crash frequency at canton level for Costa Rica. An intrinsic multivariate conditional autoregressive model is used for modeling spatial random effects. The results show that the multivariate spatial model performs better than its univariate counterpart in terms of the penalized goodness-of-fit measure Deviance Information Criteria. Additionally, the effects of the spatial smoothing due to the multivariate spatial random effects are evident in the estimation of excess equivalent property damage only crashes. Copyright © 2013 Elsevier Ltd. All rights reserved.

Multigenic Control of Measles Vaccine Immunity Mediated by Polymorphisms in Measles Receptor, Innate Pathway, and Cytokine Genes

PubMed Central

Kennedy, Richard B.; Ovsyannikova, Inna G.; Haralambieva, Iana H.; O’Byrne, Megan; Jacobson, Robert M.; Pankratz, V. Shane; Poland, Gregory A.

2012-01-01

Measles infection and vaccine response are complex biological processes that involve both viral and host genetic factors. We have previously investigated the influence of genetic polymorphisms on vaccine immune response, including measles vaccines, and have shown that polymorphisms in HLA, cytokine, cytokine receptor, and innate immune response genes are associated with variation in vaccine response but do not account for all of the inter-individual variance seen in vaccinated populations. In the current study we report the findings of a multigenic analysis of measles vaccine immunity, indicating a role for the measles virus receptor CD46, innate pattern-recognition receptors (DDX58, TLR2, 4, 5,7 and 8) and intracellular signaling intermediates (MAP3K7, NFKBIA), and key antiviral molecules (VISA, OAS2, MX1, PKR) as well as cytokines (IFNA1, IL4, IL6, IL8, IL12B) and cytokine receptor genes (IL2RB, IL6R, IL8RA) in the genetic control of both humoral and cellular immune responses. This multivariate approach provided additional insights into the genetic control of measles vaccine responses over and above the information gained by our previous univariate SNP association analyses. PMID:22265947

A Robust Bayesian Approach for Structural Equation Models with Missing Data

ERIC Educational Resources Information Center

Lee, Sik-Yum; Xia, Ye-Mao

2008-01-01

In this paper, normal/independent distributions, including but not limited to the multivariate t distribution, the multivariate contaminated distribution, and the multivariate slash distribution, are used to develop a robust Bayesian approach for analyzing structural equation models with complete or missing data. In the context of a nonlinear…

Treatment Adherence and Its Impact on Disease-Free Survival in the Breast International Group 1-98 Trial of Tamoxifen and Letrozole, Alone and in Sequence.

PubMed

Chirgwin, Jacquie H; Giobbie-Hurder, Anita; Coates, Alan S; Price, Karen N; Ejlertsen, Bent; Debled, Marc; Gelber, Richard D; Goldhirsch, Aron; Smith, Ian; Rabaglio, Manuela; Forbes, John F; Neven, Patrick; Láng, István; Colleoni, Marco; Thürlimann, Beat

2016-07-20

To investigate adherence to endocrine treatment and its relationship with disease-free survival (DFS) in the Breast International Group (BIG) 1-98 clinical trial. The BIG 1-98 trial is a double-blind trial that randomly assigned 6,193 postmenopausal women with hormone receptor-positive early breast cancer in the four-arm option to 5 years of tamoxifen (Tam), letrozole (Let), or the agents in sequence (Let-Tam, Tam-Let). This analysis included 6,144 women who received at least one dose of study treatment. Conditional landmark analyses and marginal structural Cox proportional hazards models were used to evaluate the relationship between DFS and treatment adherence (persistence [duration] and compliance with dosage). Competing risks regression was used to assess demographic, disease, and treatment characteristics of the women who stopped treatment early because of adverse events. Both aspects of low adherence (early cessation of letrozole and a compliance score of < 90%) were associated with reduced DFS (multivariable model hazard ratio, 1.45; 95% CI, 1.09 to 1.93; P = .01; and multivariable model hazard ratio, 1.61; 95% CI, 1.08 to 2.38; P = .02, respectively). Sequential treatments were associated with higher rates of nonpersistence (Tam-Let, 20.8%; Let-Tam, 20.3%; Tam 16.9%; Let 17.6%). Adverse events were the reason for most trial treatment early discontinuations (82.7%). Apart from sequential treatment assignment, reduced adherence was associated with older age, smoking, node negativity, or prior thromboembolic event. Both persistence and compliance are associated with DFS. Toxicity management and, for sequential treatments, patient and physician awareness, may improve adherence. © 2016 by American Society of Clinical Oncology.

A Comparison of Three Multivariate Models for Estimating Test Battery Reliability.

ERIC Educational Resources Information Center

Wood, Terry M.; Safrit, Margaret J.

1987-01-01

A comparison of three multivariate models (canonical reliability model, maximum generalizability model, canonical correlation model) for estimating test battery reliability indicated that the maximum generalizability model showed the least degree of bias, smallest errors in estimation, and the greatest relative efficiency across all experimental…

Application of multivariate Gaussian detection theory to known non-Gaussian probability density functions

NASA Astrophysics Data System (ADS)

Schwartz, Craig R.; Thelen, Brian J.; Kenton, Arthur C.

1995-06-01

A statistical parametric multispectral sensor performance model was developed by ERIM to support mine field detection studies, multispectral sensor design/performance trade-off studies, and target detection algorithm development. The model assumes target detection algorithms and their performance models which are based on data assumed to obey multivariate Gaussian probability distribution functions (PDFs). The applicability of these algorithms and performance models can be generalized to data having non-Gaussian PDFs through the use of transforms which convert non-Gaussian data to Gaussian (or near-Gaussian) data. An example of one such transform is the Box-Cox power law transform. In practice, such a transform can be applied to non-Gaussian data prior to the introduction of a detection algorithm that is formally based on the assumption of multivariate Gaussian data. This paper presents an extension of these techniques to the case where the joint multivariate probability density function of the non-Gaussian input data is known, and where the joint estimate of the multivariate Gaussian statistics, under the Box-Cox transform, is desired. The jointly estimated multivariate Gaussian statistics can then be used to predict the performance of a target detection algorithm which has an associated Gaussian performance model.

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution.

PubMed

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section.

Modeling Multivalent Ligand-Receptor Interactions with Steric Constraints on Configurations of Cell-Surface Receptor Aggregates

PubMed Central

Monine, Michael I.; Posner, Richard G.; Savage, Paul B.; Faeder, James R.; Hlavacek, William S.

2010-01-01

Abstract We use flow cytometry to characterize equilibrium binding of a fluorophore-labeled trivalent model antigen to bivalent IgE-FcεRI complexes on RBL cells. We find that flow cytometric measurements are consistent with an equilibrium model for ligand-receptor binding in which binding sites are assumed to be equivalent and ligand-induced receptor aggregates are assumed to be acyclic. However, this model predicts extensive receptor aggregation at antigen concentrations that yield strong cellular secretory responses, which is inconsistent with the expectation that large receptor aggregates should inhibit such responses. To investigate possible explanations for this discrepancy, we evaluate four rule-based models for interaction of a trivalent ligand with a bivalent cell-surface receptor that relax simplifying assumptions of the equilibrium model. These models are simulated using a rule-based kinetic Monte Carlo approach to investigate the kinetics of ligand-induced receptor aggregation and to study how the kinetics and equilibria of ligand-receptor interaction are affected by steric constraints on receptor aggregate configurations and by the formation of cyclic receptor aggregates. The results suggest that formation of linear chains of cyclic receptor dimers may be important for generating secretory signals. Steric effects that limit receptor aggregation and transient formation of small receptor aggregates may also be important. PMID:20085718

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol lowering drugs

PubMed Central

Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G.; Shah, Arvind K.; Lin, Jianxin

2013-01-01

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data (IPD) in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the Deviance Information Criterion (DIC) is used to select the best transformation model. Since the model is quite complex, a novel Monte Carlo Markov chain (MCMC) sampling scheme is developed to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol lowering drugs where the goal is to jointly model the three dimensional response consisting of Low Density Lipoprotein Cholesterol (LDL-C), High Density Lipoprotein Cholesterol (HDL-C), and Triglycerides (TG) (LDL-C, HDL-C, TG). Since the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately: however, a multivariate approach would be more appropriate since these variables are correlated with each other. A detailed analysis of these data is carried out using the proposed methodology. PMID:23580436

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol-lowering drugs.

PubMed

Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G; Shah, Arvind K; Lin, Jianxin

2013-10-15

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the deviance information criterion is used to select the best transformation model. Because the model is quite complex, we develop a novel Monte Carlo Markov chain sampling scheme to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol-lowering drugs where the goal is to jointly model the three-dimensional response consisting of low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), and triglycerides (TG) (LDL-C, HDL-C, TG). Because the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately; however, a multivariate approach would be more appropriate because these variables are correlated with each other. We carry out a detailed analysis of these data by using the proposed methodology. Copyright © 2013 John Wiley & Sons, Ltd.

Intratumor Heterogeneity of the Estrogen Receptor and the Long-term Risk of Fatal Breast Cancer.

PubMed

Lindström, Linda S; Yau, Christina; Czene, Kamila; Thompson, Carlie K; Hoadley, Katherine A; Van't Veer, Laura J; Balassanian, Ron; Bishop, John W; Carpenter, Philip M; Chen, Yunn-Yi; Datnow, Brian; Hasteh, Farnaz; Krings, Gregor; Lin, Fritz; Zhang, Yanhong; Nordenskjöld, Bo; Stål, Olle; Benz, Christopher C; Fornander, Tommy; Borowsky, Alexander D; Esserman, Laura J

2018-01-19

Breast cancer patients with estrogen receptor (ER)-positive disease have a continuous long-term risk for fatal breast cancer, but the biological factors influencing this risk are unknown. We aimed to determine whether high intratumor heterogeneity of ER predicts an increased long-term risk (25 years) of fatal breast cancer. The STO-3 trial enrolled 1780 postmenopausal lymph node-negative breast cancer patients randomly assigned to receive adjuvant tamoxifen vs not. The fraction of cancer cells for each ER intensity level was scored by breast cancer pathologists, and intratumor heterogeneity of ER was calculated using Rao's quadratic entropy and categorized into high and low heterogeneity using a predefined cutoff at the second tertile (67%). Long-term breast cancer-specific survival analyses by intra-tumor heterogeneity of ER were performed using Kaplan-Meier and multivariable Cox proportional hazard modeling adjusting for patient and tumor characteristics. A statistically significant difference in long-term survival by high vs low intratumor heterogeneity of ER was seen for all ER-positive patients (P < .001) and for patients with luminal A subtype tumors (P = .01). In multivariable analyses, patients with high intratumor heterogeneity of ER had a twofold increased long-term risk as compared with patients with low intratumor heterogeneity (ER-positive: hazard ratio [HR] = 1.98, 95% confidence interval [CI] = 1.31 to 3.00; luminal A subtype tumors: HR = 2.43, 95% CI = 1.18 to 4.99). Patients with high intratumor heterogeneity of ER had an increased long-term risk of fatal breast cancer. Interestingly, a similar long-term risk increase was seen in patients with luminal A subtype tumors. Our findings suggest that intratumor heterogeneity of ER is an independent long-term prognosticator with potential to change clinical management, especially for patients with luminal A tumors. © The Author(s) 2018. Published by Oxford University Press.

Association of Circulating Adipokines With Echocardiographic Measures of Cardiac Structure and Function in a Community-Based Cohort.

PubMed

von Jeinsen, Beatrice; Short, Meghan I; Xanthakis, Vanessa; Carneiro, Herman; Cheng, Susan; Mitchell, Gary F; Vasan, Ramachandran S

2018-06-21

Adipokines mediate cardiometabolic risk associated with obesity but their role in the pathogenesis of obesity-associated heart failure remains uncertain. We investigated the associations between circulating adipokine concentrations and echocardiographic measures in a community-based sample. We evaluated 3514 Framingham Heart Study participants (mean age 40 years, 53.8% women) who underwent routine echocardiography and had select circulating adipokines measured, ie, leptin, soluble leptin receptor, fatty acid-binding protein 4, retinol-binding protein 4, fetuin-A, and adiponectin. We used multivariable linear regression, adjusting for known correlates (including weight), to relate adipokine concentrations (independent variables) to the following echocardiographic measures (dependent variables): left ventricular mass index, left atrial diameter in end systole, fractional shortening, and E/e'. In multivariable-adjusted analysis, left ventricular mass index was inversely related to circulating leptin and fatty acid-binding protein 4 concentrations but positively related to retinol-binding protein 4 and leptin receptor levels ( P ≤0.002 for all). Left atrial end-systolic dimension was inversely related to leptin but positively related to retinol-binding protein 4 concentrations ( P ≤0.0001). E/e' was inversely related to leptin receptor levels ( P =0.0002). We observed effect modification by body weight for select associations (leptin receptor and fatty acid-binding protein 4 with left ventricular mass index, and leptin with left atrial diameter in end systole; P <0.05 for interactions). Fractional shortening was not associated with any of the adipokines. No echocardiographic trait was associated with fetuin-A or adiponectin concentrations. In our cross-sectional study of a large, young to middle-aged, relatively healthy community-based sample, key indices of subclinical cardiac remodeling were associated with higher or lower circulating concentrations of prohypertrophic and antihypertrophic adipokines in a context-specific manner. These observations may offer insights into the pathogenesis of the cardiomyopathy of obesity. © 2018 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

Semiparametric Thurstonian Models for Recurrent Choices: A Bayesian Analysis

ERIC Educational Resources Information Center

Ansari, Asim; Iyengar, Raghuram

2006-01-01

We develop semiparametric Bayesian Thurstonian models for analyzing repeated choice decisions involving multinomial, multivariate binary or multivariate ordinal data. Our modeling framework has multiple components that together yield considerable flexibility in modeling preference utilities, cross-sectional heterogeneity and parameter-driven…

Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor.

PubMed

Jain, Rajan; Poisson, Laila M; Gutman, David; Scarpace, Lisa; Hwang, Scott N; Holder, Chad A; Wintermark, Max; Rao, Arvind; Colen, Rivka R; Kirby, Justin; Freymann, John; Jaffe, C Carl; Mikkelsen, Tom; Flanders, Adam

2014-08-01

To correlate patient survival with morphologic imaging features and hemodynamic parameters obtained from the nonenhancing region (NER) of glioblastoma (GBM), along with clinical and genomic markers. An institutional review board waiver was obtained for this HIPAA-compliant retrospective study. Forty-five patients with GBM underwent baseline imaging with contrast material-enhanced magnetic resonance (MR) imaging and dynamic susceptibility contrast-enhanced T2*-weighted perfusion MR imaging. Molecular and clinical predictors of survival were obtained. Single and multivariable models of overall survival (OS) and progression-free survival (PFS) were explored with Kaplan-Meier estimates, Cox regression, and random survival forests. Worsening OS (log-rank test, P = .0103) and PFS (log-rank test, P = .0223) were associated with increasing relative cerebral blood volume of NER (rCBVNER), which was higher with deep white matter involvement (t test, P = .0482) and poor NER margin definition (t test, P = .0147). NER crossing the midline was the only morphologic feature of NER associated with poor survival (log-rank test, P = .0125). Preoperative Karnofsky performance score (KPS) and resection extent (n = 30) were clinically significant OS predictors (log-rank test, P = .0176 and P = .0038, respectively). No genomic alterations were associated with survival, except patients with high rCBVNER and wild-type epidermal growth factor receptor (EGFR) mutation had significantly poor survival (log-rank test, P = .0306; area under the receiver operating characteristic curve = 0.62). Combining resection extent with rCBVNER marginally improved prognostic ability (permutation, P = .084). Random forest models of presurgical predictors indicated rCBVNER as the top predictor; also important were KPS, age at diagnosis, and NER crossing the midline. A multivariable model containing rCBVNER, age at diagnosis, and KPS can be used to group patients with more than 1 year of difference in observed median survival (0.49-1.79 years). Patients with high rCBVNER and NER crossing the midline and those with high rCBVNER and wild-type EGFR mutation showed poor survival. In multivariable survival models, however, rCBVNER provided unique prognostic information that went above and beyond the assessment of all NER imaging features, as well as clinical and genomic features.

Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor

PubMed Central

Poisson, Laila M.; Gutman, David; Scarpace, Lisa; Hwang, Scott N.; Holder, Chad A.; Wintermark, Max; Rao, Arvind; Colen, Rivka R.; Kirby, Justin; Freymann, John; Jaffe, C. Carl; Mikkelsen, Tom; Flanders, Adam

2014-01-01

Purpose To correlate patient survival with morphologic imaging features and hemodynamic parameters obtained from the nonenhancing region (NER) of glioblastoma (GBM), along with clinical and genomic markers. Materials and Methods An institutional review board waiver was obtained for this HIPAA-compliant retrospective study. Forty-five patients with GBM underwent baseline imaging with contrast material–enhanced magnetic resonance (MR) imaging and dynamic susceptibility contrast-enhanced T2*-weighted perfusion MR imaging. Molecular and clinical predictors of survival were obtained. Single and multivariable models of overall survival (OS) and progression-free survival (PFS) were explored with Kaplan-Meier estimates, Cox regression, and random survival forests. Results Worsening OS (log-rank test, P = .0103) and PFS (log-rank test, P = .0223) were associated with increasing relative cerebral blood volume of NER (rCBVNER), which was higher with deep white matter involvement (t test, P = .0482) and poor NER margin definition (t test, P = .0147). NER crossing the midline was the only morphologic feature of NER associated with poor survival (log-rank test, P = .0125). Preoperative Karnofsky performance score (KPS) and resection extent (n = 30) were clinically significant OS predictors (log-rank test, P = .0176 and P = .0038, respectively). No genomic alterations were associated with survival, except patients with high rCBVNER and wild-type epidermal growth factor receptor (EGFR) mutation had significantly poor survival (log-rank test, P = .0306; area under the receiver operating characteristic curve = 0.62). Combining resection extent with rCBVNER marginally improved prognostic ability (permutation, P = .084). Random forest models of presurgical predictors indicated rCBVNER as the top predictor; also important were KPS, age at diagnosis, and NER crossing the midline. A multivariable model containing rCBVNER, age at diagnosis, and KPS can be used to group patients with more than 1 year of difference in observed median survival (0.49–1.79 years). Conclusion Patients with high rCBVNER and NER crossing the midline and those with high rCBVNER and wild-type EGFR mutation showed poor survival. In multivariable survival models, however, rCBVNER provided unique prognostic information that went above and beyond the assessment of all NER imaging features, as well as clinical and genomic features. © RSNA, 2014 Online supplemental material is available for this article. PMID:24646147

Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure.

PubMed

Allegrini, Franco; Braga, Jez W B; Moreira, Alessandro C O; Olivieri, Alejandro C

2018-06-29

A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS). Copyright © 2018 Elsevier B.V. All rights reserved.

Effects of Covariance Heterogeneity on Three Procedures for Analyzing Multivariate Repeated Measures Designs.

ERIC Educational Resources Information Center

Vallejo, Guillermo; Fidalgo, Angel; Fernandez, Paula

2001-01-01

Estimated empirical Type I error rate and power rate for three procedures for analyzing multivariate repeated measures designs: (1) the doubly multivariate model; (2) the Welch-James multivariate solution (H. Keselman, M. Carriere, a nd L. Lix, 1993); and (3) the multivariate version of the modified Brown-Forsythe procedure (M. Brown and A.…

Multiple expression patterns of biopathological markers in primary invasive breast carcinoma: a useful tool for elucidating its biological behaviour.

PubMed

Ceccarelli, C; Santini, D; Chieco, P; Taffurelli, M; Marrano, D; Mancini, A M

1995-03-01

Commonly used clinical and morphologic criteria have been reported to be of limited value in predicting the outcome of malignant tumours of the breast. Integrated information from the quantitative analysis in tumour tissue of biological parameters such as oestrogen and progesterone receptors (ER and PGR), proliferative activity, and proto-oncogene p53, c-erB2, and bcl-2 expression, may be useful for defining the biology of growth of breast carcinoma and to plan effective therapeutic strategies. Immunohistochemistry with antibodies recognizing ER, PGR, Ki-67, and the p53, c-erbB2, and bcl-2 encoded proteins was performed on 291 primary breast carcinomas. Results were integrated with clinico-pathological indicators and examined with multivariate statistical procedures and modeling. P53, c-erbB2, and bcl-2 gene products were detected, respectively, in 30.6%, 31.6%, and 85.9% of the examined invasive breast carcinomas, revealing variable associations with cellular differentiation and proliferation as defined by ER/PGR status, Ki-67, tumour mass and histologic and nuclear grading. A multivariate graphical display on a subset of the most informative cases revealed that bcl-2 expression parallels ER/PGR status and is of importance in separating tumour clusters with different degrees of aggressiveness. The results of this study indicate that multivariate explorative analyses conducted on biological and clinico-pathological parameters might constitute an integrated approach to data analysis useful for distinguishing different biological behaviours and therapeutic groups in breast carcinoma. Our findings also suggest that bcl-2 expression may play a pivotal role in tumours lacking ER-mediated growth regulation.

Associations between ACE-Inhibitors, Angiotensin Receptor Blockers, and Lean Body Mass in Community Dwelling Older Women.

PubMed

Bea, Jennifer W; Wassertheil-Smoller, Sylvia; Wertheim, Betsy C; Klimentidis, Yann; Chen, Zhao; Zaslavsky, Oleg; Manini, Todd M; Womack, Catherine R; Kroenke, Candyce H; LaCroix, Andrea Z; Thomson, Cynthia A

2018-01-01

Studies suggest that ACE-inhibitors (ACE-I) and angiotensin receptor blockers (ARBs) may preserve skeletal muscle with aging. We evaluated longitudinal differences in lean body mass (LBM) among women diagnosed with hypertension and classified as ACE-I/ARB users and nonusers among Women's Health Initiative participants that received dual energy X-ray absorptiometry scans to estimate body composition ( n =10,635) at baseline and at years 3 and 6 of follow-up. Of those, 2642 were treated for hypertension at baseline. Multivariate linear regression models, adjusted for relevant demographics, behaviors, and medications, assessed ACE-I/ARB use/nonuse and LBM associations at baseline, as well as change in LBM over 3 and 6 years. Although BMI did not differ by ACE-I/ARB use, LBM (%) was significantly higher in ACE-I/ARB users versus nonusers at baseline (52.2% versus 51.3%, resp., p =0.001). There was no association between ACE-I/ARB usage and change in LBM over time. Reasons for higher LBM with ACE-I/ARB use cross sectionally, but not longitundinally, are unclear and may reflect a threshold effect of these medications on LBM that is attenuated over time. Nevertheless, ACE-I/ARB use does not appear to negatively impact LBM in the long term.

Home sleep study for patients with myasthenia gravis.

PubMed

Yeh, J-H; Lin, C-M; Chiu, H-C; Bai, C-H

2015-09-01

The objective of the study was to examine predictors for sleep-disordered breathing (SDB) in patients with myasthenia gravis (MG) using Watch-PAT. We prospectively studied 58 consecutive patients with MG without respiratory symptoms for a full-night Watch-PAT with concomitant recording of the MG score and acetylcholine receptor antibody concentration and analyzed potential risk factors of SDB. Twenty-four patients (41%) had definitive SDB, which was mild in 12 patients, moderate in six, and severe in six. Assessing risk factors with multivariate models, we found four significant predictors (BMI, age, male gender, and use of azathioprine); BMI was the most powerful predictor. The severity and prevalence of sleep-disordered breathing had no significant association with MG score, myasthenia stage, or seropositivity of acetylcholine receptor antibody. The prevalence of SDB in myasthenic patients with mild and moderate weakness was high when using the Watch-PAT. Both myasthenia-specific factors (use of azathioprine) and general predictors in terms of BMI, age, and male gender predisposed the development of SDB in patients with myasthenia gravis. Careful screening of patients with myasthenia gravis at risk of SDB using Watch-PAT might improve the quality of sleep and cardiovascular health through proper treatment of underlying SDB. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Genetic polymorphisms of vitamin D receptor (VDR) in Fabry disease.

PubMed

Teitcher, Michael; Weinerman, Sarah; Whybra, Catharina; Beck, Michael; Sharon, Nir; Elstein, Deborah; Altarescu, Gheona

2008-11-01

Fabry disease, an X-linked inborn error of metabolism, is characterized by multi-organ involvement including cardiac signs of left ventricular hypertrophy and abnormal intima-medial (IMT) thickening of arteries, progressive renal failure, neurological involvement, and more. The vitamin D receptor (VDR) and an enzyme producing vitamin D3 result in an autocrine loop with direct effects on blood vessels. The purpose of this study is to assess VDR polymorphisms (BsmI, FokI, ApaI, and TaqI) relative to clinically important disease parameters using a disease-specific severity score (MSSI) and haplotype analysis. There were statistically significant differences between females (43% of 74 patients) and males in MSSI total scores, and in general and neurologic sub-scores. There appears to be a protective effect of the TaqI tt genotype so that there were significantly lower scores in clinical categories between those with the tt genotype versus those with the TT genotype. Multivariate models of haplotypes with MSSI scores reveal that T-A-f-B and t-a-F-b haplotypes of the VDR gene polymorphisms are significantly associated with variation in the Fabry phenotype. Despite the limitations of using the MSSI score as a clinical correlate, these results are provocative and further studies in larger cohorts with more males are recommended.

Incidence of endometrial spotting or bleeding during continuous-combined estrogen-progestin therapy in postmenopausal women with and without hypertension.

PubMed

Sriprasert, Intira; Beydoun, Hind; Barnabei, Vanessa; Nassir, Rami; LaCroix, Andrea Z; Archer, David F

2015-10-01

Endometrial spotting or bleeding is a common adverse effect among women taking continuous-combined estrogen-progestin therapy. The renin-angiotensin-aldosterone system plays a major role in hypertension and is present in the endometrium. We hypothesized that postmenopausal women with hypertension would have a higher incidence of bleeding compared with postmenopausal women without hypertension. A multivariate mixed-effects logistic model estimated the odds ratios for the relationship of hypertension status or use of antihypertensive drugs with endometrial bleeding using the Women's Health Initiative database. The incidence of spotting or bleeding in the first 12 months of estrogen-progestin use was 42% in women aged 50 to 79 years. Women with hypertension were more likely to experience bleeding than women without hypertension (odds ratio, 1.07; 95% CI, 1.02-1.13). Overall antihypertensive medication use increased bleeding with an odds ratio of 1.24, whereas angiotensin II receptor antagonists had a reduced odds ratio (0.53). Postmenopausal women with hypertension are more likely to bleed than postmenopausal women without hypertension when taking continuous estrogen-progestin, with less bleeding in women using angiotensin II receptor antagonists. This finding is novel and supports our hypothesis that the endometrial renin-angiotensin-aldosterone system may contribute to endometrial bleeding.

Metabolomic and Lipidomic Analysis of the Heart of Peroxisome Proliferator-Activated Receptor-γ Coactivator 1-β Knock Out Mice on a High Fat Diet.

PubMed

McCombie, Gregor; Medina-Gomez, Gema; Lelliott, Christopher J; Vidal-Puig, Antonio; Griffin, Julian L

2012-06-18

The peroxisome proliferator-activated receptor-γ coactivators (PGC-1) are transcriptional coactivators with an important role in mitochondrial biogenesis and regulation of genes involved in the electron transport chain and oxidative phosphorylation in oxidative tissues including cardiac tissue. These coactivators are thought to play a key role in the development of obesity, type 2 diabetes and the metabolic syndrome. In this study we have used a combined metabolomic and lipidomic analysis of cardiac tissue from the PGC-1β null mouse to examine the effects of a high fat diet on this organ. Multivariate statistics readily separated tissue from PGC-1β null mice from their wild type controls either in gender specific models or in combined datasets. This was associated with an increase in creatine and a decrease in taurine in the null mouse, and an increase in myristic acid and a reduction in long chain polyunsaturated fatty acids for both genders. The most profound changes were detected by liquid chromatography mass spectrometry analysis of intact lipids with the tissue from the null mouse having a profound increase in a number of triglycerides. The metabolomic and lipodomic changes indicate PGC-1β has a profound influence on cardiac metabolism.

Serum CA125 predicts extrauterine disease and survival in uterine carcinosarcoma

PubMed Central

Huang, Gloria S.; Chiu, Lydia G.; Gebb, Juliana S.; Gunter, Marc J.; Sukumvanich, Paniti; Goldberg, Gary L.; Einstein, Mark H.

2009-01-01

Objective The purpose of this study was to determine the clinical utility of CA125 measurement in patients with uterine carcinosarcoma (CS). Methods Ninety-five consecutive patients treated for CS at a single institution were identified. All 54 patients who underwent preoperative CA125 measurement were included in the study. Data were abstracted from the medical records. Tests of association between preoperative CA125 and previously identified clinicopathologic prognostic factors were performed using Fisher’s exact test and Pearson chi-square test. To evaluate relationship of CA125 elevation and survival, a Cox proportional hazard model was used for multivariate analysis, incorporating all of prognostic factors identified by univariate analysis. Results Preoperative CA125 was significantly associated with the presence of extrauterine disease (P<0.001), deep myometrial invasion (P<0.001), and serous histology of the epithelial component (P=0.005). Using univariate survival analysis, stage (HR=1.808, P=0.004), postoperative CA125 level (HR=9.855, P<0.001), and estrogen receptor positivity (HR=0.314, P=0.029) were significantly associated with survival. In the multivariate model, only postoperative CA125 level remained significantly associated with poor survival (HR=5.725, P=0.009). Conclusion Preoperative CA125 elevation is a marker of extrauterine disease and deep myometrial invasion in patients with uterine CS. Postoperative CA125 elevation is an independent prognostic factor for poor survival. These findings indicate that CA125 may be a clinically useful serum marker in the management of patients with CS. PMID:17935762

Source apportionment of heavy metals in agricultural soil based on PMF: A case study in Hexi Corridor, northwest China.

PubMed

Guan, Qingyu; Wang, Feifei; Xu, Chuanqi; Pan, Ninghui; Lin, Jinkuo; Zhao, Rui; Yang, Yanyan; Luo, Haiping

2018-02-01

Hexi Corridor is the most important base of commodity grain and producing area for cash crops. However, the rapid development of agriculture and industry has inevitably led to heavy metal contamination in the soils. Multivariate statistical analysis, GIS-based geostatistical methods and Positive Matrix Factorization (PMF) receptor modeling techniques were used to understand the levels of heavy metals and their source apportionment for agricultural soil in Hexi Corridor. The results showed that the average concentrations of Cr, Cu, Ni, Pb and Zn were lower than the secondary standard of soil environmental quality; however, the concentrations of eight metals (Cr, Cu, Mn, Ni, Pb, Ti, V and Zn) were higher than background values, and their corresponding enrichment factor values were significantly greater than 1. Different degrees of heavy metal pollution occurred in the agricultural soils; specifically, Ni had the most potential for impacting human health. The results from the multivariate statistical analysis and GIS-based geostatistical methods indicated both natural sources (Co and W) and anthropogenic sources (Cr, Cu, Mn, Ni, Pb, Ti, V and Zn). To better identify pollution sources of heavy metals in the agricultural soils, the PMF model was applied. Further source apportionment revealed that enrichments of Pb and Zn were attributed to traffic sources; Cr and Ni were closely related to industrial activities, including mining, smelting, coal combustion, iron and steel production and metal processing; Zn and Cu originated from agricultural activities; and V, Ti and Mn were derived from oil- and coal-related activities. Copyright © 2017 Elsevier Ltd. All rights reserved.

On the Numerical Formulation of Parametric Linear Fractional Transformation (LFT) Uncertainty Models for Multivariate Matrix Polynomial Problems

NASA Technical Reports Server (NTRS)

Belcastro, Christine M.

1998-01-01

Robust control system analysis and design is based on an uncertainty description, called a linear fractional transformation (LFT), which separates the uncertain (or varying) part of the system from the nominal system. These models are also useful in the design of gain-scheduled control systems based on Linear Parameter Varying (LPV) methods. Low-order LFT models are difficult to form for problems involving nonlinear parameter variations. This paper presents a numerical computational method for constructing and LFT model for a given LPV model. The method is developed for multivariate polynomial problems, and uses simple matrix computations to obtain an exact low-order LFT representation of the given LPV system without the use of model reduction. Although the method is developed for multivariate polynomial problems, multivariate rational problems can also be solved using this method by reformulating the rational problem into a polynomial form.

Multivariate Methods for Meta-Analysis of Genetic Association Studies.

PubMed

Dimou, Niki L; Pantavou, Katerina G; Braliou, Georgia G; Bagos, Pantelis G

2018-01-01

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research.

PubMed

Meeker, Daniella; Jiang, Xiaoqian; Matheny, Michael E; Farcas, Claudiu; D'Arcy, Michel; Pearlman, Laura; Nookala, Lavanya; Day, Michele E; Kim, Katherine K; Kim, Hyeoneui; Boxwala, Aziz; El-Kareh, Robert; Kuo, Grace M; Resnic, Frederic S; Kesselman, Carl; Ohno-Machado, Lucila

2015-11-01

Centralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner. The objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies. Based on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network. The authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws. Federated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient-level data transport, allowing institutions with strict local data storage requirements to participate in sophisticated analyses based on federated research networks. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Soy consumption reduces the risk of non-small-cell lung cancers with epidermal growth factor receptor mutations among Japanese.

PubMed

Matsuo, Keitaro; Hiraki, Akio; Ito, Hidemi; Kosaka, Takayuki; Suzuki, Takeshi; Hirose, Kaoru; Wakai, Kenji; Yatabe, Yasushi; Mitsudomi, Tetsuya; Tajima, Kazuo

2008-06-01

Epidermal growth factor receptor (EGFR) mutations play substantial roles in genesis and proliferation of non-small-cell lung cancers (NSCLCs). We recently found that reproductive factors have a substantial impact on risk of development of NSCLCs featuring such EGFR mutations. Therefore, we explored the influence of dietary habits on NSCLC risk with reference to the EGFR mutational status. We conducted a case-control study using 353 patients with NSCLCs (122 EGFR mutated and 231 EGFR wild-type) and 1765 age-sex matched non-cancer control subjects. Dietary exposure was based on a semiquantitative food frequency questionnaire and impact of major food items, like meats, seafoods, vegetables and soybean products was assessed by multivariate logistic regression. Soybean products demonstrated a protective association with EGFR mutated, but not EGFR wild-type NSCLCs, with multivariate-adjusted odds ratios and 95% confidence intervals for the 2nd and 3rd tertile of soybean product consumption of 0.79 (0.50-1.27) and 0.56 (0.34-0.93) relative to those in the lowest tertile (trend P = 0.023). In conclusion, soy consumption may exert a protective association against the development of NSCLCs with EGFR mutations, providing possible insights into mechanisms of their genesis.

Association between Breast Cancer Recurrence and Cellular Dissociation Assessed Using Fine-Needle Aspiration.

PubMed

Koike, Etsuko; Iwaya, Keiichi; Watanabe, Akinori; Miyake, Shinji; Sato, Eiichi; Ishikawa, Takashi

2016-01-01

To determine the associations between breast cancer recurrence and cytological findings of fine-needle aspiration cytology (FNAC). The study included 117 women who had undergone a modified radical mastectomy for invasive ductal carcinoma of the breast. FNAC samples of these patients were reexamined, and cytological findings, such as cellular dissociation, nuclear pleomorphism, nuclear atypia, chromatin pattern, and nuclear size, were scored. Uni- and multivariate analyses were performed to determine the prognostic significance of the cytological findings. Corresponding cancer tissues were immunostained for estrogen receptor, progesterone receptor, human epidermal growth factor 2 (HER2), p53, and E-cadherin to determine their associations with cytological findings. Coexpression of Arp2 and WAVE2 was also examined immunohistochemically as a cell locomotion signal. Cellular dissociation (p = 0.0259) and nuclear size (p = 0.0417) were significantly associated with cancer recurrence. Multivariate analysis showed that cellular dissociation and histological grade were significant independent predictors of cancer recurrence. Cellular dissociation was found to be associated with coexpression of Arp2 and WAVE2 (p = 0.0356) and HER2 (p = 0.0469). The cytological finding of cell dissociation was associated with the activation of Arp2 and WAVE2 signals and was an independent predictor of recurrence. © 2016 S. Karger AG, Basel.

beta3-Adrenergic receptor Trp64Arg polymorphism and increased body mass index in sleep apnoea.

PubMed

Piérola, J; Barceló, A; de la Peña, M; Barbé, F; Soriano, J B; Sánchez Armengol, A; Martínez, C; Agustí, A

2007-10-01

Obesity is an important risk factor for obstructive sleep apnoea syndrome (OSAS), insulin resistance and cardiovascular disease. The substitution of tryptophan 64 with arginine (Trp64Arg) polymorphism (Arg variant) of the beta(3)-adrenergic receptor (ADRB3) has been associated with obesity. In this study, the prevalence of the Trp64Arg ADRB3 polymorphism in a large group of patients with OSAS and its association with body mass index (BMI), insulin resistance and hypertension were evaluated. ADRB3 genotype was determined in 387 patients with OSAS and 137 healthy subjects recruited from three Spanish tertiary hospitals. The distributions of the ADRB3 genotypes were similar in OSAS and controls, and, in a multivariate model, the risk of OSAS was not associated with the presence of the Arg variant of the ADRB3 gene. However, BMI was higher in those patients with OSAS who carried this genetic variant than in those with the Trp variant. Furthermore, a linear trend for higher BMI was found in those with the Arg variant (56, 75 and 100% for Trp/Trp, Trp/Arg and Arg/Arg, respectively). Insulin resistance, blood pressures and serum levels of lipids and glucose were not associated with the presence of the Arg variant of the ADRB3 gene. The presence of the arginine 64 allele of the beta(3)-adrenergic receptor gene does not increase the risk of obstructive sleep apnoea syndrome, but is associated with the development of obesity in those patients who suffer obstructive sleep apnoea syndrome.

Long non-coding RNA metastasis associated in lung adenocarcinoma transcript 1 (MALAT1) interacts with estrogen receptor and predicted poor survival in breast cancer.

PubMed

Huang, Nai-Si; Chi, Ya-Yun; Xue, Jing-Yan; Liu, Meng-Ying; Huang, Sheng; Mo, Miao; Zhou, Shu-Ling; Wu, Jiong

2016-06-21

Metastasis associated in lung adenocarcinoma transcript 1 (MALAT1), a lncRNA that was first recognized as a prognostic parameter for patient survival of stage I lung cancer, is up-regulated in multiple human malignancies, including breast cancer. However, the mechanism of its function remained elusive. In the current study, by examining MALAT1 expression on mRNA level, we demonstrated that compared with MCF10A, MALAT1 expression was up-regulated in the majority of breast cancer cell lines (9/12). In 26 pairs of estrogen receptor (ER)-positive breast cancer samples, MALAT1 expression was significantly up-regulated compared with adjacent normal tissues (P = 0.012). Furthermore, of 204 breast cancer patients, high MALAT1 expression was associated with positive ER (P = 0.023) and progesterone receptor (PR) (P = 0.024) status. Further analysis using TCGA database revealed that ER and its target genes PGR and CCND1, were overexpressed in MALAT1 altered group compared with unaltered group, both on the mRNA and protein level. Lastly, we verified MALAT1's prognostic value in breast cancer. At the cut-off value of 75%, MALAT1 was the only independent prognostic factor of recurrence-free survival (RFS) in ER-negative patients in a multivariate Cox regression model (hazard ratio [HR] = 2.83, 95% confidence interval [CI] 1.02-7.83). MALAT1 overexpression was also associated with poor RFS in tamoxifen treated ER-positive breast cancer patients, which might serve as a potential biomarker to predict endocrine treatment sensitivity.

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution

PubMed Central

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section. PMID:28983398

Quantifying the impact of between-study heterogeneity in multivariate meta-analyses

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2012-01-01

Measures that quantify the impact of heterogeneity in univariate meta-analysis, including the very popular I2 statistic, are now well established. Multivariate meta-analysis, where studies provide multiple outcomes that are pooled in a single analysis, is also becoming more commonly used. The question of how to quantify heterogeneity in the multivariate setting is therefore raised. It is the univariate R2 statistic, the ratio of the variance of the estimated treatment effect under the random and fixed effects models, that generalises most naturally, so this statistic provides our basis. This statistic is then used to derive a multivariate analogue of I2, which we call . We also provide a multivariate H2 statistic, the ratio of a generalisation of Cochran's heterogeneity statistic and its associated degrees of freedom, with an accompanying generalisation of the usual I2 statistic, . Our proposed heterogeneity statistics can be used alongside all the usual estimates and inferential procedures used in multivariate meta-analysis. We apply our methods to some real datasets and show how our statistics are equally appropriate in the context of multivariate meta-regression, where study level covariate effects are included in the model. Our heterogeneity statistics may be used when applying any procedure for fitting the multivariate random effects model. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22763950

Up-scaling of multi-variable flood loss models from objects to land use units at the meso-scale

NASA Astrophysics Data System (ADS)

Kreibich, Heidi; Schröter, Kai; Merz, Bruno

2016-05-01

Flood risk management increasingly relies on risk analyses, including loss modelling. Most of the flood loss models usually applied in standard practice have in common that complex damaging processes are described by simple approaches like stage-damage functions. Novel multi-variable models significantly improve loss estimation on the micro-scale and may also be advantageous for large-scale applications. However, more input parameters also reveal additional uncertainty, even more in upscaling procedures for meso-scale applications, where the parameters need to be estimated on a regional area-wide basis. To gain more knowledge about challenges associated with the up-scaling of multi-variable flood loss models the following approach is applied: Single- and multi-variable micro-scale flood loss models are up-scaled and applied on the meso-scale, namely on basis of ATKIS land-use units. Application and validation is undertaken in 19 municipalities, which were affected during the 2002 flood by the River Mulde in Saxony, Germany by comparison to official loss data provided by the Saxon Relief Bank (SAB).In the meso-scale case study based model validation, most multi-variable models show smaller errors than the uni-variable stage-damage functions. The results show the suitability of the up-scaling approach, and, in accordance with micro-scale validation studies, that multi-variable models are an improvement in flood loss modelling also on the meso-scale. However, uncertainties remain high, stressing the importance of uncertainty quantification. Thus, the development of probabilistic loss models, like BT-FLEMO used in this study, which inherently provide uncertainty information are the way forward.

An error bound for a discrete reduced order model of a linear multivariable system

NASA Technical Reports Server (NTRS)

Al-Saggaf, Ubaid M.; Franklin, Gene F.

1987-01-01

The design of feasible controllers for high dimension multivariable systems can be greatly aided by a method of model reduction. In order for the design based on the order reduction to include a guarantee of stability, it is sufficient to have a bound on the model error. Previous work has provided such a bound for continuous-time systems for algorithms based on balancing. In this note an L-infinity bound is derived for model error for a method of order reduction of discrete linear multivariable systems based on balancing.

Multivariate Radiological-Based Models for the Prediction of Future Knee Pain: Data from the OAI

PubMed Central

Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Treviño, Victor; Tamez-Peña, José G.

2015-01-01

In this work, the potential of X-ray based multivariate prognostic models to predict the onset of chronic knee pain is presented. Using X-rays quantitative image assessments of joint-space-width (JSW) and paired semiquantitative central X-ray scores from the Osteoarthritis Initiative (OAI), a case-control study is presented. The pain assessments of the right knee at the baseline and the 60-month visits were used to screen for case/control subjects. Scores were analyzed at the time of pain incidence (T-0), the year prior incidence (T-1), and two years before pain incidence (T-2). Multivariate models were created by a cross validated elastic-net regularized generalized linear models feature selection tool. Univariate differences between cases and controls were reported by AUC, C-statistics, and ODDs ratios. Univariate analysis indicated that the medial osteophytes were significantly more prevalent in cases than controls: C-stat 0.62, 0.62, and 0.61, at T-0, T-1, and T-2, respectively. The multivariate JSW models significantly predicted pain: AUC = 0.695, 0.623, and 0.620, at T-0, T-1, and T-2, respectively. Semiquantitative multivariate models predicted paint with C-stat = 0.671, 0.648, and 0.645 at T-0, T-1, and T-2, respectively. Multivariate models derived from plain X-ray radiography assessments may be used to predict subjects that are at risk of developing knee pain. PMID:26504490

Preliminary Multi-Variable Parametric Cost Model for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Hendrichs, Todd

2010-01-01

This slide presentation reviews creating a preliminary multi-variable cost model for the contract costs of making a space telescope. There is discussion of the methodology for collecting the data, definition of the statistical analysis methodology, single variable model results, testing of historical models and an introduction of the multi variable models.

Polymorphisms in chemokine and receptor genes and gastric cancer risk and survival in a high risk Polish population.

PubMed

Gawron, Andrew J; Fought, Angela J; Lissowska, Jolanta; Ye, Weimin; Zhang, Xiao; Chow, Wong-Ho; Beane Freeman, Laura E; Hou, Lifang

2011-03-01

To examine if genetic variations in chemokine receptor and ligand genes are associated with gastric cancer risk and survival. The study included 298 cases and 417 controls from a population-based study of gastric cancer conducted in Warsaw, Poland in 1994-1996. We investigated seven single nucleotide polymorphisms in a chemokine ligand (CXCL12) and chemokine receptor (CCR2, CCR5, CX3CR1) genes and one frameshift deletion (CCR5) in blood leukocyte DNA in relation to gastric cancer risk and survival. Genotyping was conducted at the NCI Core Genotyping Facility. Odds ratios and 95% confidence intervals were computed using univariate and multivariate logistic regression models. Survival analysis was performed using Cox proportional hazards models. Gastric cancer risk was not associated with single chemokine polymorphisms. A CCR5 haplotype that contained the common alleles of IVS1+151 G>T (rs2734648), IVS2+80 C>T (rs1800024) and minor allele of IVS1+246 A>G (rs1799987) was associated with a borderline significantly increased risk (OR = 1.5, 95% CI: 1.0?2.2). For gastric cancer cases, there was a greater risk of death for carriers of the minor alleles of CCR2 Ex2+241 G>A (rs1799864) (HR = 1.5, 95% CI: 1.1-2.1) and CCR5 IVS2+80 C>T (rs1800024) (HR = 1.5, 95% CI: 1.1-2.1). Carriers of the CCR5 minor allele of IVS1+151 G>T (rs2734648) had a decreased risk of death compared to homozygote carriers of the common allele (HR = 0.8, 95% CI: 0.6-1.0). Our findings do not support an association between gastric cancer risk and single chemokine genetic variation. The observed associations between cancer risk and a CCR5 haplotype and between survival and polymorphisms in CCR2 and CCR5 need replication in future studies.

Integration of metabolomics, lipidomics and clinical data using a machine learning method.

PubMed

Acharjee, Animesh; Ament, Zsuzsanna; West, James A; Stanley, Elizabeth; Griffin, Julian L

2016-11-22

The recent pandemic of obesity and the metabolic syndrome (MetS) has led to the realisation that new drug targets are needed to either reduce obesity or the subsequent pathophysiological consequences associated with excess weight gain. Certain nuclear hormone receptors (NRs) play a pivotal role in lipid and carbohydrate metabolism and have been highlighted as potential treatments for obesity. This realisation started a search for NR agonists in order to understand and successfully treat MetS and associated conditions such as insulin resistance, dyslipidaemia, hypertension, hypertriglyceridemia, obesity and cardiovascular disease. The most studied NRs for treating metabolic diseases are the peroxisome proliferator-activated receptors (PPARs), PPAR-α, PPAR-γ, and PPAR-δ. However, prolonged PPAR treatment in animal models has led to adverse side effects including increased risk of a number of cancers, but how these receptors change metabolism long term in terms of pathology, despite many beneficial effects shorter term, is not fully understood. In the current study, changes in male Sprague Dawley rat liver caused by dietary treatment with a PPAR-pan (PPAR-α, -γ, and -δ) agonist were profiled by classical toxicology (clinical chemistry) and high throughput metabolomics and lipidomics approaches using mass spectrometry. In order to integrate an extensive set of nine different multivariate metabolic and lipidomics datasets with classical toxicological parameters we developed a hypotheses free, data driven machine learning approach. From the data analysis, we examined how the nine datasets were able to model dose and clinical chemistry results, with the different datasets having very different information content. We found lipidomics (Direct Infusion-Mass Spectrometry) data the most predictive for different dose responses. In addition, associations with the metabolic and lipidomic data with aspartate amino transaminase (AST), a hepatic leakage enzyme to assess organ damage, and albumin, indicative of altered liver synthetic function, were established. Furthermore, by establishing correlations and network connections between eicosanoids, phospholipids and triacylglycerols, we provide evidence that these lipids function as a key link between inflammatory processes and intermediary metabolism.

Multivariate Models for Normal and Binary Responses in Intervention Studies

ERIC Educational Resources Information Center

Pituch, Keenan A.; Whittaker, Tiffany A.; Chang, Wanchen

2016-01-01

Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in…

Molecular modeling study of the differential ligand-receptor interaction at the μ, δ and κ opioid receptors

NASA Astrophysics Data System (ADS)

Filizola, Marta; Carteni-Farina, Maria; Perez, Juan J.

1999-07-01

3D models of the opioid receptors μ, δ and κ were constructed using BUNDLE, an in-house program to build de novo models of G-protein coupled receptors at the atomic level. Once the three opioid receptors were constructed and before any energy refinement, models were assessed for their compatibility with the results available from point-site mutations carried out on these receptors. In a subsequent step, three selective antagonists to each of three receptors (naltrindole, naltrexone and nor-binaltorphamine) were docked onto each of the three receptors and subsequently energy minimized. The nine resulting complexes were checked for their ability to explain known results of structure-activity studies. Once the models were validated, analysis of the distances between different residues of the receptors and the ligands were computed. This analysis permitted us to identify key residues tentatively involved in direct interaction with the ligand.

Binding modes of dihydroquinoxalinones in a homology model of bradykinin receptor 1.

PubMed

Ha, Sookhee N; Hey, Pat J; Ransom, Rick W; Harrell, C Meacham; Murphy, Kathryn L; Chang, Ray; Chen, Tsing-Bau; Su, Dai-Shi; Markowitz, M Kristine; Bock, Mark G; Freidinger, Roger M; Hess, Fred J

2005-05-27

We report the first homology model of human bradykinin receptor B1 generated from the crystal structure of bovine rhodopsin as a template. Using an automated docking procedure, two B1 receptor antagonists of the dihydroquinoxalinone structural class were docked into the receptor model. Site-directed mutagenesis data of the amino acid residues in TM1, TM3, TM6, and TM7 were incorporated to place the compounds in the binding site of the homology model of the human B1 bradykinin receptor. The best pose in agreement with the mutation data was selected for detailed study of the receptor-antagonist interaction. To test the model, the calculated antagonist-receptor binding energy was correlated with the experimentally measured binding affinity (K(i)) for nine dihydroquinoxalinone analogs. The model was used to gain insight into the molecular mechanism for receptor function and to optimize the dihydroquinoxalinone analogs.

Normalization methods in time series of platelet function assays

PubMed Central

Van Poucke, Sven; Zhang, Zhongheng; Roest, Mark; Vukicevic, Milan; Beran, Maud; Lauwereins, Bart; Zheng, Ming-Hua; Henskens, Yvonne; Lancé, Marcus; Marcus, Abraham

2016-01-01

Abstract Platelet function can be quantitatively assessed by specific assays such as light-transmission aggregometry, multiple-electrode aggregometry measuring the response to adenosine diphosphate (ADP), arachidonic acid, collagen, and thrombin-receptor activating peptide and viscoelastic tests such as rotational thromboelastometry (ROTEM). The task of extracting meaningful statistical and clinical information from high-dimensional data spaces in temporal multivariate clinical data represented in multivariate time series is complex. Building insightful visualizations for multivariate time series demands adequate usage of normalization techniques. In this article, various methods for data normalization (z-transformation, range transformation, proportion transformation, and interquartile range) are presented and visualized discussing the most suited approach for platelet function data series. Normalization was calculated per assay (test) for all time points and per time point for all tests. Interquartile range, range transformation, and z-transformation demonstrated the correlation as calculated by the Spearman correlation test, when normalized per assay (test) for all time points. When normalizing per time point for all tests, no correlation could be abstracted from the charts as was the case when using all data as 1 dataset for normalization. PMID:27428217

Tyrosine phosphorylation of platelet derived growth factor β receptors in coronary artery lesions: implications for vascular remodelling after directional coronary atherectomy and unstable angina pectoris

PubMed Central

Abe, J; Deguchi, J; Takuwa, Y; Hara, K; Ikari, Y; Tamura, T; Ohno, M; Kurokawa, K

1998-01-01

Background—Growth factors such as platelet derived growth factor (PDGF) have been postulated to be important mediators of neointimal proliferation observed in atherosclerotic plaques and restenotic lesions following coronary interventions. Binding of PDGF to its receptor results in intrinsic receptor tyrosine kinase activation and subsequent cellular migration, proliferation, and vascular contraction.
Aims—To investigate whether the concentration of PDGF β receptor tyrosine phosphorylation obtained from directional coronary atherectomy (DCA) samples correlate with atherosclerotic plaque burden, the ability of diseased vessels to remodel, coronary risk factors, and clinical events.
Methods—DCA samples from 59 patients and 15 non-atherosclerotic left internal thoracic arteries (LITA) were analysed for PDGF β receptor tyrosine phosphorylation content by receptor immunoprecipitation and antiphosphotyrosine western blot. The amount of PDGF β receptor phosphorylation was analysed in relation to angiographic follow up data and clinical variables.
Results—PDGF β receptor tyrosine phosphorylation in the 59 DCA samples was greater than in the 15 non-atherosclerotic LITA (mean (SD) 0.84 (0.67) v 0.17 (0.08) over a control standard, p < 0.0001). As evaluated by stepwise regression analysis, incorporation of both PDGF β receptor tyrosine phosphorylation and immediate gain correlated strongly (adjusted r2 = 0.579) with late loss, although PDGF β receptor tyramine phosphorylation alone correlated poorly with late loss. Multivariate regression analysis of coronary risk factors and clinical events revealed unstable angina as the most significant correlate of PDGF β receptor tyrosine phosphorylation (F value 20.009, p < 0.0001).
Conclusions—PDGF β receptor tyrosine phosphorylation in atherosclerotic lesions is increased compared with non-atherosclerotic arterial tissues. The association of PDGF β receptor tyrosine phosphorylation with immediate gain strongly correlates with vascular remodelling. PDGF β receptor tyrosine phosphorylation correlates with unstable angina pectoris.

 Keywords: PDGF receptors;  atherosclerosis;  directional coronary atherectomy;  restenosis PMID:9616351

Increased risk of SSEs in bone-only metastatic breast cancer patients treated with zoledronic acid.

PubMed

Yanae, Masashi; Fujimoto, Shinichiro; Tane, Kaori; Tanioka, Maki; Fujiwara, Kimiko; Tsubaki, Masanobu; Yamazoe, Yuzuru; Morishima, Yoshiyuki; Chiba, Yasutaka; Takao, Shintaro; Komoike, Yoshifumi; Tsurutani, Junji; Nakagawa, Kazuhiko; Nishida, Shozo

2017-09-01

Bone represents one of the most common sites to which breast cancer cells metastasize. Patients experience skeletal related adverse events (pathological fractures, spinal cord compressions, and irradiation for deteriorated pain on bone) even during treatment with zoledronic acid (ZA). Therefore, we conducted a retrospective cohort study to investigate the predictive factors for symptomatic skeletal events (SSEs) in bone-metastasized breast cancer (b-MBC) patients. We retrospectively collected data on b-MBC patients treated with ZA. Patient characteristics, including age, subtype, the presence of non-bone lesions, the presence of multiple bone metastases at the commencement of ZA therapy, duration of ZA therapy, the time interval between breast cancer diagnosis and the initiation of ZA therapy, and type of systemic therapy, presence of previous SSE were analyzed using multivariable logistic regression analysis. The medical records of 183 patients were reviewed and 176 eligible patients were analyzed. The median age was 59 (range, 30-87) years. Eighty-seven patients were aged ≥60 years and 89 patients were aged < 60 years. The proportions of patients with estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2-positive disease were 81.8%, 63.1%, and 17.6%, respectively. Fifty-three patients had bone-only MBC at the commencement of ZA therapy. SSEs were observed in 42 patients. In the multivariable logistic regression analysis, bone-only MBC but not a breast cancer subtype was an independent risk factor for an SSE during ZA therapy (odds ratio: 3.878, 95% confidence interval: 1.647-9.481; p = 0.002). Bone-only MBC patients are more likely to experience an SSE even after treatment with ZA.

Risk factors for keratinocyte skin cancer in patients diagnosed with melanoma, a large retrospective study.

PubMed

Espinosa, Pablo; Pfeiffer, Ruth M; García-Casado, Zaida; Requena, Celia; Landi, Maria Teresa; Kumar, Rajiv; Nagore, Eduardo

2016-01-01

Melanoma survivors are at an increased risk of developing other malignancies, including keratinocyte skin cancer (KSC). While it is known that many risk factors for melanoma also impact risk of KSC in the general population, no previous study has investigated risk factors for KSC development in melanoma patients. We assessed associations of personal and clinical characteristics, including skin phenotype and variations in the melanocortin 1 receptor (MC1R) gene, with KSC risk in melanoma patients. We used prospective follow-up information on 1200 patients treated for melanoma at the Instituto Valenciano de Oncología, Spain, between 2000 and 2011. We computed hazard ratios and 95% confidence intervals (CIs) for the association of clinical, personal and genetic characteristics with risk of KSC, squamous cell carcinoma (SCC), or basal cell carcinoma (BCC) from Cox proportional hazard models. Five-year cumulative incidence based on competing risk models of SCC, BCC or KSC overall was computed using multivariate subdistribution hazard models. To assess predictive performance of the models, we computed areas under the receiver-operating characteristic curves (AUCs, discriminatory power) using cross-validation. Median follow-up was 57.2 months; a KSC was detected in 163 patients (13.6%). In multivariable Cox models, age, sex, sunburns, chronic sun exposure, past personal history of non-melanoma skin cancer or other non-cutaneous neoplasia, and the MC1R variants p.D294H and p.R163Q were significantly associated with KSC risk. A cumulative incidence model including age, sex, personal history of KSC, and of other non-cutaneous neoplasia had an AUC of 0.76 (95% CI: 0.71-0.80). When p.D294H and p.R163Q variants were added to the model, the AUC increased to 0.81 (95% CI: 0.77-0.84) (p-value for difference <0.0001). In addition to age, sex, skin characteristics, and sun exposure, p.R163Q and p.D294H MC1R variants significantly increased KSC risk among melanoma patients. Our findings may help identify patients who could benefit most from preventive measures. Copyright © 2015 Elsevier Ltd. All rights reserved.

Sigma receptor type 1 gene variation in a group of Polish patients with Alzheimer's disease and mild cognitive impairment.

PubMed

Maruszak, Aleksandra; Safranow, Krzysztof; Gacia, Magdalena; Gabryelewicz, Tomasz; Słowik, Agnieszka; Styczyńska, Maria; Pepłońska, Beata; Golan, Maciej P; Zekanowski, Cezary; Barcikowska, Maria

2007-01-01

The sigma-1 receptor (SIGMAR1) is a subtype of a nonopioid sigma receptor family and is implicated in numerous functions connected with Alzheimer's disease (AD). Two common genetic variants were identified in SIGMAR1: GC-241 -240TT and Q2P (A61C). It was suggested that the TT-C haplotype is a protective factor for AD. We decided to investigate a putative link between the variants of SIGMAR1 and AD in a group of Polish patients with late-onset AD, in patients with mild cognitive impairment, and in a control group. We observed no significant differences for the SIGMAR1 allele, genotype, haplotype, and diplotype distributions between the studied groups. Multivariate logistic regression analysis showed no interaction between the APOE4 and SIGMAR1 polymorphisms. Further studies using data from different populations are required to elucidate the effect of SIGMAR1 polymorphisms on AD.

Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

NASA Astrophysics Data System (ADS)

Cannon, Alex J.

2018-01-01

Most bias correction algorithms used in climatology, for example quantile mapping, are applied to univariate time series. They neglect the dependence between different variables. Those that are multivariate often correct only limited measures of joint dependence, such as Pearson or Spearman rank correlation. Here, an image processing technique designed to transfer colour information from one image to another—the N-dimensional probability density function transform—is adapted for use as a multivariate bias correction algorithm (MBCn) for climate model projections/predictions of multiple climate variables. MBCn is a multivariate generalization of quantile mapping that transfers all aspects of an observed continuous multivariate distribution to the corresponding multivariate distribution of variables from a climate model. When applied to climate model projections, changes in quantiles of each variable between the historical and projection period are also preserved. The MBCn algorithm is demonstrated on three case studies. First, the method is applied to an image processing example with characteristics that mimic a climate projection problem. Second, MBCn is used to correct a suite of 3-hourly surface meteorological variables from the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) across a North American domain. Components of the Canadian Forest Fire Weather Index (FWI) System, a complicated set of multivariate indices that characterizes the risk of wildfire, are then calculated and verified against observed values. Third, MBCn is used to correct biases in the spatial dependence structure of CanRCM4 precipitation fields. Results are compared against a univariate quantile mapping algorithm, which neglects the dependence between variables, and two multivariate bias correction algorithms, each of which corrects a different form of inter-variable correlation structure. MBCn outperforms these alternatives, often by a large margin, particularly for annual maxima of the FWI distribution and spatiotemporal autocorrelation of precipitation fields.

Partial Least Squares Calibration Modeling Towards the Multivariate Limit of Detection for Enriched Isotopic Mixtures via Laser Ablation Molecular Isotopic Spectroscopy

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

Harris, Candace; Profeta, Luisa; Akpovo, Codjo

The psuedo univariate limit of detection was calculated to compare to the multivariate interval. ompared with results from the psuedounivariate LOD, the multivariate LOD includes other factors (i.e. signal uncertainties) and the reveals the significance in creating models that not only use the analyte’s emission line but also its entire molecular spectra.

Multiple imputation for handling missing outcome data when estimating the relative risk.

PubMed

Sullivan, Thomas R; Lee, Katherine J; Ryan, Philip; Salter, Amy B

2017-09-06

Multiple imputation is a popular approach to handling missing data in medical research, yet little is known about its applicability for estimating the relative risk. Standard methods for imputing incomplete binary outcomes involve logistic regression or an assumption of multivariate normality, whereas relative risks are typically estimated using log binomial models. It is unclear whether misspecification of the imputation model in this setting could lead to biased parameter estimates. Using simulated data, we evaluated the performance of multiple imputation for handling missing data prior to estimating adjusted relative risks from a correctly specified multivariable log binomial model. We considered an arbitrary pattern of missing data in both outcome and exposure variables, with missing data induced under missing at random mechanisms. Focusing on standard model-based methods of multiple imputation, missing data were imputed using multivariate normal imputation or fully conditional specification with a logistic imputation model for the outcome. Multivariate normal imputation performed poorly in the simulation study, consistently producing estimates of the relative risk that were biased towards the null. Despite outperforming multivariate normal imputation, fully conditional specification also produced somewhat biased estimates, with greater bias observed for higher outcome prevalences and larger relative risks. Deleting imputed outcomes from analysis datasets did not improve the performance of fully conditional specification. Both multivariate normal imputation and fully conditional specification produced biased estimates of the relative risk, presumably since both use a misspecified imputation model. Based on simulation results, we recommend researchers use fully conditional specification rather than multivariate normal imputation and retain imputed outcomes in the analysis when estimating relative risks. However fully conditional specification is not without its shortcomings, and so further research is needed to identify optimal approaches for relative risk estimation within the multiple imputation framework.

A simplified parsimonious higher order multivariate Markov chain model

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, a simplified parsimonious higher-order multivariate Markov chain model (SPHOMMCM) is presented. Moreover, parameter estimation method of TPHOMMCM is give. Numerical experiments shows the effectiveness of TPHOMMCM.

Piecewise multivariate modelling of sequential metabolic profiling data.

PubMed

Rantalainen, Mattias; Cloarec, Olivier; Ebbels, Timothy M D; Lundstedt, Torbjörn; Nicholson, Jeremy K; Holmes, Elaine; Trygg, Johan

2008-02-19

Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints. A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted. The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.

A tridiagonal parsimonious higher order multivariate Markov chain model

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, we present a tridiagonal parsimonious higher-order multivariate Markov chain model (TPHOMMCM). Moreover, estimation method of the parameters in TPHOMMCM is give. Numerical experiments illustrate the effectiveness of TPHOMMCM.

MULTIVARIATE LINEAR MIXED MODELS FOR MULTIPLE OUTCOMES. (R824757)

EPA Science Inventory

We propose a multivariate linear mixed (MLMM) for the analysis of multiple outcomes, which generalizes the latent variable model of Sammel and Ryan. The proposed model assumes a flexible correlation structure among the multiple outcomes, and allows a global test of the impact of ...

Electricity Consumption in the Industrial Sector of Jordan: Application of Multivariate Linear Regression and Adaptive Neuro-Fuzzy Techniques

NASA Astrophysics Data System (ADS)

Samhouri, M.; Al-Ghandoor, A.; Fouad, R. H.

2009-08-01

In this study two techniques, for modeling electricity consumption of the Jordanian industrial sector, are presented: (i) multivariate linear regression and (ii) neuro-fuzzy models. Electricity consumption is modeled as function of different variables such as number of establishments, number of employees, electricity tariff, prevailing fuel prices, production outputs, capacity utilizations, and structural effects. It was found that industrial production and capacity utilization are the most important variables that have significant effect on future electrical power demand. The results showed that both the multivariate linear regression and neuro-fuzzy models are generally comparable and can be used adequately to simulate industrial electricity consumption. However, comparison that is based on the square root average squared error of data suggests that the neuro-fuzzy model performs slightly better for future prediction of electricity consumption than the multivariate linear regression model. Such results are in full agreement with similar work, using different methods, for other countries.

Comparing Within-Person Effects from Multivariate Longitudinal Models

ERIC Educational Resources Information Center

Bainter, Sierra A.; Howard, Andrea L.

2016-01-01

Several multivariate models are motivated to answer similar developmental questions regarding within-person (intraindividual) effects between 2 or more constructs over time, yet the within-person effects tested by each model are distinct. In this article, the authors clarify the types of within-person inferences that can be made from each model.…

Applying the multivariate time-rescaling theorem to neural population models

PubMed Central

Gerhard, Felipe; Haslinger, Robert; Pipa, Gordon

2011-01-01

Statistical models of neural activity are integral to modern neuroscience. Recently, interest has grown in modeling the spiking activity of populations of simultaneously recorded neurons to study the effects of correlations and functional connectivity on neural information processing. However any statistical model must be validated by an appropriate goodness-of-fit test. Kolmogorov-Smirnov tests based upon the time-rescaling theorem have proven to be useful for evaluating point-process-based statistical models of single-neuron spike trains. Here we discuss the extension of the time-rescaling theorem to the multivariate (neural population) case. We show that even in the presence of strong correlations between spike trains, models which neglect couplings between neurons can be erroneously passed by the univariate time-rescaling test. We present the multivariate version of the time-rescaling theorem, and provide a practical step-by-step procedure for applying it towards testing the sufficiency of neural population models. Using several simple analytically tractable models and also more complex simulated and real data sets, we demonstrate that important features of the population activity can only be detected using the multivariate extension of the test. PMID:21395436

Treatment and prognosis of breast cancer patients with brain metastases according to intrinsic subtype.

PubMed

Kuba, Sayaka; Ishida, Mayumi; Nakamura, Yoshiaki; Yamanouchi, Kosho; Minami, Shigeki; Taguchi, Kenichi; Eguchi, Susumu; Ohno, Shinji

2014-11-01

How breast cancer subtypes should affect treatment decisions for breast cancer patients with brain metastases is unclear. We analyzed local brain metastases treatments and their outcomes according to subtype in patients with breast cancer and brain metastases. We reviewed records and database information for women treated at the National Kyushu Cancer Center between 2001 and 2010. Patients were divided into three breast cancer subtype groups: Luminal (estrogen receptor positive and/or progesterone receptor positive, but human epidermal growth factor receptor 2 negative); human epidermal growth factor receptor 2 positive and triple negative (estrogen receptor negative, progesterone receptor negative and human epidermal growth factor receptor 2 negative). Of 524 advanced breast cancer patients, we reviewed 65 (12%) with brain metastases and records showing estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 status, as well as outcome data; there were 26 (40%) Luminal, 26 (40%) had human epidermal growth factor receptor 2 and 13 (20%) had triple negative subtypes. There was no statistical difference in the number of brain metastases among subtypes; however, rates of stereotactic radiosurgery or surgery for brain metastases differed significantly by subtype (human epidermal growth factor receptor 2: 81%, Luminal: 42% and triple negative: 47%; P = 0.03). Patients having the human epidermal growth factor receptor 2 subtype, a performance status of ≤1 and ≤4 brain metastases, who underwent systemic therapy after brain metastases and underwent stereotactic radiosurgery or surgery, were predicted to have longer overall survival after brain metastases. Multivariate analysis demonstrated that not having systemic therapy and not having the human epidermal growth factor receptor 2 subtype were independent factors associated with an increased risk of death (hazard ratio 2.4, 95% confidence interval 1.01-5.6; P = 0.05 and hazard ratio 2.9, 95% confidence interval 1.5-5.8; P = 0.003, respectively). Our study showed that local brain treatments and prognosis differed by subtype in breast cancer patients with brain metastases. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Remote-sensing data processing with the multivariate regression analysis method for iron mineral resource potential mapping: a case study in the Sarvian area, central Iran

NASA Astrophysics Data System (ADS)

Mansouri, Edris; Feizi, Faranak; Jafari Rad, Alireza; Arian, Mehran

2018-03-01

This paper uses multivariate regression to create a mathematical model for iron skarn exploration in the Sarvian area, central Iran, using multivariate regression for mineral prospectivity mapping (MPM). The main target of this paper is to apply multivariate regression analysis (as an MPM method) to map iron outcrops in the northeastern part of the study area in order to discover new iron deposits in other parts of the study area. Two types of multivariate regression models using two linear equations were employed to discover new mineral deposits. This method is one of the reliable methods for processing satellite images. ASTER satellite images (14 bands) were used as unique independent variables (UIVs), and iron outcrops were mapped as dependent variables for MPM. According to the results of the probability value (p value), coefficient of determination value (R2) and adjusted determination coefficient (Radj2), the second regression model (which consistent of multiple UIVs) fitted better than other models. The accuracy of the model was confirmed by iron outcrops map and geological observation. Based on field observation, iron mineralization occurs at the contact of limestone and intrusive rocks (skarn type).

Association of C5L2 genetic polymorphisms with coronary artery disease in a Han population in Xinjiang, China.

PubMed

Zheng, Ying-Ying; Xie, Xiang; Ma, Yi-Tong; Fu, Zhen-Yan; Ma, Xiang; Yang, Yi-Ning; Li, Xiao-Mei; Pan, Shuo; Adi, Dilare; Chen, Bang-Dang; Liu, Fen

2017-01-31

C5aR-like receptor 2 (C5L2) has been identified as a receptor for the inflammatory factor Complement 5a (C5a) and acylation-stimulating protein (ASP). ASP binding to C5L2 leading to a net accumulation of TG stores and glucose transporter. The aim of the present study is to evaluate the association of the SNPs of C5L2 gene with coronary artery disease (CAD) in a Chinese population. We examined the role of the tagging single nucleotide polymorphisms (SNPs) of C5L2 gene for CAD using a case-control design. We determined the prevalence of C5L2 genotypes in 505 CAD patients and 469 age and sex-matched healthy control subjects of Han population. There was significant difference in genotype distributions of rs2972607 and rs8112962 between CAD patients and control subjects. The rs2972607 was found to be associated with CAD in a dominant model (AA vs. AG + GG, P<0.001). Similarly, the rs8112962 was found to be associated with CAD in a dominant model (TT vs CT + CC, P=0.016). The difference remained statistically significant after multivariate adjustment (OR =1.401, 95% confidence interval [CI]:1.026~1.914, P=0.034; OR = 1.541, 95%CI:1.093~ 2.172, P=0.014; respectively). The results of this study indicate that both rs2972607 and rs8112962 of C5L2 gene are associated with CAD in a Han population of China.

Changes in Body Mass Index, Leptin, and Leptin Receptor Polymorphisms and Breast Cancer Risk.

PubMed

Liu, Chun-Rong; Li, Qin; Hou, Can; Li, Hui; Shuai, Ping; Zhao, Min; Zhong, Xiao-Rong; Xu, Zhu-Ping; Li, Jia-Yuan

2018-03-01

Obesity is a strong risk factor for breast cancer. The polymorphisms of leptin (LEP) and leptin receptor (LEPR) may be associated with breast cancer by regulator of adipose tissue mass and tumor cell growth. A total of 794 cases and 805 matched controls were sequentially enrolled. Time-of-flight mass spectrometry was used to determine the LEPrs7799039, LEPRrs1137100, and LEPRrs1137101 genotypes for each participant. Associations between polymorphisms of these genes, change in body mass index (BMI), and breast cancer risk were assessed by unconditional multivariable logistic regression models. The unconditional logistic regression model showed that persistent overweight (BMI ≥24 kg/m 2 ) over the preceding 10 years was associated with increased breast cancer risk in premenopausal women (odds ratio [OR] = 1.67, 95% confidence interval [CI]: 1.19-2.35). No associations between LEPrs7799039, LEPRrs1137100, or LEPRrs1137101 polymorphisms alone and breast cancer risk were found. Persistent overweight over the preceding 10 years and carrying the LEPrs7799039 AA genotype together increased breast cancer risk in premenopausal women (OR adj  = 2.00, 95% CI: 1.26-3.16). Persistent overweight over the preceding 10 years and carrying the LEPRrs1137100 GG genotype increased breast cancer risk in premenopausal women (OR adj  = 1.68, 95% CI: 1.06-2.68). In premenopausal women, persistent overweight (BMI ≥24 kg/m 2 ) over the preceding 10 years increases breast cancer risk. Persistent overweight along with LEPrs7799039 AA or LEPRrs1137100 GG genotypes synergistically increase risk of breast cancer among premenopausal women.

Dimer-based model for heptaspanning membrane receptors.

PubMed

Franco, Rafael; Casadó, Vicent; Mallol, Josefa; Ferré, Sergi; Fuxe, Kjell; Cortés, Antonio; Ciruela, Francisco; Lluis, Carmen; Canela, Enric I

2005-07-01

The existence of intramembrane receptor-receptor interactions for heptaspanning membrane receptors is now fully accepted, but a model considering dimers as the basic unit that binds to two ligand molecules is lacking. Here, we propose a two-state-dimer model in which the ligand-induced conformational changes from one component of the dimer are communicated to the other. Our model predicts cooperativity in binding, which is relevant because the other current models fail to address this phenomenon satisfactorily. Our two-state-dimer model also predicts the variety of responses elicited by full or partial agonists, neutral antagonists and inverse agonists. This model can aid our understanding of the operation of heptaspanning receptors and receptor channels, and, potentially, be important for improving the treatment of cardiovascular, neurological and neuropsychyatric diseases.

Meta-Analytic Structural Equation Modeling (MASEM): Comparison of the Multivariate Methods

ERIC Educational Resources Information Center

Zhang, Ying

2011-01-01

Meta-analytic Structural Equation Modeling (MASEM) has drawn interest from many researchers recently. In doing MASEM, researchers usually first synthesize correlation matrices across studies using meta-analysis techniques and then analyze the pooled correlation matrix using structural equation modeling techniques. Several multivariate methods of…

TSHR intronic polymorphisms (rs179247 and rs12885526) and their role in the susceptibility of the Brazilian population to Graves' disease and Graves' ophthalmopathy.

PubMed

Bufalo, N E; Dos Santos, R B; Marcello, M A; Piai, R P; Secolin, R; Romaldini, J H; Ward, L S

2015-05-01

Intronic thyroid-stimulating hormone receptor polymorphisms have been associated with the risk for both Graves' disease and Graves' ophthalmopathy, but results have been inconsistent among different populations. We aimed to investigate the influence of thyroid-stimulating hormone receptor intronic polymorphisms in a large well-characterized population of GD patients. We studied 279 Graves' disease patients (231 females and 48 males, 39.80 ± 11.69 years old), including 144 with Graves' ophthalmopathy, matched to 296 healthy control individuals. Thyroid-stimulating hormone receptor genotypes of rs179247 and rs12885526 were determined by Real Time PCR TaqMan(®) SNP Genotyping. A multivariate analysis showed that the inheritance of the thyroid-stimulating hormone receptor AA genotype for rs179247 increased the risk for Graves' disease (OR = 2.821; 95 % CI 1.595-4.990; p = 0.0004), whereas the thyroid-stimulating hormone receptor GG genotype for rs12885526 increased the risk for Graves' ophthalmopathy (OR = 2.940; 95 % CI 1.320-6.548; p = 0.0083). Individuals with Graves' ophthalmopathy also presented lower mean thyrotropin receptor antibodies levels (96.3 ± 143.9 U/L) than individuals without Graves' ophthalmopathy (98.3 ± 201.9 U/L). We did not find any association between the investigated polymorphisms and patients clinical features or outcome. We demonstrate that thyroid-stimulating hormone receptor intronic polymorphisms are associated with the susceptibility to Graves' disease and Graves' ophthalmopathy in the Brazilian population, but do not appear to influence the disease course.

Expression of the ERBB Family of Ligands and Receptors in Gastric Cancer.

PubMed

Byeon, Sun-Ju; Lee, Hye Seung; Kim, Min-A; Lee, Byung Lan; Kim, Woo Ho

2017-01-01

Gastric cancer (GC) is the second most common cancer and the third leading cause of cancer-related death in Korea. Alterations in the ERBB (homology to the erythroblastoma viral gene product, v-erbB) receptor family and ERBB-related signaling pathways are frequently observed in GC. However, the roles of the ERBB receptors and their ligands in GC are not well established. We evaluated the expression levels of various ERBB receptor ligands (i.e., heparin-binding epidermal growth factor-like growth factor [HBEGF], transforming growth factor-α [TGFA], amphiregulin [AREG], epiregulin [EREG], epidermal growth factor [EGF], and betacellulin [BTC]) and 3 ERBB family receptors (i.e., epidermal growth factor receptor [EGFR], human EGFR2 [HER2], and ERBB3) in 313 cases of GC using immunohistochemistry, fluorescence in situ hybridization, and mRNA in situ hybridization. A high expression of EGFR, HER2, and ERBB3 was observed in 30, 32, and 27 cases, respectively. A high expression of HBEGF, TGFA, AREG, EREG, EGF, and BTC was observed in 91, 97, 151, 74, 26, and 37 cases, respectively. A high expression of TGFA was associated with better survival, while a high expression of BTC was associated with worse survival. These results were confirmed using Cox proportional hazards analysis. HBEGF, TGFA, AREG, tumor-node-metastasis classification, Lauren's classification, and ERBB3 were significant survival parameters in multivariate analysis. Among the ERBB family receptors and ligands examined, 3 ligands (i.e., TGFA, HBEGF, and AREG) and ERBB3 had a prognostic impact. © 2017 S. Karger AG, Basel.

Homology Models of Melatonin Receptors: Challenges and Recent Advances

PubMed Central

Pala, Daniele; Lodola, Alessio; Bedini, Annalida; Spadoni, Gilberto; Rivara, Silvia

2013-01-01

Melatonin exerts many of its actions through the activation of two G protein-coupled receptors (GPCRs), named MT1 and MT2. So far, a number of different MT1 and MT2 receptor homology models, built either from the prototypic structure of rhodopsin or from recently solved X-ray structures of druggable GPCRs, have been proposed. These receptor models differ in the binding modes hypothesized for melatonin and melatonergic ligands, with distinct patterns of ligand-receptor interactions and putative bioactive conformations of ligands. The receptor models will be described, and they will be discussed in light of the available information from mutagenesis experiments and ligand-based pharmacophore models. The ability of these ligand-receptor complexes to rationalize structure-activity relationships of known series of melatonergic compounds will be commented upon. PMID:23584026

Regression Models For Multivariate Count Data

PubMed Central

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2016-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data. PMID:28348500

Regression Models For Multivariate Count Data.

PubMed

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2017-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.

A "Model" Multivariable Calculus Course.

ERIC Educational Resources Information Center

Beckmann, Charlene E.; Schlicker, Steven J.

1999-01-01

Describes a rich, investigative approach to multivariable calculus. Introduces a project in which students construct physical models of surfaces that represent real-life applications of their choice. The models, along with student-selected datasets, serve as vehicles to study most of the concepts of the course from both continuous and discrete…

Bayesian Estimation of Multivariate Latent Regression Models: Gauss versus Laplace

ERIC Educational Resources Information Center

Culpepper, Steven Andrew; Park, Trevor

2017-01-01

A latent multivariate regression model is developed that employs a generalized asymmetric Laplace (GAL) prior distribution for regression coefficients. The model is designed for high-dimensional applications where an approximate sparsity condition is satisfied, such that many regression coefficients are near zero after accounting for all the model…

A Sandwich-Type Standard Error Estimator of SEM Models with Multivariate Time Series

ERIC Educational Resources Information Center

Zhang, Guangjian; Chow, Sy-Miin; Ong, Anthony D.

2011-01-01

Structural equation models are increasingly used as a modeling tool for multivariate time series data in the social and behavioral sciences. Standard error estimators of SEM models, originally developed for independent data, require modifications to accommodate the fact that time series data are inherently dependent. In this article, we extend a…

Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden” Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method. PMID:20454705

Regular and low-dose aspirin, other non-steroidal anti-inflammatory medications and prospective risk of HER2-defined breast cancer: the California Teachers Study.

PubMed

Clarke, Christina A; Canchola, Alison J; Moy, Lisa M; Neuhausen, Susan L; Chung, Nadia T; Lacey, James V; Bernstein, Leslie

2017-05-01

Regular users of aspirin may have reduced risk of breast cancer. Few studies have addressed whether risk reduction pertains to specific breast cancer subtypes defined jointly by hormone receptor (estrogen and progesterone receptor) and human epidermal growth factor receptor 2 (HER2) expression. This study assessed the prospective risk of breast cancer (overall and by subtype) according to use of aspirin and other non-steroidal anti-inflammatory medications (NSAIDs) in a cohort of female public school professionals in California. In 1995 - 1996, participants in the California Teachers Study completed a baseline questionnaire on family history of cancer and other conditions, use of NSAIDs, menstrual and reproductive history, self-reported weight and height, living environment, diet, alcohol use, and physical activity. In 2005-2006, 57,164 participants provided some updated information, including use of NSAIDs and 1457 of these participants developed invasive breast cancer before January 2013. Multivariable Cox proportional hazards regression models provided hazard rate ratios (HRR) for the association between NSAID use and risk of invasive breast cancer as well as hormone receptor- and HER2-defined subtypes. Developing breast cancer was associated inversely with taking three or more tablets of low-dose aspirin per week (23% of participants). Among women reporting this exposure, the HRR was 0.84 (95% confidence interval (CI) 0.72-0.98) compared to those not taking NSAIDs and this was particularly evident in women with the hormone receptor-positive/HER2-negative subtype (HRR = 0.80, 95% CI 0.66-0.96). Use of three or more tablets of "other" NSAIDs was marginally associated with lower risk of breast cancer (HRR = 0.79, 95% CI 0.62-1.00). Other associations with NSAIDs were generally null. Our observation of reduced risk of breast cancer, among participants who took three or more tablets of low-dose aspirin weekly, is consistent with other reports looking at aspirin without differentiation by dose. This is the first report to suggest that the reduction in risk occurs for low-dose aspirin and not for regular-dose aspirin and only among women with the hormone receptor-positive/HER2-negative subtype. This preliminary study builds on previous knowledge and further supports the need for formal cancer chemoprevention studies of low-dose aspirin.

A joint modeling and estimation method for multivariate longitudinal data with mixed types of responses to analyze physical activity data generated by accelerometers.

PubMed

Li, Haocheng; Zhang, Yukun; Carroll, Raymond J; Keadle, Sarah Kozey; Sampson, Joshua N; Matthews, Charles E

2017-11-10

A mixed effect model is proposed to jointly analyze multivariate longitudinal data with continuous, proportion, count, and binary responses. The association of the variables is modeled through the correlation of random effects. We use a quasi-likelihood type approximation for nonlinear variables and transform the proposed model into a multivariate linear mixed model framework for estimation and inference. Via an extension to the EM approach, an efficient algorithm is developed to fit the model. The method is applied to physical activity data, which uses a wearable accelerometer device to measure daily movement and energy expenditure information. Our approach is also evaluated by a simulation study. Copyright © 2017 John Wiley & Sons, Ltd.

Influence of ionotropic receptor location on their dynamics at glutamatergic synapses.

PubMed

Allam, Sushmita L; Bouteiller, Jean-Marie C; Hu, Eric; Greget, Renaud; Ambert, Nicolas; Bischoff, Serge; Baudry, Michel; Berger, Theodore W

2012-01-01

In this paper we study the effects of the location of ionotropic receptors, especially AMPA and NMDA receptors, on their function at excitatory glutamatergic synapses. As few computational models only allow to evaluate the influence of receptor location on state transition and receptor dynamics, we present an elaborate computational model of a glutamatergic synapse that takes into account detailed parametric models of ionotropic receptors along with glutamate diffusion within the synaptic cleft. Our simulation results underscore the importance of the wide spread distribution of AMPA receptors which is required to avoid massive desensitization of these receptors following a single glutamate release event while NMDA receptor location is potentially optimal relative to the glutamate release site thus, emphasizing the contribution of location dependent effects of the two major ionotropic receptors to synaptic efficacy.

Structural and Molecular Modeling Features of P2X Receptors

PubMed Central

Alves, Luiz Anastacio; da Silva, João Herminio Martins; Ferreira, Dinarte Neto Moreira; Fidalgo-Neto, Antonio Augusto; Teixeira, Pedro Celso Nogueira; de Souza, Cristina Alves Magalhães; Caffarena, Ernesto Raúl; de Freitas, Mônica Santos

2014-01-01

Currently, adenosine 5′-triphosphate (ATP) is recognized as the extracellular messenger that acts through P2 receptors. P2 receptors are divided into two subtypes: P2Y metabotropic receptors and P2X ionotropic receptors, both of which are found in virtually all mammalian cell types studied. Due to the difficulty in studying membrane protein structures by X-ray crystallography or NMR techniques, there is little information about these structures available in the literature. Two structures of the P2X4 receptor in truncated form have been solved by crystallography. Molecular modeling has proven to be an excellent tool for studying ionotropic receptors. Recently, modeling studies carried out on P2X receptors have advanced our knowledge of the P2X receptor structure-function relationships. This review presents a brief history of ion channel structural studies and shows how modeling approaches can be used to address relevant questions about P2X receptors. PMID:24637936

Modelling the interdependence between the stoichiometry of receptor oligomerization and ligand binding for a coexisting dimer/tetramer receptor system.

PubMed

Rovira, X; Vivó, M; Serra, J; Roche, D; Strange, P G; Giraldo, J

2009-01-01

Many G protein-coupled receptors have been shown to exist as oligomers, but the oligomerization state and the effects of this on receptor function are unclear. For some G protein-coupled receptors, in ligand binding assays, different radioligands provide different maximal binding capacities. Here we have developed mathematical models for co-expressed dimeric and tetrameric species of receptors. We have considered models where the dimers and tetramers are in equilibrium and where they do not interconvert and we have also considered the potential influence of the ligands on the degree of oligomerization. By analogy with agonist efficacy, we have considered ligands that promote, inhibit or have no effect on oligomerization. Cell surface receptor expression and the intrinsic capacity of receptors to oligomerize are quantitative parameters of the equations. The models can account for differences in the maximal binding capacities of radioligands in different preparations of receptors and provide a conceptual framework for simulation and data fitting in complex oligomeric receptor situations.

Load compensation in a lean burn natural gas vehicle

NASA Astrophysics Data System (ADS)

Gangopadhyay, Anupam

A new multivariable PI tuning technique is developed in this research that is primarily developed for regulation purposes. Design guidelines are developed based on closed-loop stability. The new multivariable design is applied in a natural gas vehicle to combine idle and A/F ratio control loops. This results in better recovery during low idle operation of a vehicle under external step torques. A powertrain model of a natural gas engine is developed and validated for steady-state and transient operation. The nonlinear model has three states: engine speed, intake manifold pressure and fuel fraction in the intake manifold. The model includes the effect of fuel partial pressure in the intake manifold filling and emptying dynamics. Due to the inclusion of fuel fraction as a state, fuel flow rate into the cylinders is also accurately modeled. A linear system identification is performed on the nonlinear model. The linear model structure is predicted analytically from the nonlinear model and the coefficients of the predicted transfer function are shown to be functions of key physical parameters in the plant. Simulations of linear system and model parameter identification is shown to converge to the predicted values of the model coefficients. The multivariable controller developed in this research could be designed in an algebraic fashion once the plant model is known. It is thus possible to implement the multivariable PI design in an adaptive fashion combining the controller with identified plant model on-line. This will result in a self-tuning regulator (STR) type controller where the underlying design criteria is the multivariable tuning technique designed in this research.

Practical robustness measures in multivariable control system analysis. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Lehtomaki, N. A.

1981-01-01

The robustness of the stability of multivariable linear time invariant feedback control systems with respect to model uncertainty is considered using frequency domain criteria. Available robustness tests are unified under a common framework based on the nature and structure of model errors. These results are derived using a multivariable version of Nyquist's stability theorem in which the minimum singular value of the return difference transfer matrix is shown to be the multivariable generalization of the distance to the critical point on a single input, single output Nyquist diagram. Using the return difference transfer matrix, a very general robustness theorem is presented from which all of the robustness tests dealing with specific model errors may be derived. The robustness tests that explicitly utilized model error structure are able to guarantee feedback system stability in the face of model errors of larger magnitude than those robustness tests that do not. The robustness of linear quadratic Gaussian control systems are analyzed.

A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2013-01-01

Multivariate meta-analysis is becoming more commonly used. Methods for fitting the multivariate random effects model include maximum likelihood, restricted maximum likelihood, Bayesian estimation and multivariate generalisations of the standard univariate method of moments. Here, we provide a new multivariate method of moments for estimating the between-study covariance matrix with the properties that (1) it allows for either complete or incomplete outcomes and (2) it allows for covariates through meta-regression. Further, for complete data, it is invariant to linear transformations. Our method reduces to the usual univariate method of moments, proposed by DerSimonian and Laird, in a single dimension. We illustrate our method and compare it with some of the alternatives using a simulation study and a real example. PMID:23401213

Pharmacoepidemiology of anemia in kidney transplant recipients.

PubMed

Winkelmayer, Wolfgang C; Kewalramani, Reshma; Rutstein, Mark; Gabardi, Steven; Vonvisger, Tania; Chandraker, Anil

2004-05-01

ABSTRACT. Anemia has long been known to be a complication of end-stage renal disease (ESRD), and it has been linked to cardiovascular morbidity and mortality. Although kidney transplant recipients (KTR) are prone to experiencing cardiovascular outcomes, little is known about the epidemiology of anemia in this population. With few exceptions, studies to date have not fully evaluated the associations between posttransplant anemia (PTA) and medications commonly used in KTR, particularly immunosuppressant drugs, angiotensin-converting enzyme inhibitors (ACEI) and angiotensin II receptor blockers (ARB). The authors aimed to specifically investigate possible associations between these drugs and PTA. Detailed medical information was retrospectively collected on 374 consecutive KTR from our transplant clinic. Univariate/multivariate linear regression models were used to test for associations between hematocrit (HCT) and other covariates, and logistic regression models were used to detect independent predictors of PTA, defined as HCT <33%. The mean time since transplantation was 7.7 yr, and mean creatinine was 2.2 mg/dl. The prevalence of PTA was 28.6%. Ten percent of all patients were on erythropoietin therapy, but only 41.6% of patients whose HCT was <30 received this treatment. From multivariate analyses, the authors found that female gender and lower renal function were associated with lower HCT (both P < 0.001). Patients on ACEI had significantly lower HCT (P = 0.005) compared with patients without such treatment. In addition, a significant curvilinear dose-response relationship was found between ACEI dose and HCT. Among the immunosuppressant drugs, mycophenolate mofetil (P = 0.05) and tacrolimus (P = 0.02) were associated with a lower HCT. The authors conclude that PTA is prevalent and undertreated in KTR. Several medications that are possibly modifiable correlates of PTR deserve further study.

Proton pump inhibitors and histamine 2 blockers are associated with improved overall survival in patients with head and neck squamous carcinoma (HNSCC)

PubMed Central

Papagerakis, Silvana; Bellile, Emily; Peterson, Lisa A.; Pliakas, Maria; Balaskas, Katherine; Selman, Sara; Hanauer, David; Taylor, Jeremy M.G.; Duffy, Sonia; Wolf, Gregory

2015-01-01

It has been postulated that gastroesophageal reflux plays a role in the etiology of head and neck squamous cell carcinomas (HNSCC) and contributes to complications after surgery or during radiotherapy. Antacid medications are commonly used in HNSCC patients for the management of acid reflux however their relationship with outcomes has not been well studied. Associations between histamine receptor-2 antagonists (H2RAs) and proton pump inhibitors (PPIs) use and treatment outcomes were determined in 596 previously untreated HNSCC patients enrolled in our SPORE epidemiology program from 2003–2008 (median follow-up 55-month). Comprehensive clinical information was entered prospectively in our database. Risk strata were created based on possible confounding prognostic variables (age, demographics, socioeconomics, tumor stage, primary site, smoking status, HPV-16 status and treatment modality); correlations within risk strata were analyzed in a multivariable model. Patients taking antacid medications had significantly better overall survival (PPI alone: p<0.001: H2RA alone, p=0.0479; both PPI+H2RA, p=0.0133). Using multivariable Cox models and adjusting for significant prognostic covariates, both PPIs and H2RAs use were significant prognostic factors for overall survival, but only H2RAs use for recurrence-free survival in HPV16-positive oropharyngeal patients. We found significant associations between use of H2RAs and PPIs, alone or in combination, and various clinical characteristics. The findings in this large cohort study indicate that routine use of antacid medications may have significant therapeutic benefit in HNSCC patients. The reasons for this association remain an active area of investigation and could lead to identification of new treatment and prevention approaches with agents that have minimal toxicities. PMID:25468899

Spatio-temporal patterns and source apportionment of pollution in Qiantang River (China) using neural-based modeling and multivariate statistical techniques

NASA Astrophysics Data System (ADS)

Su, Shiliang; Zhi, Junjun; Lou, Liping; Huang, Fang; Chen, Xia; Wu, Jiaping

Characterizing the spatio-temporal patterns and apportioning the pollution sources of water bodies are important for the management and protection of water resources. The main objective of this study is to describe the dynamics of water quality and provide references for improving river pollution control practices. Comprehensive application of neural-based modeling and different multivariate methods was used to evaluate the spatio-temporal patterns and source apportionment of pollution in Qiantang River, China. Measurement data were obtained and pretreated for 13 variables from 41 monitoring sites for the period of 2001-2004. A self-organizing map classified the 41 monitoring sites into three groups (Group A, B and C), representing different pollution characteristics. Four significant parameters (dissolved oxygen, biochemical oxygen demand, total phosphorus and total lead) were identified by discriminant analysis for distinguishing variations of different years, with about 80% correct assignment for temporal variation. Rotated principal component analysis (PCA) identified four potential pollution sources for Group A (domestic sewage and agricultural pollution, industrial wastewater pollution, mineral weathering, vehicle exhaust and sand mining), five for Group B (heavy metal pollution, agricultural runoff, vehicle exhaust and sand mining, mineral weathering, chemical plants discharge) and another five for Group C (vehicle exhaust and sand mining, chemical plants discharge, soil weathering, biochemical pollution, mineral weathering). The identified potential pollution sources explained 75.6% of the total variances for Group A, 75.0% for Group B and 80.0% for Group C, respectively. Receptor-based source apportionment was applied to further estimate source contributions for each pollution variable in the three groups, which facilitated and supported the PCA results. These results could assist managers to develop optimal strategies and determine priorities for river pollution control and effective water resources management.

Source apportionment of trace metals in river sediments: A comparison of three methods.

PubMed

Chen, Haiyang; Teng, Yanguo; Li, Jiao; Wu, Jin; Wang, Jinsheng

2016-04-01

Increasing trace metal pollution in river sediment poses a significant threat to watershed ecosystem health. Identifying potential sources of sediment metals and apportioning their contributions are of key importance for proposing prevention and control strategies of river pollution. In this study, three advanced multivariate receptor models, factor analysis with nonnegative constraints (FA-NNC), positive matrix factorization (PMF), and multivariate curve resolution weighted-alternating least-squares (MCR-WALS), were comparatively employed for source apportionment of trace metals in river sediments and applied to the Le'an River, a main tributary of Poyang Lake which is the largest freshwater lake in China. The pollution assessment with contamination factor and geoaccumulation index suggested that the river sediments in Le'an River were contaminated severely by trace metals due to human activities. With the three apportionment tools, similar source profiles of trace metals in sediments were extracted. Especially, the MCR-WALS and PMF models produced essentially the same results. Comparatively speaking, the weighted schemes might give better solutions than the unweighted FA-NNC because the uncertainty information of environmental data was considered by PMF and MCR-WALS. Anthropogenic sources were apportioned as the most important pollution sources influencing the sediment metals in Le'an River with contributions of about 90%. Among them, copper tailings occupied the largest contribution (38.4-42.2%), followed by mining wastewater (29.0-33.5%), and agricultural activities (18.2-18.7%). To protect the ecosystem of Le'an River and Poyang Lake, special attention should be paid to the discharges of mining wastewater and the leachates of copper tailing ponds in that region. Copyright © 2015 Elsevier Ltd. All rights reserved.

Pharmacotherapy Treatment Patterns, Outcomes, and Health Resource Utilization Among Patients with Heart Failure with Reduced Ejection Fraction at a U.S. Academic Medical Center.

PubMed

Bress, Adam P; King, Jordan B; Brixner, Diana; Kielhorn, Adrian; Patel, Harshali K; Maya, Juan; Lee, Vinson C; Biskupiak, Joseph; Munger, Mark

2016-02-01

To assess clinical characteristics, pharmacotherapy treatment patterns, resource utilization and associated charges, and morbidity and mortality outcomes among a real-world cohort of patients with heart failure with reduced ejection fraction (HFrEF) in an academic medical center setting. Retrospective analysis. Electronic health record database that includes clinical, laboratory, and administrative data for all facilities of the University of Utah Health Care System. A total of 989 adults with prevalent (preexisting) HFrEF, identified by using the International Classification of Diseases, Ninth Revision, Clinical Modification code 428.x (heart failure) between January 1, 2007, and June 30, 2013, and who had a left ventricular ejection fraction of 40% or lower. The cohort had a mean age of 64 ± 15 years and was predominantly white (71%) and male (74%). Patients received β-blockers, angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin II receptor blockers (ARBs), and aldosterone receptor antagonists (ARAs) at rates of 79%, 69%, and 29%, respectively. Patients achieved target doses of β-blockers, ACEIs, and ARBs at rates of only 24%, 31%, and 13%, respectively. Overall, 58% of patients were prescribed dual therapy with a β-blocker and an ACEI or ARB, and 19% were prescribed triple therapy (β-blocker, an ACEI or ARB, and an ARA). Univariate and multivariate logistic regression models were used to assess the association between baseline characteristics with the presence of triple therapy. Two variables were statistically significant in both models: increasing age was associated with a lower odds of triple therapy (univariate: odds ratio [OR] 0.760, 95% confidence interval [CI] 0.673-0.857; multivariate: OR 0.768, 95% CI 0.625-0.942), whereas receipt of an implantable cardiac device was associated with a 2-fold increase in the odds of triple therapy (univariate: OR 2.1, 95% CI 1.4-3.1; multivariate: OR 2.1, 95% CI 1.3-3.5). During a mean ± SD follow-up of 36 ± 27 months, all-cause mortality was 0.12 per person-year. There were 1311 all-cause hospitalizations of which 611 (47%) were for worsening heart failure. The rate of all-cause and heart failure-specific hospitalizations was 0.44 and 0.21 per person-year of follow-up, respectively. The median length of stay was 6.4 ± 8.8 days, and the median charge was $22,310. The 30-day all-cause readmission rate was 20%, and the primary reason for readmission was heart failure in 65% of cases. This study demonstrates the continuing significant disease and economic burden for patients with HFrEF. Challenges remain in utilization of established disease-modifying therapy and in the treatment of patients with HFrEF and multiple comorbidities. © 2016 Pharmacotherapy Publications, Inc.

Multivariate Phylogenetic Comparative Methods: Evaluations, Comparisons, and Recommendations.

PubMed

Adams, Dean C; Collyer, Michael L

2018-01-01

Recent years have seen increased interest in phylogenetic comparative analyses of multivariate data sets, but to date the varied proposed approaches have not been extensively examined. Here we review the mathematical properties required of any multivariate method, and specifically evaluate existing multivariate phylogenetic comparative methods in this context. Phylogenetic comparative methods based on the full multivariate likelihood are robust to levels of covariation among trait dimensions and are insensitive to the orientation of the data set, but display increasing model misspecification as the number of trait dimensions increases. This is because the expected evolutionary covariance matrix (V) used in the likelihood calculations becomes more ill-conditioned as trait dimensionality increases, and as evolutionary models become more complex. Thus, these approaches are only appropriate for data sets with few traits and many species. Methods that summarize patterns across trait dimensions treated separately (e.g., SURFACE) incorrectly assume independence among trait dimensions, resulting in nearly a 100% model misspecification rate. Methods using pairwise composite likelihood are highly sensitive to levels of trait covariation, the orientation of the data set, and the number of trait dimensions. The consequences of these debilitating deficiencies are that a user can arrive at differing statistical conclusions, and therefore biological inferences, simply from a dataspace rotation, like principal component analysis. By contrast, algebraic generalizations of the standard phylogenetic comparative toolkit that use the trace of covariance matrices are insensitive to levels of trait covariation, the number of trait dimensions, and the orientation of the data set. Further, when appropriate permutation tests are used, these approaches display acceptable Type I error and statistical power. We conclude that methods summarizing information across trait dimensions, as well as pairwise composite likelihood methods should be avoided, whereas algebraic generalizations of the phylogenetic comparative toolkit provide a useful means of assessing macroevolutionary patterns in multivariate data. Finally, we discuss areas in which multivariate phylogenetic comparative methods are still in need of future development; namely highly multivariate Ornstein-Uhlenbeck models and approaches for multivariate evolutionary model comparisons. © The Author(s) 2017. Published by Oxford University Press on behalf of the Systematic Biology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Evaluating the Significance of CDK2-PELP1 Axis in Tumorigenesis and Hormone Therapy Resistance

DTIC Science & Technology

2010-02-01

them into the cell cycle. Analysis of cell lysates on a 4-12% gradient gel revealed that the phospho PELP1 antibody recognized both forms (Fig 3D...Hulin,M., Lidereau,R. and Bieche,I. Expression analysis of estrogen receptor alpha coregulators in breast carcinoma: evidence that NCOR1 expression...Pohl et al, 2003). This trial included 512 randomized patients wherein multivariate analysis revealed decreased p27 expression to be correlated

Detrimental effects of melanocortin-1 receptor (MC1R) variants on the clinical outcomes of BRAF V600 metastatic melanoma patients treated with BRAF inhibitors.

PubMed

Guida, Michele; Strippoli, Sabino; Ferretta, Anna; Bartolomeo, Nicola; Porcelli, Letizia; Maida, Immacolata; Azzariti, Amalia; Tommasi, Stefania; Grieco, Claudia; Guida, Stefania; Albano, Anna; Lorusso, Vito; Guida, Gabriella

2016-11-01

Melanocortin-1 receptor (MC1R) plays a key role in skin pigmentation, and its variants are linked with a higher melanoma risk. The influence of MC1R variants on the outcomes of patients with metastatic melanoma (MM) treated with BRAF inhibitors (BRAFi) is unknown. We studied the MC1R status in a cohort of 53 consecutive BRAF-mutated patients with MM treated with BRAFi. We also evaluated the effect of vemurafenib in four V600 BRAF melanoma cell lines with/without MC1R variants. We found a significant correlation between the presence of MC1R variants and worse outcomes in terms of both overall response rate (ORR; 59% versus 95%, P = 0.011 univariate, P = 0.028 multivariate analysis) and progression-free survival (PFS) shorter than 6 months (72% versus 33%, P = 0.012 univariate, P = 0.027 multivariate analysis). No difference in overall survival (OS) was reported, probably due to subsequent treatments. Data in vitro showed a significant different phosphorylation of Erk1/2 and p38 MAPK during treatment, associated with a greater increase in vemurafenib IC50 in MC1R variant cell lines. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

VEGF and Ki-67 Overexpression in Predicting Poor Overall Survival in Adenoid Cystic Carcinoma.

PubMed

Park, Seongyeol; Nam, Soo Jeong; Keam, Bhumsuk; Kim, Tae Min; Jeon, Yoon Kyung; Lee, Se-Hoon; Hah, J Hun; Kwon, Tack-Kyun; Kim, Dong-Wan; Sung, Myung-Whun; Heo, Dae Seog; Bang, Yung-Jue

2016-04-01

The purpose of this study was to evaluate potential prognostic factors in patients with adenoid cystic carcinoma (ACC). A total of 68 patients who underwent curative surgery and had available tissue were enrolled in this study. Their medical records and pathologic slides were reviewed and immunohistochemistry for basic fibroblast growth factor, fibroblast growth factor receptor (FGFR) 2, FGFR3, c-kit, Myb proto-oncogene protein, platelet-derived growth factor receptor beta, vascular endothelial growth factor (VEGF), and Ki-67 was performed. Univariate and multivariate analysis was performed for determination of disease-free survival (DFS) and overall survival (OS). In univariate analyses, primary site of nasal cavity and paranasal sinus (p=0.022) and Ki-67 expression of more than 7% (p=0.001) were statistically significant factors for poor DFS. Regarding OS, perineural invasion (p=0.032), high expression of VEGF (p=0.033), and high expression of Ki-67 (p=0.007) were poor prognostic factors. In multivariate analyses, primary site of nasal cavity and paranasal sinus (p=0.028) and high expression of Ki-67 (p=0.004) were independent risk factors for poor DFS, and high expression of VEGF (p=0.011) and Ki-67 (p=0.011) showed independent association with poor OS. High expression of VEGF and Ki-67 were independent poor prognostic factors for OS in ACC.

Describing the Elephant: Structure and Function in Multivariate Data.

ERIC Educational Resources Information Center

McDonald, Roderick P.

1986-01-01

There is a unity underlying the diversity of models for the analysis of multivariate data. Essentially, they constitute a family of models, most generally nonlinear, for structural/functional relations between variables drawn from a behavior domain. (Author)

Reduced risk of breast cancer associated with recreational physical activity varies by HER2 status

PubMed Central

Ma, Huiyan; Xu, Xinxin; Ursin, Giske; Simon, Michael S; Marchbanks, Polly A; Malone, Kathleen E; Lu, Yani; McDonald, Jill A; Folger, Suzanne G; Weiss, Linda K; Sullivan-Halley, Jane; Deapen, Dennis M; Press, Michael F; Bernstein, Leslie

2015-01-01

Convincing epidemiologic evidence indicates that physical activity is inversely associated with breast cancer risk. Whether this association varies by the tumor protein expression status of the estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), or p53 is unclear. We evaluated the effects of recreational physical activity on risk of invasive breast cancer classified by the four biomarkers, fitting multivariable unconditional logistic regression models to data from 1195 case and 2012 control participants in the population-based Women’s Contraceptive and Reproductive Experiences Study. Self-reported recreational physical activity at different life periods was measured as average annual metabolic equivalents of energy expenditure [MET]-hours per week. Our biomarker-specific analyses showed that lifetime recreational physical activity was negatively associated with the risks of ER-positive (ER+) and of HER2-negative (HER2−) subtypes (both Ptrend ≤ 0.04), but not with other subtypes (all Ptrend > 0.10). Analyses using combinations of biomarkers indicated that risk of invasive breast cancer varied only by HER2 status. Risk of HER2–breast cancer decreased with increasing number of MET-hours of recreational physical activity in each specific life period examined, although some trend tests were only marginally statistically significant (all Ptrend ≤ 0.06). The test for homogeneity of trends (HER2– vs. HER2+ ) reached statistical significance only when evaluating physical activity during the first 10 years after menarche (Phomogeneity = 0.03). Our data suggest that physical activity reduces risk of invasive breast cancers that lack HER2 overexpression, increasing our understanding of the biological mechanisms by which physical activity acts. PMID:25924995

Effects of early weaning and social isolation on the expression of glucocorticoid and mineralocorticoid receptor and 11beta-hydroxysteroid dehydrogenase 1 and 2 mRNAs in the frontal cortex and hippocampus of piglets.

PubMed

Poletto, R; Steibel, J P; Siegford, J M; Zanella, A J

2006-01-05

Pigs weaned at young ages show more abnormal and aggressive behaviors and cognitive deficits compared to later weaned pigs. We investigated the effects of age, weaning and/or social isolation on the expression of genes regulating glucocorticoid response [glucocorticoid receptor (GR), mineralocorticoid receptor (MR), 11beta-hydroxysteroid dehydrogenases 1 and 2 (11beta-HSD1 and 11beta-HSD2)] in the frontal cortex and hippocampus. Early- (EW; n = 6) and conventionally-weaned (CW; n = 6) piglets were weaned at 10 and 21 days after birth, respectively. Non-weaned (NW) piglets of both ages (NW; n = 6/group) remained with their dams. Immediately before euthanasia, half of CW, EW and NW animals were socially isolated for 15 min at 12 (EW, NW) and 23 (CW, NW) days of age. Differences in amounts of 11beta-HSD1, 11beta-HSD2, GR and MR mRNA were determined by quantitative real-time RT-PCR and data subjected to multivariate linear mixed model analysis. When compared with NW piglets at 12 days of age, the hippocampi of EW piglets showed decreased gene expression (P < 0.01). Social isolation decreased gene expression (P < 0.05) in the frontal cortex of all piglets. Twelve-day-old piglets showed higher MR mRNA in the frontal cortex (P < 0.01) and lower 11beta-HSD2 and GR mRNA (P < 0.05) in the hippocampus compared to 23-day-old animals. Results indicate that EW affected the hippocampus of piglets at 12 days of age, while social isolation affected frontal cortex regardless of age. These results may be correlated with behavioral and cognitive changes reported in EW piglets.

Dietary Energy Density and Postmenopausal Breast Cancer Incidence in the Cancer Prevention Study II Nutrition Cohort.

PubMed

Hartman, Terryl J; Gapstur, Susan M; Gaudet, Mia M; Shah, Roma; Flanders, W Dana; Wang, Ying; McCullough, Marjorie L

2016-10-01

Dietary energy density (ED) is a measure of diet quality that estimates the amount of energy per unit of food (kilocalories per gram) consumed. Low-ED diets are generally high in fiber and fruits and vegetables and low in fat. Dietary ED has been positively associated with body mass index (BMI) and other risk factors for postmenopausal breast cancer. We evaluated the associations of total dietary ED and energy-dense (high-ED) foods with postmenopausal breast cancer incidence. Analyses included 56,795 postmenopausal women from the Cancer Prevention Study II Nutrition Cohort with no previous history of breast or other cancers and who provided information on diet, lifestyle, and medical history in 1999. Multivariable-adjusted breast cancer incidence rate ratios (RRs and 95% CIs) were estimated for quintiles of total dietary ED and for the consumption of high-ED foods in Cox proportional hazards regression models. During a median follow-up of 11.7 y, 2509 invasive breast cancer cases were identified, including 1857 estrogen receptor-positive and 277 estrogen receptor-negative tumors. Median dietary ED was 1.5 kcal/g (IQR: 1.3-1.7 kcal/g). After adjusting for age, race, education, reproductive characteristics, and family history, high compared with low dietary ED was associated with a statistically significantly higher risk of breast cancer (RR for fifth quintile compared with first quintile: 1.20; 95% CI: 1.05, 1.36; P-trend = 0.03). The association between the amount of high-ED foods consumed and breast cancer risk was not statistically significant. We observed no differences by estrogen receptor status or effect modification by BMI, age, or physical activity. These results suggest a modest positive association between total dietary ED and risk of postmenopausal breast cancer. © 2016 American Society for Nutrition.

TransCONFIRM: Identification of a Genetic Signature of Response to Fulvestrant in Advanced Hormone Receptor-Positive Breast Cancer.

PubMed

Jeselsohn, Rinath; Barry, William T; Migliaccio, Ilenia; Biagioni, Chiara; Zhao, Jin; De Tribolet-Hardy, Jonas; Guarducci, Cristina; Bonechi, Martina; Laing, Naomi; Winer, Eric P; Brown, Myles; Leo, Angelo Di; Malorni, Luca

2016-12-01

Fulvestrant is an estrogen receptor (ER) antagonist and an approved treatment for metastatic estrogen receptor-positive (ER + ) breast cancer. With the exception of ER levels, there are no established predictive biomarkers of response to single-agent fulvestrant. We attempted to identify a gene signature of response to fulvestrant in advanced breast cancer. Primary tumor samples from 134 patients enrolled in the phase III CONFIRM study of patients with metastatic ER + breast cancer comparing treatment with either 250 mg or 500 mg fulvestrant were collected for genome-wide transcriptomic analysis. Gene expression profiling was performed using Affymetrix microarrays. An exploratory analysis was performed to identify biologic pathways and new signatures associated with response to fulvestrant. Pathway analysis demonstrated that increased EGF pathway and FOXA1 transcriptional signaling is associated with decreased response to fulvestrant. Using a multivariate Cox model, we identified a novel set of 37 genes with an expression that is independently associated with progression-free survival (PFS). TFAP2C, a known regulator of ER activity, was ranked second in this gene set, and high expression was associated with a decreased response to fulvestrant. The negative predictive value of TFAP2C expression at the protein level was confirmed by IHC. We identified biologic pathways and a novel gene signature in primary ER + breast cancers that predicts for response to treatment in the CONFIRM study. These results suggest potential new therapeutic targets and warrant further validation as predictive biomarkers of fulvestrant treatment in metastatic breast cancer. Clin Cancer Res; 22(23); 5755-64. ©2016 AACR. ©2016 American Association for Cancer Research.

Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models.

PubMed

Chen, Zewei; Zhang, Xin; Zhang, Zhuoyong

2016-12-01

Timely risk assessment of chronic kidney disease (CKD) and proper community-based CKD monitoring are important to prevent patients with potential risk from further kidney injuries. As many symptoms are associated with the progressive development of CKD, evaluating risk of CKD through a set of clinical data of symptoms coupled with multivariate models can be considered as an available method for prevention of CKD and would be useful for community-based CKD monitoring. Three common used multivariate models, i.e., K-nearest neighbor (KNN), support vector machine (SVM), and soft independent modeling of class analogy (SIMCA), were used to evaluate risk of 386 patients based on a series of clinical data taken from UCI machine learning repository. Different types of composite data, in which proportional disturbances were added to simulate measurement deviations caused by environment and instrument noises, were also utilized to evaluate the feasibility and robustness of these models in risk assessment of CKD. For the original data set, three mentioned multivariate models can differentiate patients with CKD and non-CKD with the overall accuracies over 93 %. KNN and SVM have better performances than SIMCA has in this study. For the composite data set, SVM model has the best ability to tolerate noise disturbance and thus are more robust than the other two models. Using clinical data set on symptoms coupled with multivariate models has been proved to be feasible approach for assessment of patient with potential CKD risk. SVM model can be used as useful and robust tool in this study.

Cole-Cole, linear and multivariate modeling of capacitance data for on-line monitoring of biomass.

PubMed

Dabros, Michal; Dennewald, Danielle; Currie, David J; Lee, Mark H; Todd, Robert W; Marison, Ian W; von Stockar, Urs

2009-02-01

This work evaluates three techniques of calibrating capacitance (dielectric) spectrometers used for on-line monitoring of biomass: modeling of cell properties using the theoretical Cole-Cole equation, linear regression of dual-frequency capacitance measurements on biomass concentration, and multivariate (PLS) modeling of scanning dielectric spectra. The performance and robustness of each technique is assessed during a sequence of validation batches in two experimental settings of differing signal noise. In more noisy conditions, the Cole-Cole model had significantly higher biomass concentration prediction errors than the linear and multivariate models. The PLS model was the most robust in handling signal noise. In less noisy conditions, the three models performed similarly. Estimates of the mean cell size were done additionally using the Cole-Cole and PLS models, the latter technique giving more satisfactory results.

Structure-based discovery and binding site analysis of histamine receptor ligands.

PubMed

Kiss, Róbert; Keserű, György M

2016-12-01

The application of structure-based drug discovery in histamine receptor projects was previously hampered by the lack of experimental structures. The publication of the first X-ray structure of the histamine H1 receptor has been followed by several successful virtual screens and binding site analysis studies of H1-antihistamines. This structure together with several other recently solved aminergic G-protein coupled receptors (GPCRs) enabled the development of more realistic homology models for H2, H3 and H4 receptors. Areas covered: In this paper, the authors review the development of histamine receptor models and their application in drug discovery. Expert opinion: In the authors' opinion, the application of atomistic histamine receptor models has played a significant role in understanding key ligand-receptor interactions as well as in the discovery of novel chemical starting points. The recently solved H1 receptor structure is a major milestone in structure-based drug discovery; however, our analysis also demonstrates that for building H3 and H4 receptor homology models, other GPCRs may be more suitable as templates. For these receptors, the authors envisage that the development of higher quality homology models will significantly contribute to the discovery and optimization of novel H3 and H4 ligands.

Multivariate regression model for predicting lumber grade volumes of northern red oak sawlogs

Treesearch

Daniel A. Yaussy; Robert L. Brisbin

1983-01-01

A multivariate regression model was developed to predict green board-foot yields for the seven common factory lumber grades processed from northern red oak (Quercus rubra L.) factory grade logs. The model uses the standard log measurements of grade, scaling diameter, length, and percent defect. It was validated with an independent data set. The model...

A Hierarchical Multivariate Bayesian Approach to Ensemble Model output Statistics in Atmospheric Prediction

DTIC Science & Technology

2017-09-01

efficacy of statistical post-processing methods downstream of these dynamical model components with a hierarchical multivariate Bayesian approach to...Bayesian hierarchical modeling, Markov chain Monte Carlo methods , Metropolis algorithm, machine learning, atmospheric prediction 15. NUMBER OF PAGES...scale processes. However, this dissertation explores the efficacy of statistical post-processing methods downstream of these dynamical model components

Predictive and mechanistic multivariate linear regression models for reaction development

PubMed Central

Santiago, Celine B.; Guo, Jing-Yao

2018-01-01

Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis. PMID:29719711

Linear regression analysis and its application to multivariate chromatographic calibration for the quantitative analysis of two-component mixtures.

PubMed

Dinç, Erdal; Ozdemir, Abdil

2005-01-01

Multivariate chromatographic calibration technique was developed for the quantitative analysis of binary mixtures enalapril maleate (EA) and hydrochlorothiazide (HCT) in tablets in the presence of losartan potassium (LST). The mathematical algorithm of multivariate chromatographic calibration technique is based on the use of the linear regression equations constructed using relationship between concentration and peak area at the five-wavelength set. The algorithm of this mathematical calibration model having a simple mathematical content was briefly described. This approach is a powerful mathematical tool for an optimum chromatographic multivariate calibration and elimination of fluctuations coming from instrumental and experimental conditions. This multivariate chromatographic calibration contains reduction of multivariate linear regression functions to univariate data set. The validation of model was carried out by analyzing various synthetic binary mixtures and using the standard addition technique. Developed calibration technique was applied to the analysis of the real pharmaceutical tablets containing EA and HCT. The obtained results were compared with those obtained by classical HPLC method. It was observed that the proposed multivariate chromatographic calibration gives better results than classical HPLC.

Power of Models in Longitudinal Study: Findings from a Full-Crossed Simulation Design

ERIC Educational Resources Information Center

Fang, Hua; Brooks, Gordon P.; Rizzo, Maria L.; Espy, Kimberly Andrews; Barcikowski, Robert S.

2009-01-01

Because the power properties of traditional repeated measures and hierarchical multivariate linear models have not been clearly determined in the balanced design for longitudinal studies in the literature, the authors present a power comparison study of traditional repeated measures and hierarchical multivariate linear models under 3…

Species distribution modelling for plant communities: Stacked single species or multivariate modelling approaches?

Treesearch

Emilie B. Henderson; Janet L. Ohmann; Matthew J. Gregory; Heather M. Roberts; Harold S.J. Zald

2014-01-01

Landscape management and conservation planning require maps of vegetation composition and structure over large regions. Species distribution models (SDMs) are often used for individual species, but projects mapping multiple species are rarer. We compare maps of plant community composition assembled by stacking results from many SDMs with multivariate maps constructed...

IRT-ZIP Modeling for Multivariate Zero-Inflated Count Data

ERIC Educational Resources Information Center

Wang, Lijuan

2010-01-01

This study introduces an item response theory-zero-inflated Poisson (IRT-ZIP) model to investigate psychometric properties of multiple items and predict individuals' latent trait scores for multivariate zero-inflated count data. In the model, two link functions are used to capture two processes of the zero-inflated count data. Item parameters are…

Impact of family history of cancer on the incidence of mutation in epidermal growth factor receptor gene in non-small cell lung cancer patients.

PubMed

He, Yayi; Li, Shuai; Ren, Shengxiang; Cai, Weijing; Li, Xuefei; Zhao, Chao; Li, Jiayu; Chen, Xiaoxia; Gao, Guanghui; Li, Wei; Zhou, Fei; Zhou, Caicun

2013-08-01

Epidermal growth factor receptor (EGFR) activating mutation is an important predictive biomarker of EGFR tyrosine kinase inhibitors (TKIs) in non-small cell lung cancer (NSCLC), while family history of cancer also plays an important role in the neoplasia of lung cancer. This study aimed to investigate the association between family history of cancer and EGFR mutation status in NSCLC population. From February 2008 to May 2012, 538 consecutive NSCLC patients with known EGFR mutation status were included into this study. Amplification refractory mutation system (ARMS) method was used to detect EGFR mutation. The associations between EGFR mutation and family history of cancer were evaluated using logistic regression models. EGFR activating mutation was found in 220 patients and 117 patients had family cancer histories among first-degree relatives. EGFR mutation was more frequently detected in adenocarcinoma patients (p < 0.001), never-smoker (p < 0.001) and with family history of cancer (p = 0.031), especially who had family history of lung cancer (p = 0.008). In multivariate analysis, the association of EGFR mutation with family history of cancer also existed (p = 0.027). NSCLC patients with family history of cancer, especially family history of lung cancer, might have a significantly higher incidence of EGFR activating mutation. Crown Copyright © 2013. Published by Elsevier Ireland Ltd. All rights reserved.

Expression of decoy receptor 3 in kidneys is associated with allograft survival after kidney transplant rejection.

PubMed

Weng, Shuo-Chun; Shu, Kuo-Hsiung; Wu, Ming-Ju; Wen, Mei-Chin; Hsieh, Shie-Liang; Chen, Nien-Jung; Tarng, Der-Cherng

2015-09-03

Decoy receptor 3 (DcR3) expression in kidneys has been shown to predict progression of chronic kidney disease. We prospectively investigated a cohort comprising 96 renal transplant recipients (RTRs) undergoing graft kidney biopsies. Computer-assisted quantitative immunohistochemical staining value of DcR3 in renal tubular epithelial cells (RTECs) was used to determine the predictive role of DcR3 in kidney disease progression. The primary end point was doubling of serum creatinine and/or graft failure. A multivariate Cox proportional hazards model was used to assess the risk of DcR3 expression in rejected kidney grafts toward the renal end point. In total, RTRs with kidney allograft rejection were evaluated and the median follow-up was 30.9 months. The greater expression of DcR3 immunoreactivity in RTECs was correlated with a higher rate of the histopathological concordance of acute T cell-mediated rejection. Compared with 65 non-progressors, 31 progressors had higher DcR3 expression (HDE) regardless of the traditional risk factors. Cox regression analysis showed HDE was significantly associated with the risk of renal end point with a hazard ratio of 3.19 (95% confidence interval, 1.40 to 7.27; P = 0.006) after adjusting for other variables. In repetitive biopsies, HDE in tissue showed rapid kidney disease progression due to persistent inflammation.

No association between coffee, tea or caffeine consumption and breast cancer risk in a prospective cohort study.

PubMed

Fagherazzi, Guy; Touillaud, Marina S; Boutron-Ruault, Marie-Christine; Clavel-Chapelon, Françoise; Romieu, Isabelle

2011-07-01

Numerous mechanisms for the effects of coffee, tea and caffeine on the risk of breast cancer have been suggested. Caffeine intake has already been associated with high plasma levels of female hormones, but associations have not been clearly demonstrated in epidemiological studies. We examined prospectively the association of coffee, tea and caffeine consumption with breast cancer risk in a French cohort study. Dietary information was obtained from a 208-item diet history questionnaire self-administered in 1993-1995. Multivariable Cox proportional hazards regression models were used to estimate hazards ratios and 95 % confidence intervals. The study was conducted on 67 703 women with available dietary information. During a median follow-up of 11 years, 2868 breast cancer cases were diagnosed. Median intake was 280 ml/d (2·2 cups/d) for coffee and 214 ml/d (1·7 cups/d) for tea. Median caffeine intake was 164 mg/d. No association was found between consumption of coffee, tea or caffeine and breast cancer risk. Sub-analyses by tumour receptor status, menopausal status, type of coffee (regular or decaffeinated) and meals at which beverages were drunk led to the same conclusion. Results from this prospective study showed no relationship between coffee, tea or caffeine intake and breast cancer risk overall or by hormone receptor status.

Towards structural models of molecular recognition in olfactory receptors.

PubMed

Afshar, M; Hubbard, R E; Demaille, J

1998-02-01

The G protein coupled receptors (GPCR) are an important class of proteins that act as signal transducers through the cytoplasmic membrane. Understanding the structure and activation mechanism of these proteins is crucial for understanding many different aspects of cellular signalling. The olfactory receptors correspond to the largest family of GPCRs. Very little is known about how the structures of the receptors govern the specificity of interaction which enables identification of particular odorant molecules. In this paper, we review recent developments in two areas of molecular modelling: methods for modelling the configuration of trans-membrane helices and methods for automatic docking of ligands into receptor structures. We then show how a subset of these methods can be combined to construct a model of a rat odorant receptor interacting with lyral for which experimental data are available. This modelling can help us make progress towards elucidating the specificity of interactions between receptors and odorant molecules.

A flexible model for multivariate interval-censored survival times with complex correlation structure.

PubMed

Falcaro, Milena; Pickles, Andrew

2007-02-10

We focus on the analysis of multivariate survival times with highly structured interdependency and subject to interval censoring. Such data are common in developmental genetics and genetic epidemiology. We propose a flexible mixed probit model that deals naturally with complex but uninformative censoring. The recorded ages of onset are treated as possibly censored ordinal outcomes with the interval censoring mechanism seen as arising from a coarsened measurement of a continuous variable observed as falling between subject-specific thresholds. This bypasses the requirement for the failure times to be observed as falling into non-overlapping intervals. The assumption of a normal age-of-onset distribution of the standard probit model is relaxed by embedding within it a multivariate Box-Cox transformation whose parameters are jointly estimated with the other parameters of the model. Complex decompositions of the underlying multivariate normal covariance matrix of the transformed ages of onset become possible. The new methodology is here applied to a multivariate study of the ages of first use of tobacco and first consumption of alcohol without parental permission in twins. The proposed model allows estimation of the genetic and environmental effects that are shared by both of these risk behaviours as well as those that are specific. 2006 John Wiley & Sons, Ltd.

Can multivariate models based on MOAKS predict OA knee pain? Data from the Osteoarthritis Initiative

NASA Astrophysics Data System (ADS)

Luna-Gómez, Carlos D.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Galván-Tejada, Carlos E.; Celaya-Padilla, José M.

2017-03-01

Osteoarthritis is the most common rheumatic disease in the world. Knee pain is the most disabling symptom in the disease, the prediction of pain is one of the targets in preventive medicine, this can be applied to new therapies or treatments. Using the magnetic resonance imaging and the grading scales, a multivariate model based on genetic algorithms is presented. Using a predictive model can be useful to associate minor structure changes in the joint with the future knee pain. Results suggest that multivariate models can be predictive with future knee chronic pain. All models; T0, T1 and T2, were statistically significant, all p values were < 0.05 and all AUC > 0.60.

RECEPTOR MODEL DEVELOPMENT AND APPLICATION

EPA Science Inventory

Source apportionment (receptor) models are mathematical procedures for identifying and quantifying the sources of ambient air pollutants and their effects at a site (the receptor), primarily on the basis of species concentration measurements at the receptor, and generally without...

PM SOURCE APPORTIONMENT/RECEPTOR MODELING

EPA Science Inventory

Source apportionment (receptor) models are mathematical procedures for identifying and quantifying the sources of ambient air pollutants and their effects at a site (the receptor), primarily on the basis of species concentration measurements at the receptor, and generally without...

Preclinical Testing of Novel Oxytocin Receptor Activators in Models of Autism Phenotypes

DTIC Science & Technology

2014-09-01

AD_________________ Award Number: TITLE: Preclinical Testing of Novel Oxytocin Receptor Activators in Models of Autism ...AUG 2013-7 Aug 2014 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Preclinical Testing of Novel Oxytocin Receptor Activators in Models of Autism ...a genetic mouse model of autism -like phenotypes, the Grin1 knockdown mouse. The Grin1 gene encodes the NR1 subunit of the NMDA receptor . In the

Multivariate-$t$ nonlinear mixed models with application to censored multi-outcome AIDS studies.

PubMed

Lin, Tsung-I; Wang, Wan-Lun

2017-10-01

In multivariate longitudinal HIV/AIDS studies, multi-outcome repeated measures on each patient over time may contain outliers, and the viral loads are often subject to a upper or lower limit of detection depending on the quantification assays. In this article, we consider an extension of the multivariate nonlinear mixed-effects model by adopting a joint multivariate-$t$ distribution for random effects and within-subject errors and taking the censoring information of multiple responses into account. The proposed model is called the multivariate-$t$ nonlinear mixed-effects model with censored responses (MtNLMMC), allowing for analyzing multi-outcome longitudinal data exhibiting nonlinear growth patterns with censorship and fat-tailed behavior. Utilizing the Taylor-series linearization method, a pseudo-data version of expectation conditional maximization either (ECME) algorithm is developed for iteratively carrying out maximum likelihood estimation. We illustrate our techniques with two data examples from HIV/AIDS studies. Experimental results signify that the MtNLMMC performs favorably compared to its Gaussian analogue and some existing approaches. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Multivariate analysis of longitudinal rates of change.

PubMed

Bryan, Matthew; Heagerty, Patrick J

2016-12-10

Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed in the literature. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, 'accelerated time' methods have been developed which assume that covariates rescale time in longitudinal models for disease progression. In this manuscript, we detail an alternative multivariate model formulation that directly structures longitudinal rates of change and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Voxelwise multivariate analysis of multimodality magnetic resonance imaging

PubMed Central

Naylor, Melissa G.; Cardenas, Valerie A.; Tosun, Duygu; Schuff, Norbert; Weiner, Michael; Schwartzman, Armin

2015-01-01

Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remains a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available. PMID:23408378

Preliminary Multivariable Cost Model for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip

2010-01-01

Parametric cost models are routinely used to plan missions, compare concepts and justify technology investments. Previously, the authors published two single variable cost models based on 19 flight missions. The current paper presents the development of a multi-variable space telescopes cost model. The validity of previously published models are tested. Cost estimating relationships which are and are not significant cost drivers are identified. And, interrelationships between variables are explored

GIS Modeling of Air Toxics Releases from TRI-Reporting and Non-TRI-Reporting Facilities: Impacts for Environmental Justice

PubMed Central

Dolinoy, Dana C.; Miranda, Marie Lynn

2004-01-01

The Toxics Release Inventory (TRI) requires facilities with 10 or more full-time employees that process > 25,000 pounds in aggregate or use > 10,000 pounds of any one TRI chemical to report releases annually. However, little is known about releases from non-TRI-reporting facilities, nor has attention been given to the very localized equity impacts associated with air toxics releases. Using geographic information systems and industrial source complex dispersion modeling, we developed methods for characterizing air releases from TRI-reporting as well as non-TRI-reporting facilities at four levels of geographic resolution. We characterized the spatial distribution and concentration of air releases from one representative industry in Durham County, North Carolina (USA). Inclusive modeling of all facilities rather than modeling of TRI sites alone significantly alters the magnitude and spatial distribution of modeled air concentrations. Modeling exposure receptors at more refined levels of geographic resolution reveals localized, neighborhood-level exposure hot spots that are not apparent at coarser geographic scales. Multivariate analysis indicates that inclusive facility modeling at fine levels of geographic resolution reveals exposure disparities by income and race. These new methods significantly enhance the ability to model air toxics, perform equity analysis, and clarify conflicts in the literature regarding environmental justice findings. This work has substantial implications for how to structure TRI reporting requirements, as well as methods and types of analysis that will successfully elucidate the spatial distribution of exposure potentials across geographic, income, and racial lines. PMID:15579419

Exact solutions to a spatially extended model of kinase-receptor interaction.

PubMed

Szopa, Piotr; Lipniacki, Tomasz; Kazmierczak, Bogdan

2011-10-01

B and Mast cells are activated by the aggregation of the immune receptors. Motivated by this phenomena we consider a simple spatially extended model of mutual interaction of kinases and membrane receptors. It is assumed that kinase activates membrane receptors and in turn the kinase molecules bound to the active receptors are activated by transphosphorylation. Such a type of interaction implies positive feedback and may lead to bistability. In this study we apply the Steklov eigenproblem theory to analyze the linearized model and find exact solutions in the case of non-uniformly distributed membrane receptors. This approach allows us to determine the critical value of receptor dephosphorylation rate at which cell activation (by arbitrary small perturbation of the inactive state) is possible. We found that cell sensitivity grows with decreasing kinase diffusion and increasing anisotropy of the receptor distribution. Moreover, these two effects are cooperating. We showed that the cell activity can be abruptly triggered by the formation of the receptor aggregate. Since the considered activation mechanism is not based on receptor crosslinking by polyvalent antigens, the proposed model can also explain B cell activation due to receptor aggregation following binding of monovalent antigens presented on the antigen presenting cell.

Multivariate modelling of endophenotypes associated with the metabolic syndrome in Chinese twins.

PubMed

Pang, Z; Zhang, D; Li, S; Duan, H; Hjelmborg, J; Kruse, T A; Kyvik, K O; Christensen, K; Tan, Q

2010-12-01

The common genetic and environmental effects on endophenotypes related to the metabolic syndrome have been investigated using bivariate and multivariate twin models. This paper extends the pairwise analysis approach by introducing independent and common pathway models to Chinese twin data. The aim was to explore the common genetic architecture in the development of these phenotypes in the Chinese population. Three multivariate models including the full saturated Cholesky decomposition model, the common factor independent pathway model and the common factor common pathway model were fitted to 695 pairs of Chinese twins representing six phenotypes including BMI, total cholesterol, total triacylglycerol, fasting glucose, HDL and LDL. Performances of the nested models were compared with that of the full Cholesky model. Cross-phenotype correlation coefficients gave clear indication of common genetic or environmental backgrounds in the phenotypes. Decomposition of phenotypic correlation by the Cholesky model revealed that the observed phenotypic correlation among lipid phenotypes had genetic and unique environmental backgrounds. Both pathway models suggest a common genetic architecture for lipid phenotypes, which is distinct from that of the non-lipid phenotypes. The declining performance with model restriction indicates biological heterogeneity in development among some of these phenotypes. Our multivariate analyses revealed common genetic and environmental backgrounds for the studied lipid phenotypes in Chinese twins. Model performance showed that physiologically distinct endophenotypes may follow different genetic regulations.

Methodological challenges to multivariate syndromic surveillance: a case study using Swiss animal health data.

PubMed

Vial, Flavie; Wei, Wei; Held, Leonhard

2016-12-20

In an era of ubiquitous electronic collection of animal health data, multivariate surveillance systems (which concurrently monitor several data streams) should have a greater probability of detecting disease events than univariate systems. However, despite their limitations, univariate aberration detection algorithms are used in most active syndromic surveillance (SyS) systems because of their ease of application and interpretation. On the other hand, a stochastic modelling-based approach to multivariate surveillance offers more flexibility, allowing for the retention of historical outbreaks, for overdispersion and for non-stationarity. While such methods are not new, they are yet to be applied to animal health surveillance data. We applied an example of such stochastic model, Held and colleagues' two-component model, to two multivariate animal health datasets from Switzerland. In our first application, multivariate time series of the number of laboratories test requests were derived from Swiss animal diagnostic laboratories. We compare the performance of the two-component model to parallel monitoring using an improved Farrington algorithm and found both methods yield a satisfactorily low false alarm rate. However, the calibration test of the two-component model on the one-step ahead predictions proved satisfactory, making such an approach suitable for outbreak prediction. In our second application, the two-component model was applied to the multivariate time series of the number of cattle abortions and the number of test requests for bovine viral diarrhea (a disease that often results in abortions). We found that there is a two days lagged effect from the number of abortions to the number of test requests. We further compared the joint modelling and univariate modelling of the number of laboratory test requests time series. The joint modelling approach showed evidence of superiority in terms of forecasting abilities. Stochastic modelling approaches offer the potential to address more realistic surveillance scenarios through, for example, the inclusion of times series specific parameters, or of covariates known to have an impact on syndrome counts. Nevertheless, many methodological challenges to multivariate surveillance of animal SyS data still remain. Deciding on the amount of corroboration among data streams that is required to escalate into an alert is not a trivial task given the sparse data on the events under consideration (e.g. disease outbreaks).

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

NASA Astrophysics Data System (ADS)

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-03-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states.

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

PubMed Central

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-01-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states. PMID:26996254

Multivariate synaptic and behavioral profiling reveals new developmental endophenotypes in the prefrontal cortex

PubMed Central

Iafrati, Jillian; Malvache, Arnaud; Gonzalez Campo, Cecilia; Orejarena, M. Juliana; Lassalle, Olivier; Bouamrane, Lamine; Chavis, Pascale

2016-01-01

The postnatal maturation of the prefrontal cortex (PFC) represents a period of increased vulnerability to risk factors and emergence of neuropsychiatric disorders. To disambiguate the pathophysiological mechanisms contributing to these disorders, we revisited the endophenotype approach from a developmental viewpoint. The extracellular matrix protein reelin which contributes to cellular and network plasticity, is a risk factor for several psychiatric diseases. We mapped the aggregate effect of the RELN risk allele on postnatal development of PFC functions by cross-sectional synaptic and behavioral analysis of reelin-haploinsufficient mice. Multivariate analysis of bootstrapped datasets revealed subgroups of phenotypic traits specific to each maturational epoch. The preeminence of synaptic AMPA/NMDA receptor content to pre-weaning and juvenile endophenotypes shifts to long-term potentiation and memory renewal during adolescence followed by NMDA-GluN2B synaptic content in adulthood. Strikingly, multivariate analysis shows that pharmacological rehabilitation of reelin haploinsufficient dysfunctions is mediated through induction of new endophenotypes rather than reversion to wild-type traits. By delineating previously unknown developmental endophenotypic sequences, we conceived a promising general strategy to disambiguate the molecular underpinnings of complex psychiatric disorders and for the rational design of pharmacotherapies in these disorders. PMID:27765946

TFF3 is a valuable predictive biomarker of endocrine response in metastatic breast cancer

PubMed Central

May, Felicity E B; Westley, Bruce R

2015-01-01

The stratification of breast cancer patients for endocrine therapies by oestrogen or progesterone receptor expression is effective but imperfect. The present study aims were to validate microarray studies that demonstrate TFF3 regulation by oestrogen and its association with oestrogen receptors in breast cancer, to evaluate TFF3 as a biomarker of endocrine response, and to investigate TFF3 function. Microarray data were validated by quantitative RT-PCR and northern and western transfer analyses. TFF3 was induced by oestrogen, and its induction was inhibited by antioestrogens, tamoxifen, 4-hydroxytamoxifen and fulvestrant in oestrogen-responsive breast cancer cells. The expression of TFF3 mRNA was associated with oestrogen receptor mRNA in breast tumours (Pearson's coefficient=0.762, P=0.000). Monoclonal antibodies raised against the TFF3 protein detected TFF3 by immunohistochemistry in oesophageal submucosal glands, intestinal goblet and neuroendocrine cells, Barrett's metaplasia and intestinal metaplasia. TFF3 protein expression was associated with oestrogen receptor, progesterone receptor and TFF1 expression in malignant breast cells. TFF3 is a specific and sensitive predictive biomarker of response to endocrine therapy, degree of response and duration of response in unstratified metastatic breast cancer patients (P=0.000, P=0.002 and P=0.002 respectively). Multivariate binary logistic regression analysis demonstrated that TFF3 is an independent biomarker of endocrine response and degree of response, and this was confirmed in a validation cohort. TFF3 stimulated migration and invasion of breast cancer cells. In conclusion, TFF3 expression is associated with response to endocrine therapy, and outperforms oestrogen receptor, progesterone receptor and TFF1 as an independent biomarker, possibly because it mediates the malign effects of oestrogen on invasion and metastasis. PMID:25900183

Cyto- and receptor architecture of area 32 in human and macaque brains.

PubMed

Palomero-Gallagher, Nicola; Zilles, Karl; Schleicher, Axel; Vogt, Brent A

2013-10-01

Human area 32 plays crucial roles in emotion and memory consolidation. It has subgenual (s32), pregenual (p32), dorsal, and midcingulate components. We seek to determine whether macaque area 32 has subgenual and pregenual subdivisions and the extent to which they are comparable to those in humans by means of NeuN immunohistochemistry and multireceptor analysis of laminar profiles. The macaque has areas s32 and p32. In s32, layer IIIa/b neurons are larger than those of layer IIIc. This relationship is reversed in p32. Layer Va is thicker and Vb thinner in s32. Area p32 contains higher kainate, benzodiazepine (BZ), and serotonin (5-HT)1A but lower N-methyl-D-aspartate (NMDA) and α2 receptor densities. Most differences were found in layers I, II, and VI. Together, these differences support the dual nature of macaque area 32. Comparative analysis of human and macaque s32 and p32 supports equivalences in cyto- and receptor architecture. Although there are differences in mean areal receptor densities, there are considerable similarities at the layer level. Laminar receptor distribution patterns in each area are comparable in the two species in layers III-Va for kainate, NMDA, γ-aminobutyric acid (GABA)B , BZ, and 5-HT1A receptors. Multivariate statistical analysis of laminar receptor densities revealed that human s32 is more similar to macaque s32 and p32 than to human p32. Thus, macaque 32 is more complex than hitherto known. Our data suggest a homologous neural architecture in anterior cingulate s32 and p32 in human and macaque brains. © 2013 Wiley Periodicals, Inc.

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy.

PubMed

Dankers, Frank; Wijsman, Robin; Troost, Esther G C; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L

2017-05-07

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy

NASA Astrophysics Data System (ADS)

Dankers, Frank; Wijsman, Robin; Troost, Esther G. C.; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L.

2017-05-01

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

Multivariate meta-analysis: potential and promise.

PubMed

Jackson, Dan; Riley, Richard; White, Ian R

2011-09-10

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day 'Multivariate meta-analysis' event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd.

Multivariate meta-analysis: Potential and promise

PubMed Central

Jackson, Dan; Riley, Richard; White, Ian R

2011-01-01

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day ‘Multivariate meta-analysis’ event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd. PMID:21268052

Stress and Personal Resource as Predictors of the Adjustment of Parents to Autistic Children: A Multivariate Model

ERIC Educational Resources Information Center

Siman-Tov, Ayelet; Kaniel, Shlomo

2011-01-01

The research validates a multivariate model that predicts parental adjustment to coping successfully with an autistic child. The model comprises four elements: parental stress, parental resources, parental adjustment and the child's autism symptoms. 176 parents of children aged between 6 to 16 diagnosed with PDD answered several questionnaires…

Multivariate mixed linear model analysis of longitudinal data: an information-rich statistical technique for analyzing disease resistance data

USDA-ARS?s Scientific Manuscript database

The mixed linear model (MLM) is currently among the most advanced and flexible statistical modeling techniques and its use in tackling problems in plant pathology has begun surfacing in the literature. The longitudinal MLM is a multivariate extension that handles repeatedly measured data, such as r...

Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method

Treesearch

Denis Valle; Benjamin Baiser; Christopher W. Woodall; Robin Chazdon; Jerome Chave

2014-01-01

We propose a novel multivariate method to analyse biodiversity data based on the Latent Dirichlet Allocation (LDA) model. LDA, a probabilistic model, reduces assemblages to sets of distinct component communities. It produces easily interpretable results, can represent abrupt and gradual changes in composition, accommodates missing data and allows for coherent estimates...

Multivariate Regression Analysis and Slaughter Livestock,

DTIC Science & Technology

AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

Impact of changes in pill appearance in the adherence to angiotensin receptor blockers and in the blood pressure levels: a retrospective cohort study

PubMed Central

Lumbreras, B; López-Pintor, E

2017-01-01

Objective To assess the level of adherence to angiotensin receptor blockers (ARBs) in patients regularly attending a community pharmacy and the influence of a change in patients' adherence to pharmacological treatment. Design Retrospective cohort study of a random sample of consecutive patients collecting their medication. Setting 40 community pharmacies in Alicante (Southeast Spain). Participants 602 consecutive ≥18 years old patients following treatment with ARBs at least 3 previous refills were included. Main outcome measures Prevalence of uncontrolled blood pressure (BP) and adherence to prescribed pharmacological treatment (measured through both the Batalla and the Morisky-Green tests). A multivariate Poisson regression model was used to estimate the adjusted risk ratio (RRa) for non-adherence to pharmacological treatment by the presence of a change in patient's adherence and other significant variables. Results 161/602 (13.7%) patients presented uncontrolled BP. According to the Morisky test, 410/602 (68.2%) patients were considered adherent to pharmacological treatment and 231/602 (38.4%) patients according to the Batalla test. According to the Morisky-Green test, in the multivariable analysis, patients with a previous change in pill appearance were less likely to be adherent than those patients with no change in their pharmacological treatment (RRa 0.45; CI 95% 0.22 to 0.90; p=0.024). Systolic BP was higher in patients with a change in pill appearance in the previous 3 refills (median BP 142 mm Hg; IQR 136–148) than in those who did not have a change (median BP 127 mm Hg; IQR 118–135; p<0.001). Conclusions There was a low percentage of adherence and nearly 15% of uncontrolled BP in patients who regularly collected their medication. Switching between pills of different appearances was associated with lower patient adherence to pharmacological treatment and a higher uncontrolled BP than no change in pharmacological treatment or change only in package but not in pill appearance. PMID:28363919

Can Locoregional Treatment of the Primary Tumor Improve Outcomes for Women With Stage IV Breast Cancer at Diagnosis?

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

Nguyen, David H.A., E-mail: dhanguyen@yahoo.com; Departement de Radio-Oncologie, Hopital Maisonneuve-Rosemont, Montreal, Quebec; Truong, Pauline T.

2012-09-01

Purpose: To examine the effect of locoregional treatment (LRT) of the primary tumor on survival in patients with Stage IV breast cancer at diagnosis. Methods and Materials: The study cohort comprised 733 women referred to the British Columbia Cancer Agency between 1996 and 2005 with newly diagnosed clinical or pathologic M1 breast cancer. Tumor and treatment characteristics, overall survival (OS), and locoregional progression-free survival were compared between patients treated with (n = 378) and without (n = 355) LRT of the primary disease. Multivariable analysis was performed with Cox regression modeling. Results: The median follow-up time was 1.9 years. LRTmore » consisted of surgery alone in 67% of patients, radiotherapy alone in 22%, and both in 11%. LRT was used more commonly in women with age <50 years, Eastern Cooperative Oncology Group (ECOG) performance status 0-1, Stage T1-2 tumors, N0-1 disease, limited M1 burden, and asymptomatic M1 disease (all p < 0.05). Systemic therapy was used in 92% of patients who underwent LRT and 85% of patients who did not. In patients treated with LRT compared with those without LRT, the 5-year OS rates were 21% vs. 14% (p < 0.001), and the rates of locoregional progression-free survival were 72% vs. 46% (p < 0.001). Among 378 patients treated with LRT, the rates of 5-year OS were higher in patients with age <50, ECOG performance status 0-1, estrogen receptor-positive disease, clear surgical margins, single subsite, bone-only metastasis, and one to four metastatic lesions (all p < 0.003). On multivariable analysis, LRT was associated with improved OS (hazard ratio, 0.78; 95% confidence interval, 0.64-0.94, p = 0.009). Conclusion: Locoregional treatment of the primary disease is associated with improved survival in some women with Stage IV breast cancer at diagnosis. Among those treated with LRT, the most favorable rates of survival were observed in subsets with young age, good performance status, estrogen receptor-positive disease, clear margins, and distant disease limited to one subsite, bone-only involvement, or fewer than five metastatic lesions.« less

Univariate and multivariate spatial models of health facility utilisation for childhood fevers in an area on the coast of Kenya.

PubMed

Ouma, Paul O; Agutu, Nathan O; Snow, Robert W; Noor, Abdisalan M

2017-09-18

Precise quantification of health service utilisation is important for the estimation of disease burden and allocation of health resources. Current approaches to mapping health facility utilisation rely on spatial accessibility alone as the predictor. However, other spatially varying social, demographic and economic factors may affect the use of health services. The exclusion of these factors can lead to the inaccurate estimation of health facility utilisation. Here, we compare the accuracy of a univariate spatial model, developed only from estimated travel time, to a multivariate model that also includes relevant social, demographic and economic factors. A theoretical surface of travel time to the nearest public health facility was developed. These were assigned to each child reported to have had fever in the Kenya demographic and health survey of 2014 (KDHS 2014). The relationship of child treatment seeking for fever with travel time, household and individual factors from the KDHS2014 were determined using multilevel mixed modelling. Bayesian information criterion (BIC) and likelihood ratio test (LRT) tests were carried out to measure how selected factors improve parsimony and goodness of fit of the time model. Using the mixed model, a univariate spatial model of health facility utilisation was fitted using travel time as the predictor. The mixed model was also used to compute a multivariate spatial model of utilisation, using travel time and modelled surfaces of selected household and individual factors as predictors. The univariate and multivariate spatial models were then compared using the receiver operating area under the curve (AUC) and a percent correct prediction (PCP) test. The best fitting multivariate model had travel time, household wealth index and number of children in household as the predictors. These factors reduced BIC of the time model from 4008 to 2959, a change which was confirmed by the LRT test. Although there was a high correlation of the two modelled probability surfaces (Adj R 2  = 88%), the multivariate model had better AUC compared to the univariate model; 0.83 versus 0.73 and PCP 0.61 versus 0.45 values. Our study shows that a model that uses travel time, as well as household and individual-level socio-demographic factors, results in a more accurate estimation of use of health facilities for the treatment of childhood fever, compared to one that relies on only travel time.

Adjustment of automatic control systems of production facilities at coal processing plants using multivariant physico- mathematical models

NASA Astrophysics Data System (ADS)

Evtushenko, V. F.; Myshlyaev, L. P.; Makarov, G. V.; Ivushkin, K. A.; Burkova, E. V.

2016-10-01

The structure of multi-variant physical and mathematical models of control system is offered as well as its application for adjustment of automatic control system (ACS) of production facilities on the example of coal processing plant.

A simplified parsimonious higher order multivariate Markov chain model with new convergence condition

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, we present a simplified parsimonious higher-order multivariate Markov chain model with new convergence condition. (TPHOMMCM-NCC). Moreover, estimation method of the parameters in TPHOMMCM-NCC is give. Numerical experiments illustrate the effectiveness of TPHOMMCM-NCC.

Various forms of indexing HDMR for modelling multivariate classification problems

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

Aksu, Çağrı; Tunga, M. Alper

2014-12-10

The Indexing HDMR method was recently developed for modelling multivariate interpolation problems. The method uses the Plain HDMR philosophy in partitioning the given multivariate data set into less variate data sets and then constructing an analytical structure through these partitioned data sets to represent the given multidimensional problem. Indexing HDMR makes HDMR be applicable to classification problems having real world data. Mostly, we do not know all possible class values in the domain of the given problem, that is, we have a non-orthogonal data structure. However, Plain HDMR needs an orthogonal data structure in the given problem to be modelled.more » In this sense, the main idea of this work is to offer various forms of Indexing HDMR to successfully model these real life classification problems. To test these different forms, several well-known multivariate classification problems given in UCI Machine Learning Repository were used and it was observed that the accuracy results lie between 80% and 95% which are very satisfactory.« less

Influence of semi-quantitative oestrogen receptor expression on adjuvant endocrine therapy efficacy in ductal and lobular breast cancer - a TEAM study analysis.

PubMed

van de Water, Willemien; Fontein, Duveken B Y; van Nes, Johanna G H; Bartlett, John M S; Hille, Elysée T M; Putter, Hein; Robson, Tammy; Liefers, Gerrit-Jan; Roumen, Rudi M H; Seynaeve, Caroline; Dirix, Luc Y; Paridaens, Robert; Kranenbarg, Elma Meershoek-Klein; Nortier, Johan W R; van de Velde, Cornelis J H

2013-01-01

Multiple studies suggest better efficacy of chemotherapy in invasive ductal breast carcinomas (IDC) than invasive lobular breast carcinomas (ILC). However, data on efficacy of adjuvant endocrine therapy regimens and histological subtypes are sparse. This study assessed endocrine therapy efficacy in IDC and ILC. The influence of semi-quantitative oestrogen receptor (ER) expression by Allred score was also investigated. Dutch and Belgian patients enrolled in the Tamoxifen Exemestane Adjuvant Multinational (TEAM) trial were randomized to exemestane (25mg daily) alone or following tamoxifen (20mg daily) for 5 years. Inclusion was restricted to IDC and ILC patients. Histological subtype was assessed locally; ER expression was centrally reviewed according to Allred score (ER-poor (<7; n=235); ER-rich (7; n=1789)). Primary end-point was relapse-free survival (RFS), which was the time from randomization to disease relapse. Overall, 2140 (82%) IDC and 463 (18%) ILC patients were included. RFS was similar for both endocrine treatment regimens in IDC (hazard ratio (HR) for exemestane was 0.83 (95%confidence interval (CI) 0.67-1.03)), and ILC (HR 0.69 (95%CI 0.45-1.06)). Irrespective of histological subtype, patients with ER-rich Allred scores allocated to exemestane alone had an improved RFS (multivariable HR 0.71 (95%CI 0.56-0.89)). In contrast, patients with ER-poor Allred scores allocated to exemestane had a worse RFS (multivariable HR 2.33 (95%CI 1.32-4.11)). Significant effect modification by ER-Allred score was confirmed (multivariable p=0.003). Efficacy of endocrine therapy regimens was similar for IDC and ILC. However, ER-rich patients showed superior efficacy to upfront exemestane, while ER-poor patients had better outcomes with sequential therapy, irrespective of histological subtype, emphasising the relevance of quantification of ER expression. Copyright © 2012 Elsevier Ltd. All rights reserved.

Multivariate random-parameters zero-inflated negative binomial regression model: an application to estimate crash frequencies at intersections.

PubMed

Dong, Chunjiao; Clarke, David B; Yan, Xuedong; Khattak, Asad; Huang, Baoshan

2014-09-01

Crash data are collected through police reports and integrated with road inventory data for further analysis. Integrated police reports and inventory data yield correlated multivariate data for roadway entities (e.g., segments or intersections). Analysis of such data reveals important relationships that can help focus on high-risk situations and coming up with safety countermeasures. To understand relationships between crash frequencies and associated variables, while taking full advantage of the available data, multivariate random-parameters models are appropriate since they can simultaneously consider the correlation among the specific crash types and account for unobserved heterogeneity. However, a key issue that arises with correlated multivariate data is the number of crash-free samples increases, as crash counts have many categories. In this paper, we describe a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model for jointly modeling crash counts. The full Bayesian method is employed to estimate the model parameters. Crash frequencies at urban signalized intersections in Tennessee are analyzed. The paper investigates the performance of MZINB and MRZINB regression models in establishing the relationship between crash frequencies, pavement conditions, traffic factors, and geometric design features of roadway intersections. Compared to the MZINB model, the MRZINB model identifies additional statistically significant factors and provides better goodness of fit in developing the relationships. The empirical results show that MRZINB model possesses most of the desirable statistical properties in terms of its ability to accommodate unobserved heterogeneity and excess zero counts in correlated data. Notably, in the random-parameters MZINB model, the estimated parameters vary significantly across intersections for different crash types. Copyright © 2014 Elsevier Ltd. All rights reserved.

Insights on multivariate updates of physical and biogeochemical ocean variables using an Ensemble Kalman Filter and an idealized model of upwelling

NASA Astrophysics Data System (ADS)

Yu, Liuqian; Fennel, Katja; Bertino, Laurent; Gharamti, Mohamad El; Thompson, Keith R.

2018-06-01

Effective data assimilation methods for incorporating observations into marine biogeochemical models are required to improve hindcasts, nowcasts and forecasts of the ocean's biogeochemical state. Recent assimilation efforts have shown that updating model physics alone can degrade biogeochemical fields while only updating biogeochemical variables may not improve a model's predictive skill when the physical fields are inaccurate. Here we systematically investigate whether multivariate updates of physical and biogeochemical model states are superior to only updating either physical or biogeochemical variables. We conducted a series of twin experiments in an idealized ocean channel that experiences wind-driven upwelling. The forecast model was forced with biased wind stress and perturbed biogeochemical model parameters compared to the model run representing the "truth". Taking advantage of the multivariate nature of the deterministic Ensemble Kalman Filter (DEnKF), we assimilated different combinations of synthetic physical (sea surface height, sea surface temperature and temperature profiles) and biogeochemical (surface chlorophyll and nitrate profiles) observations. We show that when biogeochemical and physical properties are highly correlated (e.g., thermocline and nutricline), multivariate updates of both are essential for improving model skill and can be accomplished by assimilating either physical (e.g., temperature profiles) or biogeochemical (e.g., nutrient profiles) observations. In our idealized domain, the improvement is largely due to a better representation of nutrient upwelling, which results in a more accurate nutrient input into the euphotic zone. In contrast, assimilating surface chlorophyll improves the model state only slightly, because surface chlorophyll contains little information about the vertical density structure. We also show that a degradation of the correlation between observed subsurface temperature and nutrient fields, which has been an issue in several previous assimilation studies, can be reduced by multivariate updates of physical and biogeochemical fields.

Multivariate generalized multifactor dimensionality reduction to detect gene-gene interactions

PubMed Central

2013-01-01

Background Recently, one of the greatest challenges in genome-wide association studies is to detect gene-gene and/or gene-environment interactions for common complex human diseases. Ritchie et al. (2001) proposed multifactor dimensionality reduction (MDR) method for interaction analysis. MDR is a combinatorial approach to reduce multi-locus genotypes into high-risk and low-risk groups. Although MDR has been widely used for case-control studies with binary phenotypes, several extensions have been proposed. One of these methods, a generalized MDR (GMDR) proposed by Lou et al. (2007), allows adjusting for covariates and applying to both dichotomous and continuous phenotypes. GMDR uses the residual score of a generalized linear model of phenotypes to assign either high-risk or low-risk group, while MDR uses the ratio of cases to controls. Methods In this study, we propose multivariate GMDR, an extension of GMDR for multivariate phenotypes. Jointly analysing correlated multivariate phenotypes may have more power to detect susceptible genes and gene-gene interactions. We construct generalized estimating equations (GEE) with multivariate phenotypes to extend generalized linear models. Using the score vectors from GEE we discriminate high-risk from low-risk groups. We applied the multivariate GMDR method to the blood pressure data of the 7,546 subjects from the Korean Association Resource study: systolic blood pressure (SBP) and diastolic blood pressure (DBP). We compare the results of multivariate GMDR for SBP and DBP to the results from separate univariate GMDR for SBP and DBP, respectively. We also applied the multivariate GMDR method to the repeatedly measured hypertension status from 5,466 subjects and compared its result with those of univariate GMDR at each time point. Results Results from the univariate GMDR and multivariate GMDR in two-locus model with both blood pressures and hypertension phenotypes indicate best combinations of SNPs whose interaction has significant association with risk for high blood pressures or hypertension. Although the test balanced accuracy (BA) of multivariate analysis was not always greater than that of univariate analysis, the multivariate BAs were more stable with smaller standard deviations. Conclusions In this study, we have developed multivariate GMDR method using GEE approach. It is useful to use multivariate GMDR with correlated multiple phenotypes of interests. PMID:24565370

Prognosis of metastatic gastric and gastroesophageal junction cancer by HER2 status: a European and USA International collaborative analysis.

PubMed

Janjigian, Y Y; Werner, D; Pauligk, C; Steinmetz, K; Kelsen, D P; Jäger, E; Altmannsberger, H-M; Robinson, E; Tafe, L J; Tang, L H; Shah, M A; Al-Batran, S-E

2012-10-01

To determine whether human epidermal growth factor receptor 2 (HER2) status is an independent prognostic factor in metastatic gastric and gastroesophageal junction (GEJ) adenocarcinoma. Formalin-fixed paraffin-embedded tumor samples from 381 metastatic gastric/GEJ cancer patients enrolled at Krankenhaus Nordwest and Memorial Sloan-Kettering Cancer Centers on six first-line trials of chemotherapy without trastuzumab were examined for HER2 by immunohistochemistry (IHC) and in situ hybridization (ISH). IHC 3+ or ISH-positive tumors were considered HER2 positive. Seventy-eight of 381 patients (20%) had HER2-positive disease. In the multivariate logistic model, there were significantly higher rates of HER2 positivity in patients with liver metastasis (liver metastasis 31%; no liver metastasis 11%; P = 0.025) and intestinal histology (intestinal 33%; diffuse/mixed 8%; P = 0.001). No significant differences in HER2 positivity were found between resections and biopsies or primaries and metastases. Patients with HER2-positive gastric cancer had longer median overall survival compared with HER2-negative gastric cancer patients (13.9 versus 11.4 months, P = 0.047), but multivariate analysis indicated that HER2 status was not an independent prognostic factor (hazard ratio 0.79; 0.44-1.14; P = 0.194). Approximately 20% of Western patients with metastatic gastric cancer are HER2 positive. Unlike breast cancer, HER2 positivity is not independently prognostic of patient outcome in metastatic gastric or GEJ.

Central nervous system medication use and incident mobility limitation in community elders: the Health, Aging, and Body Composition study.

PubMed

Boudreau, Robert M; Hanlon, Joseph T; Roumani, Yazan F; Studenski, Stephanie A; Ruby, Christine M; Wright, Rollin M; Hilmer, Sarah N; Shorr, Ronald I; Bauer, Douglas C; Simonsick, Eleanor M; Newman, Anne B

2009-10-01

To evaluate whether CNS medication use in older adults was associated with a higher risk of future incident mobility limitation. This 5-year longitudinal cohort study included 3055 participants from the health, aging and body composition (Health ABC) study who were well-functioning at baseline. CNS medication use (benzodiazepine and opioid receptor agonists, antipsychotics, and antidepressants) was determined yearly (except year 4) during in-home or in-clinic interviews. Summated standardized daily doses (low, medium, and high) and duration of CNS drug use were computed. Incident mobility limitation was operationalized as two consecutive self-reports of having any difficulty walking 1/4 mile or climbing 10 steps without resting every 6 months after baseline. Multivariable Cox proportional hazard analyses were conducted adjusting for demographics, health behaviors, health status, and common indications for CNS medications. Each year at least 13.9% of participants used a CNS medication. By year 6, overall 49% had developed incident mobility limitation. In multivariable models, CNS medication users compared to never users showed a higher risk for incident mobility limitation (adjusted hazard ratio (Adj. HR) 1.28; 95% confidence interval (CI) 1.12-1.47). Similar findings of increased risk were seen in analyses examining dose- and duration-response relationships. CNS medication use is independently associated with an increased risk of future incident mobility limitation in community dwelling elderly. Further studies are needed to determine the impact of reducing CNS medication exposure on mobility problems. 2009 John Wiley & Sons, Ltd.

Multiple templates-based homology modeling enhances structure quality of AT1 receptor: validation by molecular dynamics and antagonist docking.

PubMed

Sokkar, Pandian; Mohandass, Shylajanaciyar; Ramachandran, Murugesan

2011-07-01

We present a comparative account on 3D-structures of human type-1 receptor (AT1) for angiotensin II (AngII), modeled using three different methodologies. AngII activates a wide spectrum of signaling responses via the AT1 receptor that mediates physiological control of blood pressure and diverse pathological actions in cardiovascular, renal, and other cell types. Availability of 3D-model of AT1 receptor would significantly enhance the development of new drugs for cardiovascular diseases. However, templates of AT1 receptor with low sequence similarity increase the complexity in straightforward homology modeling, and hence there is a need to evaluate different modeling methodologies in order to use the models for sensitive applications such as rational drug design. Three models were generated for AT1 receptor by, (1) homology modeling with bovine rhodopsin as template, (2) homology modeling with multiple templates and (3) threading using I-TASSER web server. Molecular dynamics (MD) simulation (15 ns) of models in explicit membrane-water system, Ramachandran plot analysis and molecular docking with antagonists led to the conclusion that multiple template-based homology modeling outweighs other methodologies for AT1 modeling.

Is receptor oligomerization causally linked to activation of the EGF receptor kinase?

NASA Technical Reports Server (NTRS)

Rintoul, D. A.; Spooner, B. S. (Principal Investigator)

1992-01-01

Transduction of a signal from an extracellular peptide hormone to produce an intracellular response is often mediated by a cell surface receptor, which is usually a glycoprotein. The secondary intracellular signal(s) generated after hormone binding to the receptor have been intensively studied. The nature of the primary signal generated by ligand binding to the receptor is understood less well in most cases. The particular case of the epidermal growth factor (EGF) receptor is analyzed, and evidence for or against two dissimilar models of primary signal transduction is reviewed. Evidence for the most widely accepted current model is found to be unconvincing. Evidence for the other model is substantial but indirect; a direct test of this model remains to be done.

Estimation and model selection of semiparametric multivariate survival functions under general censorship.

PubMed

Chen, Xiaohong; Fan, Yanqin; Pouzo, Demian; Ying, Zhiliang

2010-07-01

We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root- n asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step nonparametric estimation of the marginal survivals. We establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application is provided.

Estimation and model selection of semiparametric multivariate survival functions under general censorship

PubMed Central

Chen, Xiaohong; Fan, Yanqin; Pouzo, Demian; Ying, Zhiliang

2013-01-01

We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root-n asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step nonparametric estimation of the marginal survivals. We establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application is provided. PMID:24790286

Usual Dietary Intakes: SAS Macros for Fitting Multivariate Measurement Error Models & Estimating Multivariate Usual Intake Distributions

Cancer.gov

The following SAS macros can be used to create a multivariate usual intake distribution for multiple dietary components that are consumed nearly every day or episodically. A SAS macro for performing balanced repeated replication (BRR) variance estimation is also included.

Comparative Robustness of Recent Methods for Analyzing Multivariate Repeated Measures Designs

ERIC Educational Resources Information Center

Seco, Guillermo Vallejo; Gras, Jaime Arnau; Garcia, Manuel Ato

2007-01-01

This study evaluated the robustness of two recent methods for analyzing multivariate repeated measures when the assumptions of covariance homogeneity and multivariate normality are violated. Specifically, the authors' work compares the performance of the modified Brown-Forsythe (MBF) procedure and the mixed-model procedure adjusted by the…

Critical elements on fitting the Bayesian multivariate Poisson Lognormal model

NASA Astrophysics Data System (ADS)

Zamzuri, Zamira Hasanah binti

2015-10-01

Motivated by a problem on fitting multivariate models to traffic accident data, a detailed discussion of the Multivariate Poisson Lognormal (MPL) model is presented. This paper reveals three critical elements on fitting the MPL model: the setting of initial estimates, hyperparameters and tuning parameters. These issues have not been highlighted in the literature. Based on simulation studies conducted, we have shown that to use the Univariate Poisson Model (UPM) estimates as starting values, at least 20,000 iterations are needed to obtain reliable final estimates. We also illustrated the sensitivity of the specific hyperparameter, which if it is not given extra attention, may affect the final estimates. The last issue is regarding the tuning parameters where they depend on the acceptance rate. Finally, a heuristic algorithm to fit the MPL model is presented. This acts as a guide to ensure that the model works satisfactorily given any data set.

Analysis/forecast experiments with a multivariate statistical analysis scheme using FGGE data

NASA Technical Reports Server (NTRS)

Baker, W. E.; Bloom, S. C.; Nestler, M. S.

1985-01-01

A three-dimensional, multivariate, statistical analysis method, optimal interpolation (OI) is described for modeling meteorological data from widely dispersed sites. The model was developed to analyze FGGE data at the NASA-Goddard Laboratory of Atmospherics. The model features a multivariate surface analysis over the oceans, including maintenance of the Ekman balance and a geographically dependent correlation function. Preliminary comparisons are made between the OI model and similar schemes employed at the European Center for Medium Range Weather Forecasts and the National Meteorological Center. The OI scheme is used to provide input to a GCM, and model error correlations are calculated for forecasts of 500 mb vertical water mixing ratios and the wind profiles. Comparisons are made between the predictions and measured data. The model is shown to be as accurate as a successive corrections model out to 4.5 days.

Multivariate Bayesian modeling of known and unknown causes of events--an application to biosurveillance.

PubMed

Shen, Yanna; Cooper, Gregory F

2012-09-01

This paper investigates Bayesian modeling of known and unknown causes of events in the context of disease-outbreak detection. We introduce a multivariate Bayesian approach that models multiple evidential features of every person in the population. This approach models and detects (1) known diseases (e.g., influenza and anthrax) by using informative prior probabilities and (2) unknown diseases (e.g., a new, highly contagious respiratory virus that has never been seen before) by using relatively non-informative prior probabilities. We report the results of simulation experiments which support that this modeling method can improve the detection of new disease outbreaks in a population. A contribution of this paper is that it introduces a multivariate Bayesian approach for jointly modeling both known and unknown causes of events. Such modeling has general applicability in domains where the space of known causes is incomplete. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

FACTOR ANALYTIC MODELS OF CLUSTERED MULTIVARIATE DATA WITH INFORMATIVE CENSORING

EPA Science Inventory

This paper describes a general class of factor analytic models for the analysis of clustered multivariate data in the presence of informative missingness. We assume that there are distinct sets of cluster-level latent variables related to the primary outcomes and to the censorin...

An Examination of the Domain of Multivariable Functions Using the Pirie-Kieren Model

ERIC Educational Resources Information Center

Sengul, Sare; Yildiz, Sevda Goktepe

2016-01-01

The aim of this study is to employ the Pirie-Kieren model so as to examine the understandings relating to the domain of multivariable functions held by primary school mathematics preservice teachers. The data obtained was categorized according to Pirie-Kieren model and demonstrated visually in tables and bar charts. The study group consisted of…

Multivariate regression model for predicting yields of grade lumber from yellow birch sawlogs

Treesearch

Andrew F. Howard; Daniel A. Yaussy

1986-01-01

A multivariate regression model was developed to predict green board-foot yields for the common grades of factory lumber processed from yellow birch factory-grade logs. The model incorporates the standard log measurements of scaling diameter, length, proportion of scalable defects, and the assigned USDA Forest Service log grade. Differences in yields between band and...

A Multivariate Model for the Meta-Analysis of Study Level Survival Data at Multiple Times

ERIC Educational Resources Information Center

Jackson, Dan; Rollins, Katie; Coughlin, Patrick

2014-01-01

Motivated by our meta-analytic dataset involving survival rates after treatment for critical leg ischemia, we develop and apply a new multivariate model for the meta-analysis of study level survival data at multiple times. Our data set involves 50 studies that provide mortality rates at up to seven time points, which we model simultaneously, and…

Interaction of DRD4 Methylation and Phthalate Metabolites Affects Continuous Performance Test Performance in ADHD.

PubMed

Kim, Johanna Inhyang; Kim, Jae-Won; Shin, Inkyung; Kim, Bung-Nyun

2018-05-01

We investigated the interaction effect between the methylation of dopamine receptor D4 (DRD4) and phthalate exposure in ADHD on continuous performance test (CPT) variables. Urine concentrations of mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), mono-(2-ethyl-5-oxohexyl) phthalate (MEOHP), and mono-n-butyl phthalate (MBP) were tested. The methylation status was analyzed for CpG sites of DRD4. Multivariable linear regression models were applied to investigate the interaction effects of methylation and phthalate levels. There was a significant interaction effect of the methylation of CpG26 and CpG28 with the MEHHP level on omission errors in ADHD patients, but not in controls. The post hoc analysis revealed a significant correlation between the MEHHP concentration and omission errors in the methylated group, but not in the unmethylated group. The interaction between the methylation status of CpG sites of DRD4, particularly CpG26 and CpG28, and phthalate metabolite levels affects the attention level in ADHD patients.

Analytical framework for reconstructing heterogeneous environmental variables from mammal community structure.

PubMed

Louys, Julien; Meloro, Carlo; Elton, Sarah; Ditchfield, Peter; Bishop, Laura C

2015-01-01

We test the performance of two models that use mammalian communities to reconstruct multivariate palaeoenvironments. While both models exploit the correlation between mammal communities (defined in terms of functional groups) and arboreal heterogeneity, the first uses a multiple multivariate regression of community structure and arboreal heterogeneity, while the second uses a linear regression of the principal components of each ecospace. The success of these methods means the palaeoenvironment of a particular locality can be reconstructed in terms of the proportions of heavy, moderate, light, and absent tree canopy cover. The linear regression is less biased, and more precisely and accurately reconstructs heavy tree canopy cover than the multiple multivariate model. However, the multiple multivariate model performs better than the linear regression for all other canopy cover categories. Both models consistently perform better than randomly generated reconstructions. We apply both models to the palaeocommunity of the Upper Laetolil Beds, Tanzania. Our reconstructions indicate that there was very little heavy tree cover at this site (likely less than 10%), with the palaeo-landscape instead comprising a mixture of light and absent tree cover. These reconstructions help resolve the previous conflicting palaeoecological reconstructions made for this site. Copyright © 2014 Elsevier Ltd. All rights reserved.

Prevalence of and risk factors for reduced serum bicarbonate in chronic kidney disease.

PubMed

Raphael, Kalani L; Zhang, Yingying; Ying, Jian; Greene, Tom

2014-10-01

The prevalence of metabolic acidosis increases as glomerular filtration rate falls. However, most patients with stage 4 chronic kidney disease have normal serum bicarbonate concentration while some with stage 3 chronic kidney disease have low serum bicarbonate, suggesting that other factors contribute to generation of acidosis. The purpose of this study is to identify risk factors, other than reduced glomerular filtration rate, for reduced serum bicarbonate in chronic kidney disease. This is a cross-sectional analysis of baseline data from the Chronic Renal Insufficiency Cohort Study. Multivariable logistic and linear regression models were used to relate predictor variables to the odds of low serum bicarbonate (< 22 mM) compared with normal serum bicarbonate (22-30 mM) and the coefficients of Δ serum bicarbonate concentration. The prevalence of low serum bicarbonate at baseline was 17.3%. Lower estimated glomerular filtration rate had the strongest relationship with low serum bicarbonate. Factors associated with higher odds of low serum bicarbonate, independent of estimated glomerular filtration rate, were urinary albumin/creatinine ≥ 10 mg/g, smoking, anaemia, hyperkalaemia, non-diuretic use and higher serum albumin. These and younger age, higher waist circumference, and use of angiotensin converting enzyme inhibitors or angiotensin receptor blockers associated with negative Δ serum bicarbonate in linear regression models. Several factors not typically considered to associate with reduced serum bicarbonate in chronic kidney disease were identified including albuminuria ≥ 10 mg/g, anaemia, smoking, higher serum albumin, higher waist circumference, and use of angiotensin converting enzyme inhibitors or angiotensin receptor blockers. Future studies should explore the longitudinal effect of these factors on serum bicarbonate concentration. © 2014 Asian Pacific Society of Nephrology.

Common variants in genes encoding adiponectin (ADIPOQ) and its receptors (ADIPOR1/2), adiponectin concentrations, and diabetes incidence in the Diabetes Prevention Program

PubMed Central

Mather, K. J.; Christophi, C. A.; Jablonski, K. A.; Knowler, W. C.; Goldberg, R. B.; Kahn, S. E.; Spector, T.; Dastani, Z.; Waterworth, D.; Richards, J. B.; Funahashi, T.; Pi-Sunyer, F. X.; Pollin, T. I.; Florez, J. C.; Franks, P. W.

2012-01-01

Aims Baseline adiponectin concentrations predict incident Type 2 diabetes mellitus in the Diabetes Prevention Program. We tested the hypothesis that common variants in the genes encoding adiponectin (ADIPOQ) and its receptors (ADIPOR1, ADIPOR2) would associate with circulating adiponectin concentrations and/or with diabetes incidence in the Diabetes Prevention Program population. Methods Seventy-seven tagging single-nucleotide polymorphisms (SNPs) in ADIPOQ (24), ADIPOR1 (22) and ADIPOR2 (31) were genotyped. Associations of SNPs with baseline adiponectin concentrations were evaluated using linear modelling. Associations of SNPs with diabetes incidence were evaluated using Cox proportional hazards modelling. Results Thirteen of 24 ADIPOQ SNPs were significantly associated with baseline adiponectin concentrations. Multivariable analysis including these 13 SNPs revealed strong independent contributions from rs17366568, rs1648707, rs17373414 and rs1403696 with adiponectin concentrations. However, no ADIPOQ SNPs were directly associated with diabetes incidence. Two ADIPOR1 SNPs (rs1342387 and rs12733285) were associated with ~18% increased diabetes incidence for carriers of the minor allele without differences across treatment groups, and without any relationship with adiponectin concentrations. Conclusions ADIPOQ SNPs are significantly associated with adiponectin concentrations in the Diabetes Prevention Program cohort. This observation extends prior observations from unselected populations of European descent into a broader multi-ethnic population, and confirms the relevance of these variants in an obese/dysglycaemic population. Despite the robust relationship between adiponectin concentrations and diabetes risk in this cohort, variants in ADIPOQ that relate to adiponectin concentrations do not relate to diabetes risk in this population. ADIPOR1 variants exerted significant effects on diabetes risk distinct from any effect of adiponectin concentrations. [Clinical Trials Registry Nos; NCT 00004992 (Diabetes Prevention Program) and NCT 00038727 (Diabetes Prevention Program Outcomes Study)] PMID:22443353

Angiotensin II type 1 receptor blockers as a first choice in patients with acute myocardial infarction.

PubMed

Lee, Jang Hoon; Bae, Myung Hwan; Yang, Dong Heon; Park, Hun Sik; Cho, Yongkeun; Lee, Won Kee; Jeong, Myung Ho; Kim, Young Jo; Cho, Myeong Chan; Kim, Chong Jin; Chae, Shung Chull

2016-03-01

Angiotensin II type 1 receptor blockers (ARBs) have not been adequately evaluated in patients without left ventricular (LV) dysfunction or heart failure after acute myocardial infarction (AMI). Between November 2005 and January 2008, 6,781 patients who were not receiving angiotensin-converting enzyme inhibitors (ACEIs) or ARBs were selected from the Korean AMI Registry. The primary endpoints were 12-month major adverse cardiac events (MACEs) including death and recurrent AMI. Seventy percent of the patients were Killip class 1 and had a LV ejection fraction ≥ 40%. The prescription rate of ARBs was 12.2%. For each patient, a propensity score, indicating the likelihood of using ARBs during hospitalization or at discharge, was calculated using a non-parsimonious multivariable logistic regression model, and was used to match the patients 1:4, yielding 715 ARB users versus 2,860 ACEI users. The effect of ARBs on in-hospital mortality and 12-month MACE occurrence was assessed using matched logistic and Cox regression models. Compared with ACEIs, ARBs significantly reduced in-hospital mortality(1.3% vs. 3.3%; hazard ratio [HR], 0.379; 95% confidence interval [CI], 0.190 to0.756; p = 0.006) and 12-month MACE occurrence (4.6% vs. 6.9%; HR, 0.661; 95% CI, 0.457 to 0.956; p = 0.028). However, the benefit of ARBs on 12-month mortality compared with ACEIs was marginal (4.3% vs. 6.2%; HR, 0.684; 95% CI, 0.467 to 1.002; p = 0.051). Our results suggest that ARBs are not inferior to, and may actually be better than ACEIs in Korean patients with AMI.

VDR gene methylation as a molecular adaption to light exposure: Historic, recent and genetic influences.

PubMed

Beckett, Emma L; Jones, Patrice; Veysey, Martin; Duesing, Konsta; Martin, Charlotte; Furst, John; Yates, Zoe; Jablonski, Nina G; Chaplin, George; Lucock, Mark

2017-09-10

The vitamin D receptor (VDR) is a member of the nuclear receptor family of transcription factors. We examined whether degree of VDR gene methylation acts as a molecular adaptation to light exposure. We explored this in the context of photoperiod at conception, recent UV irradiance at 305 nm, and gene-latitude effects. Eighty subjects were examined for VDR gene-CpG island methylation density. VDR gene variants were also examined by PCR-RFLP. Photoperiod at conception was significantly positively related to VDR methylation density, explaining 17% of the variance in methylation (r 2  = 0.17; P = .001). Within this model, photoperiod at conception and plasma 25(OH)D independently predicted methylation density at the VDR-CpG island. Recent UV exposure at 305 nm led to a fivefold increase in mean methylation density (P = .02). Again, UV exposure and plasma 25(OH)D independently predicted methylation density at the VDR-CpG island. In the presence of the BsmI mutant allele, methylation density was increased (P = .01), and in the presence of the TaqI or FokI mutant allele, methylation density was decreased (P = .007 and .04 respectively). Multivariate modelling suggests plasma 25(OH)D, photoperiod at conception, recent solar irradiance, and VDR genotype combine as independent predictors of methylation at the VDR-CpG island, explaining 34% of the variance in methylation (R 2  = 0.34, P < .0001). Duration of early-life light exposure and strength of recent irradiance, along with latitudinal genetic factors, influence degree of VDR gene methylation consistent with this epigenetic phenomenon being a molecular adaptation to variation in ambient light exposure. Findings contribute to our understanding of human biology. © 2017 Wiley Periodicals, Inc.

Induction regimen and survival in simultaneous heart-kidney transplant recipients.

PubMed

Ariyamuthu, Venkatesh K; Amin, Alpesh A; Drazner, Mark H; Araj, Faris; Mammen, Pradeep P A; Ayvaci, Mehmet; Mete, Mutlu; Ozay, Fatih; Ghanta, Mythili; Mohan, Sumit; Mohan, Prince; Tanriover, Bekir

2018-05-01

Induction therapy in simultaneous heart-kidney transplantation (SHKT) is not well studied in the setting of contemporary maintenance immunosuppression consisting of tacrolimus (TAC), mycophenolic acid (MPA), and prednisone (PRED). We analyzed the Organ Procurement and Transplant Network registry from January 1, 2000, to March 3, 2015, for recipients of SHKT (N = 623) maintained on TAC/MPA/PRED at hospital discharge. The study cohort was further stratified into 3 groups by induction choice: induction (n = 232), rabbit anti-thymoglobulin (r-ATG; n = 204), and interleukin-2 receptor-α (n = 187) antagonists. Survival rates were estimated using the Kaplan-Meier estimator. Multivariable inverse probability weighted Cox proportional hazard regression models were used to assess hazard ratios associated with post-transplant mortality as the primary outcome. The study cohort was censored on March 4, 2016, to allow at least 1-year of follow-up. During the study period, the number of SHKTs increased nearly 5-fold. The Kaplan-Meier survival curve showed superior outcomes with r-ATG compared with no induction or interleukin-2 receptor-α induction. Compared with the no-induction group, an inverse probability weighted Cox proportional hazard model showed no independent association of induction therapy with the primary outcome. In sub-group analysis, r-ATG appeared to lower mortality in sensitized patients with panel reactive antibody of 10% or higher (hazard ratio, 0.19; 95% confidence interval, 0.05-0.71). r-ATG may provide a survival benefit in SHKT, especially in sensitized patients maintained on TAC/MPA/PRED at hospital discharge. Copyright © 2017 International Society for the Heart and Lung Transplantation. Published by Elsevier Inc. All rights reserved.

Lactate dehydrogenase predicts combined progression-free survival after sequential therapy with abiraterone and enzalutamide for patients with castration-resistant prostate cancer.

PubMed

Mori, Keiichiro; Kimura, Takahiro; Onuma, Hajime; Kimura, Shoji; Yamamoto, Toshihiro; Sasaki, Hiroshi; Miki, Jun; Miki, Kenta; Egawa, Shin

2017-07-01

An array of clinical issues remains to be resolved for castration-resistant prostate cancer (CRPC), including the sequence of drug use and drug cross-resistance. At present, no clear guidelines are available for the optimal sequence of use of novel agents like androgen-receptor axis-targeted (ARAT) agents, particularly enzalutamide, and abiraterone. This study retrospectively analyzed a total of 69 patients with CRPC treated with sequential therapy using enzalutamide followed by abiraterone or vice versa. The primary outcome measure was the comparative combined progression-free survival (PFS) comprising symptomatic and/or radiographic PFS. Patients were also compared for total prostate-specific antigen (PSA)-PFS, overall survival (OS), and PSA response. The predictors of combined PFS and OS were analyzed with a backward-stepwise multivariate Cox model. Of the 69 patients, 46 received enzalutamide first, followed by abiraterone (E-A group), and 23 received abiraterone, followed by enzalutamide (A-E group). The two groups were not significantly different with regard to basic data, except for hemoglobin values. In a comparison with the E-A group, the A-E group was shown to be associated with better combined PFS in Kaplan-Meier analysis (P = 0.043). Similar results were obtained for total PSA-PFS (P = 0.049), while OS did not differ between groups (P = 0.62). Multivariate analysis demonstrated that pretreatment lactate dehydrogenase (LDH) values and age were significant predictors of longer combined PFS (P < 0.05). Likewise, multivariate analysis demonstrated that pretreatment hemoglobin values and performance status were significant predictors of longer OS (P < 0.05). The results of this study suggested the A-E sequence had longer combined PSA and total PSA-PFS compared to the E-A sequence in patients with CRPC. LDH values in sequential therapy may serve as a predictor of longer combined PFS. © 2017 Wiley Periodicals, Inc.

The cubic ternary complex receptor-occupancy model. III. resurrecting efficacy.

PubMed

Weiss, J M; Morgan, P H; Lutz, M W; Kenakin, T P

1996-08-21

Early work in pharmacology characterized the interaction of receptors and ligands in terms of two parameters, affinity and efficacy, an approach we term the bipartite view. A precise formulation of efficacy only exists for very simple pharmacological models. Here we extend the notion of efficacy to models that incorporate receptor activation and G-protein coupling. Using the cubic ternary complex model, we show that efficacy is not purely a property of the ligand-receptor interaction; it also depends upon the distributional details of the receptor species in the native receptor ensemble. This suggests a distinction between what we call potential efficacy (a vector) and realized efficacy (a scalar). To each receptor species in the native receptor ensemble we assign a part-worth utility; taken together these utilities comprise the potential efficacy vector. Realized efficacy is the expectation of these part-worth utilities with respect to the frequency distribution of receptor species in the native receptor ensemble. In the parlance of statistical decision theory, the binding of a ligand to a receptor ensemble is a random prospect and realized efficacy is the utility of this prospect. We explore the implications that our definition of efficacy has for understanding agonism and in assessing the legitimacy of the bipartite view in pharmacology.

Structure-activity relationships of seco-prezizaane and picrotoxane/picrodendrane terpenoids by Quasar receptor-surface modeling.

PubMed

Schmidt, Thomas J; Gurrath, Marion; Ozoe, Yoshihisa

2004-08-01

The seco-prezizaane-type sesquiterpenes pseudoanisatin and parviflorolide from Illicium are noncompetitive antagonists at housefly (Musca domestica) gamma-aminobutyric acid (GABA) receptors. They show selectivity toward the insect receptor and thus represent new leads toward selective insecticides. Based on the binding data for 13 seco-prezizaane terpenoids and 17 picrotoxane and picrodendrane-type terpenoids to housefly and rat GABA receptors, a QSAR study was conducted by quasi-atomistic receptor-surface modeling (Quasar). The resulting models provide insight into the structural basis of selectivity and properties of the binding sites at GABA receptor-coupled chloride channels of insects and mammals.

The NLS-Based Nonlinear Grey Multivariate Model for Forecasting Pollutant Emissions in China.

PubMed

Pei, Ling-Ling; Li, Qin; Wang, Zheng-Xin

2018-03-08

The relationship between pollutant discharge and economic growth has been a major research focus in environmental economics. To accurately estimate the nonlinear change law of China's pollutant discharge with economic growth, this study establishes a transformed nonlinear grey multivariable (TNGM (1, N )) model based on the nonlinear least square (NLS) method. The Gauss-Seidel iterative algorithm was used to solve the parameters of the TNGM (1, N ) model based on the NLS basic principle. This algorithm improves the precision of the model by continuous iteration and constantly approximating the optimal regression coefficient of the nonlinear model. In our empirical analysis, the traditional grey multivariate model GM (1, N ) and the NLS-based TNGM (1, N ) models were respectively adopted to forecast and analyze the relationship among wastewater discharge per capita (WDPC), and per capita emissions of SO₂ and dust, alongside GDP per capita in China during the period 1996-2015. Results indicated that the NLS algorithm is able to effectively help the grey multivariable model identify the nonlinear relationship between pollutant discharge and economic growth. The results show that the NLS-based TNGM (1, N ) model presents greater precision when forecasting WDPC, SO₂ emissions and dust emissions per capita, compared to the traditional GM (1, N ) model; WDPC indicates a growing tendency aligned with the growth of GDP, while the per capita emissions of SO₂ and dust reduce accordingly.

Customizing G Protein-coupled receptor models for structure-based virtual screening.

PubMed

de Graaf, Chris; Rognan, Didier

2009-01-01

This review will focus on the construction, refinement, and validation of G Protein-coupled receptor models for the purpose of structure-based virtual screening. Practical tips and tricks derived from concrete modeling and virtual screening exercises to overcome the problems and pitfalls associated with the different steps of the receptor modeling workflow will be presented. These examples will not only include rhodopsin-like (class A), but also secretine-like (class B), and glutamate-like (class C) receptors. In addition, the review will present a careful comparative analysis of current crystal structures and their implication on homology modeling. The following themes will be discussed: i) the use of experimental anchors in guiding the modeling procedure; ii) amino acid sequence alignments; iii) ligand binding mode accommodation and binding cavity expansion; iv) proline-induced kinks in transmembrane helices; v) binding mode prediction and virtual screening by receptor-ligand interaction fingerprint scoring; vi) extracellular loop modeling; vii) virtual filtering schemes. Finally, an overview of several successful structure-based screening shows that receptor models, despite structural inaccuracies, can be efficiently used to find novel ligands.

Exploration of N-arylpiperazine Binding Sites of D2 Dopaminergic Receptor.

PubMed

Soskic, Vukic; Sukalovic, Vladimir; Kostic-Rajacic, Sladjana

2015-01-01

The crystal structures of the D3 dopamine receptor and several other G-protein coupled receptors (GPCRs) were published in recent times. Those 3D structures are used by us and other scientists as a template for the homology modeling and ligand docking analysis of related GPCRs. Our main scientific interest lies in the field of pharmacologically active N-arylpiperazines that exhibit antipsychotic and/or antidepressant properties, and as such are dopaminergic and serotonergic receptor ligands. In this short review article we are presenting synthesis and biological data on the new N-arylpipereazine as well our results on molecular modeling of the interactions of those N-arylpiperazines with the model of D2 dopamine receptors. To obtain that model the crystal structure of the D3 dopamine receptor was used. Our results show that the N-arylpiperazines binding site consists of two pockets: one is the orthosteric binding site where the N-arylpiperazine part of the ligand is docked and the second is a non-canonical accessory binding site for N-arylpipereazine that is formed by a second extracellular loop (ecl2) of the receptor. Until now, the structure of this receptor region was unresolved in crystal structure analyses of the D3 dopamine receptor. To get a more complete picture of the ligand - receptor interaction, DFT quantum mechanical calculations on N-arylpiperazine were performed and the obtained models were used to examine those interactions.

Voxelwise multivariate analysis of multimodality magnetic resonance imaging.

PubMed

Naylor, Melissa G; Cardenas, Valerie A; Tosun, Duygu; Schuff, Norbert; Weiner, Michael; Schwartzman, Armin

2014-03-01

Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remain a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available. Copyright © 2013 Wiley Periodicals, Inc.

Multivariate Analysis of Longitudinal Rates of Change

PubMed Central

Bryan, Matthew; Heagerty, Patrick J.

2016-01-01

Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed by Roy and Lin [1]; Proust-Lima, Letenneur and Jacqmin-Gadda [2]; and Gray and Brookmeyer [3] among others. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, Gray and Brookmeyer [3] introduce an “accelerated time” method which assumes that covariates rescale time in longitudinal models for disease progression. In this manuscript we detail an alternative multivariate model formulation that directly structures longitudinal rates of change, and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. PMID:27417129

A Multivariate Descriptive Model of Motivation for Orthodontic Treatment.

ERIC Educational Resources Information Center

Hackett, Paul M. W.; And Others

1993-01-01

Motivation for receiving orthodontic treatment was studied among 109 young adults, and a multivariate model of the process is proposed. The combination of smallest scale analysis and Partial Order Scalogram Analysis by base Coordinates (POSAC) illustrates an interesting methodology for health treatment studies and explores motivation for dental…

Mathematical Formulation of Multivariate Euclidean Models for Discrimination Methods.

ERIC Educational Resources Information Center

Mullen, Kenneth; Ennis, Daniel M.

1987-01-01

Multivariate models for the triangular and duo-trio methods are described, and theoretical methods are compared to a Monte Carlo simulation. Implications are discussed for a new theory of multidimensional scaling which challenges the traditional assumption that proximity measures and perceptual distances are monotonically related. (Author/GDC)

A Multivariate Model of Parent-Adolescent Relationship Variables in Early Adolescence

ERIC Educational Resources Information Center

McKinney, Cliff; Renk, Kimberly

2011-01-01

Given the importance of predicting outcomes for early adolescents, this study examines a multivariate model of parent-adolescent relationship variables, including parenting, family environment, and conflict. Participants, who completed measures assessing these variables, included 710 culturally diverse 11-14-year-olds who were attending a middle…

Dimensionality of Motion and Binding Valency Govern Receptor-Ligand Kinetics As Revealed by Agent-Based Modeling.

PubMed

Lehnert, Teresa; Figge, Marc Thilo

2017-01-01

Mathematical modeling and computer simulations have become an integral part of modern biological research. The strength of theoretical approaches is in the simplification of complex biological systems. We here consider the general problem of receptor-ligand binding in the context of antibody-antigen binding. On the one hand, we establish a quantitative mapping between macroscopic binding rates of a deterministic differential equation model and their microscopic equivalents as obtained from simulating the spatiotemporal binding kinetics by stochastic agent-based models. On the other hand, we investigate the impact of various properties of B cell-derived receptors-such as their dimensionality of motion, morphology, and binding valency-on the receptor-ligand binding kinetics. To this end, we implemented an algorithm that simulates antigen binding by B cell-derived receptors with a Y-shaped morphology that can move in different dimensionalities, i.e., either as membrane-anchored receptors or as soluble receptors. The mapping of the macroscopic and microscopic binding rates allowed us to quantitatively compare different agent-based model variants for the different types of B cell-derived receptors. Our results indicate that the dimensionality of motion governs the binding kinetics and that this predominant impact is quantitatively compensated by the bivalency of these receptors.

Association Between Imaging Characteristics and Different Molecular Subtypes of Breast Cancer.

PubMed

Wu, Mingxiang; Ma, Jie

2017-04-01

Breast cancer can be divided into four major molecular subtypes based on the expression of hormone receptor (estrogen receptor and progesterone receptor), human epidermal growth factor receptor 2, HER2 status, and molecular proliferation rate (Ki67). In this study, we sought to investigate the association between breast cancer subtype and radiological findings in the Chinese population. Medical records of 300 consecutive invasive breast cancer patients were reviewed from the database: the Breast Imaging Reporting and Data System. The imaging characteristics of the lesions were evaluated. The molecular subtypes of breast cancer were classified into four types: luminal A, luminal B, HER2 overexpressed (HER2), and basal-like breast cancer (BLBC). Univariate and multivariate logistic regression analyses were performed to assess the association between the subtype (dependent variable) and mammography or 15 magnetic resonance imaging (MRI) indicators (independent variables). Luminal A and B subtypes were commonly associated with "clustered calcification distribution," "nipple invasion," or "skin invasion" (P <0.05). The BLBC subtype was more commonly associated with "rim enhancement" and persistent inflow type enhancement in delayed phase (P <0.05). HER2 overexpressed cancers showed association with persistent enhancement in the delayed phase on MRI and "clustered calcification distribution" on mammography (P <0.05). Certain radiological features are strongly associated with the molecular subtype and hormone receptor status of breast tumor, which are potentially useful tools in the diagnosis and subtyping of breast cancer. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Classical least squares multivariate spectral analysis

DOEpatents

Haaland, David M.

2002-01-01

An improved classical least squares multivariate spectral analysis method that adds spectral shapes describing non-calibrated components and system effects (other than baseline corrections) present in the analyzed mixture to the prediction phase of the method. These improvements decrease or eliminate many of the restrictions to the CLS-type methods and greatly extend their capabilities, accuracy, and precision. One new application of PACLS includes the ability to accurately predict unknown sample concentrations when new unmodeled spectral components are present in the unknown samples. Other applications of PACLS include the incorporation of spectrometer drift into the quantitative multivariate model and the maintenance of a calibration on a drifting spectrometer. Finally, the ability of PACLS to transfer a multivariate model between spectrometers is demonstrated.

Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets

USGS Publications Warehouse

Finley, Andrew O.; Banerjee, Sudipto; Cook, Bruce D.; Bradford, John B.

2013-01-01

In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.

Multivariate missing data in hydrology - Review and applications

NASA Astrophysics Data System (ADS)

Ben Aissia, Mohamed-Aymen; Chebana, Fateh; Ouarda, Taha B. M. J.

2017-12-01

Water resources planning and management require complete data sets of a number of hydrological variables, such as flood peaks and volumes. However, hydrologists are often faced with the problem of missing data (MD) in hydrological databases. Several methods are used to deal with the imputation of MD. During the last decade, multivariate approaches have gained popularity in the field of hydrology, especially in hydrological frequency analysis (HFA). However, treating the MD remains neglected in the multivariate HFA literature whereas the focus has been mainly on the modeling component. For a complete analysis and in order to optimize the use of data, MD should also be treated in the multivariate setting prior to modeling and inference. Imputation of MD in the multivariate hydrological framework can have direct implications on the quality of the estimation. Indeed, the dependence between the series represents important additional information that can be included in the imputation process. The objective of the present paper is to highlight the importance of treating MD in multivariate hydrological frequency analysis by reviewing and applying multivariate imputation methods and by comparing univariate and multivariate imputation methods. An application is carried out for multiple flood attributes on three sites in order to evaluate the performance of the different methods based on the leave-one-out procedure. The results indicate that, the performance of imputation methods can be improved by adopting the multivariate setting, compared to mean substitution and interpolation methods, especially when using the copula-based approach.

A mixed-effects regression model for longitudinal multivariate ordinal data.

PubMed

Liu, Li C; Hedeker, Donald

2006-03-01

A mixed-effects item response theory model that allows for three-level multivariate ordinal outcomes and accommodates multiple random subject effects is proposed for analysis of multivariate ordinal outcomes in longitudinal studies. This model allows for the estimation of different item factor loadings (item discrimination parameters) for the multiple outcomes. The covariates in the model do not have to follow the proportional odds assumption and can be at any level. Assuming either a probit or logistic response function, maximum marginal likelihood estimation is proposed utilizing multidimensional Gauss-Hermite quadrature for integration of the random effects. An iterative Fisher scoring solution, which provides standard errors for all model parameters, is used. An analysis of a longitudinal substance use data set, where four items of substance use behavior (cigarette use, alcohol use, marijuana use, and getting drunk or high) are repeatedly measured over time, is used to illustrate application of the proposed model.

A multivariate spatial mixture model for areal data: examining regional differences in standardized test scores

PubMed Central

Neelon, Brian; Gelfand, Alan E.; Miranda, Marie Lynn

2013-01-01

Summary Researchers in the health and social sciences often wish to examine joint spatial patterns for two or more related outcomes. Examples include infant birth weight and gestational length, psychosocial and behavioral indices, and educational test scores from different cognitive domains. We propose a multivariate spatial mixture model for the joint analysis of continuous individual-level outcomes that are referenced to areal units. The responses are modeled as a finite mixture of multivariate normals, which accommodates a wide range of marginal response distributions and allows investigators to examine covariate effects within subpopulations of interest. The model has a hierarchical structure built at the individual level (i.e., individuals are nested within areal units), and thus incorporates both individual- and areal-level predictors as well as spatial random effects for each mixture component. Conditional autoregressive (CAR) priors on the random effects provide spatial smoothing and allow the shape of the multivariate distribution to vary flexibly across geographic regions. We adopt a Bayesian modeling approach and develop an efficient Markov chain Monte Carlo model fitting algorithm that relies primarily on closed-form full conditionals. We use the model to explore geographic patterns in end-of-grade math and reading test scores among school-age children in North Carolina. PMID:26401059

Data driven discrete-time parsimonious identification of a nonlinear state-space model for a weakly nonlinear system with short data record

NASA Astrophysics Data System (ADS)

Relan, Rishi; Tiels, Koen; Marconato, Anna; Dreesen, Philippe; Schoukens, Johan

2018-05-01

Many real world systems exhibit a quasi linear or weakly nonlinear behavior during normal operation, and a hard saturation effect for high peaks of the input signal. In this paper, a methodology to identify a parsimonious discrete-time nonlinear state space model (NLSS) for the nonlinear dynamical system with relatively short data record is proposed. The capability of the NLSS model structure is demonstrated by introducing two different initialisation schemes, one of them using multivariate polynomials. In addition, a method using first-order information of the multivariate polynomials and tensor decomposition is employed to obtain the parsimonious decoupled representation of the set of multivariate real polynomials estimated during the identification of NLSS model. Finally, the experimental verification of the model structure is done on the cascaded water-benchmark identification problem.

Polymorphisms in chemokine and receptor genes and gastric cancer risk and survival in a high risk Polish population

PubMed Central

Gawron, Andrew J.; Fought, Angela J.; Lissowska, Jolanta; Ye, Weimin; Zhang, Xiao; Chow, Wong-Ho; Freeman, Laura E. Beane; Hou, Lifang

2010-01-01

Objective To examine if genetic variations in chemokine receptor and ligand genes are associated with gastric cancer risk and survival. Methods The study included 298 cases and 417 controls from a population-based study of gastric cancer conducted in Warsaw, Poland in 1994–1996. We investigated seven single nucleotide polymorphisms in a chemokine ligand (CXCL12) and chemokine receptor (CCR2, CCR5, CX3CR1) genes and one frameshift deletion (CCR5) in blood leukocyte DNA in relation to gastric cancer risk and survival. Genotyping was conducted at the NCI Core Genotyping Facility. Odds ratios and 95% confidence intervals were computed using univariate and multivariate logistic regression models. Survival analysis was performed using Cox proportional hazards models. Results Gastric cancer risk was not associated with single chemokine polymorphisms. A CCR5 haplotype that contained the common alleles of IVS1+151 G>T (rs2734648), IVS2+80 C>T (rs1800024) and minor allele of IVS1+246 A>G (rs1799987) was associated with a borderline significantly increased risk (OR = 1.5, 95% CI: 1.0–2.2). For gastric cancer cases, there was a greater risk of death for carriers of the minor alleles of CCR2 Ex2+241 G>A (rs1799864) (HR = 1.5, 95% CI: 1.1–2.1) and CCR5 IVS2+80 C>T (rs1800024) (HR = 1.5, 95% CI: 1.1–2.1). Carriers of the CCR5 minor allele of IVS1+151 G>T (rs2734648) had a decreased risk of death compared to homozygote carriers of the common allele (HR = 0.8, 95% CI: 0.6–1.0). Conclusions Our findings do not support an association between gastric cancer risk and single chemokine genetic variation. The observed associations between cancer risk and a CCR5 haplotype and between survival and polymorphisms in CCR2 and CCR5 need replication in future studies. PMID:21091093

Correlation of Various Biomarkers with Axillary Nodal Metastases: Can a Panel of Such Biomarkers Guide Selective Use of Axillary Surgery in T1 Breast Cancer?

PubMed

Dass, Tufale A; Rakesh, Sharma; Prakash, K Patil; Singh, Chandraveer

2015-12-01

To evaluate the correlation of various clinic-pathological variables with axillary nodal involvement in T1 breast cancer & to identify a sub-group of T1 cancers, on the basis of observed variables, with a low risk of axillary nodal metastases. Clinico-pathological variables observed included tumor size, lymphovascular invasion (LVI), histological grade of tumor, tumor palpability, estrogen/progesterone (ER/PR) & her2/neu receptors, age, family history, histological type of tumor, axillary nodal metastases for 100 patients without clinically palpable nodes who underwent axillary lymph node dissection in Bombay Hospital & Medical Research Center from March, 2009. Data compiled was analyzed by univariate & multivariate analysis. All the variables viz. tumor size, LVI, histological grade, tumor palpability & ER/PR/Her2 receptor profile, which were found to be significantly associated with axillary lymph node involvement (ALNI) on univariate analysis were also found to be independent predictors of ALNI on multivariate analysis. Age of the patient, family history & histological type of tumor were not significantly correlated with ALNI. None of the 12 patients with tumor biomarker profile of T1a-b tumors without LVI & with histological grade I, had ALNI. The risk of ALNI can be predicted by using various tumor biomarker variables. Based on the predicted risk of ALNI, the management strategy for axilla can be individualized. The omission of operative axillary staging may be considered in patients with low predictive risk of ALNI.

Mammographic density changes following discontinuation of tamoxifen in premenopausal women with oestrogen receptor-positive breast cancer.

PubMed

Kim, Won Hwa; Cho, Nariya; Kim, Young-Seon; Yi, Ann

2018-04-06

To evaluate the changes in mammographic density after tamoxifen discontinuation in premenopausal women with oestrogen receptor-positive breast cancers and the underlying factors METHODS: A total of 213 consecutive premenopausal women with breast cancer who received tamoxifen treatment after curative surgery and underwent three mammograms (baseline, after tamoxifen treatment, after tamoxifen discontinuation) were included. Changes in mammographic density after tamoxifen discontinuation were assessed qualitatively (decrease, no change, or increase) by two readers and measured quantitatively by semi-automated software. The association between % density change and clinicopathological factors was evaluated using univariate and multivariate regression analyses. After tamoxifen discontinuation, a mammographic density increase was observed in 31.9% (68/213, reader 1) to 22.1% (47/213, reader 2) by qualitative assessment, with a mean density increase of 1.8% by quantitative assessment compared to density before tamoxifen discontinuation. In multivariate analysis, younger age (≤ 39 years) and greater % density decline after tamoxifen treatment (≥ 17.0%) were independent factors associated with density change after tamoxifen discontinuation (p < .001 and p = .003, respectively). Tamoxifen discontinuation was associated with mammographic density change with a mean density increase of 1.8%, which was associated with younger age and greater density change after tamoxifen treatment. • Increased mammographic density after tamoxifen discontinuation can occur in premenopausal women. • Mean density increase after tamoxifen discontinuation was 1.8%. • Density increase is associated with age and density decrease after tamoxifen.

Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model.

PubMed

Snell, Kym I E; Hua, Harry; Debray, Thomas P A; Ensor, Joie; Look, Maxime P; Moons, Karel G M; Riley, Richard D

2016-01-01

Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation. We suggest multivariate meta-analysis for jointly synthesizing calibration and discrimination performance, while accounting for their correlation. The approach estimates a prediction model's average performance, the heterogeneity in performance across populations, and the probability of "good" performance in new populations. This allows different implementation strategies (e.g., recalibration) to be compared. Application is made to a diagnostic model for deep vein thrombosis (DVT) and a prognostic model for breast cancer mortality. In both examples, multivariate meta-analysis reveals that calibration performance is excellent on average but highly heterogeneous across populations unless the model's intercept (baseline hazard) is recalibrated. For the cancer model, the probability of "good" performance (defined by C statistic ≥0.7 and calibration slope between 0.9 and 1.1) in a new population was 0.67 with recalibration but 0.22 without recalibration. For the DVT model, even with recalibration, there was only a 0.03 probability of "good" performance. Multivariate meta-analysis can be used to externally validate a prediction model's calibration and discrimination performance across multiple populations and to evaluate different implementation strategies. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

Multivariate Formation Pressure Prediction with Seismic-derived Petrophysical Properties from Prestack AVO inversion and Poststack Seismic Motion Inversion

NASA Astrophysics Data System (ADS)

Yu, H.; Gu, H.

2017-12-01

A novel multivariate seismic formation pressure prediction methodology is presented, which incorporates high-resolution seismic velocity data from prestack AVO inversion, and petrophysical data (porosity and shale volume) derived from poststack seismic motion inversion. In contrast to traditional seismic formation prediction methods, the proposed methodology is based on a multivariate pressure prediction model and utilizes a trace-by-trace multivariate regression analysis on seismic-derived petrophysical properties to calibrate model parameters in order to make accurate predictions with higher resolution in both vertical and lateral directions. With prestack time migration velocity as initial velocity model, an AVO inversion was first applied to prestack dataset to obtain high-resolution seismic velocity with higher frequency that is to be used as the velocity input for seismic pressure prediction, and the density dataset to calculate accurate Overburden Pressure (OBP). Seismic Motion Inversion (SMI) is an inversion technique based on Markov Chain Monte Carlo simulation. Both structural variability and similarity of seismic waveform are used to incorporate well log data to characterize the variability of the property to be obtained. In this research, porosity and shale volume are first interpreted on well logs, and then combined with poststack seismic data using SMI to build porosity and shale volume datasets for seismic pressure prediction. A multivariate effective stress model is used to convert velocity, porosity and shale volume datasets to effective stress. After a thorough study of the regional stratigraphic and sedimentary characteristics, a regional normally compacted interval model is built, and then the coefficients in the multivariate prediction model are determined in a trace-by-trace multivariate regression analysis on the petrophysical data. The coefficients are used to convert velocity, porosity and shale volume datasets to effective stress and then to calculate formation pressure with OBP. Application of the proposed methodology to a research area in East China Sea has proved that the method can bridge the gap between seismic and well log pressure prediction and give predicted pressure values close to pressure meassurements from well testing.

Mu-Opioid (MOP) receptor mediated G-protein signaling is impaired in specific brain regions in a rat model of schizophrenia.

PubMed

Szűcs, Edina; Büki, Alexandra; Kékesi, Gabriella; Horváth, Gyöngyi; Benyhe, Sándor

2016-04-21

Schizophrenia is a complex mental health disorder. Clinical reports suggest that many patients with schizophrenia are less sensitive to pain than other individuals. Animal models do not interpret schizophrenia completely, but they can model a number of symptoms of the disease, including decreased pain sensitivities and increased pain thresholds of various modalities. Opioid receptors and endogenous opioid peptides have a substantial role in analgesia. In this biochemical study we investigated changes in the signaling properties of the mu-opioid (MOP) receptor in different brain regions, which are involved in the pain transmission, i.e., thalamus, olfactory bulb, prefrontal cortex and hippocampus. Our goal was to compare the transmembrane signaling mediated by MOP receptors in control rats and in a recently developed rat model of schizophrenia. Regulatory G-protein activation via MOP receptors were measured in [(35)S]GTPγS binding assays in the presence of a highly selective MOP receptor peptide agonist, DAMGO. It was found that the MOP receptor mediated activation of G-proteins was substantially lower in membranes prepared from the 'schizophrenic' model rats than in control animals. The potency of DAMGO to activate MOP receptor was also decreased in all brain regions studied. Taken together in our rat model of schizophrenia, MOP receptor mediated G-proteins have a reduced stimulatory activity compared to membrane preparations taken from control animals. The observed distinct changes of opioid receptor functions in different areas of the brain do not explain the augmented nociceptive threshold described in these animals. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Multiscale Modeling of Virus Entry via Receptor-Mediated Endocytosis

NASA Astrophysics Data System (ADS)

Liu, Jin

2012-11-01

Virus infections are ubiquitous and remain major threats to human health worldwide. Viruses are intracellular parasites and must enter host cells to initiate infection. Receptor-mediated endocytosis is the most common entry pathway taken by viruses, the whole process is highly complex and dictated by various events, such as virus motions, membrane deformations, receptor diffusion and ligand-receptor reactions, occurring at multiple length and time scales. We develop a multiscale model for virus entry through receptor-mediated endocytosis. The binding of virus to cell surface is based on a mesoscale three dimensional stochastic adhesion model, the internalization (endocytosis) of virus and cellular membrane deformation is based on the discretization of Helfrich Hamiltonian in a curvilinear space using Monte Carlo method. The multiscale model is based on the combination of these two models. We will implement this model to study the herpes simplex virus entry into B78 cells and compare the model predictions with experimental measurements.

Time Series Model Identification by Estimating Information.

DTIC Science & Technology

1982-11-01

principle, Applications of Statistics, P. R. Krishnaiah , ed., North-Holland: Amsterdam, 27-41. Anderson, T. W. (1971). The Statistical Analysis of Time Series...E. (1969). Multiple Time Series Modeling, Multivariate Analysis II, edited by P. Krishnaiah , Academic Press: New York, 389-409. Parzen, E. (1981...Newton, H. J. (1980). Multiple Time Series Modeling, II Multivariate Analysis - V, edited by P. Krishnaiah , North Holland: Amsterdam, 181-197. Shibata, R

Determining the Relationship Between Moral Waivers and Marine Corps Unsuitability Attrition

DTIC Science & Technology

2008-03-01

observed characteristics. However, econometric research indicates that the magnitude of interaction effects estimated via probit or logit models may...1997 to 2005. Multivariate probit models were used to analyze the effects of moral waivers on unsatisfactory service separations. 15. NUMBER OF...files from fiscal years 1997 to 2005. Multivariate probit models were used to analyze the effects of moral waivers on unsatisfactory service

Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes

PubMed Central

Parker, Joel S.; Mullins, Michael; Cheang, Maggie C.U.; Leung, Samuel; Voduc, David; Vickery, Tammi; Davies, Sherri; Fauron, Christiane; He, Xiaping; Hu, Zhiyuan; Quackenbush, John F.; Stijleman, Inge J.; Palazzo, Juan; Marron, J.S.; Nobel, Andrew B.; Mardis, Elaine; Nielsen, Torsten O.; Ellis, Matthew J.; Perou, Charles M.; Bernard, Philip S.

2009-01-01

Purpose To improve on current standards for breast cancer prognosis and prediction of chemotherapy benefit by developing a risk model that incorporates the gene expression–based “intrinsic” subtypes luminal A, luminal B, HER2-enriched, and basal-like. Methods A 50-gene subtype predictor was developed using microarray and quantitative reverse transcriptase polymerase chain reaction data from 189 prototype samples. Test sets from 761 patients (no systemic therapy) were evaluated for prognosis, and 133 patients were evaluated for prediction of pathologic complete response (pCR) to a taxane and anthracycline regimen. Results The intrinsic subtypes as discrete entities showed prognostic significance (P = 2.26E-12) and remained significant in multivariable analyses that incorporated standard parameters (estrogen receptor status, histologic grade, tumor size, and node status). A prognostic model for node-negative breast cancer was built using intrinsic subtype and clinical information. The C-index estimate for the combined model (subtype and tumor size) was a significant improvement on either the clinicopathologic model or subtype model alone. The intrinsic subtype model predicted neoadjuvant chemotherapy efficacy with a negative predictive value for pCR of 97%. Conclusion Diagnosis by intrinsic subtype adds significant prognostic and predictive information to standard parameters for patients with breast cancer. The prognostic properties of the continuous risk score will be of value for the management of node-negative breast cancers. The subtypes and risk score can also be used to assess the likelihood of efficacy from neoadjuvant chemotherapy. PMID:19204204

Structural Analysis of Chemokine Receptor–Ligand Interactions

PubMed Central

2017-01-01

This review focuses on the construction and application of structural chemokine receptor models for the elucidation of molecular determinants of chemokine receptor modulation and the structure-based discovery and design of chemokine receptor ligands. A comparative analysis of ligand binding pockets in chemokine receptors is presented, including a detailed description of the CXCR4, CCR2, CCR5, CCR9, and US28 X-ray structures, and their implication for modeling molecular interactions of chemokine receptors with small-molecule ligands, peptide ligands, and large antibodies and chemokines. These studies demonstrate how the integration of new structural information on chemokine receptors with extensive structure–activity relationship and site-directed mutagenesis data facilitates the prediction of the structure of chemokine receptor–ligand complexes that have not been crystallized. Finally, a review of structure-based ligand discovery and design studies based on chemokine receptor crystal structures and homology models illustrates the possibilities and challenges to find novel ligands for chemokine receptors. PMID:28165741

Association between p75 neurotrophin receptor gene expression and cell apoptosis in tissues surrounding hematomas in rat models of intracerebral hemorrhage.

PubMed

He, Baixiang; Bao, Gang; Guo, Shiwen; Xu, Gaofeng; Li, Qi; Wang, Ning

2012-03-15

Animal models of intracerebral hemorrhage were established by injection of autologous blood into the caudate nucleus in rats. Cell apoptosis was measured by flow cytometry and immunohistochemical staining of the p75 neurotrophin receptor. p75 neurotrophin receptor protein was detected by immunohistochemistry. p75 neurotrophin receptor mRNA was examined by quantitative real-time polymerase chain reactions. At 24 hours after modeling, cellular apoptosis occured around hematoma with upregulation of p75 neurotrophin receptor protein and mRNA was observed, which directly correlated to apoptosis. This observation indicated that p75 neurotrophin receptor upregulation was associated with cell apoptosis around hematomas after intracerebral hemorrhage.

Further evidence of no linkage between schizophrenia and the dopamine D{sub 3} receptor gene locus

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

Nanko, S.; Fukuda, R.; Hattori, M.

The dopamine hypothesis of schizophrenia proposed that dopaminergic pathways are involved in the etiology of the disease. In particular, interest among psychiatrists has focused on the D{sub 2} receptor because of its affinity to antipsychotic drugs. Recently a new dopamine receptor gene has been cloned and named the dopamine D{sub 3} receptor. The D{sub 3} receptor is a potential site for antipsychotic drug action and may be involved in the pathophysiology of schizophrenia. We have carried out a linkage study between the susceptibility gene for schizophrenia and polymorphism of the dopamine D{sub 3} receptor gene in two Japanese pedigrees. Themore » LOD scores were negative for all genetic models and for all affective status at a recombination fraction {theta} = 0. Linkage of DRD{sub 3} has been excluded for the model 1 (dominant model) and the model 3 (recessive model). The LOD score was -3.43 at {theta} = 0 for model 1 (dominant model) and broad definition of affected status. These results were consistent with previous studies. 19 refs., 2 figs., 3 tabs.« less

Role of spatial inhomogenity in GPCR dimerisation predicted by receptor association-diffusion models

NASA Astrophysics Data System (ADS)

Deshpande, Sneha A.; Pawar, Aiswarya B.; Dighe, Anish; Athale, Chaitanya A.; Sengupta, Durba

2017-06-01

G protein-coupled receptor (GPCR) association is an emerging paradigm with far reaching implications in the regulation of signalling pathways and therapeutic interventions. Recent super resolution microscopy studies have revealed that receptor dimer steady state exhibits sub-second dynamics. In particular the GPCRs, muscarinic acetylcholine receptor M1 (M1MR) and formyl peptide receptor (FPR), have been demonstrated to exhibit a fast association/dissociation kinetics, independent of ligand binding. In this work, we have developed a spatial kinetic Monte Carlo model to investigate receptor homo-dimerisation at a single receptor resolution. Experimentally measured association/dissociation kinetic parameters and diffusion coefficients were used as inputs to the model. To test the effect of membrane spatial heterogeneity on the simulated steady state, simulations were compared to experimental statistics of dimerisation. In the simplest case the receptors are assumed to be diffusing in a spatially homogeneous environment, while spatial heterogeneity is modelled to result from crowding, membrane micro-domains and cytoskeletal compartmentalisation or ‘corrals’. We show that a simple association-diffusion model is sufficient to reproduce M1MR association statistics, but fails to reproduce FPR statistics despite comparable kinetic constants. A parameter sensitivity analysis is required to reproduce the association statistics of FPR. The model reveals the complex interplay between cytoskeletal components and their influence on receptor association kinetics within the features of the membrane landscape. These results constitute an important step towards understanding the factors modulating GPCR organisation.

Repeat polymorphisms in ESR2 and AR and colorectal cancer risk and prognosis: results from a German population-based case-control study.

PubMed

Rudolph, Anja; Shi, Hong; Försti, Asta; Hoffmeister, Michael; Sainz, Juan; Jansen, Lina; Hemminki, Kari; Brenner, Hermann; Chang-Claude, Jenny

2014-11-07

Evidence has accumulated which suggests that sex steroids influence colorectal cancer development and progression. We therefore assessed the association of repeat polymorphisms in the estrogen receptor β gene (ESR2) and the androgen receptor gene (AR) with colorectal cancer risk and prognosis. The ESR2 CA and AR CAG repeat polymorphisms were genotyped in 1798 cases (746 female, 1052 male) and 1810 controls (732 female, 1078 male), matched for sex, age and county of residence. Colorectal cancer risk associations overall and specific for gender were evaluated using multivariate logistic regression models adjusted for sex, county of residence and age. Associations with overall and disease-specific survival were evaluated using Cox proportional hazard models adjusted for established prognostic factors (diagnosis of other cancer after colorectal cancer diagnosis, detection by screening, treatment with adjuvant chemotherapy, tumour extent, nodal status, distant metastasis, body mass index, age at diagnosis and year of diagnosis) and stratified for grade of differentiation. Heterogeneity in gender specific associations was assessed by comparing models with and without a multiplicative interaction term by means of a likelihood ratio test. The average number of ESR2 CA repeats was associated with a small 5% increase in colorectal cancer risk (OR = 1.05, 95% CI 1.01-1.10) without significant heterogeneity according to gender or tumoural ESR2 expression. We found no indication for an association between the AR CAG repeat polymorphisms and risk of colorectal cancer. The ESR2 CA and AR CAG repeat polymorphisms were not associated with overall survival or disease specific survival after colorectal cancer diagnosis. Higher numbers of ESR2 CA repeats are potentially associated with a small increase in colorectal cancer risk. Our study does not support an association between colorectal cancer prognosis and the investigated repeat polymorphisms.

Multiplex proteomics for prediction of major cardiovascular events in type 2 diabetes.

PubMed

Nowak, Christoph; Carlsson, Axel C; Östgren, Carl Johan; Nyström, Fredrik H; Alam, Moudud; Feldreich, Tobias; Sundström, Johan; Carrero, Juan-Jesus; Leppert, Jerzy; Hedberg, Pär; Henriksen, Egil; Cordeiro, Antonio C; Giedraitis, Vilmantas; Lind, Lars; Ingelsson, Erik; Fall, Tove; Ärnlöv, Johan

2018-05-24

Multiplex proteomics could improve understanding and risk prediction of major adverse cardiovascular events (MACE) in type 2 diabetes. This study assessed 80 cardiovascular and inflammatory proteins for biomarker discovery and prediction of MACE in type 2 diabetes. We combined data from six prospective epidemiological studies of 30-77-year-old individuals with type 2 diabetes in whom 80 circulating proteins were measured by proximity extension assay. Multivariable-adjusted Cox regression was used in a discovery/replication design to identify biomarkers for incident MACE. We used gradient-boosted machine learning and lasso regularised Cox regression in a random 75% training subsample to assess whether adding proteins to risk factors included in the Swedish National Diabetes Register risk model would improve the prediction of MACE in the separate 25% test subsample. Of 1211 adults with type 2 diabetes (32% women), 211 experienced a MACE over a mean (±SD) of 6.4 ± 2.3 years. We replicated associations (<5% false discovery rate) between risk of MACE and eight proteins: matrix metalloproteinase (MMP)-12, IL-27 subunit α (IL-27a), kidney injury molecule (KIM)-1, fibroblast growth factor (FGF)-23, protein S100-A12, TNF receptor (TNFR)-1, TNFR-2 and TNF-related apoptosis-inducing ligand receptor (TRAIL-R)2. Addition of the 80-protein assay to established risk factors improved discrimination in the separate test sample from 0.686 (95% CI 0.682, 0.689) to 0.748 (95% CI 0.746, 0.751). A sparse model of 20 added proteins achieved a C statistic of 0.747 (95% CI 0.653, 0.842) in the test sample. We identified eight protein biomarkers, four of which are novel, for risk of MACE in community residents with type 2 diabetes, and found improved risk prediction by combining multiplex proteomics with an established risk model. Multiprotein arrays could be useful in identifying individuals with type 2 diabetes who are at highest risk of a cardiovascular event.

Integrated Experimental and Model-based Analysis Reveals the Spatial Aspects of EGFR Activation Dynamics

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

Shankaran, Harish; Zhang, Yi; Chrisler, William B.

2012-10-02

The epidermal growth factor receptor (EGFR) belongs to the ErbB family of receptor tyrosine kinases, and controls a diverse set of cellular responses relevant to development and tumorigenesis. ErbB activation is a complex process involving receptor-ligand binding, receptor dimerization, phosphorylation, and trafficking (internalization, recycling and degradation), which together dictate the spatio-temporal distribution of active receptors within the cell. The ability to predict this distribution, and elucidation of the factors regulating it, would help to establish a mechanistic link between ErbB expression levels and the cellular response. Towards this end, we constructed mathematical models for deconvolving the contributions of receptor dimerizationmore » and phosphorylation to EGFR activation, and to examine the dependence of these processes on sub-cellular location. We collected experimental datasets for EGFR activation dynamics in human mammary epithelial cells, with the specific goal of model parameterization, and used the data to estimate parameters for several alternate models. Model-based analysis indicated that: 1) signal termination via receptor dephosphorylation in late endosomes, prior to degradation, is an important component of the response, 2) less than 40% of the receptors in the cell are phosphorylated at any given time, even at saturating ligand doses, and 3) receptor dephosphorylation rates at the cell surface and early endosomes are comparable. We validated the last finding by measuring EGFR dephosphorylation rates at various times following ligand addition both in whole cells, and in endosomes using ELISAs and fluorescent imaging. Overall, our results provide important information on how EGFR phosphorylation levels are regulated within cells. Further, the mathematical model described here can be extended to determine receptor dimer abundances in cells co-expressing various levels of ErbB receptors. This study demonstrates that an iterative cycle of experiments and modeling can be used to gain mechanistic insight regarding complex cell signaling networks.« less

Investigation of the anxiolytic effects of linalool, a lavender extract, in the male Sprague-Dawley rat.

PubMed

Cline, Michael; Taylor, John E; Flores, Jesus; Bracken, Samuel; McCall, Suzanne; Ceremuga, Thomas E

2008-02-01

The purpose of our study was to investigate the anxiolytic effects of linalool and its potential interaction with the GABAA receptor in Sprague-Dawley rats. Lavender has been used traditionally as an herbal remedy in the treatment of many medical conditions, including anxiety. Linalool is a major component of the essential oil of lavender. Forty-four rats were divided into 4 groups: control, linalool, midazolam (positive control), and flumazenil and linalool. The behavioral and the neurohormonal/physiological components of anxiety were evaluated. The behavioral component was examined by using the elevated plus maze (open arm time/total time) and the neurohormonal/physiological component by measuring serum catecholamine and corticosterone levels. Data analysis was performed using a 2-tailed Multivariate Analysis of Variance and Sheffe post-hoc test. Our data suggest that linalool does not produce anxiolysis by modulation of the GABAA receptor; however, linalool may modulate motor movements and locomotion.

Dopamine-Related Disruption of Functional Topography of Striatal Connections in Unmedicated Patients With Schizophrenia.

PubMed

Horga, Guillermo; Cassidy, Clifford M; Xu, Xiaoyan; Moore, Holly; Slifstein, Mark; Van Snellenberg, Jared X; Abi-Dargham, Anissa

2016-08-01

Despite the well-established role of striatal dopamine in psychosis, current views generally agree that cortical dysfunction is likely necessary for the emergence of psychotic symptoms. The topographic organization of striatal-cortical connections is central to gating and integration of higher-order information, so a disruption of such topography via dysregulated dopamine could lead to cortical dysfunction in schizophrenia. However, this hypothesis remains to be tested using multivariate methods ascertaining the global pattern of striatal connectivity and without the confounding effects of antidopaminergic medication. To examine whether the pattern of brain connectivity across striatal subregions is abnormal in unmedicated patients with schizophrenia and whether this abnormality relates to psychotic symptoms and extrastriatal dopaminergic transmission. In this multimodal, case-control study, we obtained resting-state functional magnetic resonance imaging data from 18 unmedicated patients with schizophrenia and 24 matched healthy controls from the New York State Psychiatric Institute. A subset of these (12 and 17, respectively) underwent positron emission tomography with the dopamine D2 receptor radiotracer carbon 11-labeled FLB457 before and after amphetamine administration. Data were acquired between June 16, 2011, and February 25, 2014. Data analysis was performed from September 1, 2014, to January 11, 2016. Group differences in the striatal connectivity pattern (assessed via multivariable logistic regression) across striatal subregions, the association between the multivariate striatal connectivity pattern and extrastriatal baseline D2 receptor binding potential and its change after amphetamine administration, and the association between the multivariate connectivity pattern and the severity of positive symptoms evaluated with the Positive and Negative Syndrome Scale. Of the patients with schizophrenia (mean [SEM] age, 35.6 [11.8] years), 9 (50%) were male and 9 (50%) were female. Of the controls (mean [SEM] age, 33.7 [8.8] years), 10 (42%) were male and 14 (58%) were female. Patients had an abnormal pattern of striatal connectivity, which included abnormal caudate connections with a distributed set of associative cortex regions (χ229 = 53.55, P = .004). In patients, more deviation from the multivariate pattern of striatal connectivity found in controls correlated specifically with more severe positive symptoms (ρ = -0.77, P = .002). Striatal connectivity also correlated with baseline binding potential across cortical and extrastriatal subcortical regions (t25 = 3.01, P = .01, Bonferroni corrected) but not with its change after amphetamine administration. Using a multimodal, circuit-level interrogation of striatal-cortical connections, it was demonstrated that the functional topography of these connections is globally disrupted in unmedicated patients with schizophrenia. These findings suggest that striatal-cortical dysconnectivity may underlie the effects of dopamine dysregulation on the pathophysiologic mechanism of psychotic symptoms.

A General Multivariate Latent Growth Model with Applications to Student Achievement

ERIC Educational Resources Information Center

Bianconcini, Silvia; Cagnone, Silvia

2012-01-01

The evaluation of the formative process in the University system has been assuming an ever increasing importance in the European countries. Within this context, the analysis of student performance and capabilities plays a fundamental role. In this work, the authors propose a multivariate latent growth model for studying the performances of a…

Bayesian Estimation of Random Coefficient Dynamic Factor Models

ERIC Educational Resources Information Center

Song, Hairong; Ferrer, Emilio

2012-01-01

Dynamic factor models (DFMs) have typically been applied to multivariate time series data collected from a single unit of study, such as a single individual or dyad. The goal of DFMs application is to capture dynamics of multivariate systems. When multiple units are available, however, DFMs are not suited to capture variations in dynamics across…

Rotation in the Dynamic Factor Modeling of Multivariate Stationary Time Series.

ERIC Educational Resources Information Center

Molenaar, Peter C. M.; Nesselroade, John R.

2001-01-01

Proposes a special rotation procedure for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white noise, into a univariate moving-average.…

Modeling Associations among Multivariate Longitudinal Categorical Variables in Survey Data: A Semiparametric Bayesian Approach

ERIC Educational Resources Information Center

Tchumtchoua, Sylvie; Dey, Dipak K.

2012-01-01

This paper proposes a semiparametric Bayesian framework for the analysis of associations among multivariate longitudinal categorical variables in high-dimensional data settings. This type of data is frequent, especially in the social and behavioral sciences. A semiparametric hierarchical factor analysis model is developed in which the…

Parametric Cost Models for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip

2010-01-01

A study is in-process to develop a multivariable parametric cost model for space telescopes. Cost and engineering parametric data has been collected on 30 different space telescopes. Statistical correlations have been developed between 19 variables of 59 variables sampled. Single Variable and Multi-Variable Cost Estimating Relationships have been developed. Results are being published.

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments

PubMed Central

Avalappampatty Sivasamy, Aneetha; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T2 method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T2 statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better. PMID:26357668

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments.

PubMed

Sivasamy, Aneetha Avalappampatty; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T(2) method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T(2) statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better.

Predictive model for falling in Parkinson disease patients.

PubMed

Custodio, Nilton; Lira, David; Herrera-Perez, Eder; Montesinos, Rosa; Castro-Suarez, Sheila; Cuenca-Alfaro, Jose; Cortijo, Patricia

2016-12-01

Falls are a common complication of advancing Parkinson's disease (PD). Although numerous risk factors are known, reliable predictors of future falls are still lacking. The aim of this study was to develop a multivariate model to predict falling in PD patients. Prospective cohort with forty-nine PD patients. The area under the receiver-operating characteristic curve (AUC) was calculated to evaluate predictive performance of the purposed multivariate model. The median of PD duration and UPDRS-III score in the cohort was 6 years and 24 points, respectively. Falls occurred in 18 PD patients (30%). Predictive factors for falling identified by univariate analysis were age, PD duration, physical activity, and scores of UPDRS motor, FOG, ACE, IFS, PFAQ and GDS ( p -value < 0.001), as well as fear of falling score ( p -value = 0.04). The final multivariate model (PD duration, FOG, ACE, and physical activity) showed an AUC = 0.9282 (correctly classified = 89.83%; sensitivity = 92.68%; specificity = 83.33%). This study showed that our multivariate model have a high performance to predict falling in a sample of PD patients.

The choice of prior distribution for a covariance matrix in multivariate meta-analysis: a simulation study.

PubMed

Hurtado Rúa, Sandra M; Mazumdar, Madhu; Strawderman, Robert L

2015-12-30

Bayesian meta-analysis is an increasingly important component of clinical research, with multivariate meta-analysis a promising tool for studies with multiple endpoints. Model assumptions, including the choice of priors, are crucial aspects of multivariate Bayesian meta-analysis (MBMA) models. In a given model, two different prior distributions can lead to different inferences about a particular parameter. A simulation study was performed in which the impact of families of prior distributions for the covariance matrix of a multivariate normal random effects MBMA model was analyzed. Inferences about effect sizes were not particularly sensitive to prior choice, but the related covariance estimates were. A few families of prior distributions with small relative biases, tight mean squared errors, and close to nominal coverage for the effect size estimates were identified. Our results demonstrate the need for sensitivity analysis and suggest some guidelines for choosing prior distributions in this class of problems. The MBMA models proposed here are illustrated in a small meta-analysis example from the periodontal field and a medium meta-analysis from the study of stroke. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Development of 7TM receptor-ligand complex models using ligand-biased, semi-empirical helix-bundle repacking in torsion space: application to the agonist interaction of the human dopamine D2 receptor.

PubMed

Malo, Marcus; Persson, Ronnie; Svensson, Peder; Luthman, Kristina; Brive, Lars

2013-03-01

Prediction of 3D structures of membrane proteins, and of G-protein coupled receptors (GPCRs) in particular, is motivated by their importance in biological systems and the difficulties associated with experimental structure determination. In the present study, a novel method for the prediction of 3D structures of the membrane-embedded region of helical membrane proteins is presented. A large pool of candidate models are produced by repacking of the helices of a homology model using Monte Carlo sampling in torsion space, followed by ranking based on their geometric and ligand-binding properties. The trajectory is directed by weak initial restraints to orient helices towards the original model to improve computation efficiency, and by a ligand to guide the receptor towards a chosen conformational state. The method was validated by construction of the β1 adrenergic receptor model in complex with (S)-cyanopindolol using bovine rhodopsin as template. In addition, models of the dopamine D2 receptor were produced with the selective and rigid agonist (R)-N-propylapomorphine ((R)-NPA) present. A second quality assessment was implemented by evaluating the results from docking of a library of 29 ligands with known activity, which further discriminated between receptor models. Agonist binding and recognition by the dopamine D2 receptor is interpreted using the 3D structure model resulting from the approach. This method has a potential for modeling of all types of helical transmembrane proteins for which a structural template with sequence homology sufficient for homology modeling is not available or is in an incorrect conformational state, but for which sufficient empirical information is accessible.

Studying Resist Stochastics with the Multivariate Poisson Propagation Model

DOE PAGES

Naulleau, Patrick; Anderson, Christopher; Chao, Weilun; ...

2014-01-01

Progress in the ultimate performance of extreme ultraviolet resist has arguably decelerated in recent years suggesting an approach to stochastic limits both in photon counts and material parameters. Here we report on the performance of a variety of leading extreme ultraviolet resist both with and without chemical amplification. The measured performance is compared to stochastic modeling results using the Multivariate Poisson Propagation Model. The results show that the best materials are indeed nearing modeled performance limits.

Multivariable Parametric Cost Model for Ground Optical Telescope Assembly

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia

2005-01-01

A parametric cost model for ground-based telescopes is developed using multivariable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction-limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature are examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e., multi-telescope phased-array systems). Additionally, single variable models Based on aperture diameter are derived.

Homology Modeling of Class A G Protein-Coupled Receptors

PubMed Central

Costanzi, Stefano

2012-01-01

G protein-coupled receptors (GPCRs) are a large superfamily of membrane bound signaling proteins that hold great pharmaceutical interest. Since experimentally elucidated structures are available only for a very limited number of receptors, homology modeling has become a widespread technique for the construction of GPCR models intended to study the structure-function relationships of the receptors and aid the discovery and development of ligands capable of modulating their activity. Through this chapter, various aspects involved in the constructions of homology models of the serpentine domain of the largest class of GPCRs, known as class A or rhodopsin family, are illustrated. In particular, the chapter provides suggestions, guidelines and critical thoughts on some of the most crucial aspect of GPCR modeling, including: collection of candidate templates and a structure-based alignment of their sequences; identification and alignment of the transmembrane helices of the query receptor to the corresponding domains of the candidate templates; selection of one or more templates receptor; election of homology or de novo modeling for the construction of specific extracellular and intracellular domains; construction of the three-dimensional models, with special consideration to extracellular regions, disulfide bridges, and interhelical cavity; validation of the models through controlled virtual screening experiments. PMID:22323225

Order-restricted inference for multivariate longitudinal data with applications to the natural history of hearing loss.

PubMed

Rosen, Sophia; Davidov, Ori

2012-07-20

Multivariate outcomes are often measured longitudinally. For example, in hearing loss studies, hearing thresholds for each subject are measured repeatedly over time at several frequencies. Thus, each patient is associated with a multivariate longitudinal outcome. The multivariate mixed-effects model is a useful tool for the analysis of such data. There are situations in which the parameters of the model are subject to some restrictions or constraints. For example, it is known that hearing thresholds, at every frequency, increase with age. Moreover, this age-related threshold elevation is monotone in frequency, that is, the higher the frequency, the higher, on average, is the rate of threshold elevation. This means that there is a natural ordering among the different frequencies in the rate of hearing loss. In practice, this amounts to imposing a set of constraints on the different frequencies' regression coefficients modeling the mean effect of time and age at entry to the study on hearing thresholds. The aforementioned constraints should be accounted for in the analysis. The result is a multivariate longitudinal model with restricted parameters. We propose estimation and testing procedures for such models. We show that ignoring the constraints may lead to misleading inferences regarding the direction and the magnitude of various effects. Moreover, simulations show that incorporating the constraints substantially improves the mean squared error of the estimates and the power of the tests. We used this methodology to analyze a real hearing loss study. Copyright © 2012 John Wiley & Sons, Ltd.

Exploring the Interaction between TSC2, PTEN, and the NMDA Receptor in Animal Models of Tuberous Sclerosis

DTIC Science & Technology

2014-09-01

Sunnen CN, Crowell B, Lee GH, Anderson AE, and D’Arcangelo G. Examination of the Role of Pten in Ionotropic Glutamate Receptor Expression. National...PTEN, and the NMDA Receptor in Animal Models of Tuberous Sclerosis PRINCIPAL INVESTIGATOR: D’Arcangelo, Gabriella CONTRACTING...June 2014 4. TITLE AND SUBTITLE Exploring the Interaction between TSC2, PTEN, and the NMDA Receptor in Animal Models of Tuberous Sclerosis 5a

Odor Preference Learning and Memory Modify GluA1 Phosphorylation and GluA1 Distribution in the Neonate Rat Olfactory Bulb: Testing the AMPA Receptor Hypothesis in an Appetitive Learning Model

ERIC Educational Resources Information Center

Cui, Wen; Darby-King, Andrea; Grimes, Matthew T.; Howland, John G.; Wang, Yu Tian; McLean, John H.; Harley, Carolyn W.

2011-01-01

An increase in synaptic AMPA receptors is hypothesized to mediate learning and memory. AMPA receptor increases have been reported in aversive learning models, although it is not clear if they are seen with memory maintenance. Here we examine AMPA receptor changes in a cAMP/PKA/CREB-dependent appetitive learning model: odor preference learning in…

The NLS-Based Nonlinear Grey Multivariate Model for Forecasting Pollutant Emissions in China

PubMed Central

Pei, Ling-Ling; Li, Qin

2018-01-01

The relationship between pollutant discharge and economic growth has been a major research focus in environmental economics. To accurately estimate the nonlinear change law of China’s pollutant discharge with economic growth, this study establishes a transformed nonlinear grey multivariable (TNGM (1, N)) model based on the nonlinear least square (NLS) method. The Gauss–Seidel iterative algorithm was used to solve the parameters of the TNGM (1, N) model based on the NLS basic principle. This algorithm improves the precision of the model by continuous iteration and constantly approximating the optimal regression coefficient of the nonlinear model. In our empirical analysis, the traditional grey multivariate model GM (1, N) and the NLS-based TNGM (1, N) models were respectively adopted to forecast and analyze the relationship among wastewater discharge per capita (WDPC), and per capita emissions of SO2 and dust, alongside GDP per capita in China during the period 1996–2015. Results indicated that the NLS algorithm is able to effectively help the grey multivariable model identify the nonlinear relationship between pollutant discharge and economic growth. The results show that the NLS-based TNGM (1, N) model presents greater precision when forecasting WDPC, SO2 emissions and dust emissions per capita, compared to the traditional GM (1, N) model; WDPC indicates a growing tendency aligned with the growth of GDP, while the per capita emissions of SO2 and dust reduce accordingly. PMID:29517985

Effect of epidermal growth factor receptor gene polymorphisms on prognosis in glioma patients

PubMed Central

Li, Jingjie; Yan, Mengdan; Xie, Zhilan; Zhu, Yuanyuan; Chen, Chao; Jin, Tianbo

2016-01-01

Previous studies suggested that single nucleotide polymorphisms (SNPs) in epidermal growth factor receptor (EGFR) are associated with risk of glioma. However, the associations between these SNPs and glioma patient prognosis have not yet been fully investigated. Therefore, the present study was aimed to evaluate the effects of EGFR polymorphisms on the glioma patient prognosis. We retrospectively evaluated 269 glioma patients and investigated associations between EGFR SNPs and patient prognosis using Cox proportional hazard models and Kaplan-Meier curves. Univariate analysis revealed that age, gross-total resection and chemotherapy were associated with the prognosis of glioma patients (p < 0.05). In addition, four EGFR SNPs (rs11506105, rs3752651, rs1468727 and rs845552) correlated with overall survival (OS) (Log-rank p = 0.011, 0.020, 0.008, and 0.009, respectively) and progression-free survival PFS (Log-rank p = 0.026, 0.024, 0.019 and 0.009, respectively). Multivariate analysis indicated that the rs11506105 G/G genotype, the rs3752651 and rs1468727 C/C genotype and the rs845552 A/A genotype correlated inversely with OS and PFS. In addition, OS among patients with the rs730437 C/C genotype (p = 0.030) was significantly lower OS than among patients with A/A genotype. These data suggest that five EGFR SNPs (rs11506105, rs3752651, rs1468727, rs845552 and rs730437) correlated with glioma patient prognosis, and should be furthered validated in studies of ethnically diverse patients. PMID:27437777

Tumor gene expression and prognosis in breast cancer patients with 10 or more positive lymph nodes.

PubMed

Cobleigh, Melody A; Tabesh, Bita; Bitterman, Pincas; Baker, Joffre; Cronin, Maureen; Liu, Mei-Lan; Borchik, Russell; Mosquera, Juan-Miguel; Walker, Michael G; Shak, Steven

2005-12-15

This study, along with two others, was done to develop the 21-gene Recurrence Score assay (Oncotype DX) that was validated in a subsequent independent study and is used to aid decision making about chemotherapy in estrogen receptor (ER)-positive, node-negative breast cancer patients. Patients with >or=10 nodes diagnosed from 1979 to 1999 were identified. RNA was extracted from paraffin blocks, and expression of 203 candidate genes was quantified using reverse transcription-PCR (RT-PCR). Seventy-eight patients were studied. As of August 2002, 77% of patients had distant recurrence or breast cancer death. Univariate Cox analysis of clinical and immunohistochemistry variables indicated that HER2/immunohistochemistry, number of involved nodes, progesterone receptor (PR)/immunohistochemistry (% cells), and ER/immunohistochemistry (% cells) were significantly associated with distant recurrence-free survival (DRFS). Univariate Cox analysis identified 22 genes associated with DRFS. Higher expression correlated with shorter DRFS for the HER2 adaptor GRB7 and the macrophage marker CD68. Higher expression correlated with longer DRFS for tumor protein p53-binding protein 2 (TP53BP2) and the ER axis genes PR and Bcl2. Multivariate methods, including stepwise variable selection and bootstrap resampling of the Cox proportional hazards regression model, identified several genes, including TP53BP2 and Bcl2, as significant predictors of DRFS. Tumor gene expression profiles of archival tissues, some more than 20 years old, provide significant information about risk of distant recurrence even among patients with 10 or more nodes.

Genotype distribution of estrogen receptor-alpha, catechol-O-methyltransferase, and cytochrome P450 17 gene polymorphisms in Caucasian women with uterine leiomyomas.

PubMed

Denschlag, Dominik; Bentz, Eva-Katrin; Hefler, Lukas; Pietrowski, Detlef; Zeillinger, Robert; Tempfer, Clemens; Tong, Dan

2006-02-01

To evaluate the association between the presence of uterine leiomyomas and three functional single nucleotide polymorphisms (SNPs) of the estrogen receptor alpha (ESR1), catechol-O-methyltransferase (COMT), and cytochrom P450 17 (CYP17A) genes, which have been described to modify the estrogen metabolism. Prospective case control study. Academic research institution. One hundred thirty women with clinically and surgically diagnosed uterine leiomyomas and 139 population controls. Peripheral venous puncture. Polymerase chain reaction and pyrosequencing were performed to genotype women with respect to the ESR1 IVS1-397 T/C (PvuII), COMT G158A, and the CYP17A 34T-->C SNPs. Comparing women with uterine leiomyomas and controls, no statistically significant differences with respect to allele frequency and genotype distribution were ascertained for ESR1 IVS 1-397 T/C (PvuII) (P=0.9 and P=0.6, respectively), COMT G158A (P=0.3 and P=0.6, respectively), and CYP17A 34T-->C (P=0.1 and P=0.5, respectively). When all two-way interactions of investigated SNPs were ascertained, no significant interactions were observed. In a multivariate model, no SNP was significantly associated with leiomyomas. Carriage of the ESR1 IVS1-397 T/C (PvuII), COMT G158A, and the CYP17A 34T-->C SNPs is not associated with the susceptibility to uterine leiomyoma in a Caucasian population.

Prognostic Impact of 21-Gene Recurrence Score in Patients With Stage IV Breast Cancer: TBCRC 013.

PubMed

King, Tari A; Lyman, Jaclyn P; Gonen, Mithat; Voci, Amy; De Brot, Marina; Boafo, Camilla; Sing, Amy Pratt; Hwang, E Shelley; Alvarado, Michael D; Liu, Minetta C; Boughey, Judy C; McGuire, Kandace P; Van Poznak, Catherine H; Jacobs, Lisa K; Meszoely, Ingrid M; Krontiras, Helen; Babiera, Gildy V; Norton, Larry; Morrow, Monica; Hudis, Clifford A

2016-07-10

The objective of this study was to determine whether the 21-gene Recurrence Score (RS) provides clinically meaningful information in patients with de novo stage IV breast cancer enrolled in the Translational Breast Cancer Research Consortium (TBCRC) 013. TBCRC 013 was a multicenter prospective registry that evaluated the role of surgery of the primary tumor in patients with de novo stage IV breast cancer. From July 2009 to April 2012, 127 patients from 14 sites were enrolled; 109 (86%) patients had pretreatment primary tumor samples suitable for 21-gene RS analysis. Clinical variables, time to first progression (TTP), and 2-year overall survival (OS) were correlated with the 21-gene RS by using log-rank, Kaplan-Meier, and Cox regression. Median patient age was 52 years (21 to 79 years); the majority had hormone receptor-positive/human epidermal growth factor receptor 2 (HER2)-negative (72 [66%]) or hormone receptor-positive/HER2-positive (20 [18%]) breast cancer. At a median follow-up of 29 months, median TTP was 20 months (95% CI, 16 to 26 months), and median survival was 49 months (95% CI, 40 months to not reached). An RS was generated for 101 (93%) primary tumor samples: 22 (23%) low risk (< 18), 29 (28%) intermediate risk (18 to 30); and 50 (49%) high risk (≥ 31). For all patients, RS was associated with TTP (P = .01) and 2-year OS (P = .04). In multivariable Cox regression models among 69 patients with estrogen receptor (ER)-positive/HER2-negative cancer, RS was independently prognostic for TTP (hazard ratio, 1.40; 95% CI, 1.05 to 1.86; P = .02) and 2-year OS (hazard ratio, 1.83; 95% CI, 1.14 to 2.95; P = .013). The 21-gene RS is independently prognostic for both TTP and 2-year OS in ER-positive/HER2-negative de novo stage IV breast cancer. Prospective validation is needed to determine the potential role for this assay in the clinical management of this patient subset. © 2016 by American Society of Clinical Oncology.

Space-time variation of respiratory cancers in South Carolina: a flexible multivariate mixture modeling approach to risk estimation.

PubMed

Carroll, Rachel; Lawson, Andrew B; Kirby, Russell S; Faes, Christel; Aregay, Mehreteab; Watjou, Kevin

2017-01-01

Many types of cancer have an underlying spatiotemporal distribution. Spatiotemporal mixture modeling can offer a flexible approach to risk estimation via the inclusion of latent variables. In this article, we examine the application and benefits of using four different spatiotemporal mixture modeling methods in the modeling of cancer of the lung and bronchus as well as "other" respiratory cancer incidences in the state of South Carolina. Of the methods tested, no single method outperforms the other methods; which method is best depends on the cancer under consideration. The lung and bronchus cancer incidence outcome is best described by the univariate modeling formulation, whereas the "other" respiratory cancer incidence outcome is best described by the multivariate modeling formulation. Spatiotemporal multivariate mixture methods can aid in the modeling of cancers with small and sparse incidences when including information from a related, more common type of cancer. Copyright © 2016 Elsevier Inc. All rights reserved.

Multivariate Time Series Decomposition into Oscillation Components.

PubMed

Matsuda, Takeru; Komaki, Fumiyasu

2017-08-01

Many time series are considered to be a superposition of several oscillation components. We have proposed a method for decomposing univariate time series into oscillation components and estimating their phases (Matsuda & Komaki, 2017 ). In this study, we extend that method to multivariate time series. We assume that several oscillators underlie the given multivariate time series and that each variable corresponds to a superposition of the projections of the oscillators. Thus, the oscillators superpose on each variable with amplitude and phase modulation. Based on this idea, we develop gaussian linear state-space models and use them to decompose the given multivariate time series. The model parameters are estimated from data using the empirical Bayes method, and the number of oscillators is determined using the Akaike information criterion. Therefore, the proposed method extracts underlying oscillators in a data-driven manner and enables investigation of phase dynamics in a given multivariate time series. Numerical results show the effectiveness of the proposed method. From monthly mean north-south sunspot number data, the proposed method reveals an interesting phase relationship.

Multivariate meta-analysis using individual participant data

PubMed Central

Riley, R. D.; Price, M. J.; Jackson, D.; Wardle, M.; Gueyffier, F.; Wang, J.; Staessen, J. A.; White, I. R.

2016-01-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment–covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. PMID:26099484

Operational models of pharmacological agonism.

PubMed

Black, J W; Leff, P

1983-12-22

The traditional receptor-stimulus model of agonism began with a description of drug action based on the law of mass action and has developed by a series of modifications, each accounting for new experimental evidence. By contrast, in this paper an approach to modelling agonism is taken that begins with the observation that experimental agonist-concentration effect, E/[A], curves are commonly hyperbolic and develops using the deduction that the relation between occupancy and effect must be hyperbolic if the law of mass action applies at the agonist-receptor level. The result is a general model that explicitly describes agonism by three parameters: an agonist-receptor dissociation constant, KA; the total receptor concentration, [R0]; and a parameter, KE, defining the transduction of agonist-receptor complex, AR, into pharmacological effect. The ratio, [R0]/KE, described here as the 'transducer ratio', tau, is a logical definition for the efficacy of an agonist in a system. The model may be extended to account for non-hyperbolic E/[A] curves with no loss of meaning. Analysis shows that an explicit formulation of the traditional receptor-stimulus model is one particular form of the general model but that it is not the simplest. An alternative model is proposed, representing the cognitive and transducer functions of a receptor, that describes agonist action with one fewer parameter than the traditional model. In addition, this model provides a chemical definition of intrinsic efficacy making this parameter experimentally accessible in principle. The alternative models are compared and contrasted with regard to their practical and conceptual utilities in experimental pharmacology.

Radiation Therapy Versus No Radiation Therapy to the Neo-breast Following Skin-Sparing Mastectomy and Immediate Autologous Free Flap Reconstruction for Breast Cancer: Patient-Reported and Surgical Outcomes at 1 Year-A Mastectomy Reconstruction Outcomes Consortium (MROC) Substudy.

PubMed

Cooke, Andrew L; Diaz-Abele, Julian; Hayakawa, Tom; Buchel, Ed; Dalke, Kimberly; Lambert, Pascal

2017-09-01

To determine whether adjuvant radiation therapy (RT) is associated with adverse patient-reported outcomes and surgical complications 1 year after skin-sparing mastectomy and immediate autologous free flap reconstruction for breast cancer. We compared 24 domains of patient-reported outcome measures 1 year after autologous reconstruction between patients who received adjuvant RT and those who did not. A total of 125 patients who underwent surgery between 2012 and 2015 at our institution were included from the Mastectomy Reconstruction Outcomes Consortium study database. Adjusted multivariate models were created incorporating RT technical data, age, cancer stage, estrogen receptor, chemotherapy, breast size, body mass index, and income to determine whether RT was associated with outcomes. At 1 year after surgery, European Organisation for Research and Treatment of Cancer (EORTC) Breast Cancer-Specific Quality of Life Questionnaire breast symptoms were significantly greater in 64 patients who received RT (8-point difference on 100-point ordinal scale, P<.0001) versus 61 who did not receive RT in univariate and multivariate models. EORTC arm symptoms (20-point difference on 100-point ordinal scale, P=.0200) differed on univariate analysis but not on multivariate analysis. All other outcomes-including Numerical Pain Rating Scale, BREAST-Q (Post-operative Reconstruction Module), Patient-Report Outcomes Measurement Information System Profile 29, McGill Pain Questionnaire-Short Form (MPQ-SF) score, Generalized Anxiety Disorder Scale, and Patient Health Questionnaire-were not statistically different between groups. Surgical complications were uncommon and did not differ by treatment. RT to the neo-breast compared with no RT following immediate autologous free flap reconstruction for breast cancer is well tolerated at 1 year following surgery despite patients undergoing RT also having a higher cancer stage and more intensive surgical and systemic treatment. Neo-breast symptoms are more common in patients receiving RT by the EORTC Breast Cancer-Specific Quality of Life Questionnaire but not by the BREAST-Q. Patient-reported results at 1 year after surgery suggest RT following immediate autologous free flap breast reconstruction is well tolerated. Copyright © 2017 Elsevier Inc. All rights reserved.

Exploring an interaction of adenosine A2A receptor variability with coffee and tea intake in Parkinson's disease.

PubMed

Tan, E K; Lu, Z Y; Fook-Chong, S M C; Tan, E; Shen, H; Chua, E; Yih, Y; Teo, Y Y; Zhao, Y

2006-09-05

Caffeine is an adenosine receptor A1 and A2A receptor antagonist and a putative functional genetic variant of the A2A receptor (2592C > Tins) mediates caffeine-induced anxiety. Here we investigated the potential interaction of this A2A genetic variant with the quantity of coffee and tea intake and their relationship with the risk of PD. A total of 441 subjects consisting of 222 PD and 219 race, gender and age matched controls were included. A multivariate analysis of the variables including the 2592C > Tins A2A genotypes, age of onset, gender, and the quantity of tea and coffee intake, interaction of the A2A genotypes with coffee intake, interaction of A2A genotypes with tea intake demonstrated the quantity of coffee intake to be significantly associated with PD (P < 0.0005, OR = 0.922, 95% CI: 0.881, 0.964). However, there was no significant interaction of the A2A genotypes with the quantity of coffee and tea intake in modulating the risk of PD. The dose dependent protective effect of coffee intake in PD was independent of the 2592C > Tins A2A genotype suggesting that the pharmacogenetic action of caffeine in PD may be mediated differently from other caffeine-induced neurologic syndromes.

Multivariable Parametric Cost Model for Ground Optical: Telescope Assembly

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia

2004-01-01

A parametric cost model for ground-based telescopes is developed using multi-variable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature were examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e. multi-telescope phased-array systems). Additionally, single variable models based on aperture diameter were derived.

A Multivariate Multilevel Approach to the Modeling of Accuracy and Speed of Test Takers

ERIC Educational Resources Information Center

Klein Entink, R. H.; Fox, J. P.; van der Linden, W. J.

2009-01-01

Response times on test items are easily collected in modern computerized testing. When collecting both (binary) responses and (continuous) response times on test items, it is possible to measure the accuracy and speed of test takers. To study the relationships between these two constructs, the model is extended with a multivariate multilevel…

Multivariate regression model for partitioning tree volume of white oak into round-product classes

Treesearch

Daniel A. Yaussy; David L. Sonderman

1984-01-01

Describes the development of multivariate equations that predict the expected cubic volume of four round-product classes from independent variables composed of individual tree-quality characteristics. Although the model has limited application at this time, it does demonstrate the feasibility of partitioning total tree cubic volume into round-product classes based on...

The Dirichlet-Multinomial Model for Multivariate Randomized Response Data and Small Samples

ERIC Educational Resources Information Center

Avetisyan, Marianna; Fox, Jean-Paul

2012-01-01

In survey sampling the randomized response (RR) technique can be used to obtain truthful answers to sensitive questions. Although the individual answers are masked due to the RR technique, individual (sensitive) response rates can be estimated when observing multivariate response data. The beta-binomial model for binary RR data will be generalized…

Tracking Problem Solving by Multivariate Pattern Analysis and Hidden Markov Model Algorithms

ERIC Educational Resources Information Center

Anderson, John R.

2012-01-01

Multivariate pattern analysis can be combined with Hidden Markov Model algorithms to track the second-by-second thinking as people solve complex problems. Two applications of this methodology are illustrated with a data set taken from children as they interacted with an intelligent tutoring system for algebra. The first "mind reading" application…

Four Families of Multi-Variant Issues in Graduate-Level Asynchronous Online Courses

ERIC Educational Resources Information Center

Gisburne, Jaclyn M.; Fairchild, Patricia J.

2004-01-01

This is the first of several papers developed from a faculty and student perspective describing a new distance learning (DL) model. Integral to the model are four interrelated families of multi-variant issues, referred to here as (a) the academic divide, (b) student misalignment, (c) administrative influences, and (d) the use of student…

Assessing Reliability of Student Ratings of Advisor: A Comparison of Univariate and Multivariate Generalizability Approaches.

ERIC Educational Resources Information Center

Sun, Anji; Valiga, Michael J.

In this study, the reliability of the American College Testing (ACT) Program's "Survey of Academic Advising" (SAA) was examined using both univariate and multivariate generalizability theory approaches. The primary purpose of the study was to compare the results of three generalizability theory models (a random univariate model, a mixed…

Web-Based Tools for Modelling and Analysis of Multivariate Data: California Ozone Pollution Activity

ERIC Educational Resources Information Center

Dinov, Ivo D.; Christou, Nicolas

2011-01-01

This article presents a hands-on web-based activity motivated by the relation between human health and ozone pollution in California. This case study is based on multivariate data collected monthly at 20 locations in California between 1980 and 2006. Several strategies and tools for data interrogation and exploratory data analysis, model fitting…

Multivariate Generalizations of Student's t-Distribution. ONR Technical Report. [Biometric Lab Report No. 90-3.

ERIC Educational Resources Information Center

Gibbons, Robert D.; And Others

In the process of developing a conditionally-dependent item response theory (IRT) model, the problem arose of modeling an underlying multivariate normal (MVN) response process with general correlation among the items. Without the assumption of conditional independence, for which the underlying MVN cdf takes on comparatively simple forms and can be…

Bias and Precision of Measures of Association for a Fixed-Effect Multivariate Analysis of Variance Model

ERIC Educational Resources Information Center

Kim, Soyoung; Olejnik, Stephen

2005-01-01

The sampling distributions of five popular measures of association with and without two bias adjusting methods were examined for the single factor fixed-effects multivariate analysis of variance model. The number of groups, sample sizes, number of outcomes, and the strength of association were manipulated. The results indicate that all five…

Homology Modeling of Dopamine D2 and D3 Receptors: Molecular Dynamics Refinement and Docking Evaluation

PubMed Central

Platania, Chiara Bianca Maria; Salomone, Salvatore; Leggio, Gian Marco; Drago, Filippo; Bucolo, Claudio

2012-01-01

Dopamine (DA) receptors, a class of G-protein coupled receptors (GPCRs), have been targeted for drug development for the treatment of neurological, psychiatric and ocular disorders. The lack of structural information about GPCRs and their ligand complexes has prompted the development of homology models of these proteins aimed at structure-based drug design. Crystal structure of human dopamine D3 (hD3) receptor has been recently solved. Based on the hD3 receptor crystal structure we generated dopamine D2 and D3 receptor models and refined them with molecular dynamics (MD) protocol. Refined structures, obtained from the MD simulations in membrane environment, were subsequently used in molecular docking studies in order to investigate potential sites of interaction. The structure of hD3 and hD2L receptors was differentiated by means of MD simulations and D3 selective ligands were discriminated, in terms of binding energy, by docking calculation. Robust correlation of computed and experimental Ki was obtained for hD3 and hD2L receptor ligands. In conclusion, the present computational approach seems suitable to build and refine structure models of homologous dopamine receptors that may be of value for structure-based drug discovery of selective dopaminergic ligands. PMID:22970199

Homology modeling, binding site identification and docking study of human angiotensin II type I (Ang II-AT1) receptor.

PubMed

Vyas, Vivek K; Ghate, Manjunath; Patel, Kinjal; Qureshi, Gulamnizami; Shah, Surmil

2015-08-01

Ang II-AT1 receptors play an important role in mediating virtually all of the physiological actions of Ang II. Several drugs (SARTANs) are available, which can block the AT1 receptor effectively and lower the blood pressure in the patients with hypertension. Currently, there is no experimental Ang II-AT1 structure available; therefore, in this study we modeled Ang II-AT1 receptor structure using homology modeling followed by identification and characterization of binding sites and thereby assessing druggability of the receptor. Homology models were constructed using MODELLER and I-TASSER server, refined and validated using PROCHECK in which 96.9% of 318 residues were present in the favoured regions of the Ramachandran plots. Various Ang II-AT1 receptor antagonist drugs are available in the market as antihypertensive drug, so we have performed docking study with the binding site prediction algorithms to predict different binding pockets on the modeled proteins. The identification of 3D structures and binding sites for various known drugs will guide us for the structure-based drug design of novel compounds as Ang II-AT1 receptor antagonists for the treatment of hypertension. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

MULTIVARIATE ANALYSES (CONONICAL CORRELATION AND PARTIAL LEAST SQUARE, PLS) TO MODEL AND ASSESS THE ASSOCIATION OF LANDSCAPE METRICS TO SURFACE WATER CHEMICAL AND BIOLOGICAL PROPERTIES USING SAVANNAH RIVER BASIN DATA.

EPA Science Inventory

Many multivariate methods are used in describing and predicting relation; each has its unique usage of categorical and non-categorical data. In multivariate analysis of variance (MANOVA), many response variables (y's) are related to many independent variables that are categorical...

Spring-Loaded Model Revisited: Paramyxovirus Fusion Requires Engagement of a Receptor Binding Protein beyond Initial Triggering of the Fusion Protein▿

PubMed Central

Porotto, Matteo; DeVito, Ilaria; Palmer, Samantha G.; Jurgens, Eric M.; Yee, Jia L.; Yokoyama, Christine C.; Pessi, Antonello; Moscona, Anne

2011-01-01

During paramyxovirus entry into a host cell, receptor engagement by a specialized binding protein triggers conformational changes in the adjacent fusion protein (F), leading to fusion between the viral and cell membranes. According to the existing paradigm of paramyxovirus membrane fusion, the initial activation of F by the receptor binding protein sets off a spring-loaded mechanism whereby the F protein progresses independently through the subsequent steps in the fusion process, ending in membrane merger. For human parainfluenza virus type 3 (HPIV3), the receptor binding protein (hemagglutinin-neuraminidase [HN]) has three functions: receptor binding, receptor cleaving, and activating F. We report that continuous receptor engagement by HN activates F to advance through the series of structural rearrangements required for fusion. In contrast to the prevailing model, the role of HN-receptor engagement in the fusion process is required beyond an initiating step, i.e., it is still required even after the insertion of the fusion peptide into the target cell membrane, enabling F to mediate membrane merger. We also report that for Nipah virus, whose receptor binding protein has no receptor-cleaving activity, the continuous stimulation of the F protein by a receptor-engaged binding protein is key for fusion. We suggest a general model for paramyxovirus fusion activation in which receptor engagement plays an active role in F activation, and the continued engagement of the receptor binding protein is essential to F protein function until the onset of membrane merger. This model has broad implications for the mechanism of paramyxovirus fusion and for strategies to prevent viral entry. PMID:21976650

Spring-loaded model revisited: paramyxovirus fusion requires engagement of a receptor binding protein beyond initial triggering of the fusion protein.

PubMed

Porotto, Matteo; Devito, Ilaria; Palmer, Samantha G; Jurgens, Eric M; Yee, Jia L; Yokoyama, Christine C; Pessi, Antonello; Moscona, Anne

2011-12-01

During paramyxovirus entry into a host cell, receptor engagement by a specialized binding protein triggers conformational changes in the adjacent fusion protein (F), leading to fusion between the viral and cell membranes. According to the existing paradigm of paramyxovirus membrane fusion, the initial activation of F by the receptor binding protein sets off a spring-loaded mechanism whereby the F protein progresses independently through the subsequent steps in the fusion process, ending in membrane merger. For human parainfluenza virus type 3 (HPIV3), the receptor binding protein (hemagglutinin-neuraminidase [HN]) has three functions: receptor binding, receptor cleaving, and activating F. We report that continuous receptor engagement by HN activates F to advance through the series of structural rearrangements required for fusion. In contrast to the prevailing model, the role of HN-receptor engagement in the fusion process is required beyond an initiating step, i.e., it is still required even after the insertion of the fusion peptide into the target cell membrane, enabling F to mediate membrane merger. We also report that for Nipah virus, whose receptor binding protein has no receptor-cleaving activity, the continuous stimulation of the F protein by a receptor-engaged binding protein is key for fusion. We suggest a general model for paramyxovirus fusion activation in which receptor engagement plays an active role in F activation, and the continued engagement of the receptor binding protein is essential to F protein function until the onset of membrane merger. This model has broad implications for the mechanism of paramyxovirus fusion and for strategies to prevent viral entry.

A combined ligand-based and target-based drug design approach for G-protein coupled receptors: application to salvinorin A, a selective kappa opioid receptor agonist

NASA Astrophysics Data System (ADS)

Singh, Nidhi; Chevé, Gwénaël; Ferguson, David M.; McCurdy, Christopher R.

2006-08-01

Combined ligand-based and target-based drug design approaches provide a synergistic advantage over either method individually. Therefore, we set out to develop a powerful virtual screening model to identify novel molecular scaffolds as potential leads for the human KOP (hKOP) receptor employing a combined approach. Utilizing a set of recently reported derivatives of salvinorin A, a structurally unique KOP receptor agonist, a pharmacophore model was developed that consisted of two hydrogen bond acceptor and three hydrophobic features. The model was cross-validated by randomizing the data using the CatScramble technique. Further validation was carried out using a test set that performed well in classifying active and inactive molecules correctly. Simultaneously, a bovine rhodopsin based "agonist-bound" hKOP receptor model was also generated. The model provided more accurate information about the putative binding site of salvinorin A based ligands. Several protein structure-checking programs were used to validate the model. In addition, this model was in agreement with the mutation experiments carried out on KOP receptor. The predictive ability of the model was evaluated by docking a set of known KOP receptor agonists into the active site of this model. The docked scores correlated reasonably well with experimental p K i values. It is hypothesized that the integration of these two independently generated models would enable a swift and reliable identification of new lead compounds that could reduce time and cost of hit finding within the drug discovery and development process, particularly in the case of GPCRs.

Transforming Boolean models to continuous models: methodology and application to T-cell receptor signaling

PubMed Central

Wittmann, Dominik M; Krumsiek, Jan; Saez-Rodriguez, Julio; Lauffenburger, Douglas A; Klamt, Steffen; Theis, Fabian J

2009-01-01

Background The understanding of regulatory and signaling networks has long been a core objective in Systems Biology. Knowledge about these networks is mainly of qualitative nature, which allows the construction of Boolean models, where the state of a component is either 'off' or 'on'. While often able to capture the essential behavior of a network, these models can never reproduce detailed time courses of concentration levels. Nowadays however, experiments yield more and more quantitative data. An obvious question therefore is how qualitative models can be used to explain and predict the outcome of these experiments. Results In this contribution we present a canonical way of transforming Boolean into continuous models, where the use of multivariate polynomial interpolation allows transformation of logic operations into a system of ordinary differential equations (ODE). The method is standardized and can readily be applied to large networks. Other, more limited approaches to this task are briefly reviewed and compared. Moreover, we discuss and generalize existing theoretical results on the relation between Boolean and continuous models. As a test case a logical model is transformed into an extensive continuous ODE model describing the activation of T-cells. We discuss how parameters for this model can be determined such that quantitative experimental results are explained and predicted, including time-courses for multiple ligand concentrations and binding affinities of different ligands. This shows that from the continuous model we may obtain biological insights not evident from the discrete one. Conclusion The presented approach will facilitate the interaction between modeling and experiments. Moreover, it provides a straightforward way to apply quantitative analysis methods to qualitatively described systems. PMID:19785753

Comparative effectiveness of angiotensin-receptor blockers for preventing macrovascular disease in patients with diabetes: a population-based cohort study

PubMed Central

Antoniou, Tony; Camacho, Ximena; Yao, Zhan; Gomes, Tara; Juurlink, David N.; Mamdani, Muhammad M.

2013-01-01

Background: Telmisartan, unlike other angiotensin-receptor blockers, is a partial agonist of peroxisome proliferator–activated receptor-γ, a property that has been associated with improvements in surrogate markers of cardiovascular health in small trials involving patients with diabetes. However, whether this property translates into a reduced risk of cardiovascular events and death in these patients is unknown. We sought to explore the risk of myocardial infarction, stroke and heart failure in patients with diabetes who were taking telmisartan relative to the risk of these events occurring in patients taking other angiotensin-receptor blockers. Methods: We conducted a population-based, retrospective cohort study of Ontario residents with diabetes aged 66 years and older who started treatment with candesartan, irbesartan, losartan, telmisartan or valsartan between Apr. 1, 2001, and Mar. 31, 2011. Our primary outcome was a composite of admission to hospital for acute myocardial infarction, stroke or heart failure. We examined each outcome individually in secondary analyses, in addition to all-cause mortality. Results: We identified 54 186 patients with diabetes who started taking an angiotensin-receptor blocker during the study period. After multivariable adjustment, patients who took either telmisartan (adjusted hazard ratio [HR] 0.85, 95% confidence interval [CI] 0.74–0.97) or valsartan (adjusted HR 0.86, 95% CI 0.77–0.95) had a lower risk of the composite outcome compared with patients who took irbesartan. In contrast, no significant difference in risk was seen between other angiotensin-receptor blockers and irbesartan. In secondary analyses, we found a reduced risk of admission to hospital for heart failure with telmisartan compared with irbesartan (adjusted HR 0.79, 95% CI 0.66–0.96), but no significant differences in risk were seen between angiotensin-receptor blockers in our other secondary analyses. Interpretation: Compared with other angiotensin-receptor blockers, telmisartan and valsartan were both associated with a lower risk of admission to hospital for acute myocardial infarction, stroke or heart failure among older adults with diabetes and hypertension. Telmisartan and valsartan may therefore be the preferred angiotensin-receptor blockers for use in these patients. PMID:23836857

M-type phospholipase A2 receptor autoantibodies and renal function in patients with primary membranous nephropathy.

PubMed

Hoxha, Elion; Harendza, Sigrid; Pinnschmidt, Hans; Panzer, Ulf; Stahl, Rolf A K

2014-11-07

Loss of renal function in patients with primary membranous nephropathy cannot be reliably predicted by laboratory or clinical markers at the time of diagnosis. M-type phospholipase A2 receptor autoantibodies have been shown to be associated with changes in proteinuria. Their eventual effect on renal function, however, is unclear. In this prospective, open, multicenter study, the potential role of M-type phospholipase A2 receptor autoantibodies levels on the increase of serum creatinine in 118 consecutive patients with membranous nephropathy and positivity for serum M-type phospholipase A2 receptor autoantibodies was analyzed. Patients were included in the study between April of 2010 and December of 2012 and observed until December of 2013. The clinical end point was defined as an increase of serum creatinine by ≥ 25% and serum creatinine reaching ≥ 1.3 mg/dl. Patients were divided into tertiles according to their M-type phospholipase A2 receptor autoantibody levels at the time of inclusion in the study: tertile 1 levels=20-86 units/ml (low), tertile 2 levels=87-201 units/ml (medium), and tertile 3 levels ≥ 202 units/ml (high). The median follow-up time of all patients in the study was 27 months (interquartile range=18-33 months). The clinical end point was reached in 69% of patients with high M-type phospholipase A2 receptor autoantibodies levels (tertile 3) but only 25% of patients with low M-type phospholipase A2 receptor autoantibodies levels. The average time to reach the study end point was 17.7 months in patients with high M-type phospholipase A2 receptor autoantibodies levels and 30.9 months in patients with low M-type phospholipase A2 receptor autoantibodies levels. A multivariate Cox regression analysis showed that high M-type phospholipase A2 receptor autoantibodies levels-in addition to men and older age-are an independent predictor for progressive loss of renal function. High M-type phospholipase A2 receptor autoantibodies levels were associated with more rapid loss of renal function in this cohort of patients with primary membranous nephropathy and therefore, could be helpful for treatment decisions. Copyright © 2014 by the American Society of Nephrology.

Molecular modelling studies on the ORL1-receptor and ORL1-agonists

NASA Astrophysics Data System (ADS)

Bröer, Britta M.; Gurrath, Marion; Höltje, Hans-Dieter

2003-11-01

The ORL1 ( opioid receptor like 1)- receptor is a member of the family of rhodopsin-like G protein-coupled receptors (GPCR) and represents an interesting new therapeutical target since it is involved in a variety of biomedical important processes, such as anxiety, nociception, feeding, and memory. In order to shed light on the molecular basis of the interactions of the GPCR with its ligands, the receptor protein and a dataset of specific agonists were examined using molecular modelling methods. For that purpose, the conformational space of a very potent non-peptide ORL1-receptor agonist (Ro 64-6198) with a small number of rotatable bonds was analysed in order to derive a pharmacophoric arrangement. The conformational analyses yielded a conformation that served as template for the superposition of a set of related analogues. Structural superposition was achieved by employing the program FlexS. Using the experimental binding data and the superposition of the ligands, a 3D-QSAR analysis applying the GRID/GOLPE method was carried out. After the ligand-based modelling approach, a 3D model of the ORL1-receptor has been constructed using homology modelling methods based on the crystal structure of bovine rhodopsin. A representative structure of the model taken from molecular dynamics simulations was used for a manual docking procedure. Asp-130 and Thr-305 within the ORL1-receptor model served as important hydrophilic interaction partners. Furthermore, a hydrophobic cavity was identified stabilizing the agonists within their binding site. The manual docking results were supported using FlexX, which identified the same protein-ligand interaction points.

CoMFA analyses of C-2 position salvinorin A analogs at the kappa-opioid receptor provides insights into epimer selectivity.

PubMed

McGovern, Donna L; Mosier, Philip D; Roth, Bryan L; Westkaemper, Richard B

2010-04-01

The highly potent and kappa-opioid (KOP) receptor-selective hallucinogen Salvinorin A and selected analogs have been analyzed using the 3D quantitative structure-affinity relationship technique Comparative Molecular Field Analysis (CoMFA) in an effort to derive a statistically significant and predictive model of salvinorin affinity at the KOP receptor and to provide additional statistical support for the validity of previously proposed structure-based interaction models. Two CoMFA models of Salvinorin A analogs substituted at the C-2 position are presented. Separate models were developed based on the radioligand used in the kappa-opioid binding assay, [(3)H]diprenorphine or [(125)I]6 beta-iodo-3,14-dihydroxy-17-cyclopropylmethyl-4,5 alpha-epoxymorphinan ([(125)I]IOXY). For each dataset, three methods of alignment were employed: a receptor-docked alignment derived from the structure-based docking algorithm GOLD, another from the ligand-based alignment algorithm FlexS, and a rigid realignment of the poses from the receptor-docked alignment. The receptor-docked alignment produced statistically superior results compared to either the FlexS alignment or the realignment in both datasets. The [(125)I]IOXY set (Model 1) and [(3)H]diprenorphine set (Model 2) gave q(2) values of 0.592 and 0.620, respectively, using the receptor-docked alignment, and both models produced similar CoMFA contour maps that reflected the stereoelectronic features of the receptor model from which they were derived. Each model gave significantly predictive CoMFA statistics (Model 1 PSET r(2)=0.833; Model 2 PSET r(2)=0.813). Based on the CoMFA contour maps, a binding mode was proposed for amine-containing Salvinorin A analogs that provides a rationale for the observation that the beta-epimers (R-configuration) of protonated amines at the C-2 position have a higher affinity than the corresponding alpha-epimers (S-configuration). (c) 2010. Published by Elsevier Inc.

Toward the Multivariate Modeling of Achievement, Aptitude, and Personality.

ERIC Educational Resources Information Center

Foshay, Wellesley R.; Misanchuk, Earl R.

1981-01-01

A multivariate investigation of the dynamics of cumulative achievement studied the influence of course grades, personality traits, environmental variables, and previous performance. The latter was the best single predictor of performance. (CJ)

Kinetic operational models of agonism for G-protein-coupled receptors.

PubMed

Hoare, Samuel R J; Pierre, Nicolas; Moya, Arturo Gonzalez; Larson, Brad

2018-06-07

The application of kinetics to research and therapeutic development of G-protein-coupled receptors has become increasingly valuable. Pharmacological models provide the foundation of pharmacology, providing concepts and measurable parameters such as efficacy and potency that have underlain decades of successful drug discovery. Currently there are few pharmacological models that incorporate kinetic activity in such a way as to yield experimentally-accessible drug parameters. In this study, a kinetic model of pharmacological response was developed that provides a kinetic descriptor of efficacy (the transduction rate constant, k τ ) and allows measurement of receptor-ligand binding kinetics from functional data. The model assumes: (1) receptor interacts with a precursor of the response ("Transduction potential") and converts it to the response. (2) The response can decay. Familiar response vs time plots emerge, depending on whether transduction potential is depleted and/or response decays. These are the straight line, the "association" exponential curve, and the rise-and-fall curve. Convenient, familiar methods are described for measuring the model parameters and files are provided for the curve-fitting program Prism (GraphPad Software) that can be used as a guide. The efficacy parameter k τ is straightforward to measure and accounts for receptor reserve; all that is required is measurement of response over time at a maximally-stimulating concentration of agonist. The modular nature of the model framework allows it to be extended. Here this is done to incorporate antagonist-receptor binding kinetics and slow agonist-receptor equilibration. In principle, the modular framework can incorporate other cellular processes, such as receptor desensitization. The kinetic response model described here can be applied to measure kinetic pharmacological parameters than can be used to advance the understanding of GPCR pharmacology and optimize new and improved therapeutics. Copyright © 2018 Elsevier Ltd. All rights reserved.

A combined computational and structural model of the full-length human prolactin receptor

PubMed Central

Bugge, Katrine; Papaleo, Elena; Haxholm, Gitte W.; Hopper, Jonathan T. S.; Robinson, Carol V.; Olsen, Johan G.; Lindorff-Larsen, Kresten; Kragelund, Birthe B.

2016-01-01

The prolactin receptor is an archetype member of the class I cytokine receptor family, comprising receptors with fundamental functions in biology as well as key drug targets. Structurally, each of these receptors represent an intriguing diversity, providing an exceptionally challenging target for structural biology. Here, we access the molecular architecture of the monomeric human prolactin receptor by combining experimental and computational efforts. We solve the NMR structure of its transmembrane domain in micelles and collect structural data on overlapping fragments of the receptor with small-angle X-ray scattering, native mass spectrometry and NMR spectroscopy. Along with previously published data, these are integrated by molecular modelling to generate a full receptor structure. The result provides the first full view of a class I cytokine receptor, exemplifying the architecture of more than 40 different receptor chains, and reveals that the extracellular domain is merely the tip of a molecular iceberg. PMID:27174498

Role of Exonic Variation in Chemokine Receptor Genes on AIDS: CCRL2 F167Y Association with Pneumocystis Pneumonia

PubMed Central

An, Ping; Li, Rongling; Wang, Ji Ming; Yoshimura, Teizo; Takahashi, Munehisa; Samudralal, Ram; O'Brien, Stephen J.; Phair, John; Goedert, James J.; Kirk, Gregory D.; Troyer, Jennifer L.; Sezgin, Efe; Buchbinder, Susan P.; Donfield, Sharyne; Nelson, George W.; Winkler, Cheryl A.

2011-01-01

Chromosome 3p21–22 harbors two clusters of chemokine receptor genes, several of which serve as major or minor coreceptors of HIV-1. Although the genetic association of CCR5 and CCR2 variants with HIV-1 pathogenesis is well known, the role of variation in other nearby chemokine receptor genes remain unresolved. We genotyped exonic single nucleotide polymorphisms (SNPs) in chemokine receptor genes: CCR3, CCRL2, and CXCR6 (at 3p21) and CCR8 and CX3CR1 (at 3p22), the majority of which were non-synonymous. The individual SNPs were tested for their effects on disease progression and outcomes in five treatment-naïve HIV-1/AIDS natural history cohorts. In addition to the known CCR5 and CCR2 associations, significant associations were identified for CCR3, CCR8, and CCRL2 on progression to AIDS. A multivariate survival analysis pointed to a previously undetected association of a non-conservative amino acid change F167Y in CCRL2 with AIDS progression: 167F is associated with accelerated progression to AIDS (RH = 1.90, P = 0.002, corrected). Further analysis indicated that CCRL2-167F was specifically associated with more rapid development of pneumocystis pneumonia (PCP) (RH = 2.84, 95% CI 1.28–6.31) among four major AIDS–defining conditions. Considering the newly defined role of CCRL2 in lung dendritic cell trafficking, this atypical chemokine receptor may affect PCP through immune regulation and inducing inflammation. PMID:22046140

Predicting vascular complications in percutaneous coronary interventions.

PubMed

Piper, Winthrop D; Malenka, David J; Ryan, Thomas J; Shubrooks, Samuel J; O'Connor, Gerald T; Robb, John F; Farrell, Karen L; Corliss, Mary S; Hearne, Michael J; Kellett, Mirle A; Watkins, Matthew W; Bradley, William A; Hettleman, Bruce D; Silver, Theodore M; McGrath, Paul D; O'Mears, John R; Wennberg, David E

2003-06-01

Using a large, current, regional registry of percutaneous coronary interventions (PCI), we identified risk factors for postprocedure vascular complications and developed a scoring system to estimate individual patient risk. A vascular complication (access-site injury requiring treatment or bleeding requiring transfusion) is a potentially avoidable outcome of PCI. Data were collected on 18,137 consecutive patients undergoing PCI in northern New England from January 1997 to December 1999. Multivariate regression was used to identify characteristics associated with vascular complications and to develop a scoring system to predict risk. The rate of vascular complication was 2.98% (541 cases). Variables associated with increased risk in the multivariate analysis included age >or=70, odds ratio (OR) 2.7, female sex (OR 2.4), body surface area <1.6 m(2) (OR 1.9), history of congestive heart failure (OR 1.4), chronic obstructive pulmonary disease (OR 1.5), renal failure (OR 1.9), lower extremity vascular disease (OR 1.4), bleeding disorder (OR 1.68), emergent priority (OR 2.3), myocardial infarction (OR 1.7), shock (1.86), >or=1 type B2 (OR 1.32) or type C (OR 1.7) lesions, 3-vessel PCI (OR 1.5), use of thienopyridines (OR 1.4) or use of glycoprotein IIb/IIIa receptor inhibitors (OR 1.9). The model performed well in tests for significance, discrimination, and calibration. The scoring system captured 75% of actual vascular complications in its highest quintiles of predicted risk. Predicting the risk of post-PCI vascular complications is feasible. This information may be useful for clinical decision-making and institutional efforts at quality improvement.

An exploratory factor analysis of nutritional biomarkers associated with major depression in pregnancy

PubMed Central

Bodnar, Lisa M.; Wisner, Katherine L.; Luther, James F.; Powers, Robert W.; Evans, Rhobert W.; Gallaher, Marcia J.; Newby, P.K.

2011-01-01

Objective Major depressive disorder (MDD) during pregnancy increases the risk of adverse maternal and infant outcomes. Maternal nutritional status may be a modifiable risk factor for antenatal depression. We evaluated the association between patterns in mid-pregnancy nutritional biomarkers and MDD. Design Prospective cohort study Setting Pittsburgh, Pennsylvania, USA Subjects Women who enrolled at ≤20 weeks gestation had a diagnosis of MDD made with the Structured Clinical Interview for DSM-IV at 20-, 30-, and 36-week study visits. A total of 135 women contributed 345 person-visits. Non-fasting blood drawn at enrollment was assayed for red cell essential fatty acids, plasma folate, homocysteine, and ascorbic acid; serum 25-hydroxyvitamin D, retinol, vitamin E, carotenoids, ferritin, and soluble transferrin receptors. Nutritional biomarkers were entered into principal components analysis. Results Three factors emerged: Factor 1, Essential Fatty Acids; Factor 2, Micronutrients; and Factor 3, Carotenoids. MDD was prevalent in 21.5% of women. In longitudinal multivariable logistic models, there was no association between the Essential Fatty Acid or Micronutrient patterns and MDD either before or after adjustment for employment, education, or prepregnancy BMI. In unadjusted analysis, women with Carotenoid factor scores in the middle and upper tertiles were 60% less likely than women in the bottom tertile to have MDD during pregnancy, but after adjustment for confounders, the associations were no longer statistically significant. Conclusions While meaningful patterns were derived using nutritional biomarkers, significant associations with MDD were not observed in multivariable adjusted analyses. Larger, more diverse samples are needed to understand nutrition-depression relationships during pregnancy. PMID:22152590

An exploratory factor analysis of nutritional biomarkers associated with major depression in pregnancy.

PubMed

Bodnar, Lisa M; Wisner, Katherine L; Luther, James F; Powers, Robert W; Evans, Rhobert W; Gallaher, Marcia J; Newby, P K

2012-06-01

Major depressive disorder (MDD) during pregnancy increases the risk of adverse maternal and infant outcomes. Maternal nutritional status may be a modifiable risk factor for antenatal depression. We evaluated the association between patterns in mid-pregnancy nutritional biomarkers and MDD. Prospective cohort study. Pittsburgh, Pennsylvania, USA. Women who enrolled at ≤20 weeks' gestation and had a diagnosis of MDD made with the Structured Clinical Interview for DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, 4th edition) at 20-, 30- and 36-week study visits. A total of 135 women contributed 345 person-visits. Non-fasting blood drawn at enrolment was assayed for red cell essential fatty acids, plasma folate, homocysteine and ascorbic acid; serum 25-hydroxyvitamin D, retinol, vitamin E, carotenoids, ferritin and soluble transferrin receptors. Nutritional biomarkers were entered into principal components analysis. Three factors emerged: Factor 1, Essential Fatty Acids; Factor 2, Micronutrients; and Factor 3, Carotenoids. MDD was prevalent in 21·5 % of women. In longitudinal multivariable logistic models, there was no association between the Essential Fatty Acids or Micronutrients pattern and MDD either before or after adjustment for employment, education or pre-pregnancy BMI. In unadjusted analysis, women with factor scores for Carotenoids in the middle and upper tertiles were 60 % less likely than women in the bottom tertile to have MDD during pregnancy, but after adjustment for confounders the associations were no longer statistically significant. While meaningful patterns were derived using nutritional biomarkers, significant associations with MDD were not observed in multivariable adjusted analyses. Larger, more diverse samples are needed to understand nutrition-depression relationships during pregnancy.

Efficacy and safety of icotinib in Chinese patients with advanced non-small cell lung cancer after failure of chemotherapy.

PubMed

Shao, Lan; Zhang, Beibei; He, Chunxiao; Lin, Baochai; Song, Zhengbo; Lou, Guangyuan; Yu, Xinmin; Zhang, Yiping

2014-01-01

The preclinical experiments and several clinical studies showed icotinib, an oral epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor, in Chinese patients with advanced non-small cell lung cancer (NSCLC) who failed previous chemotherapy. We performed a retrospective study of the efficacy and safety of icotinib monotherapy in a different and more recent sample of Chinese patients. The clinical data of 149 patients with advanced NSCLC who were admitted to Zhejiang Cancer Hospital from August 1, 2011 to July 31, 2012 were retrospectively analyzed. All patients were given icotinib treatment after the failure of previous chemotherapy. Univariate and multivariate analyses were conducted based on the Kaplan Meier method and Cox proportional hazards model. The objective response rate was 33/149 and disease control rate was 105/149. No complete response occurred. Median progression free survival (PFS) with icotinib treatment was 5.03 months (95% CI: 3.51 to 6.55). Median overall survival was 12.3 months (95% CI: 10.68 to 13.92). Multivariate analysis showed that the mutation of EGFR and one regimen of prior chemotherapy were significantly associated with longer PFS. At least one drug related adverse event was observed in 65.8% (98/149) of patients, but mostly grade 1 or 2 and reversible and none grade 4 toxicity. Icotinib monotherapy is an effective and well tolerated regimen for Chinese patients with NSCLC after the failure of chemotherapy. It is a promising agent and further study with icotinib in properly conducted trials with larger patient samples and other ethnic groups is warranted.

Image-based compound profiling reveals a dual inhibitor of tyrosine kinase and microtubule polymerization.

PubMed

Tanabe, Kenji

2016-04-27

Small-molecule compounds are widely used as biological research tools and therapeutic drugs. Therefore, uncovering novel targets of these compounds should provide insights that are valuable in both basic and clinical studies. I developed a method for image-based compound profiling by quantitating the effects of compounds on signal transduction and vesicle trafficking of epidermal growth factor receptor (EGFR). Using six signal transduction molecules and two markers of vesicle trafficking, 570 image features were obtained and subjected to multivariate analysis. Fourteen compounds that affected EGFR or its pathways were classified into four clusters, based on their phenotypic features. Surprisingly, one EGFR inhibitor (CAS 879127-07-8) was classified into the same cluster as nocodazole, a microtubule depolymerizer. In fact, this compound directly depolymerized microtubules. These results indicate that CAS 879127-07-8 could be used as a chemical probe to investigate both the EGFR pathway and microtubule dynamics. The image-based multivariate analysis developed herein has potential as a powerful tool for discovering unexpected drug properties.

Bayesian transformation cure frailty models with multivariate failure time data.

PubMed

Yin, Guosheng

2008-12-10

We propose a class of transformation cure frailty models to accommodate a survival fraction in multivariate failure time data. Established through a general power transformation, this family of cure frailty models includes the proportional hazards and the proportional odds modeling structures as two special cases. Within the Bayesian paradigm, we obtain the joint posterior distribution and the corresponding full conditional distributions of the model parameters for the implementation of Gibbs sampling. Model selection is based on the conditional predictive ordinate statistic and deviance information criterion. As an illustration, we apply the proposed method to a real data set from dentistry.

Factors related to clinical pregnancy after vitrified-warmed embryo transfer: a retrospective and multivariate logistic regression analysis of 2313 transfer cycles.

PubMed

Shi, Wenhao; Zhang, Silin; Zhao, Wanqiu; Xia, Xue; Wang, Min; Wang, Hui; Bai, Haiyan; Shi, Juanzi

2013-07-01

What factors does multivariate logistic regression show to be significantly associated with the likelihood of clinical pregnancy in vitrified-warmed embryo transfer (VET) cycles? Assisted hatching (AH) and if the reason to freeze embryos was to avoid the risk of ovarian hyperstimulation syndrome (OHSS) were significantly positively associated with a greater likelihood of clinical pregnancy. Single factor analysis has shown AH, number of embryos transferred and the reason of freezing for OHSS to be positively and damaged blastomere to be negatively significantly associated with the chance of clinical pregnancy after VET. It remains unclear what factors would be significant after multivariate analysis. The study was a retrospective analysis of 2313 VET cycles from 1481 patients performed between January 2008 and April 2012. A multivariate logistic regression analysis was performed to identify the factors to affect clinical pregnancy outcome of VET. There were 22 candidate variables selected based on clinical experiences and the literature. With the thresholds of α entry = α removal= 0.05 for both variable entry and variable removal, eight variables were chosen to contribute the multivariable model by the bootstrap stepwise variable selection algorithm (n = 1000). Eight variables were age at controlled ovarian hyperstimulation (COH), reason for freezing, AH, endometrial thickness, damaged blastomere, number of embryos transferred, number of good-quality embryos, and blood presence on transfer catheter. A descriptive comparison of the relative importance was accomplished by the proportion of explained variation (PEV). Among the reasons for freezing, the OHSS group showed a higher OR than the surplus embryo group when compared with other reasons for VET groups (OHSS versus Other, OR: 2.145; CI: 1.4-3.286; Surplus embryos versus Other, OR: 1.152; CI: 0.761-1.743) and high PEV (marginal 2.77%, P = 0.2911; partial 1.68%; CI of area under receptor operator characteristic curve (ROC): 0.5576-0.6000). AH also showed a high OR (OR: 2.105, CI: 1.554-2.85) and high PEV (marginal 1.97%; partial 1.02%; CI of area under ROC: 0.5344-0.5647). The number of good-quality embryos showed the highest marginal PEV and partial PEV (marginal 3.91%, partial 2.28%; CI of area under ROC: 0.5886-0.6343). This was a retrospective multivariate analysis of the data obtained in 5 years from a single IVF center. Repeated cycles in the same woman were treated as independent observations, which could introduce bias. Results are based on clinical pregnancy and not live births. Prospective analysis of a larger data set from a multicenter study based on live births is necessary to confirm the findings. Paying attention to the quality of embryos, the number of good embryos, AH and the reasons for freezing that are associated with clinical pregnancy after VET will assist the improvement of success rates.

All-Atom Structural Models of the Transmembrane Domains of Insulin and Type 1 Insulin-Like Growth Factor Receptors

PubMed Central

Mohammadiarani, Hossein; Vashisth, Harish

2016-01-01

The receptor tyrosine kinase superfamily comprises many cell-surface receptors including the insulin receptor (IR) and type 1 insulin-like growth factor receptor (IGF1R) that are constitutively homodimeric transmembrane glycoproteins. Therefore, these receptors require ligand-triggered domain rearrangements rather than receptor dimerization for activation. Specifically, binding of peptide ligands to receptor ectodomains transduces signals across the transmembrane domains for trans-autophosphorylation in cytoplasmic kinase domains. The molecular details of these processes are poorly understood in part due to the absence of structures of full-length receptors. Using MD simulations and enhanced conformational sampling algorithms, we present all-atom structural models of peptides containing 51 residues from the transmembrane and juxtamembrane regions of IR and IGF1R. In our models, the transmembrane regions of both receptors adopt helical conformations with kinks at Pro961 (IR) and Pro941 (IGF1R), but the C-terminal residues corresponding to the juxtamembrane region of each receptor adopt unfolded and flexible conformations in IR as opposed to a helix in IGF1R. We also observe that the N-terminal residues in IR form a kinked-helix sitting at the membrane–solvent interface, while homologous residues in IGF1R are unfolded and flexible. These conformational differences result in a larger tilt-angle of the membrane-embedded helix in IGF1R in comparison to IR to compensate for interactions with water molecules at the membrane–solvent interfaces. Our metastable/stable states for the transmembrane domain of IR, observed in a lipid bilayer, are consistent with a known NMR structure of this domain determined in detergent micelles, and similar states in IGF1R are consistent with a previously reported model of the dimerized transmembrane domains of IGF1R. Our all-atom structural models suggest potentially unique structural organization of kinase domains in each receptor. PMID:27379020

Pathological reorganization of NMDA receptors subunits and postsynaptic protein PSD-95 distribution in Alzheimer's disease.

PubMed

Leuba, Genevieve; Vernay, Andre; Kraftsik, Rudolf; Tardif, Eric; Riederer, Beat Michel; Savioz, Armand

2014-01-01

In Alzheimer's disease (AD), synaptic alterations play a major role and are often correlated with cognitive changes. In order to better understand synaptic modifications, we compared alterations in NMDA receptors and postsynaptic protein PSD-95 expression in the entorhinal cortex (EC) and frontal cortex (FC; area 9) of AD and control brains. We combined immunohistochemical and image analysis methods to quantify on consecutive sections the distribution of PSD-95 and NMDA receptors GluN1, GluN2A and GluN2B in EC and FC from 25 AD and control cases. The density of stained receptors was analyzed using multivariate statistical methods to assess the effect of neurodegeneration. In both regions, the number of neuronal profiles immunostained for GluN1 receptors subunit and PSD-95 protein was significantly increased in AD compared to controls (3-6 fold), while the number of neuronal profiles stained for GluN2A and GluN2B receptors subunits was on the contrary decreased (3-4 fold). The increase in marked neuronal profiles was more prominent in a cortical band corresponding to layers 3 to 5 with large pyramidal cells. Neurons positive for GluN1 or PSD-95 staining were often found in the same localization on consecutive sections and they were also reactive for the anti-tau antibody AD2, indicating a neurodegenerative process. Differences in the density of immunoreactive puncta representing neuropile were not statistically significant. Altogether these data indicate that GluN1 and PSD-95 accumulate in the neuronal perikarya, but this is not the case for GluN2A and GluN2B, while the neuropile compartment is less subject to modifications. Thus, important variations in the pattern of distribution of the NMDA receptors subunits and PSD-95 represent a marker in AD and by impairing the neuronal network, contribute to functional deterioration.

Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

DOE PAGES

Liu, Chao; Akintayo, Adedotun; Jiang, Zhanhong; ...

2017-12-18

Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load components has thus far mostly been studied using univariate data, e.g., using only whole building electricity consumption time series to identify a certain type of end-use such as lighting load. However, using additional variables in the form of multivariate time series data may provide more information in terms of extracting distinguishable features in the context of energy disaggregation. In this work, a novel probabilistic graphical modeling approach, namely the spatiotemporal pattern network (STPN) is proposed for energy disaggregation using multivariate time-series data. The STPN framework is shownmore » to be capable of handling diverse types of multivariate time-series to improve the energy disaggregation performance. The technique outperforms the state of the art factorial hidden Markov models (FHMM) and combinatorial optimization (CO) techniques in multiple real-life test cases. Furthermore, based on two homes' aggregate electric consumption data, a similarity metric is defined for the energy disaggregation of one home using a trained model based on the other home (i.e., out-of-sample case). The proposed similarity metric allows us to enhance scalability via learning supervised models for a few homes and deploying such models to many other similar but unmodeled homes with significantly high disaggregation accuracy.« less

A simple prognostic model for overall survival in metastatic renal cell carcinoma.

PubMed

Assi, Hazem I; Patenaude, Francois; Toumishey, Ethan; Ross, Laura; Abdelsalam, Mahmoud; Reiman, Tony

2016-01-01

The primary purpose of this study was to develop a simpler prognostic model to predict overall survival for patients treated for metastatic renal cell carcinoma (mRCC) by examining variables shown in the literature to be associated with survival. We conducted a retrospective analysis of patients treated for mRCC at two Canadian centres. All patients who started first-line treatment were included in the analysis. A multivariate Cox proportional hazards regression model was constructed using a stepwise procedure. Patients were assigned to risk groups depending on how many of the three risk factors from the final multivariate model they had. There were three risk factors in the final multivariate model: hemoglobin, prior nephrectomy, and time from diagnosis to treatment. Patients in the high-risk group (two or three risk factors) had a median survival of 5.9 months, while those in the intermediate-risk group (one risk factor) had a median survival of 16.2 months, and those in the low-risk group (no risk factors) had a median survival of 50.6 months. In multivariate analysis, shorter survival times were associated with hemoglobin below the lower limit of normal, absence of prior nephrectomy, and initiation of treatment within one year of diagnosis.

A simple prognostic model for overall survival in metastatic renal cell carcinoma

PubMed Central

Assi, Hazem I.; Patenaude, Francois; Toumishey, Ethan; Ross, Laura; Abdelsalam, Mahmoud; Reiman, Tony

2016-01-01

Introduction: The primary purpose of this study was to develop a simpler prognostic model to predict overall survival for patients treated for metastatic renal cell carcinoma (mRCC) by examining variables shown in the literature to be associated with survival. Methods: We conducted a retrospective analysis of patients treated for mRCC at two Canadian centres. All patients who started first-line treatment were included in the analysis. A multivariate Cox proportional hazards regression model was constructed using a stepwise procedure. Patients were assigned to risk groups depending on how many of the three risk factors from the final multivariate model they had. Results: There were three risk factors in the final multivariate model: hemoglobin, prior nephrectomy, and time from diagnosis to treatment. Patients in the high-risk group (two or three risk factors) had a median survival of 5.9 months, while those in the intermediate-risk group (one risk factor) had a median survival of 16.2 months, and those in the low-risk group (no risk factors) had a median survival of 50.6 months. Conclusions: In multivariate analysis, shorter survival times were associated with hemoglobin below the lower limit of normal, absence of prior nephrectomy, and initiation of treatment within one year of diagnosis. PMID:27217858

Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

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

Liu, Chao; Akintayo, Adedotun; Jiang, Zhanhong

Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load components has thus far mostly been studied using univariate data, e.g., using only whole building electricity consumption time series to identify a certain type of end-use such as lighting load. However, using additional variables in the form of multivariate time series data may provide more information in terms of extracting distinguishable features in the context of energy disaggregation. In this work, a novel probabilistic graphical modeling approach, namely the spatiotemporal pattern network (STPN) is proposed for energy disaggregation using multivariate time-series data. The STPN framework is shownmore » to be capable of handling diverse types of multivariate time-series to improve the energy disaggregation performance. The technique outperforms the state of the art factorial hidden Markov models (FHMM) and combinatorial optimization (CO) techniques in multiple real-life test cases. Furthermore, based on two homes' aggregate electric consumption data, a similarity metric is defined for the energy disaggregation of one home using a trained model based on the other home (i.e., out-of-sample case). The proposed similarity metric allows us to enhance scalability via learning supervised models for a few homes and deploying such models to many other similar but unmodeled homes with significantly high disaggregation accuracy.« less

A High-Dimensional, Multivariate Copula Approach to Modeling Multivariate Agricultural Price Relationships and Tail Dependencies

Treesearch

Xuan Chi; Barry Goodwin

2012-01-01

Spatial and temporal relationships among agricultural prices have been an important topic of applied research for many years. Such research is used to investigate the performance of markets and to examine linkages up and down the marketing chain. This research has empirically evaluated price linkages by using correlation and regression models and, later, linear and...

Validation of cross-sectional time series and multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents using doubly labeled water

USDA-ARS?s Scientific Manuscript database

Accurate, nonintrusive, and inexpensive techniques are needed to measure energy expenditure (EE) in free-living populations. Our primary aim in this study was to validate cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models based on observable participant cha...

Identifying pleiotropic genes in genome-wide association studies from related subjects using the linear mixed model and Fisher combination function.

PubMed

Yang, James J; Williams, L Keoki; Buu, Anne

2017-08-24

A multivariate genome-wide association test is proposed for analyzing data on multivariate quantitative phenotypes collected from related subjects. The proposed method is a two-step approach. The first step models the association between the genotype and marginal phenotype using a linear mixed model. The second step uses the correlation between residuals of the linear mixed model to estimate the null distribution of the Fisher combination test statistic. The simulation results show that the proposed method controls the type I error rate and is more powerful than the marginal tests across different population structures (admixed or non-admixed) and relatedness (related or independent). The statistical analysis on the database of the Study of Addiction: Genetics and Environment (SAGE) demonstrates that applying the multivariate association test may facilitate identification of the pleiotropic genes contributing to the risk for alcohol dependence commonly expressed by four correlated phenotypes. This study proposes a multivariate method for identifying pleiotropic genes while adjusting for cryptic relatedness and population structure between subjects. The two-step approach is not only powerful but also computationally efficient even when the number of subjects and the number of phenotypes are both very large.

Copula-based prediction of economic movements

NASA Astrophysics Data System (ADS)

García, J. E.; González-López, V. A.; Hirsh, I. D.

2016-06-01

In this paper we model the discretized returns of two paired time series BM&FBOVESPA Dividend Index and BM&FBOVESPA Public Utilities Index using multivariate Markov models. The discretization corresponds to three categories, high losses, high profits and the complementary periods of the series. In technical terms, the maximal memory that can be considered for a Markov model, can be derived from the size of the alphabet and dataset. The number of parameters needed to specify a discrete multivariate Markov chain grows exponentially with the order and dimension of the chain. In this case the size of the database is not large enough for a consistent estimation of the model. We apply a strategy to estimate a multivariate process with an order greater than the order achieved using standard procedures. The new strategy consist on obtaining a partition of the state space which is constructed from a combination, of the partitions corresponding to the two marginal processes and the partition corresponding to the multivariate Markov chain. In order to estimate the transition probabilities, all the partitions are linked using a copula. In our application this strategy provides a significant improvement in the movement predictions.

Cross-country transferability of multi-variable damage models

NASA Astrophysics Data System (ADS)

Wagenaar, Dennis; Lüdtke, Stefan; Kreibich, Heidi; Bouwer, Laurens

2017-04-01

Flood damage assessment is often done with simple damage curves based only on flood water depth. Additionally, damage models are often transferred in space and time, e.g. from region to region or from one flood event to another. Validation has shown that depth-damage curve estimates are associated with high uncertainties, particularly when applied in regions outside the area where the data for curve development was collected. Recently, progress has been made with multi-variable damage models created with data-mining techniques, i.e. Bayesian Networks and random forest. However, it is still unknown to what extent and under which conditions model transfers are possible and reliable. Model validations in different countries will provide valuable insights into the transferability of multi-variable damage models. In this study we compare multi-variable models developed on basis of flood damage datasets from Germany as well as from The Netherlands. Data from several German floods was collected using computer aided telephone interviews. Data from the 1993 Meuse flood in the Netherlands is available, based on compensations paid by the government. The Bayesian network and random forest based models are applied and validated in both countries on basis of the individual datasets. A major challenge was the harmonization of the variables between both datasets due to factors like differences in variable definitions, and regional and temporal differences in flood hazard and exposure characteristics. Results of model validations and comparisons in both countries are discussed, particularly in respect to encountered challenges and possible solutions for an improvement of model transferability.

Integrated experimental and model-based analysis reveals the spatial aspects of EGFR activation dynamics

PubMed Central

Shankaran, Harish; Zhang, Yi; Chrisler, William B.; Ewald, Jonathan A.; Wiley, H. Steven; Resat, Haluk

2012-01-01

The epidermal growth factor receptor (EGFR) belongs to the ErbB family of receptor tyrosine kinases, and controls a diverse set of cellular responses relevant to development and tumorigenesis. ErbB activation is a complex process involving receptor-ligand binding, receptor dimerization, phosphorylation, and trafficking (internalization, recycling and degradation), which together dictate the spatio-temporal distribution of active receptors within the cell. The ability to predict this distribution, and elucidation of the factors regulating it, would help to establish a mechanistic link between ErbB expression levels and the cellular response. Towards this end, we constructed mathematical models to determine the contributions of receptor dimerization and phosphorylation to EGFR activation, and to examine the dependence of these processes on sub-cellular location. We collected experimental datasets for EGFR activation dynamics in human mammary epithelial cells, with the specific goal of model parameterization, and used the data to estimate parameters for several alternate models. Model-based analysis indicated that: 1) signal termination via receptor dephosphorylation in late endosomes, prior to degradation, is an important component of the response, 2) less than 40% of the receptors in the cell are phosphorylated at any given time, even at saturating ligand doses, and 3) receptor phosphorylation kinetics at the cell surface and early endosomes are comparable. We validated the last finding by measuring the EGFR dephosphorylation rates at various times following ligand addition both in whole cells and in endosomes using ELISAs and fluorescent imaging. Overall, our results provide important information on how EGFR phosphorylation levels are regulated within cells. This study demonstrates that an iterative cycle of experiments and modeling can be used to gain mechanistic insight regarding complex cell signaling networks. PMID:22952062

Receptor-based 3D QSAR analysis of estrogen receptor ligands - merging the accuracy of receptor-based alignments with the computational efficiency of ligand-based methods

NASA Astrophysics Data System (ADS)

Sippl, Wolfgang

2000-08-01

One of the major challenges in computational approaches to drug design is the accurate prediction of binding affinity of biomolecules. In the present study several prediction methods for a published set of estrogen receptor ligands are investigated and compared. The binding modes of 30 ligands were determined using the docking program AutoDock and were compared with available X-ray structures of estrogen receptor-ligand complexes. On the basis of the docking results an interaction energy-based model, which uses the information of the whole ligand-receptor complex, was generated. Several parameters were modified in order to analyze their influence onto the correlation between binding affinities and calculated ligand-receptor interaction energies. The highest correlation coefficient ( r 2 = 0.617, q 2 LOO = 0.570) was obtained considering protein flexibility during the interaction energy evaluation. The second prediction method uses a combination of receptor-based and 3D quantitative structure-activity relationships (3D QSAR) methods. The ligand alignment obtained from the docking simulations was taken as basis for a comparative field analysis applying the GRID/GOLPE program. Using the interaction field derived with a water probe and applying the smart region definition (SRD) variable selection, a significant and robust model was obtained ( r 2 = 0.991, q 2 LOO = 0.921). The predictive ability of the established model was further evaluated by using a test set of six additional compounds. The comparison with the generated interaction energy-based model and with a traditional CoMFA model obtained using a ligand-based alignment ( r 2 = 0.951, q 2 LOO = 0.796) indicates that the combination of receptor-based and 3D QSAR methods is able to improve the quality of the underlying model.

Multivariate Prediction Equations for HbA1c Lowering, Weight Change, and Hypoglycemic Events Associated with Insulin Rescue Medication in Type 2 Diabetes Mellitus: Informing Economic Modeling.

PubMed

Willis, Michael; Asseburg, Christian; Nilsson, Andreas; Johnsson, Kristina; Kartman, Bernt

2017-03-01

Type 2 diabetes mellitus (T2DM) is chronic and progressive and the cost-effectiveness of new treatment interventions must be established over long time horizons. Given the limited durability of drugs, assumptions regarding downstream rescue medication can drive results. Especially for insulin, for which treatment effects and adverse events are known to depend on patient characteristics, this can be problematic for health economic evaluation involving modeling. To estimate parsimonious multivariate equations of treatment effects and hypoglycemic event risks for use in parameterizing insulin rescue therapy in model-based cost-effectiveness analysis. Clinical evidence for insulin use in T2DM was identified in PubMed and from published reviews and meta-analyses. Study and patient characteristics and treatment effects and adverse event rates were extracted and the data used to estimate parsimonious treatment effect and hypoglycemic event risk equations using multivariate regression analysis. Data from 91 studies featuring 171 usable study arms were identified, mostly for premix and basal insulin types. Multivariate prediction equations for glycated hemoglobin A 1c lowering and weight change were estimated separately for insulin-naive and insulin-experienced patients. Goodness of fit (R 2 ) for both outcomes were generally good, ranging from 0.44 to 0.84. Multivariate prediction equations for symptomatic, nocturnal, and severe hypoglycemic events were also estimated, though considerable heterogeneity in definitions limits their usefulness. Parsimonious and robust multivariate prediction equations were estimated for glycated hemoglobin A 1c and weight change, separately for insulin-naive and insulin-experienced patients. Using these in economic simulation modeling in T2DM can improve realism and flexibility in modeling insulin rescue medication. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

The innovative concept of three-dimensional hybrid receptor modeling

NASA Astrophysics Data System (ADS)

Stojić, A.; Stanišić Stojić, S.

2017-09-01

The aim of this study was to improve the current understanding of air pollution transport processes at regional and long-range scale. For this purpose, three-dimensional (3D) potential source contribution function and concentration weighted trajectory models, as well as new hybrid receptor model, concentration weighted boundary layer (CWBL), which uses a two-dimensional grid and a planetary boundary layer height as a frame of reference, are presented. The refined approach to hybrid receptor modeling has two advantages. At first, it considers whether each trajectory endpoint meets the inclusion criteria based on planetary boundary layer height, which is expected to provide a more realistic representation of the spatial distribution of emission sources and pollutant transport pathways. Secondly, it includes pollutant time series preprocessing to make hybrid receptor models more applicable for suburban and urban locations. The 3D hybrid receptor models presented herein are designed to identify altitude distribution of potential sources, whereas CWBL can be used for analyzing the vertical distribution of pollutant concentrations along the transport pathway.

Generalized paired-agent kinetic model for in vivo quantification of cancer cell-surface receptors under receptor saturation conditions

NASA Astrophysics Data System (ADS)

Sadeghipour, N.; Davis, S. C.; Tichauer, K. M.

2017-01-01

New precision medicine drugs oftentimes act through binding to specific cell-surface cancer receptors, and thus their efficacy is highly dependent on the availability of those receptors and the receptor concentration per cell. Paired-agent molecular imaging can provide quantitative information on receptor status in vivo, especially in tumor tissue; however, to date, published approaches to paired-agent quantitative imaging require that only ‘trace’ levels of imaging agent exist compared to receptor concentration. This strict requirement may limit applicability, particularly in drug binding studies, which seek to report on a biological effect in response to saturating receptors with a drug moiety. To extend the regime over which paired-agent imaging may be used, this work presents a generalized simplified reference tissue model (GSRTM) for paired-agent imaging developed to approximate receptor concentration in both non-receptor-saturated and receptor-saturated conditions. Extensive simulation studies show that tumor receptor concentration estimates recovered using the GSRTM are more accurate in receptor-saturation conditions than the standard simple reference tissue model (SRTM) (% error (mean  ±  sd): GSRTM 0  ±  1 and SRTM 50  ±  1) and match the SRTM accuracy in non-saturated conditions (% error (mean  ±  sd): GSRTM 5  ±  5 and SRTM 0  ±  5). To further test the approach, GSRTM-estimated receptor concentration was compared to SRTM-estimated values extracted from tumor xenograft in vivo mouse model data. The GSRTM estimates were observed to deviate from the SRTM in tumors with low receptor saturation (which are likely in a saturated regime). Finally, a general ‘rule-of-thumb’ algorithm is presented to estimate the expected level of receptor saturation that would be achieved in a given tissue provided dose and pharmacokinetic information about the drug or imaging agent being used, and physiological information about the tissue. These studies suggest that the GSRTM is necessary when receptor saturation exceeds 20% and highlight the potential for GSRTM to accurately measure receptor concentrations under saturation conditions, such as might be required during high dose drug studies, or for imaging applications where high concentrations of imaging agent are required to optimize signal-to-noise conditions. This model can also be applied to PET and SPECT imaging studies that tend to suffer from noisier data, but require one less parameter to fit if images are converted to imaging agent concentration (quantitative PET/SPECT).

An assessment of the effects of serotonin 6 (5-HT6) receptor antagonists in rodent models of learning.

PubMed

Lindner, Mark D; Hodges, Donald B; Hogan, John B; Orie, Anitra F; Corsa, Jason A; Barten, Donna M; Polson, Craig; Robertson, Barbara J; Guss, Valerie L; Gillman, Kevin W; Starrett, John E; Gribkoff, Valentin K

2003-11-01

Antagonists of serotonin 6 (5-HT6) receptors have been reported to enhance cognition in animal models of learning, although this finding has not been universal. We have assessed the therapeutic potential of the specific 5-HT6 receptor antagonists 4-amino-N-(2,6-bis-methylamino-pyrimidin-4-yl)-benzenesulfonamide (Ro 04-6790) and 5-chloro-N-(4-methoxy-3-piperazin-1-yl-phenyl)-3-methyl-2-benzothiophenesulfonamide (SB-271046) in rodent models of cognitive function. Although mice express the 5-HT6 receptor and the function of this receptor has been investigated in mice, all reports of activity with 5-HT6 receptor antagonists have used rat models. In the present study, receptor binding revealed that the pharmacological properties of the mouse receptor are different from the rat and human receptor: Ro 04-6790 does not bind to the mouse 5-HT6 receptor, so all in vivo testing included in the present report was conducted in rats. We replicated previous reports that 5-HT6 receptor antagonists produce a stretching syndrome previously shown to be mediated through cholinergic mechanisms, but Ro 04-6790 and SB-271046 failed to attenuate scopolamine-induced deficits in a test of contextual fear conditioning. We also failed to replicate the significant effects reported previously in both an autoshaping task and in a version of the Morris water maze. The results of our experiments are not consistent with previous reports that suggested that 5-HT6 antagonists might have therapeutic potential for cognitive disorders.

Melanocortin MC1 receptor in human genetics and model systems

PubMed Central

Beaumont, Kimberley A.; Wong, Shu S.; Ainger, Stephen A.; Liu, Yan Yan; Patel, Mira P.; Millhauser, Glenn L.; Smith, Jennifer J.; Alewood, Paul F.; Leonard, J. Helen; Sturm, Richard A.

2011-01-01

The melanocortin MC1 receptor is a G -protein coupled receptor expressed in melanocytes of the skin and hair and is known for its key role in regulation of human pigmentation. Melanocortin MC1 receptor activation after ultraviolet radiation exposure results in a switch from the red/yellow pheomelanin to the brown/black eumelanin pigment synthesis within cutaneous melanocytes; this pigment is then transferred to the surrounding keratinocytes of the skin. The increase in melanin maturation and uptake results in tanning of the skin, providing a physical protection of skin cells from ultraviolet radiation induced DNA damage. Melanocortin MC 1 receptor polymorphism is widespread within the Caucasian population and some variant alleles are associated with red hair colour, fair skin, poor tanning and increased risk of skin cancer. Here we will discuss the use of mouse coat colour models, human genetic association studies, and in vitro cell culture studies to determine the complex functions of the melanocortin MC1 receptor and the molecular mechanisms underlying the association between melanocortin MC1 receptor variant alleles and the red hair colour phenotype. Recent research indicates that melanocortin MC1 receptor has many non-pigmentary functions, and that the increased risk of skin cancer conferred by melanocortin MC1 receptor variant alleles is to some extent independent of pigmentation phenotypes. The use of new transgenic mouse models, the study of novel melanocortin MC1 receptor response genes and the use of more advanced human skin models such as 3D skin reconstruction may provide key elements in understanding the pharmacogenetics of human melanocortin MC1 receptor polymorphism . PMID:21199646

A competitive binding model predicts the response of mammalian olfactory receptors to mixtures

NASA Astrophysics Data System (ADS)

Singh, Vijay; Murphy, Nicolle; Mainland, Joel; Balasubramanian, Vijay

Most natural odors are complex mixtures of many odorants, but due to the large number of possible mixtures only a small fraction can be studied experimentally. To get a realistic understanding of the olfactory system we need methods to predict responses to complex mixtures from single odorant responses. Focusing on mammalian olfactory receptors (ORs in mouse and human), we propose a simple biophysical model for odor-receptor interactions where only one odor molecule can bind to a receptor at a time. The resulting competition for occupancy of the receptor accounts for the experimentally observed nonlinear mixture responses. We first fit a dose-response relationship to individual odor responses and then use those parameters in a competitive binding model to predict mixture responses. With no additional parameters, the model predicts responses of 15 (of 18 tested) receptors to within 10 - 30 % of the observed values, for mixtures with 2, 3 and 12 odorants chosen from a panel of 30. Extensions of our basic model with odorant interactions lead to additional nonlinearities observed in mixture response like suppression, cooperativity, and overshadowing. Our model provides a systematic framework for characterizing and parameterizing such mixing nonlinearities from mixture response data.

Modelling and simulation of biased agonism dynamics at a G protein-coupled receptor.

PubMed

Bridge, L J; Mead, J; Frattini, E; Winfield, I; Ladds, G

2018-04-07

Theoretical models of G protein-coupled receptor (GPCR) concentration-response relationships often assume an agonist producing a single functional response via a single active state of the receptor. These models have largely been analysed assuming steady-state conditions. There is now much experimental evidence to suggest that many GPCRs can exist in multiple receptor conformations and elicit numerous functional responses, with ligands having the potential to activate different signalling pathways to varying extents-a concept referred to as biased agonism, functional selectivity or pluri-dimensional efficacy. Moreover, recent experimental results indicate a clear possibility for time-dependent bias, whereby an agonist's bias with respect to different pathways may vary dynamically. Efforts towards understanding the implications of temporal bias by characterising and quantifying ligand effects on multiple pathways will clearly be aided by extending current equilibrium binding and biased activation models to include G protein activation dynamics. Here, we present a new model of time-dependent biased agonism, based on ordinary differential equations for multiple cubic ternary complex activation models with G protein cycle dynamics. This model allows simulation and analysis of multi-pathway activation bias dynamics at a single receptor for the first time, at the level of active G protein (α GTP ), towards the analysis of dynamic functional responses. The model is generally applicable to systems with N G G proteins and N* active receptor states. Numerical simulations for N G =N * =2 reveal new insights into the effects of system parameters (including cooperativities, and ligand and receptor concentrations) on bias dynamics, highlighting new phenomena including the dynamic inter-conversion of bias direction. Further, we fit this model to 'wet' experimental data for two competing G proteins (G i and G s ) that become activated upon stimulation of the adenosine A 1 receptor with adenosine derivative compounds. Finally, we show that our model can qualitatively describe the temporal dynamics of this competing G protein activation. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

An Allosteric Coagonist Model for Propofol Effects on α1β2γ2L γ-Aminobutyric Acid Type A Receptors

PubMed Central

Ruesch, Dirk; Neumann, Elena; Wulf, Hinnerk; Forman, Stuart A.

2011-01-01

Background Propofol produces its major actions via γ-aminobutyric acid type A (GABAA) receptors. At low concentrations, propofol enhances agonist-stimulated GABAA receptor activity, and high propofol concentrations directly activate receptors. Etomidate produces similar effects, and there is convincing evidence that a single class of etomidate sites mediate both agonist modulation and direct GABAA receptor activation. It is unknown if the propofol binding site(s) on GABAA receptors that modulate agonist-induced activity also mediate direct activation. Methods GABAA α1β2γ2L receptors were heterologously expressed in Xenopus oocytes and activity was quantified using voltage clamp electrophysiology. We tested whether propofol and etomidate display the same linkage between agonist modulation and direct activation of GABAA receptors by identifying equi-efficacious drug solutions for direct activation. We then determined whether these drug solutions produce equal modulation of GABA-induced receptor activity. We also measured propofol-dependent direct activation and modulation of low GABA responses. Allosteric coagonist models similar to that established for etomidate, but with variable numbers of propofol sites, were fitted to combined data. Results Solutions of 19 μM propofol and 10 μM etomidate were found to equally activate GABAA receptors. These two drug solutions also produced indistinguishable modulation of GABA-induced receptor activity. Combined electrophysiological data behaved in a manner consistent with allosteric co-agonist models with more than one propofol site. The best fit was observed when the model assumed three equivalent propofol sites. Conclusions Our results support the hypothesis that propofol, like etomidate, acts at GABAA receptor sites mediating both GABA modulation and direct activation. PMID:22104494

Molecular modeling of ligand-receptor interactions in the OR5 olfactory receptor.

PubMed

Singer, M S; Shepherd, G M

1994-06-02

Olfactory receptors belong to the superfamily of seven transmembrane domain, G protein-coupled receptors. In order to begin analysis of mechanisms of receptor activation, a computer model of the OR5 olfactory receptor has been constructed and compared with other members of this superfamily. We have tested docking of the odor molecule lyral, which is known to activate the OR5 receptor. The results point to specific ligand-binding residues on helices III through VII that form a binding pocket in the receptor. Some of these residues occupy sequence positions identical to ligand-binding residues conserved among other superfamily members. The results provide new insights into possible molecular mechanisms of odor recognition and suggest hypotheses to guide future experimental studies using site-directed mutagenesis.

A multivariate mixed model system for wood specific gravity and moisture content of planted loblolly pine stands in the southern United States

Treesearch

Finto Antony; Laurence R. Schimleck; Alex Clark; Richard F. Daniels

2012-01-01

Specific gravity (SG) and moisture content (MC) both have a strong influence on the quantity and quality of wood fiber. We proposed a multivariate mixed model system to model the two properties simultaneously. Disk SG and MC at different height levels were measured from 3 trees in 135 stands across the natural range of loblolly pine and the stand level values were used...

The heterodimeric sweet taste receptor has multiple potential ligand binding sites.

PubMed

Cui, Meng; Jiang, Peihua; Maillet, Emeline; Max, Marianna; Margolskee, Robert F; Osman, Roman

2006-01-01

The sweet taste receptor is a heterodimer of two G protein coupled receptors, T1R2 and T1R3. This discovery has increased our understanding at the molecular level of the mechanisms underlying sweet taste. Previous experimental studies using sweet receptor chimeras and mutants show that there are at least three potential binding sites in this heterodimeric receptor. Receptor activity toward the artificial sweeteners aspartame and neotame depends on residues in the amino terminal domain of human T1R2. In contrast, receptor activity toward the sweetener cyclamate and the sweet taste inhibitor lactisole depends on residues within the transmembrane domain of human T1R3. Furthermore, receptor activity toward the sweet protein brazzein depends on the cysteine rich domain of human T1R3. Although crystal structures are not available for the sweet taste receptor, useful homology models can be developed based on appropriate templates. The amino terminal domain, cysteine rich domain and transmembrane helix domain of T1R2 and T1R3 have been modeled based on the crystal structures of metabotropic glutamate receptor type 1, tumor necrosis factor receptor, and bovine rhodopsin, respectively. We have used homology models of the sweet taste receptors, molecular docking of sweet ligands to the receptors, and site-directed mutagenesis of the receptors to identify potential ligand binding sites of the sweet taste receptor. These studies have led to a better understanding of the structure and function of this heterodimeric receptor, and can act as a guide for rational structure-based design of novel non-caloric sweeteners, which can be used in the fighting against obesity and diabetes.

Multivariate curve resolution-alternating least squares and kinetic modeling applied to near-infrared data from curing reactions of epoxy resins: mechanistic approach and estimation of kinetic rate constants.

PubMed

Garrido, M; Larrechi, M S; Rius, F X

2006-02-01

This study describes the combination of multivariate curve resolution-alternating least squares with a kinetic modeling strategy for obtaining the kinetic rate constants of a curing reaction of epoxy resins. The reaction between phenyl glycidyl ether and aniline is monitored by near-infrared spectroscopy under isothermal conditions for several initial molar ratios of the reagents. The data for all experiments, arranged in a column-wise augmented data matrix, are analyzed using multivariate curve resolution-alternating least squares. The concentration profiles recovered are fitted to a chemical model proposed for the reaction. The selection of the kinetic model is assisted by the information contained in the recovered concentration profiles. The nonlinear fitting provides the kinetic rate constants. The optimized rate constants are in agreement with values reported in the literature.

Quantitative in vivo receptor binding. III. Tracer kinetic modeling of muscarinic cholinergic receptor binding

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

Frey, K.A.; Hichwa, R.D.; Ehrenkaufer, R.L.

1985-10-01

A tracer kinetic method is developed for the in vivo estimation of high-affinity radioligand binding to central nervous system receptors. Ligand is considered to exist in three brain pools corresponding to free, nonspecifically bound, and specifically bound tracer. These environments, in addition to that of intravascular tracer, are interrelated by a compartmental model of in vivo ligand distribution. A mathematical description of the model is derived, which allows determination of regional blood-brain barrier permeability, nonspecific binding, the rate of receptor-ligand association, and the rate of dissociation of bound ligand, from the time courses of arterial blood and tissue tracer concentrations.more » The term ''free receptor density'' is introduced to describe the receptor population measured by this method. The technique is applied to the in vivo determination of regional muscarinic acetylcholine receptors in the rat, with the use of (TH)scopolamine. Kinetic estimates of free muscarinic receptor density are in general agreement with binding capacities obtained from previous in vivo and in vitro equilibrium binding studies. In the striatum, however, kinetic estimates of free receptor density are less than those in the neocortex--a reversal of the rank ordering of these regions derived from equilibrium determinations. A simplified model is presented that is applicable to tracers that do not readily dissociate from specific binding sites during the experimental period.« less

The neuropsychopharmacology of phencyclidine: from NMDA receptor hypofunction to the dopamine hypothesis of schizophrenia.

PubMed

Jentsch, J D; Roth, R H

1999-03-01

Administration of noncompetitive NMDA/glutamate receptor antagonists, such as phencyclidine (PCP) and ketamine, to humans induces a broad range of schizophrenic-like symptomatology, findings that have contributed to a hypoglutamatergic hypothesis of schizophrenia. Moreover, a history of experimental investigations of the effects of these drugs in animals suggests that NMDA receptor antagonists may model some behavioral symptoms of schizophrenia in nonhuman subjects. In this review, the usefulness of PCP administration as a potential animal model of schizophrenia is considered. To support the contention that NMDA receptor antagonist administration represents a viable model of schizophrenia, the behavioral and neurobiological effects of these drugs are discussed, especially with regard to differing profiles following single-dose and long-term exposure. The neurochemical effects of NMDA receptor antagonist administration are argued to support a neurobiological hypothesis of schizophrenia, which includes pathophysiology within several neurotransmitter systems, manifested in behavioral pathology. Future directions for the application of NMDA receptor antagonist models of schizophrenia to preclinical and pathophysiological research are offered.

The PIT-trap-A "model-free" bootstrap procedure for inference about regression models with discrete, multivariate responses.

PubMed

Warton, David I; Thibaut, Loïc; Wang, Yi Alice

2017-01-01

Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not identically distributed (thus not amenable to bootstrapping)-common examples including logistic or Poisson regression and generalizations to handle clustered or multivariate data, such as generalised estimating equations. We propose a bootstrap method based on probability integral transform (PIT-) residuals, which we call the PIT-trap, which assumes data come from some marginal distribution F of known parametric form. This method can be understood as a type of "model-free bootstrap", adapted to the problem of discrete and highly multivariate data. PIT-residuals have the key property that they are (asymptotically) pivotal. The PIT-trap thus inherits the key property, not afforded by any other residual resampling approach, that the marginal distribution of data can be preserved under PIT-trapping. This in turn enables the derivation of some standard bootstrap properties, including second-order correctness of pivotal PIT-trap test statistics. In multivariate data, bootstrapping rows of PIT-residuals affords the property that it preserves correlation in data without the need for it to be modelled, a key point of difference as compared to a parametric bootstrap. The proposed method is illustrated on an example involving multivariate abundance data in ecology, and demonstrated via simulation to have improved properties as compared to competing resampling methods.

The PIT-trap—A “model-free” bootstrap procedure for inference about regression models with discrete, multivariate responses

PubMed Central

Thibaut, Loïc; Wang, Yi Alice

2017-01-01

Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not identically distributed (thus not amenable to bootstrapping)—common examples including logistic or Poisson regression and generalizations to handle clustered or multivariate data, such as generalised estimating equations. We propose a bootstrap method based on probability integral transform (PIT-) residuals, which we call the PIT-trap, which assumes data come from some marginal distribution F of known parametric form. This method can be understood as a type of “model-free bootstrap”, adapted to the problem of discrete and highly multivariate data. PIT-residuals have the key property that they are (asymptotically) pivotal. The PIT-trap thus inherits the key property, not afforded by any other residual resampling approach, that the marginal distribution of data can be preserved under PIT-trapping. This in turn enables the derivation of some standard bootstrap properties, including second-order correctness of pivotal PIT-trap test statistics. In multivariate data, bootstrapping rows of PIT-residuals affords the property that it preserves correlation in data without the need for it to be modelled, a key point of difference as compared to a parametric bootstrap. The proposed method is illustrated on an example involving multivariate abundance data in ecology, and demonstrated via simulation to have improved properties as compared to competing resampling methods. PMID:28738071

Plasma 25-hydroxyvitamin D and risk of breast cancer in the Nurses' Health Study II

PubMed Central

2011-01-01

Introduction Experimental evidence indicates vitamin D may play an important role in breast cancer etiology but epidemiologic evidence to date is inconsistent. Vitamin D comes from dietary intake and sun exposure and plasma levels of 25-hydroxyvitamin D (25(OH)D) are considered the best measure of vitamin D status. Methods We conducted a prospective nested case-control study within the Nurses' Health Study II (NHSII). Plasma samples collected in 1996 to 1999 were assayed for 25(OH)D in 613 cases, diagnosed after blood collection and before 1 June 2007, and in 1,218 matched controls. Multivariate relative risks (RR) and 95% confidence intervals (CI) were calculated by conditional logistic regression, adjusting for several breast cancer risk factors. Results No significant association was observed between plasma 25(OH)D levels and breast cancer risk (top vs. bottom quartile multivariate RR = 1.20, 95% CI (0.88 to 1.63), P-value, test for trend = 0.32). Results were similar when season-specific quartile cut points were used. Results did not change when restricted to women who were premenopausal at blood collection or premenopausal at diagnosis. Results were similar between estrogen receptor (ER)+/progesterone receptor (PR)+ and ER-/PR- tumors (P-value, test for heterogeneity = 0.51). The association did not vary by age at blood collection or season of blood collection, but did vary when stratified by body mass index (P-value, test for heterogeneity = 0.01). Conclusions Circulating 25(OH)D levels were not significantly associated with breast cancer risk in this predominantly premenopausal population. PMID:21569367

Estrogen Receptor Expression in Atypical Hyperplasia: Lack of Association with Breast Cancer

PubMed Central

Barr Fritcher, Emily G.; Degnim, Amy C.; Hartmann, Lynn C.; Radisky, Derek C.; Boughey, Judy C.; Anderson, Stephanie S.; Vierkant, Robert A.; Frost, Marlene H.; Visscher, Daniel W.; Reynolds, Carol

2011-01-01

Background Estrogen receptor (ER) is expressed in normal and malignant breast epithelium, and expression levels have been found to increase with age in normal breast epithelium but not in atypical hyperplasia (AH) and carcinoma in situ. Here we assess ER expression in AH and its association with later breast cancer. Methods ER expression was assessed immunohistochemically in archival sections from 246 women with AH who had open benign breast biopsy from 1967–1991. The ACISRIII (Dako, Carpinteria, CA) was utilized to calculate ER expression in all atypical foci. Using multivariate linear regression, we examined associations of ER expression with age at biopsy, indication for biopsy, type of atypia, number of atypical foci, involution status, and family history. Breast cancer risk across levels of ER expression was also assessed compared to the Iowa SEER control population. Results Among 246 women, 87 (35%) had atypical ductal hyperplasia (ADH), 141 (57%) had atypical lobular hyperplasia (ALH), and 18 (7%) had both. Forty-nine (20%) developed breast cancer (median follow-up of 14.4 years). Multivariate analysis indicated that type of atypia and age at diagnosis were significantly associated with ER percent staining and intensity [p<0.05]. ER expression was increased in women with ADH and/or those over age 55. ER expression did not significantly impact breast cancer risk in patients diagnosed with atypia. Conclusion We found increasing ER expression in atypical hyperplasia with increasing age. ER expression in atypical hyperplasia does not further discriminate breast cancer risk in women with atypia. PMID:21209395

Effects of icotinib on early-stage non-small-cell lung cancer as neoadjuvant treatment with different epidermal growth factor receptor phenotypes.

PubMed

Wang, Tao; Liu, Yang; Zhou, Bin; Wang, Zhi; Liang, Naichao; Zhang, Yundong; Dong, Zhouhuan; Li, Jie

2016-01-01

Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) have demonstrated efficacy in treating advanced non-small-cell lung cancer (NSCLC). Preliminary findings suggested that EGFR-TKIs might also be beneficial in neoadjuvant therapy in treating NSCLC. Therefore, this study aimed to evaluate the efficacy and safety of neoadjuvant therapy with icotinib in patients with early-stage NSCLC. We retrospectively reviewed the medical history of patients who were initially diagnosed with stage IA-IIIA NSCLC and were under icotinib administration before surgery between December 2011 and December 2014. Tumor assessment was conducted between the second and fourth week from initial icotinib treatment. The association between personal characteristics, smoking status, disease stage, EGFR mutation status, and clinical outcomes were investigated using multivariate logistic regression analysis. A total of 67 patients with NSCLC were reviewed, and approximately half (38/67) of them were identified as having EGFR-mutant tumors. The overall response rate of all patients was 26.7% at 2-4 weeks' assessment. Multivariate analysis showed that female sex (38.5% versus 10.7% in males, P=0.028) and EGFR mutation status (42.1% versus 6.9% in EGFR wild type, P=0.011) were independent predictive factors. The analysis also showed that the most common adverse effects were rash (43.3%) and dry skin (34.4%), which were tolerable. Icotinib induced clinical response with minimal toxicity as neoadjuvant treatment in early NSCLC, especially in patients with common EGFR mutations. Further studies are warranted to confirm our findings.

GENETIC INTERACTIONS BETWEEN ADRB2 AND PTGER4 ON RESPONSES TO SALMETEROL OR MONTELUKAST IN JAPANESE PATIENTS WITH MILD PERSISTENT ASTHMA.

PubMed

Yamada, Hideyasu; Masuko, Hironori; Inui, Toshihide; Kanazawa, Jun; Yatagai, Yohei; Sakamoto, Tohru; Iijima, Hiroaki; Konno, Satoshi; Shimizu, Kaoruko; Makita, Hironi; Nishimura, Masaharu; Kokubu, Fumio; Saito, Takefumi; Endo, Takeo; Ninomiya, Hiroki; Kaneko, Norihiro; Hizawa, Nobuyuki

2016-01-01

Long-acting β 2 -agonists (LABA) and leukotriene receptor antagonists (LTRA) are two principal agents that can be added to inhaled corticosteroids (ICS) for patients with asthma that is not adequately controlled by ICS alone. In our previous study, the Gly16Arg genotype of the β 2 -adrenergic receptor (ADRB2) gene did not influence the differential bronchodilator effect of salmeterol versus montelukast as an add-on therapy to ICS within 16 weeks of follow-up (the J-Blossom study). We examined if genes encoding CYSLTR1, CYSLTR2, PTGER2 or PTGER4 could explain differential responses to salmeterol versus montelukast using the participants of the J-Blossom study. This study included 76 patients with mild-to-moderate asthma. The difference in peak expiratory flow (PEF) (ΔPEF, l/min) after 16 weeks of treatment with salmeterol (ΔPEFsal) versus montelukast (ΔPEFmon) was associated with the genotypes at each of 4 genes. In addition, multivariate analyses were used to identify a gene-gene interaction between ADRB2 gene and each of these 4 genes. Although none of 4 genes were associated with ΔPEFsal-ΔPEFmon in the univariate analyses, multivariate analysis showed that PTGER4 gene, interacting with ADRB2 Gly16Arg, was associated with ΔPEFsal-ΔPEFmon (p=0.0032). Our findings suggested that the interactions between two genetic loci at ADRB2 and PTGER4 is important in determining the differential response to salmeterol versus montelukast in patients with chronic adult asthma.

Synthesis of a control model for a liquid nitrogen cooled, closed circuit, cryogenic nitrogen wind tunnel and its validation

NASA Technical Reports Server (NTRS)

Balakrishna, S.; Goglia, G. L.

1979-01-01

The details of the efforts to synthesize a control-compatible multivariable model of a liquid nitrogen cooled, gaseous nitrogen operated, closed circuit, cryogenic pressure tunnel are presented. The synthesized model was transformed into a real-time cryogenic tunnel simulator, and this model is validated by comparing the model responses to the actual tunnel responses of the 0.3 m transonic cryogenic tunnel, using the quasi-steady-state and the transient responses of the model and the tunnel. The global nature of the simple, explicit, lumped multivariable model of a closed circuit cryogenic tunnel is demonstrated.

Constrained positive matrix factorization: Elemental ratios, spatial distinction, and chemical transport model source contributions

NASA Astrophysics Data System (ADS)

Sturtz, Timothy M.

Source apportionment models attempt to untangle the relationship between pollution sources and the impacts at downwind receptors. Two frameworks of source apportionment models exist: source-oriented and receptor-oriented. Source based apportionment models use presumed emissions and atmospheric processes to estimate the downwind source contributions. Conversely, receptor based models leverage speciated concentration data from downwind receptors and apply statistical methods to predict source contributions. Integration of both source-oriented and receptor-oriented models could lead to a better understanding of the implications sources have on the environment and society. The research presented here investigated three different types of constraints applied to the Positive Matrix Factorization (PMF) receptor model within the framework of the Multilinear Engine (ME-2): element ratio constraints, spatial separation constraints, and chemical transport model (CTM) source attribution constraints. PM10-2.5 mass and trace element concentrations were measured in Winston-Salem, Chicago, and St. Paul at up to 60 sites per city during two different seasons in 2010. PMF was used to explore the underlying sources of variability. Information on previously reported PM10-2.5 tire and brake wear profiles were used to constrain these features in PMF by prior specification of selected species ratios. We also modified PMF to allow for combining the measurements from all three cities into a single model while preserving city-specific soil features. Relatively minor differences were observed between model predictions with and without the prior ratio constraints, increasing confidence in our ability to identify separate brake wear and tire wear features. Using separate data, source contributions to total fine particle carbon predicted by a CTM were incorporated into the PMF receptor model to form a receptor-oriented hybrid model. The level of influence of the CTM versus traditional PMF was varied using a weighting parameter applied to an object function as implemented in ME-2. The resulting hybrid model was used to quantify the contributions of total carbon from both wildfires and biogenic sources at two Interagency Monitoring of Protected Visual Environment monitoring sites, Monture and Sula Peak, Montana, from 2006 through 2008.

Elevated nuclear expression of the SMRT corepressor in breast cancer is associated with earlier tumor recurrence

PubMed Central

Migliaccio, Ilenia; Chaubal, Vaishali; Wu, Meng-Fen; Pace, Margaret C.; Hartmaier, Ryan; Jiang, Shiming; Edwards, Dean P.; Gutiérrez, M. Carolina; Hilsenbeck, Susan G.; Oesterreich, Steffi

2012-01-01

Silencing mediator of retinoic acid and thyroid hormone receptor (SMRT), also known as nuclear corepressor 2 (NCOR2) is a transcriptional corepressor for multiple members of the nuclear receptor superfamily of transcription factors, including estrogen receptor-α (ERα). In the classical model of corepressor action, SMRT binds to antiestrogen-bound ERα at target promoters and represses ERα transcriptional activity and gene expression. Herein SMRT mRNA and protein expression was examined in a panel of 30 breast cancer cell lines. Expression of both parameters was found to vary considerably amongst lines and the correlation between protein and mRNA expression was very poor (R2 = 0.0775). Therefore, SMRT protein levels were examined by immunohistochemical staining of a tissue microarray of 866 patients with stage I–II breast cancer. Nuclear and cytoplasmic SMRT were scored separately according to the Allred score. The majority of tumors (67 %) were negative for cytoplasmic SMRT, which when detected was found at very low levels. In contrast, nuclear SMRT was broadly detected. There was no significant difference in time to recurrence (TTR) according to SMRT expression levels in the ERα-positive tamoxifen-treated patients (P = 0.297) but the difference was significant in the untreated patients (P = 0.01). In multivariate analysis, ERα-positive tamoxifen-untreated patients with high nuclear SMRT expression (SMRT 5-8, i.e., 2nd to 4th quartile) had a shorter TTR (HR = 1.94, 95 % CI, 1.24–3.04; P = 0.004) while there was no association with SMRT expression for ERα-positive tamoxifen-treated patients. There was no association between SMRT expression and overall survival for patients, regardless of whether they received tamoxifen. Thus while SMRT protein expression was not predictive of outcome after antiestrogen therapy, it may have value in predicting tumor recurrence in patients not receiving adjuvant tamoxifen therapy. PMID:23015261

Increased plasma DPP4 activities predict new-onset atherosclerosis in association with its proinflammatory effects in Chinese over a four year period: A prospective study.

PubMed

Zheng, T P; Yang, F; Gao, Y; Baskota, A; Chen, T; Tian, H M; Ran, X W

2014-08-01

DPP4, a novel proinflammatory cytokine, is involved in the inflammatory process through its interaction with IGF-II/M6P receptor. We aimed to investigate whether it could predict new-onset atherosclerosis in Chinese. A prospective study was conducted of 590 adults (213 men and 377 women) aged 18-70 years without atherosclerosis examined in 2007(baseline) and 2011(follow-up). Circulating DPP4 activity, inflammatory markers, IGF-II/M6P receptor and common carotid artery Intima-Media Thickness (C-IMT) were measured at baseline and four years later. At baseline, individuals in the highest quartile of DPP4 activity had higher age, WHR, BMI, SBP, fasting insulin, 2h-PG, TG, LDL-C, IL-6, hs-CRP, IGF-II/M6P-R, C-IMT and lower HDL-C compared with individuals in the lowest quartile. After a 4-year follow-up, 71 individuals developed atherosclerosis. In multiple linear regression analysis, baseline DPP4 activity was an independent predictor of an increase in inflammatory markers, IGF-II/M6P receptor, and C-IMT over a 4-year period (all P < 0.01). In multivariable-adjusted models, the odds ratio (OR) for incident atherosclerosis comparing the highest with the lowest quartiles of DPP4 activity was 3.17 (95%CI 1.33-7.58) after adjustment for confounding risk factors (P = 0.009). The incidence of atherosclerosis owing to DPP4 activity increased by 12.41%. DPP4 activity is an important predictor of the onset of inflammation and atherosclerosis in apparently healthy Chinese. This finding may have important implications for understanding the proinflammatory role of DPP-4 in the pathogenesis of atherosclerosis. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Epidermal growth factor receptor (EGFr); results of a 6 year follow-up study in operable breast cancer with emphasis on the node negative subgroup.

PubMed Central

Nicholson, S.; Richard, J.; Sainsbury, C.; Halcrow, P.; Kelly, P.; Angus, B.; Wright, C.; Henry, J.; Farndon, J. R.; Harris, A. L.

1991-01-01

More accurate criteria are required for the selection of patients with node-negative breast cancer for systemic adjuvant therapy. Expression of epidermal growth factor receptor (EGFr) has been shown previously to be inversely related to oestrogen receptor (ER) in patients with operable breast cancer and to be associated with a poorer prognosis. Analysis of EGFr and ER was performed on tumour samples from 231 patients with operable breast cancer followed for up to 6 years after surgery. The median duration of follow-up in patients still alive at the time of analysis was 45 months. Thirty-five percent of patients (82) had tumours with greater than 10 fmol mg-1 I125-EGF binding (EGFr+) and 47% (109) and cystolic ER concentrations greater than 5 fmol mg-1 (ER+), with a marked inverse relationship between EGFr and ER (P less than 0.00001). In a univariate analysis EGFr was second only to axillary node status as a prognostic marker for all patients both in terms of relapse-free and overall survival (P less than 0.001, log rank). For patients with histologically negative axillary nodes EGFr was superior to ER in predicting relapse and survival (P less than 0.01 and P less than 0.005 respectively compared to P less than 0.1 and P less than 0.1, log rank). In a multivariate (Cox model) analysis only EGFr, out of EGFr, ER, size and grade, was predictive for either relapse-free or overall survival for patients with node-negative disease (P = 0.05 and P = 0.026 respectively). EGFr has been shown to be a marker of poor prognosis for patients with node-negative breast cancer. Since patients with EGFr+ tumours are unlikely to respond to hormone therapy it may be possible to select them for trials of systemic adjuvant chemotherapy. PMID:1846551

Epidermal growth factor receptor (EGFr); results of a 6 year follow-up study in operable breast cancer with emphasis on the node negative subgroup.

PubMed

Nicholson, S; Richard, J; Sainsbury, C; Halcrow, P; Kelly, P; Angus, B; Wright, C; Henry, J; Farndon, J R; Harris, A L

1991-01-01

More accurate criteria are required for the selection of patients with node-negative breast cancer for systemic adjuvant therapy. Expression of epidermal growth factor receptor (EGFr) has been shown previously to be inversely related to oestrogen receptor (ER) in patients with operable breast cancer and to be associated with a poorer prognosis. Analysis of EGFr and ER was performed on tumour samples from 231 patients with operable breast cancer followed for up to 6 years after surgery. The median duration of follow-up in patients still alive at the time of analysis was 45 months. Thirty-five percent of patients (82) had tumours with greater than 10 fmol mg-1 I125-EGF binding (EGFr+) and 47% (109) and cystolic ER concentrations greater than 5 fmol mg-1 (ER+), with a marked inverse relationship between EGFr and ER (P less than 0.00001). In a univariate analysis EGFr was second only to axillary node status as a prognostic marker for all patients both in terms of relapse-free and overall survival (P less than 0.001, log rank). For patients with histologically negative axillary nodes EGFr was superior to ER in predicting relapse and survival (P less than 0.01 and P less than 0.005 respectively compared to P less than 0.1 and P less than 0.1, log rank). In a multivariate (Cox model) analysis only EGFr, out of EGFr, ER, size and grade, was predictive for either relapse-free or overall survival for patients with node-negative disease (P = 0.05 and P = 0.026 respectively). EGFr has been shown to be a marker of poor prognosis for patients with node-negative breast cancer. Since patients with EGFr+ tumours are unlikely to respond to hormone therapy it may be possible to select them for trials of systemic adjuvant chemotherapy.

Association between injury pattern of patients with multiple injuries and circulating levels of soluble tumor necrosis factor receptors, interleukin-6 and interleukin-10, and polymorphonuclear neutrophil elastase.

PubMed

Hensler, Thorsten; Sauerland, Stefan; Bouillon, Bertil; Raum, Marcus; Rixen, Dieter; Helling, Hanns-J; Andermahr, Jonas; Neugebauer, Edmund A M

2002-05-01

Our knowledge about the bidirectional interactions between brain and whole organism after trauma is still limited. It was the purpose of this prospective clinical study to determine the influence of severe head trauma (SHT) as well as trauma in different anatomic injury regions on posttraumatic inflammatory mediator levels from patients with multiple injuries. Thirty-five healthy controls, 33 patients with an isolated SHT, 47 patients with multiple injuries without SHT, and 45 patients with both SHT and multiple injuries were studied. The posttraumatic plasma levels of soluble tumor necrosis factor receptors p55 and p75, interleukin (IL)-6, IL-10, and polymorphonuclear neutrophil (PMN) elastase were monitored using enzyme-linked immunosorbent assay technique. The influence of head injuries as well as thorax, abdomen, and extremity injuries on the mediator release from patients with multiple injuries was investigated by multivariate linear regression models. The soluble tumor necrosis factor receptor p55/p75 ratio was significantly elevated within 3 hours of trauma in all three injury groups and returned to reference ratios after 12 hours. The lowest increase was found in patients suffering from an isolated SHT. Lowest mediator levels in this patient population were also found for IL-6, IL-10, and PMN elastase during the first 36 hours after trauma. Additional injuries to the head, thorax, abdomen, and extremity modulated mediator levels to a different degree. No specific effect was found for SHT when compared with other injury groups. Thorax injuries caused the quickest rise in mediator levels, whereas abdominal injuries significantly increased PMN elastase levels 12 to 24 hours after trauma. Traumatic injuries cause the liberation of various mediators, without any specific association between anatomic injury pattern and the pattern of mediator release.

Prognostic Significance of Progesterone Receptor–Positive Tumor Cells Within Immunohistochemically Defined Luminal A Breast Cancer

PubMed Central

Prat, Aleix; Cheang, Maggie Chon U.; Martín, Miguel; Parker, Joel S.; Carrasco, Eva; Caballero, Rosalía; Tyldesley, Scott; Gelmon, Karen; Bernard, Philip S.; Nielsen, Torsten O.; Perou, Charles M.

2013-01-01

Purpose Current immunohistochemical (IHC)-based definitions of luminal A and B breast cancers are imperfect when compared with multigene expression-based assays. In this study, we sought to improve the IHC subtyping by examining the pathologic and gene expression characteristics of genomically defined luminal A and B subtypes. Patients and Methods Gene expression and pathologic features were collected from primary tumors across five independent cohorts: British Columbia Cancer Agency (BCCA) tamoxifen-treated only, Grupo Español de Investigación en Cáncer de Mama 9906 trial, BCCA no systemic treatment cohort, PAM50 microarray training data set, and a combined publicly available microarray data set. Optimal cutoffs of percentage of progesterone receptor (PR) –positive tumor cells to predict survival were derived and independently tested. Multivariable Cox models were used to test the prognostic significance. Results Clinicopathologic comparisons among luminal A and B subtypes consistently identified higher rates of PR positivity, human epidermal growth factor receptor 2 (HER2) negativity, and histologic grade 1 in luminal A tumors. Quantitative PR gene and protein expression were also found to be significantly higher in luminal A tumors. An empiric cutoff of more than 20% of PR-positive tumor cells was statistically chosen and proved significant for predicting survival differences within IHC-defined luminal A tumors independently of endocrine therapy administration. Finally, no additional prognostic value within hormonal receptor (HR) –positive/HER2-negative disease was observed with the use of the IHC4 score when intrinsic IHC-based subtypes were used that included the more than 20% PR-positive tumor cells and vice versa. Conclusion Semiquantitative IHC expression of PR adds prognostic value within the current IHC-based luminal A definition by improving the identification of good outcome breast cancers. The new proposed IHC-based definition of luminal A tumors is HR positive/HER2 negative/Ki-67 less than 14%, and PR more than 20%. PMID:23233704

Joint prognostic effect of obesity and chronic systemic inflammation in patients with metastatic colorectal cancer.

PubMed

Shah, Manasi S; Fogelman, David R; Raghav, Kanwal Pratap Singh; Heymach, John V; Tran, Hai T; Jiang, Zhi-Qin; Kopetz, Scott; Daniel, Carrie R

2015-09-01

Obesity is strongly linked with chronic systemic inflammation, and each has been linked with disease progression and survival in patients with colorectal cancer (CRC). The authors investigated the joint prognostic effects of obesity and circulating cytokines in patients with metastatic CRC (mCRC), an understudied patient group. In 242 chemotherapy-naive patients with mCRC, the authors measured a multiplex cytokine panel and abstracted clinicopathological features, height, and weight from medical records. Overall survival (OS) was calculated from the date of mCRC diagnosis until the date of death from any cause and evaluated by Kaplan-Meier analysis and multivariable Cox proportional hazards regression models. Cut points for cytokines were determined by restricted cubic spline regression. In multivariable models, elevated interleukin (IL)-8, IL-2 receptor alpha, and lactate dehydrogenase (LDH) emerged as significant predictors of poor OS (hazard ratio [HR] and 95% confidence interval [95% CI] for above vs below the (referent) knot point: 2.5 [95% CI, 1.7-3.7], 1.9 [95% CI, 1.3-2.7], and 2.2 [95% CI, 1.6-3.1], respectively; all P<.001). Obesity (body mass index ≥30 kg/m(2) ) was not found to be associated with OS, but appeared to modify the relationships observed with IL-8 and LDH, which were associated with a significant 4-fold and 5-fold risk of death, respectively, in obese patients compared with a 2-fold risk of death in nonobese patients (P for interaction of .06 and .04, respectively). Similar results emerged from joint effects analysis, in which obese patients with high IL-8 (or LDH) experienced the highest risk of death. Although obesity itself was not found to be independently associated with survival in patients with mCRC, the adverse prognostic significance of LDH and IL-8 was found to be enhanced in obese patients. © 2015 American Cancer Society.

Squamous cell carcinoma of the breast in the United States: incidence, demographics, tumor characteristics, and survival.

PubMed

Yadav, Siddhartha; Yadav, Dhiraj; Zakalik, Dana

2017-07-01

Squamous cell carcinoma of breast accounts for less than 0.1% of all breast cancers. The purpose of this study is to describe the epidemiology and survival of this rare malignancy. Data were extracted from the National Cancer Institute's Surveillance, Epidemiology and End Results Registry to identify women diagnosed with squamous cell carcinoma of breast between 1998 and 2013. SEER*Stat 8.3.1 was used to calculate age-adjusted incidence, age-wise distribution, and annual percentage change in incidence. Kaplan-Meier curves were plotted for survival analysis. Univariate and multivariate Cox proportional hazard regression model was used to determine predictors of survival. A total of 445 cases of squamous cell carcinoma of breast were diagnosed during the study period. The median age of diagnosis was 67 years. The overall age-adjusted incidence between 1998 and 2013 was 0.62 per 1,000,000 per year, and the incidence has been on a decline. Approximately half of the tumors were poorly differentiated. Stage II was the most common stage at presentation. Majority of the cases were negative for expression of estrogen and progesterone receptor. One-third of the cases underwent breast conservation surgery while more than half of the cases underwent mastectomy (unilateral or bilateral). Approximately one-third of cases received radiation treatment. The 1-year and 5-year cause-specific survival was 81.6 and 63.5%, respectively. Excluding patient with metastasis or unknown stage at presentation, in multivariate Cox proportional hazard model, older age at diagnosis and higher tumor stage (T3 or T4) or nodal stage at presentation were significant predictors of poor survival. Our study describes the unique characteristics of squamous cell carcinoma of breast and demonstrates that it is an aggressive tumor with a poor survival. Older age and higher tumor or nodal stages at presentation were independent predictors of poor survival for loco-regional stages.

64Cu-DOTATATE PET/MRI for Detection of Activated Macrophages in Carotid Atherosclerotic Plaques: Studies in Patients Undergoing Endarterectomy.

PubMed

Pedersen, Sune Folke; Sandholt, Benjamin Vikjær; Keller, Sune Høgild; Hansen, Adam Espe; Clemmensen, Andreas Ettrup; Sillesen, Henrik; Højgaard, Liselotte; Ripa, Rasmus Sejersten; Kjær, Andreas

2015-07-01

A feature of vulnerable atherosclerotic plaques of the carotid artery is high activity and abundance of lesion macrophages. There is consensus that this is of importance for plaque vulnerability, which may lead to clinical events, such as stroke and transient ischemic attack. We used positron emission tomography (PET) and the novel PET ligand [(64)Cu] [1,4,7,10-tetraazacyclododecane-N,N',N″,N‴-tetraacetic acid]-d-Phe1,Tyr3-octreotate ((64)Cu-DOTATATE) to specifically target macrophages via the somatostatin receptor subtype-2 in vivo. Ten patients underwent simultaneous PET/MRI to measure (64)Cu-DOTATATE uptake in carotid artery plaques before carotid endarterectomy. (64)Cu-DOTATATE uptake was significantly higher in symptomatic plaque versus the contralateral carotid artery (P<0.001). Subsequently, a total of 62 plaque segments were assessed for gene expression of selected markers of plaque vulnerability using real-time quantitative polymerase chain reaction. These results were compared with in vivo (64)Cu-DOTATATE uptake calculated as the mean standardized uptake value. Univariate analysis of real-time quantitative polymerase chain reaction and PET showed that cluster of differentiation 163 (CD163) and CD68 gene expression correlated significantly but weakly with mean standardized uptake value in scans performed 85 minutes post injection (P<0.001 and P=0.015, respectively). Subsequent multivariate analysis showed that CD163 correlated independently with (64)Cu-DOTATATE uptake (P=0.031) whereas CD68 did not contribute significantly to the final model. The novel PET tracer (64)Cu-DOTATATE accumulates in atherosclerotic plaques of the carotid artery. CD163 gene expression correlated independently with (64)Cu-DOTATATE uptake measured by real-time quantitative polymerase chain reaction in the final multivariate model, indicating that (64)Cu-DOTATATE PET is detecting alternatively activated macrophages. This association could potentially improve noninvasive identification and characterization of vulnerable plaques. © 2015 The Authors.

Improving the Prognostic Ability through Better Use of Standard Clinical Data - The Nottingham Prognostic Index as an Example

PubMed Central

Winzer, Klaus-Jürgen; Buchholz, Anika; Schumacher, Martin; Sauerbrei, Willi

2016-01-01

Background Prognostic factors and prognostic models play a key role in medical research and patient management. The Nottingham Prognostic Index (NPI) is a well-established prognostic classification scheme for patients with breast cancer. In a very simple way, it combines the information from tumor size, lymph node stage and tumor grade. For the resulting index cutpoints are proposed to classify it into three to six groups with different prognosis. As not all prognostic information from the three and other standard factors is used, we will consider improvement of the prognostic ability using suitable analysis approaches. Methods and Findings Reanalyzing overall survival data of 1560 patients from a clinical database by using multivariable fractional polynomials and further modern statistical methods we illustrate suitable multivariable modelling and methods to derive and assess the prognostic ability of an index. Using a REMARK type profile we summarize relevant steps of the analysis. Adding the information from hormonal receptor status and using the full information from the three NPI components, specifically concerning the number of positive lymph nodes, an extended NPI with improved prognostic ability is derived. Conclusions The prognostic ability of even one of the best established prognostic index in medicine can be improved by using suitable statistical methodology to extract the full information from standard clinical data. This extended version of the NPI can serve as a benchmark to assess the added value of new information, ranging from a new single clinical marker to a derived index from omics data. An established benchmark would also help to harmonize the statistical analyses of such studies and protect against the propagation of many false promises concerning the prognostic value of new measurements. Statistical methods used are generally available and can be used for similar analyses in other diseases. PMID:26938061

Multivariate analysis of prognostic factors in male breast cancer in Serbia.

PubMed

Sipetic-Grujicic, Sandra Branko; Murtezani, Zafir Hajdar; Neskovic-Konstatinovic, Zora Borivoje; Marinkovic, Jelena Milutin; Kovcin, Vladimir Nikola; Andric, Zoran Gojko; Kostic, Sanja Vladeta; Ratkov, Isidora Stojan; Maksimovic, Jadranka Milutin

2014-01-01

The aim of this study was to analyze the demographic and clinical characteristics of male breast cancer patients in Serbia, and furthermore to determine overall survival and predictive factors for prognosis. In the period of 1996-2006 histopathological diagnosis of breast cancer was made in 84 males at the Institute for Oncology and Radiology of Serbia. For statistical analyses the Kaplan-Meier method, long-rank test and Cox proportional hazards regression model were used. The mean age at diagnosis with breast cancer was 64.3±10.5 years with a range from 35-84 years. Nearly 80% of the tumors showed ductal histology. About 44% had early tumor stages (I and II) whereas 46.4% and 9.5% of the male exhibited stages III and IV, respectively. Only 7.1% of male patients were grade one. One-fifth of all patients had tumors measuring ≤2 cm, and 14.3% larger than 5 cm. Lymph node metastasis was recorded in 40.4% patients and 47% relapse. Estrogen and progesterone receptor expression was positive in 66.7% and 58.3%, respectively. Among 14.3% of individuals tumor was HER2 positive. About two-thirds of all male patients had radical mastectomy (66.7%). Adjuvant hormonal (tamoxifene), systematic chemotherapy (CMF or FAC) and adjuvant radiotherapy were given to 59.5%, 35.7% and 29.8% patients respectively. Overall survival rates at five and ten years for male breast cancer were 55.0% and 43.9%, respectively. According to the multivariate Cox regression predictive model, a lower initial disease stage, a lower tumor grade, application of adjuvant hormone therapy and no relapse occurrence were significant independent predictors for good overall survival. Results of the treatment would be better if disease is discovered earlier and therefore health education and screening are an imperative in solving this problem.

Mechanistic analysis of the function of agonists and allosteric modulators: reconciling two-state and operational models

PubMed Central

Roche, David; Gil, Debora; Giraldo, Jesús

2013-01-01

Two-state and operational models of both agonism and allosterism are compared to identify and characterize common pharmacological parameters. To account for the receptor-dependent basal response, constitutive receptor activity is considered in the operational models. By arranging two-state models as the fraction of active receptors and operational models as the fractional response relative to the maximum effect of the system, a one-by-one correspondence between parameters is found. The comparative analysis allows a better understanding of complex allosteric interactions. In particular, the inclusion of constitutive receptor activity in the operational model of allosterism allows the characterization of modulators able to lower the basal response of the system; that is, allosteric modulators with negative intrinsic efficacy. Theoretical simulations and overall goodness of fit of the models to simulated data suggest that it is feasible to apply the models to experimental data and constitute one step forward in receptor theory formalism. Linked Articles Another recent review on allosteric modulation can be found at: Kenakin, T (2013). New concepts in pharmacological efficacy at 7TM receptors: IUPHAR Review 2. British Journal of Pharmacology 168: 554–575. doi: 10.1111/j.1476-5381.2012.02223.x And in this issue of BJP there is an article on a new allosteric modulator: Newman AS, Batis N, Grafton G, Caputo F, Brady CA, Lambert J, Peters JA, Gordon J, Brain KL, Powell AD and Barnes NM (2013). 5-Chloroindole: a potent allosteric modulator of the 5-HT3 receptor. British Journal of Pharmacology 169: 1228–1238. doi: 10.1111/bph.12213 PMID:23647200

Source apportionment of VOCs in the Los Angeles area using positive matrix factorization

NASA Astrophysics Data System (ADS)

Brown, Steven G.; Frankel, Anna; Hafner, Hilary R.

Eight 3-h speciated hydrocarbon measurements were collected daily by the South Coast Air Quality Management District (SCAQMD) as part of the Photochemical Assessment Monitoring Stations (PAMS) program during the summers of 2001-03 at two sites in the Los Angeles air basin, Azusa and Hawthorne. Over 30 hydrocarbons from over 500 samples at Azusa and 600 samples at Hawthorne were subsequently analyzed using the multivariate receptor model positive matrix factorization (PMF). At Azusa and Hawthorne, five and six factors were identified, respectively, with a good comparison between predicted and measured mass. At Azusa, evaporative emissions (a median of 31% of the total mass), motor vehicle exhaust (22%), liquid/unburned gasoline (27%), coatings (17%), and biogenic emissions (3%) factors were identified. Factors identified at Hawthorne were evaporative emissions (a median of 34% of the total mass), motor vehicle exhaust (24%), industrial process losses (15%), natural gas (13%), liquid/unburned gasoline (13%), and biogenic emissions (1%). Together, the median contribution from mobile source-related factors (exhaust, evaporative emissions, and liquid/unburned gasoline) was 80% and 71% at Azusa and Hawthorne, respectively, similar to previous source apportionment results using the chemical mass balance (CMB) model. There is a difference in the distribution among mobile source factors compared to the CMB work, with an increase in the contribution from evaporative emissions, though the cause (changes in emissions or differences between models) is unknown.

(−) Arctigenin and (+) Pinoresinol Are Antagonists of the Human Thyroid Hormone Receptor β

PubMed Central

2015-01-01

Lignans are important biologically active dietary polyphenolic compounds. Consumption of foods that are rich in lignans is associated with positive health effects. Using modeling tools to probe the ligand-binding pockets of molecular receptors, we found that lignans have high docking affinity for the human thyroid hormone receptor β. Follow-up experimental results show that lignans (−) arctigenin and (+) pinoresinol are antagonists of the human thyroid hormone receptor β. The modeled complexes show key plausible interactions between the two ligands and important amino acid residues of the receptor. PMID:25383984

Serum Potassium Levels and Outcome in Acute Heart Failure (Data from the PROTECT and COACH Trials).

PubMed

Tromp, Jasper; Ter Maaten, Jozine M; Damman, Kevin; O'Connor, Christopher M; Metra, Marco; Dittrich, Howard C; Ponikowski, Piotr; Teerlink, John R; Cotter, Gad; Davison, Beth; Cleland, John G F; Givertz, Michael M; Bloomfield, Daniel M; van der Wal, Martje H L; Jaarsma, Tiny; van Veldhuisen, Dirk J; Hillege, Hans L; Voors, Adriaan A; van der Meer, Peter

2017-01-15

Serum potassium is routinely measured at admission for acute heart failure (AHF), but information on association with clinical variables and prognosis is limited. Potassium measurements at admission were available in 1,867 patients with AHF in the original cohort of 2,033 patients included in the Patients Hospitalized with acute heart failure and Volume Overload to Assess Treatment Effect on Congestion and Renal FuncTion trial. Patients were grouped according to low potassium (<3.5 mEq/l), normal potassium (3.5 to 5.0 mEq/l), and high potassium (>5.0 mEq/l) levels. Results were verified in a validation cohort of 1,023 patients. Mean age of patients was 71 ± 11 years, and 66% were men. Low potassium was present in 115 patients (6%), normal potassium in 1,576 (84%), and high potassium in 176 (9%). Potassium levels increased during hospitalization (0.18 ± 0.69 mEq/l). Patients with high potassium more often used angiotensin-converting enzyme inhibitors and mineralocorticoid receptor antagonists before admission, had impaired baseline renal function and a better diuretic response (p = 0.005), independent of mineralocorticoid receptor antagonist usage. During 180-day follow-up, a total of 330 patients (18%) died. Potassium levels at admission showed a univariate linear association with mortality (hazard ratio [log] 2.36, 95% confidence interval 1.07 to 5.23; p = 0.034) but not after multivariate adjustment. Changes of potassium levels during hospitalization or potassium levels at discharge were not associated with outcome after multivariate analysis. Results in the validation cohort were similar to the index cohort. In conclusion, high potassium levels at admission are associated with an impaired renal function but a better diuretic response. Changes in potassium levels are common, and overall levels increase during hospitalization. In conclusion, potassium levels at admission or its change during hospitalization are not associated with mortality after multivariate adjustment. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

The impact of covariance misspecification in multivariate Gaussian mixtures on estimation and inference: an application to longitudinal modeling.

PubMed

Heggeseth, Brianna C; Jewell, Nicholas P

2013-07-20

Multivariate Gaussian mixtures are a class of models that provide a flexible parametric approach for the representation of heterogeneous multivariate outcomes. When the outcome is a vector of repeated measurements taken on the same subject, there is often inherent dependence between observations. However, a common covariance assumption is conditional independence-that is, given the mixture component label, the outcomes for subjects are independent. In this paper, we study, through asymptotic bias calculations and simulation, the impact of covariance misspecification in multivariate Gaussian mixtures. Although maximum likelihood estimators of regression and mixing probability parameters are not consistent under misspecification, they have little asymptotic bias when mixture components are well separated or if the assumed correlation is close to the truth even when the covariance is misspecified. We also present a robust standard error estimator and show that it outperforms conventional estimators in simulations and can indicate that the model is misspecified. Body mass index data from a national longitudinal study are used to demonstrate the effects of misspecification on potential inferences made in practice. Copyright © 2013 John Wiley & Sons, Ltd.

Multivariate meta-analysis using individual participant data.

PubMed

Riley, R D; Price, M J; Jackson, D; Wardle, M; Gueyffier, F; Wang, J; Staessen, J A; White, I R

2015-06-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment-covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. © 2014 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.

Determination of rice syrup adulterant concentration in honey using three-dimensional fluorescence spectra and multivariate calibrations

NASA Astrophysics Data System (ADS)

Chen, Quansheng; Qi, Shuai; Li, Huanhuan; Han, Xiaoyan; Ouyang, Qin; Zhao, Jiewen

2014-10-01

To rapidly and efficiently detect the presence of adulterants in honey, three-dimensional fluorescence spectroscopy (3DFS) technique was employed with the help of multivariate calibration. The data of 3D fluorescence spectra were compressed using characteristic extraction and the principal component analysis (PCA). Then, partial least squares (PLS) and back propagation neural network (BP-ANN) algorithms were used for modeling. The model was optimized by cross validation, and its performance was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in prediction set. The results showed that BP-ANN model was superior to PLS models, and the optimum prediction results of the mixed group (sunflower ± longan ± buckwheat ± rape) model were achieved as follow: RMSEP = 0.0235 and R = 0.9787 in the prediction set. The study demonstrated that the 3D fluorescence spectroscopy technique combined with multivariate calibration has high potential in rapid, nondestructive, and accurate quantitative analysis of honey adulteration.

Multivariable model predictive control design of reactive distillation column for Dimethyl Ether production

NASA Astrophysics Data System (ADS)

Wahid, A.; Putra, I. G. E. P.

2018-03-01

Dimethyl ether (DME) as an alternative clean energy has attracted a growing attention in the recent years. DME production via reactive distillation has potential for capital cost and energy requirement savings. However, combination of reaction and distillation on a single column makes reactive distillation process a very complex multivariable system with high non-linearity of process and strong interaction between process variables. This study investigates a multivariable model predictive control (MPC) based on two-point temperature control strategy for the DME reactive distillation column to maintain the purities of both product streams. The process model is estimated by a first order plus dead time model. The DME and water purity is maintained by controlling a stage temperature in rectifying and stripping section, respectively. The result shows that the model predictive controller performed faster responses compared to conventional PI controller that are showed by the smaller ISE values. In addition, the MPC controller is able to handle the loop interactions well.

The CCK(-like) receptor in the animal kingdom: functions, evolution and structures.

PubMed

Staljanssens, Dorien; Azari, Elnaz Karimian; Christiaens, Olivier; Beaufays, Jérôme; Lins, Laurence; Van Camp, John; Smagghe, Guy

2011-03-01

In this review, the cholecystokinin (CCK)(-like) receptors throughout the animal kingdom are compared on the level of physiological functions, evolutionary basis and molecular structure. In vertebrates, the CCK receptor is an important member of the G-protein coupled receptors as it is involved in the regulation of many physiological functions like satiety, gastrointestinal motility, gastric acid secretion, gall bladder contraction, pancreatic secretion, panic, anxiety and memory and learning processes. A homolog for this receptor is also found in nematodes and arthropods, called CK receptor and sulfakinin (SK) receptor, respectively. These receptors seem to have evolved from a common ancestor which is probably still closely related to the nematode CK receptor. The SK receptor is more closely related to the CCK receptor and seems to have similar functions. A molecular 3D-model for the CCK receptor type 1 has been built together with the docking of the natural ligands for the CCK and SK receptors in the CCK receptor type 1. These molecular models can help to study ligand-receptor interactions, that can in turn be useful in the development of new CCK(-like) receptor agonists and antagonists with beneficial health effects in humans or potential for pest control. Copyright © 2010 Elsevier Inc. All rights reserved.

An Investigation of Multivariate Adaptive Regression Splines for Modeling and Analysis of Univariate and Semi-Multivariate Time Series Systems

DTIC Science & Technology

1991-09-01

However, there is no guarantee that this would work; for instance if the data were generated by an ARCH model (Tong, 1990 pp. 116-117) then a simple...Hill, R., Griffiths, W., Lutkepohl, H., and Lee, T., Introduction to the Theory and Practice of Econometrics , 2th ed., Wiley, 1985. Kendall, M., Stuart

Fluctuation correlation models for receptor immobilization

NASA Astrophysics Data System (ADS)

Fourcade, B.

2017-12-01

Nanoscale dynamics with cycles of receptor diffusion and immobilization by cell-external-or-internal factors is a key process in living cell adhesion phenomena at the origin of a plethora of signal transduction pathways. Motivated by modern correlation microscopy approaches, the receptor correlation functions in physical models based on diffusion-influenced reaction is studied. Using analytical and stochastic modeling, this paper focuses on the hybrid regime where diffusion and reaction are not truly separable. The time receptor autocorrelation functions are shown to be indexed by different time scales and their asymptotic expansions are given. Stochastic simulations show that this analysis can be extended to situations with a small number of molecules. It is also demonstrated that this analysis applies when receptor immobilization is coupled to environmental noise.

Shifting physician prescribing to a preferred histamine-2-receptor antagonist. Effects of a multifactorial intervention in a mixed-model health maintenance organization.

PubMed

Brufsky, J W; Ross-Degnan, D; Calabrese, D; Gao, X; Soumerai, S B

1998-03-01

This study was undertaken to determine whether a program of education, therapeutic reevaluation of eligible patients, and performance feedback could shift prescribing to cimetidine from other histamine-2 receptor antagonists, which commonly are used in the management of ulcers and reflux, and reduce costs without increasing rates of ulcer-related hospital admissions. This study used an interrupted monthly time series with comparison series in a large mixed-model health maintenance organization. Physicians employed in health centers (staff model) and physicians in independent medical groups contracting to provide health maintenance organization services (group model) participated. The comparative percentage prescribed of specific histamine-2 receptor antagonists (market share), total histamine-2 receptor antagonist prescribing, cost per histamine-2 receptor antagonist prescription, and the rate of hospitalization for gastrointestinal illness were assessed. In the staff model, therapeutic reevaluation resulted in a sudden increase in market share of the preferred histamine-2 receptor antagonist cimetidine (+53.8%) and a sudden decrease in ranitidine (-44.7%) and famotidine (-4.8%); subsequently, cimetidine market share grew by 1.1% per month. In the group model, therapeutic reevaluation resulted in increased cimetidine market share (+9.7%) and decreased prescribing of other histamine-2 receptor antagonists (ranitidine -11.6%; famotidine -1.2%). Performance feedback did not result in further changes in prescribing in either setting. Use of omeprazole, an expensive alternative, essentially was unchanged by the interventions, as were overall histamine-2 receptor antagonist prescribing and hospital admissions for gastrointestinal illnesses. This intervention, which cost approximately $60,000 to implement, resulted in estimated annual savings in histamine-2 receptor antagonist expenditures of $1.06 million. Annual savings in histamine-2 receptor antagonist expenditures after this multifaceted intervention were more than implementation costs, with no discernible effects on numbers of hospitalizations. The magnitude of effect and cost savings were much greater in the staff model; organizational factors and economic incentives may have contributed to these differences. More research is needed to determine the generalizability of this approach to other technologies and managed care settings.

Stimulation of postsynapse adrenergic α2A receptor improves attention/cognition performance in an animal model of attention deficit hyperactivity disorder.

PubMed

Kawaura, Kazuaki; Karasawa, Jun-ichi; Chaki, Shigeyuki; Hikichi, Hirohiko

2014-08-15

A 5-trial inhibitory avoidance test using spontaneously hypertensive rat (SHR) pups has been used as an animal model of attention deficit hyperactivity disorder (ADHD). However, the roles of noradrenergic systems, which are involved in the pathophysiology of ADHD, have not been investigated in this model. In the present study, the effects of adrenergic α2 receptor stimulation, which has been an effective treatment for ADHD, on attention/cognition performance were investigated in this model. Moreover, neuronal mechanisms mediated through adrenergic α2 receptors were investigated. We evaluated the effects of both clonidine, a non-selective adrenergic α2 receptor agonist, and guanfacine, a selective adrenergic α2A receptor agonist, using a 5-trial inhibitory avoidance test with SHR pups. Juvenile SHR exhibited a shorter transfer latency, compared with juvenile Wistar Kyoto (WKY) rats. Both clonidine and guanfacine significantly prolonged the transfer latency of juvenile SHR. The effects of clonidine and guanfacine were significantly blocked by pretreatment with an adrenergic α2A receptor antagonist. In contrast, the effect of clonidine was not attenuated by pretreatment with an adrenergic α2B receptor antagonist, or an adrenergic α2C receptor antagonist, while it was attenuated by a non-selective adrenergic α2 receptor antagonist. Furthermore, the effects of neither clonidine nor guanfacine were blocked by pretreatment with a selective noradrenergic neurotoxin. These results suggest that the stimulation of the adrenergic α2A receptor improves the attention/cognition performance of juvenile SHR in the 5-trial inhibitory avoidance test and that postsynaptic, rather than presynaptic, adrenergic α2A receptor is involved in this effect. Copyright © 2014 Elsevier B.V. All rights reserved.

Receptors as a master key for synchronization of rhythms

NASA Astrophysics Data System (ADS)

Nagano, Seido

2004-03-01

A simple, but general scheme to achieve synchronization of rhythms was derived. The scheme has been inductively generalized from the modelling study of cellular slime mold. It was clarified that biological receptors work as apparatuses that can convert external stimulus to the form of nonlinear interaction within individual oscillators. Namely, the mathematical model receptor works as a nonlinear coupling apparatus between nonlinear oscillators. Thus, synchronization is achieved as a result of competition between two kinds of non-linearities, and to achieve synchronization, even a small external stimulation via model receptors can change the characteristics of individual oscillators significantly. The derived scheme is very simple mathematically, but it is a very powerful scheme as numerically demonstrated. The biological receptor scheme should significantly help understanding of synchronization phenomena in biology since groups of limit cycle oscillators and receptors are ubiquitous in biological systems. Reference: S. Nagano, Phys Rev. E67, 056215(2003)

Association of calcium sensing receptor polymorphisms at rs1801725 with circulating calcium in breast cancer patients.

PubMed

Wang, Li; Widatalla, Sarrah E; Whalen, Diva S; Ochieng, Josiah; Sakwe, Amos M

2017-08-02

Breast cancer (BC) patients with late-stage and/or rapidly growing tumors are prone to develop high serum calcium levels which have been shown to be associated with larger and aggressive breast tumors in post and premenopausal women respectively. Given the pivotal role of the calcium sensing receptor (CaSR) in calcium homeostasis, we evaluated whether polymorphisms of the CASR gene at rs1801725 and rs1801726 SNPs in exon 7, are associated with circulating calcium levels in African American and Caucasian control subjects and BC cases. In this retrospective case-control study, we assessed the mean circulating calcium levels, the distribution of two inactivating CaSR SNPs at rs1801725 and rs1801726 in 199 cases and 384 age-matched controls, and used multivariable regression analysis to determine whether these SNPs are associated with circulating calcium in control subjects and BC cases. We found that the mean circulating calcium levels in African American subjects were higher than those in Caucasian subjects (p < 0.001). As expected, the mean calcium levels were higher in BC cases compared to control subjects (p < 0.001), but the calcium levels in BC patients were independent of race. We also show that in BC cases and control subjects, the major alleles at rs1801725 (G/T, A986S) and at rs1801726 (C/G, Q1011E) were common among Caucasians and African Americans respectively. Compared to the wild type alleles, polymorphisms at the rs1801725 SNP were associated with higher calcium levels (p = 0.006) while those at rs1801726 were not. Using multivariable linear mixed-effects models and adjusting for age and race, we show that circulating calcium levels in BC cases were associated with tumor grade (p = 0.009), clinical stage (p = 0.003) and more importantly, with inactivating mutations of the CASR at the rs1801725 SNP (p = 0.038). These data suggest that decreased sensitivity of the CaSR to calcium due to inactivating polymorphisms at rs1801725, may predispose up to 20% of BC cases to high circulating calcium-associated larger and/or aggressive breast tumors.

Low p53 Binding Protein 1 (53BP1) Expression Is Associated With Increased Local Recurrence in Breast Cancer Patients Treated With Breast-Conserving Surgery and Radiotherapy

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

Neboori, Hanmanth J.R.; Haffty, Bruce G., E-mail: hafftybg@umdnj.edu; Wu Hao

2012-08-01

Purpose: To investigate whether the expression of p53 binding protein 1 (53BP1) has prognostic significance in a cohort of early-stage breast cancer patients treated with breast-conserving surgery and radiotherapy (BCS+RT). Methods and Materials: A tissue microarray of early-stage breast cancer treated with BCS+RT from a cohort of 514 women was assayed for 53BP1, estrogen receptor, progesterone receptor, and HER2 expression by immunohistochemistry. Through log-rank tests and univariate and multivariate models, the staining profile of each tumor was correlated with clinical endpoints, including ipsilateral breast recurrence-free survival (IBRFS), distant metastasis-free survival (DMFS), cause-specific survival (CSS), recurrence-free survival (RFS), and overall survivalmore » (OS). Results: Of the 477 (93%) evaluable tumors, 63 (13%) were scored as low. Low expression of 53BP1 was associated with worse outcomes for all endpoints studied, including 10-year IBRFS (76.8% vs. 90.5%; P=.01), OS (66.4% vs. 81.7%; P=.02), CSS (66.0% vs. 87.4%; P<.01), DMFS (55.9% vs. 87.0%; P<.01), and RFS (45.2% vs. 80.6%; P<.01). Multivariate analysis incorporating various clinico-pathologic markers and 53BP1 expression found that 53BP1 expression was again an independent predictor of all endpoints (IBRFS: P=.0254; OS: P=.0094; CSS: P=.0033; DMFS: P=.0006; RFS: P=.0002). Low 53BP1 expression was also found to correlate with triple-negative (TN) phenotype (P<.01). Furthermore, in subset analysis of all TN breast cancer, negative 53BP1 expression trended for lower IBRFS (72.3% vs. 93.9%; P=.0361) and was significant for worse DMFS (48.2% vs. 86.8%; P=.0035) and RFS (37.8% vs. 83.7%; P=.0014). Conclusion: Our data indicate that low 53BP1 expression is an independent prognostic indicator for local relapse among other endpoints in early-stage breast cancer and TN breast cancer patients treated with BCS+RT. These results should be verified in larger cohorts of patients to validate their clinical significance.« less

Y-box-binding protein YB-1 identifies high-risk patients with primary breast cancer benefiting from rapidly cycled tandem high-dose adjuvant chemotherapy.

PubMed

Gluz, Oleg; Mengele, Karin; Schmitt, Manfred; Kates, Ronald; Diallo-Danebrock, Raihana; Neff, Frauke; Royer, Hans-Dieter; Eckstein, Niels; Mohrmann, Svjetlana; Ting, Evelyn; Kiechle, Marion; Poremba, Christopher; Nitz, Ulrike; Harbeck, Nadia

2009-12-20

To investigate the potential of Y-box-binding protein YB-1, a multifunctional protein linked to tumor aggressiveness and multidrug resistance, to identify patients with breast cancer likely to benefit from dose-intensified chemotherapy regimens. YB-1 was immunohistochemically determined in 211 primary tumors from the prospective, randomized West German Study Group WSG-AM-01 trial in high-risk (> or = 10 involved lymph-nodes) breast cancer (HRBC). Predictive impact of YB-1 was assessed by multivariate survival analysis, including time-varying factor-therapy interactions. At median follow-up of 61.7 months, patients receiving rapidly cycled tandem high-dose therapy (HD; two cycles [2x] epirubicin 90 mg/m(2) and cyclophosphamide 600 mg/m(2) every 14 days, followed by 2x epirubicin 90 mg/m(2), cyclophosphamide 3,000 mg/m(2), and thiotepa 400 mg/m(2) every 21 days) had better disease-free survival (DFS; hazard ratio [HR] = 0.62; 95% CI, 0.44 to 0.89) and overall survival (OS; HR = 0.59; 95% CI, 0.4 to 0.89) than those receiving conventional dose-dense chemotherapy (DD; 4x epirubicin 90 mg/m(2) and cyclophosphamide 600 mg/m(2), followed by 3x cyclophosphamide 600 mg/m(2), methotrexate 40 mg/m(2), and fluorouracil 600 mg/m(2) every 14 days). High YB-1 was associated with aggressive tumor phenotype (negative steroid hormone receptor status, positive human epidermal growth factor receptor 2 and p53 status, high MIB-1, unfavorable tumor grade) and poor OS (median 78 v 97 months; P = .01). In patients with high YB-1, HD yielded a 63-month median DFS (P = .001) and a 46-month median OS advantage (P = .002) versus DD. In multivariate models, patients with high B-1 receiving HD (v DD) had one third the hazard rate after 20 months for DFS and one sixth after 40 months for OS. In a randomized prospective cancer therapy trial, for the first time, a strong predictive impact of YB-1 on survival has been demonstrated: enhanced benefit from HD (v DD) therapy occurs in HRBC with high YB-1. Future trials could therefore address optimal chemotherapeutic strategies,taking YB-1 into account.

Locating the Seventh Cervical Spinous Process: Development and Validation of a Multivariate Model Using Palpation and Personal Information.

PubMed

Ferreira, Ana Paula A; Póvoa, Luciana C; Zanier, José F C; Ferreira, Arthur S

2017-02-01

The aim of this study was to develop and validate a multivariate prediction model, guided by palpation and personal information, for locating the seventh cervical spinous process (C7SP). A single-blinded, cross-sectional study at a primary to tertiary health care center was conducted for model development and temporal validation. One-hundred sixty participants were prospectively included for model development (n = 80) and time-split validation stages (n = 80). The C7SP was located using the thorax-rib static method (TRSM). Participants underwent chest radiography for assessment of the inner body structure located with TRSM and using radio-opaque markers placed over the skin. Age, sex, height, body mass, body mass index, and vertex-marker distance (D V-M ) were used to predict the distance from the C7SP to the vertex (D V-C7 ). Multivariate linear regression modeling, limits of agreement plot, histogram of residues, receiver operating characteristic curves, and confusion tables were analyzed. The multivariate linear prediction model for D V-C7 (in centimeters) was D V-C7 = 0.986D V-M + 0.018(mass) + 0.014(age) - 1.008. Receiver operating characteristic curves had better discrimination of D V-C7 (area under the curve = 0.661; 95% confidence interval = 0.541-0.782; P = .015) than D V-M (area under the curve = 0.480; 95% confidence interval = 0.345-0.614; P = .761), with respective cutoff points at 23.40 cm (sensitivity = 41%, specificity = 63%) and 24.75 cm (sensitivity = 69%, specificity = 52%). The C7SP was correctly located more often when using predicted D V-C7 in the validation sample than when using the TRSM in the development sample: n = 53 (66%) vs n = 32 (40%), P < .001. Better accuracy was obtained when locating the C7SP by use of a multivariate model that incorporates palpation and personal information. Copyright © 2016. Published by Elsevier Inc.

DasPy – Open Source Multivariate Land Data Assimilation Framework with High Performance Computing

NASA Astrophysics Data System (ADS)

Han, Xujun; Li, Xin; Montzka, Carsten; Kollet, Stefan; Vereecken, Harry; Hendricks Franssen, Harrie-Jan

2015-04-01

Data assimilation has become a popular method to integrate observations from multiple sources with land surface models to improve predictions of the water and energy cycles of the soil-vegetation-atmosphere continuum. In recent years, several land data assimilation systems have been developed in different research agencies. Because of the software availability or adaptability, these systems are not easy to apply for the purpose of multivariate land data assimilation research. Multivariate data assimilation refers to the simultaneous assimilation of observation data for multiple model state variables into a simulation model. Our main motivation was to develop an open source multivariate land data assimilation framework (DasPy) which is implemented using the Python script language mixed with C++ and Fortran language. This system has been evaluated in several soil moisture, L-band brightness temperature and land surface temperature assimilation studies. The implementation allows also parameter estimation (soil properties and/or leaf area index) on the basis of the joint state and parameter estimation approach. LETKF (Local Ensemble Transform Kalman Filter) is implemented as the main data assimilation algorithm, and uncertainties in the data assimilation can be represented by perturbed atmospheric forcings, perturbed soil and vegetation properties and model initial conditions. The CLM4.5 (Community Land Model) was integrated as the model operator. The CMEM (Community Microwave Emission Modelling Platform), COSMIC (COsmic-ray Soil Moisture Interaction Code) and the two source formulation were integrated as observation operators for assimilation of L-band passive microwave, cosmic-ray soil moisture probe and land surface temperature measurements, respectively. DasPy is parallelized using the hybrid MPI (Message Passing Interface) and OpenMP (Open Multi-Processing) techniques. All the input and output data flow is organized efficiently using the commonly used NetCDF file format. Online 1D and 2D visualization of data assimilation results is also implemented to facilitate the post simulation analysis. In summary, DasPy is a ready to use open source parallel multivariate land data assimilation framework.

Computer modeling of the neurotoxin binding site of acetylcholine receptor spanning residues 185 through 196

NASA Technical Reports Server (NTRS)

Garduno-Juarez, R.; Shibata, M.; Zielinski, T. J.; Rein, R.

1987-01-01

A model of the complex between the acetylcholine receptor and the snake neurotoxin, cobratoxin, was built by molecular model building and energy optimization techniques. The experimentally identified functionally important residues of cobratoxin and the dodecapeptide corresponding to the residues 185-196 of acetylcholine receptor alpha subunit were used to build the model. Both cis and trans conformers of cyclic L-cystine portion of the dodecapeptide were examined. Binding residues independently identified on cobratoxin are shown to interact with the dodecapeptide AChR model.

A comparison of bivariate, multivariate random-effects, and Poisson correlated gamma-frailty models to meta-analyze individual patient data of ordinal scale diagnostic tests.

PubMed

Simoneau, Gabrielle; Levis, Brooke; Cuijpers, Pim; Ioannidis, John P A; Patten, Scott B; Shrier, Ian; Bombardier, Charles H; de Lima Osório, Flavia; Fann, Jesse R; Gjerdingen, Dwenda; Lamers, Femke; Lotrakul, Manote; Löwe, Bernd; Shaaban, Juwita; Stafford, Lesley; van Weert, Henk C P M; Whooley, Mary A; Wittkampf, Karin A; Yeung, Albert S; Thombs, Brett D; Benedetti, Andrea

2017-11-01

Individual patient data (IPD) meta-analyses are increasingly common in the literature. In the context of estimating the diagnostic accuracy of ordinal or semi-continuous scale tests, sensitivity and specificity are often reported for a given threshold or a small set of thresholds, and a meta-analysis is conducted via a bivariate approach to account for their correlation. When IPD are available, sensitivity and specificity can be pooled for every possible threshold. Our objective was to compare the bivariate approach, which can be applied separately at every threshold, to two multivariate methods: the ordinal multivariate random-effects model and the Poisson correlated gamma-frailty model. Our comparison was empirical, using IPD from 13 studies that evaluated the diagnostic accuracy of the 9-item Patient Health Questionnaire depression screening tool, and included simulations. The empirical comparison showed that the implementation of the two multivariate methods is more laborious in terms of computational time and sensitivity to user-supplied values compared to the bivariate approach. Simulations showed that ignoring the within-study correlation of sensitivity and specificity across thresholds did not worsen inferences with the bivariate approach compared to the Poisson model. The ordinal approach was not suitable for simulations because the model was highly sensitive to user-supplied starting values. We tentatively recommend the bivariate approach rather than more complex multivariate methods for IPD diagnostic accuracy meta-analyses of ordinal scale tests, although the limited type of diagnostic data considered in the simulation study restricts the generalization of our findings. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

PHI and PCA3 improve the prognostic performance of PRIAS and Epstein criteria in predicting insignificant prostate cancer in men eligible for active surveillance.

PubMed

Cantiello, Francesco; Russo, Giorgio Ivan; Cicione, Antonio; Ferro, Matteo; Cimino, Sebastiano; Favilla, Vincenzo; Perdonà, Sisto; De Cobelli, Ottavio; Magno, Carlo; Morgia, Giuseppe; Damiano, Rocco

2016-04-01

To assess the performance of prostate health index (PHI) and prostate cancer antigen 3 (PCA3) when added to the PRIAS or Epstein criteria in predicting the presence of pathologically insignificant prostate cancer (IPCa) in patients who underwent radical prostatectomy (RP) but eligible for active surveillance (AS). An observational retrospective study was performed in 188 PCa patients treated with laparoscopic or robot-assisted RP but eligible for AS according to Epstein or PRIAS criteria. Blood and urinary specimens were collected before initial prostate biopsy for PHI and PCA3 measurements. Multivariate logistic regression analyses and decision curve analysis were carried out to identify predictors of IPCa using the updated ERSPC definition. At the multivariate analyses, the inclusion of both PCA3 and PHI significantly increased the accuracy of the Epstein multivariate model in predicting IPCa with an increase of 17 % (AUC = 0.77) and of 32 % (AUC = 0.92), respectively. The inclusion of both PCA3 and PHI also increased the predictive accuracy of the PRIAS multivariate model with an increase of 29 % (AUC = 0.87) and of 39 % (AUC = 0.97), respectively. DCA revealed that the multivariable models with the addition of PHI or PCA3 showed a greater net benefit and performed better than the reference models. In a direct comparison, PHI outperformed PCA3 performance resulting in higher net benefit. In a same cohort of patients eligible for AS, the addition of PHI and PCA3 to Epstein or PRIAS models improved their prognostic performance. PHI resulted in greater net benefit in predicting IPCa compared to PCA3.

Seizure-Onset Mapping Based on Time-Variant Multivariate Functional Connectivity Analysis of High-Dimensional Intracranial EEG: A Kalman Filter Approach.

PubMed

Lie, Octavian V; van Mierlo, Pieter

2017-01-01

The visual interpretation of intracranial EEG (iEEG) is the standard method used in complex epilepsy surgery cases to map the regions of seizure onset targeted for resection. Still, visual iEEG analysis is labor-intensive and biased due to interpreter dependency. Multivariate parametric functional connectivity measures using adaptive autoregressive (AR) modeling of the iEEG signals based on the Kalman filter algorithm have been used successfully to localize the electrographic seizure onsets. Due to their high computational cost, these methods have been applied to a limited number of iEEG time-series (<60). The aim of this study was to test two Kalman filter implementations, a well-known multivariate adaptive AR model (Arnold et al. 1998) and a simplified, computationally efficient derivation of it, for their potential application to connectivity analysis of high-dimensional (up to 192 channels) iEEG data. When used on simulated seizures together with a multivariate connectivity estimator, the partial directed coherence, the two AR models were compared for their ability to reconstitute the designed seizure signal connections from noisy data. Next, focal seizures from iEEG recordings (73-113 channels) in three patients rendered seizure-free after surgery were mapped with the outdegree, a graph-theory index of outward directed connectivity. Simulation results indicated high levels of mapping accuracy for the two models in the presence of low-to-moderate noise cross-correlation. Accordingly, both AR models correctly mapped the real seizure onset to the resection volume. This study supports the possibility of conducting fully data-driven multivariate connectivity estimations on high-dimensional iEEG datasets using the Kalman filter approach.

Quantitative monitoring of sucrose, reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Das, Bappa; Sahoo, Rabi N.; Pargal, Sourabh; Krishna, Gopal; Verma, Rakesh; Chinnusamy, Viswanathan; Sehgal, Vinay K.; Gupta, Vinod K.; Dash, Sushanta K.; Swain, Padmini

2018-03-01

In the present investigation, the changes in sucrose, reducing and total sugar content due to water-deficit stress in rice leaves were modeled using visible, near infrared (VNIR) and shortwave infrared (SWIR) spectroscopy. The objectives of the study were to identify the best vegetation indices and suitable multivariate technique based on precise analysis of hyperspectral data (350 to 2500 nm) and sucrose, reducing sugar and total sugar content measured at different stress levels from 16 different rice genotypes. Spectral data analysis was done to identify suitable spectral indices and models for sucrose estimation. Novel spectral indices in near infrared (NIR) range viz. ratio spectral index (RSI) and normalised difference spectral indices (NDSI) sensitive to sucrose, reducing sugar and total sugar content were identified which were subsequently calibrated and validated. The RSI and NDSI models had R2 values of 0.65, 0.71 and 0.67; RPD values of 1.68, 1.95 and 1.66 for sucrose, reducing sugar and total sugar, respectively for validation dataset. Different multivariate spectral models such as artificial neural network (ANN), multivariate adaptive regression splines (MARS), multiple linear regression (MLR), partial least square regression (PLSR), random forest regression (RFR) and support vector machine regression (SVMR) were also evaluated. The best performing multivariate models for sucrose, reducing sugars and total sugars were found to be, MARS, ANN and MARS, respectively with respect to RPD values of 2.08, 2.44, and 1.93. Results indicated that VNIR and SWIR spectroscopy combined with multivariate calibration can be used as a reliable alternative to conventional methods for measurement of sucrose, reducing sugars and total sugars of rice under water-deficit stress as this technique is fast, economic, and noninvasive.

Using Time Series Analysis to Predict Cardiac Arrest in a PICU.

PubMed

Kennedy, Curtis E; Aoki, Noriaki; Mariscalco, Michele; Turley, James P

2015-11-01

To build and test cardiac arrest prediction models in a PICU, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data. Retrospective cohort study. Thirty-one bed academic PICU that provides care for medical and general surgical (not congenital heart surgery) patients. Patients experiencing a cardiac arrest in the PICU and requiring external cardiac massage for at least 2 minutes. None. One hundred three cases of cardiac arrest and 109 control cases were used to prepare a baseline dataset that consisted of 1,025 variables in four data classes: multivariate, raw time series, clinical calculations, and time series trend analysis. We trained 20 arrest prediction models using a matrix of five feature sets (combinations of data classes) with four modeling algorithms: linear regression, decision tree, neural network, and support vector machine. The reference model (multivariate data with regression algorithm) had an accuracy of 78% and 87% area under the receiver operating characteristic curve. The best model (multivariate + trend analysis data with support vector machine algorithm) had an accuracy of 94% and 98% area under the receiver operating characteristic curve. Cardiac arrest predictions based on a traditional model built with multivariate data and a regression algorithm misclassified cases 3.7 times more frequently than predictions that included time series trend analysis and built with a support vector machine algorithm. Although the final model lacks the specificity necessary for clinical application, we have demonstrated how information from time series data can be used to increase the accuracy of clinical prediction models.

Chemiluminescence-based multivariate sensing of local equivalence ratios in premixed atmospheric methane-air flames

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

Tripathi, Markandey M.; Krishnan, Sundar R.; Srinivasan, Kalyan K.

Chemiluminescence emissions from OH*, CH*, C2, and CO2 formed within the reaction zone of premixed flames depend upon the fuel-air equivalence ratio in the burning mixture. In the present paper, a new partial least square regression (PLS-R) based multivariate sensing methodology is investigated and compared with an OH*/CH* intensity ratio-based calibration model for sensing equivalence ratio in atmospheric methane-air premixed flames. Five replications of spectral data at nine different equivalence ratios ranging from 0.73 to 1.48 were used in the calibration of both models. During model development, the PLS-R model was initially validated with the calibration data set using themore » leave-one-out cross validation technique. Since the PLS-R model used the entire raw spectral intensities, it did not need the nonlinear background subtraction of CO2 emission that is required for typical OH*/CH* intensity ratio calibrations. An unbiased spectral data set (not used in the PLS-R model development), for 28 different equivalence ratio conditions ranging from 0.71 to 1.67, was used to predict equivalence ratios using the PLS-R and the intensity ratio calibration models. It was found that the equivalence ratios predicted with the PLS-R based multivariate calibration model matched the experimentally measured equivalence ratios within 7%; whereas, the OH*/CH* intensity ratio calibration grossly underpredicted equivalence ratios in comparison to measured equivalence ratios, especially under rich conditions ( > 1.2). The practical implications of the chemiluminescence-based multivariate equivalence ratio sensing methodology are also discussed.« less

Application of agonist-receptor modeling to the sweetness synergy between high fructose corn syrup and sucralose, and between high-potency sweeteners.

PubMed

Wolf, P A; Bridges, J R; Wicklund, R

2010-03-01

The agonist-receptor-transducer model of D. Ennis is applied to beverage formulations sweetened with high fructose corn syrup, sucralose, and other high-potency sweeteners, confirming the utility of the model, and supports the growing volume of evidence for multiple binding sites on the sweetness receptor. The model is further simplified to require less parameters for other sweetener blend systems whenever potency information is available for the single sweeteners.

Donor and recipient chemokine receptor CCR5 genotype is associated with survival after bone marrow transplantation

PubMed Central

McDermott, David H.; Conway, Susan E.; Wang, Tao; Ricklefs, Stacy M.; Agovi, Manza A.; Porcella, Stephen F.; Tran, Huong Thi Bich; Milford, Edgar; Spellman, Stephen

2010-01-01

Despite continual improvement, morbidity and mortality after hematopoietic stem cell transplantation (HSCT) remain high. The importance of chemokines in HSCT lies in their regulation of immune responses that determine transplantation outcomes. We investigated the role of recipient and donor chemokine system gene polymorphisms by using a candidate gene approach on the incidence of graft-versus-host disease and posttransplantation outcomes in 1370 extensively human leukocyte antigen–matched, unrelated donor-recipient pairs by using multivariate Cox regression models. Our analysis identified that recipients homozygous for a common CCR5 haplotype (H1/H1) had better disease-free survival (DFS; P = .005) and overall survival (P = .021). When the same genotype of both the donor and recipient were considered in the models, a highly significant association with DFS and overall survival was noted (P < .001 and P = .007, respectively) with absolute differences in survival of up to 20% seen between the groups at 3 years after transplantation (50% DFS for pairs with recipient CCR5 H1/H1 vs 30% for pairs with donor CCR5 H1/H1). This finding suggests that donor and/or recipient CCR5 genotypes may be associated with HSCT outcome and suggests new diagnostic and therapeutic strategies for optimizing therapy. PMID:20068218

Comparison of Kinetic Models for Dual-Tracer Receptor Concentration Imaging in Tumors

PubMed Central

Hamzei, Nazanin; Samkoe, Kimberley S; Elliott, Jonathan T; Holt, Robert W; Gunn, Jason R; Hasan, Tayyaba; Pogue, Brian W; Tichauer, Kenneth M

2014-01-01

Molecular differences between cancerous and healthy tissue have become key targets for novel therapeutics specific to tumor receptors. However, cancer cell receptor expression can vary within and amongst different tumors, making strategies that can quantify receptor concentration in vivo critical for the progression of targeted therapies. Recently a dual-tracer imaging approach capable of providing quantitative measures of receptor concentration in vivo was developed. It relies on the simultaneous injection and imaging of receptor-targeted tracer and an untargeted tracer (to account for non-specific uptake of the targeted tracer). Early implementations of this approach have been structured on existing “reference tissue” imaging methods that have not been optimized for or validated in dual-tracer imaging. Using simulations and mouse tumor model experimental data, the salient findings in this study were that all widely used reference tissue kinetic models can be used for dual-tracer imaging, with the linearized simplified reference tissue model offering a good balance of accuracy and computational efficiency. Moreover, an alternate version of the full two-compartment reference tissue model can be employed accurately by assuming that the K1s of the targeted and untargeted tracers are similar to avoid assuming an instantaneous equilibrium between bound and free states (made by all other models). PMID:25414912

Is the Acute NMDA Receptor Hypofunction a Valid Model of Schizophrenia?

PubMed Central

Adell, Albert; Jiménez-Sánchez, Laura; López-Gil, Xavier; Romón, Tamara

2012-01-01

Several genetic, neurodevelopmental, and pharmacological animal models of schizophrenia have been established. This short review examines the validity of one of the most used pharmacological model of the illness, ie, the acute administration of N-methyl-D-aspartate (NMDA) receptor antagonists in rodents. In some cases, data on chronic or prenatal NMDA receptor antagonist exposure have been introduced for comparison. The face validity of acute NMDA receptor blockade is granted inasmuch as hyperlocomotion and stereotypies induced by phencyclidine, ketamine, and MK-801 are regarded as a surrogate for the positive symptoms of schizophrenia. In addition, the loss of parvalbumin-containing cells (which is one of the most compelling finding in postmortem schizophrenia brain) following NMDA receptor blockade adds construct validity to this model. However, the lack of changes in glutamic acid decarboxylase (GAD67) is at variance with human studies. It is possible that changes in GAD67 are more reflective of the neurodevelopmental condition of schizophrenia. Finally, the model also has predictive validity, in that its behavioral and transmitter activation in rodents are responsive to antipsychotic treatment. Overall, although not devoid of drawbacks, the acute administration of NMDA receptor antagonists can be considered as a good model of schizophrenia bearing a satisfactory degree of validity. PMID:21965469

Origin of soluble chemical species in bulk precipitation collected in Tokyo, Japan: Statistical evaluation of source materials

NASA Astrophysics Data System (ADS)

Tsurumi, Makoto; Takahashi, Akira; Ichikuni, Masami

An iterative least-squares method with a receptor model was applied to the analytical data of the precipitation samples collected at 23 points in the suburban area of Tokyo, and the number and composition of the source materials were determined. Thirty-nine monthly bulk precipitation samples were collected in the spring and summer of 1987 from the hilly and mountainous area of Tokyo and analyzed for Na +, K +, NH 4+, Mg 2+, Ca 2+, F -, Cl -, Br -, NO 3- and SO 42- by atomic absorption spectrometry and ion chromatography. The pH of the samples was also measured. A multivariate ion balance approach (Tsurumi, 1982, Anal. Chim. Acta138, 177-182) showed that the solutes in the precipitation were derived from just three major sources; sea salt, acid substance (a mixture of 53% HNO 3, 39% H 2SO 4 and 8% HCl in equivalent) and CaSO 4. The contributions of each source to the precipitation were calculated for every sampling site. Variations of the contributions with the distance from the coast were also discussed.

Source Apportionment and Risk Assessment of Emerging Contaminants: An Approach of Pharmaco-Signature in Water Systems

PubMed Central

Jiang, Jheng Jie; Lee, Chon Lin; Fang, Meng Der; Boyd, Kenneth G.; Gibb, Stuart W.

2015-01-01

This paper presents a methodology based on multivariate data analysis for characterizing potential source contributions of emerging contaminants (ECs) detected in 26 river water samples across multi-scape regions during dry and wet seasons. Based on this methodology, we unveil an approach toward potential source contributions of ECs, a concept we refer to as the “Pharmaco-signature.” Exploratory analysis of data points has been carried out by unsupervised pattern recognition (hierarchical cluster analysis, HCA) and receptor model (principal component analysis-multiple linear regression, PCA-MLR) in an attempt to demonstrate significant source contributions of ECs in different land-use zone. Robust cluster solutions grouped the database according to different EC profiles. PCA-MLR identified that 58.9% of the mean summed ECs were contributed by domestic impact, 9.7% by antibiotics application, and 31.4% by drug abuse. Diclofenac, ibuprofen, codeine, ampicillin, tetracycline, and erythromycin-H2O have significant pollution risk quotients (RQ>1), indicating potentially high risk to aquatic organisms in Taiwan. PMID:25874375

Effects of the construction of Scroby Sands offshore wind farm on the prey base of Little tern Sternula albifrons at its most important UK colony.

PubMed

Perrow, Martin R; Gilroy, James J; Skeate, Eleanor R; Tomlinson, Mark L

2011-08-01

Despite widespread interest in the impacts of wind farms upon birds, few researchers have examined the potential for indirect or trophic (predator-prey) effects. Using surface trawls, we monitored prey abundance before and after construction of a 30 turbine offshore wind farm sited close to an internationally important colony of Little terns. Observations confirmed that young-of-the-year clupeids dominated chick diet, which trawl samples suggested were mainly herring. Multivariate modelling indicated a significant reduction in herring abundance from 2004 onwards that could not be explained by environmental factors. Intensely noisy monopile installation during the winter spawning period was suggested to be responsible. Reduced prey abundance corresponded with a significant decline in Little tern foraging success. Unprecedented egg abandonment and lack of chick hatching tentatively suggested a colony-scale response in some years. We urge a precautionary approach to the timing and duration of pile-driving activity supported with long-term targeted monitoring of sensitive receptors. Copyright © 2011 Elsevier Ltd. All rights reserved.

Integration of co-localized glandular morphometry and protein biomarker expression in immunofluorescent images for prostate cancer prognosis

NASA Astrophysics Data System (ADS)

Scott, Richard; Khan, Faisal M.; Zeineh, Jack; Donovan, Michael; Fernandez, Gerardo

2015-03-01

Immunofluorescent (IF) image analysis of tissue pathology has proven to be extremely valuable and robust in developing prognostic assessments of disease, particularly in prostate cancer. There have been significant advances in the literature in quantitative biomarker expression as well as characterization of glandular architectures in discrete gland rings. However, while biomarker and glandular morphometric features have been combined as separate predictors in multivariate models, there is a lack of integrative features for biomarkers co-localized within specific morphological sub-types; for example the evaluation of androgen receptor (AR) expression within Gleason 3 glands only. In this work we propose a novel framework employing multiple techniques to generate integrated metrics of morphology and biomarker expression. We demonstrate the utility of the approaches in predicting clinical disease progression in images from 326 prostate biopsies and 373 prostatectomies. Our proposed integrative approaches yield significant improvements over existing IF image feature metrics. This work presents some of the first algorithms for generating innovative characteristics in tissue diagnostics that integrate co-localized morphometry and protein biomarker expression.

Coupled stochastic spatial and non-spatial simulations of ErbB1 signaling pathways demonstrate the importance of spatial organization in signal transduction.

PubMed

Costa, Michelle N; Radhakrishnan, Krishnan; Wilson, Bridget S; Vlachos, Dionisios G; Edwards, Jeremy S

2009-07-23

The ErbB family of receptors activates intracellular signaling pathways that control cellular proliferation, growth, differentiation and apoptosis. Given these central roles, it is not surprising that overexpression of the ErbB receptors is often associated with carcinogenesis. Therefore, extensive laboratory studies have been devoted to understanding the signaling events associated with ErbB activation. Systems biology has contributed significantly to our current understanding of ErbB signaling networks. However, although computational models have grown in complexity over the years, little work has been done to consider the spatial-temporal dynamics of receptor interactions and to evaluate how spatial organization of membrane receptors influences signaling transduction. Herein, we explore the impact of spatial organization of the epidermal growth factor receptor (ErbB1/EGFR) on the initiation of downstream signaling. We describe the development of an algorithm that couples a spatial stochastic model of membrane receptors with a nonspatial stochastic model of the reactions and interactions in the cytosol. This novel algorithm provides a computationally efficient method to evaluate the effects of spatial heterogeneity on the coupling of receptors to cytosolic signaling partners. Mathematical models of signal transduction rarely consider the contributions of spatial organization due to high computational costs. A hybrid stochastic approach simplifies analyses of the spatio-temporal aspects of cell signaling and, as an example, demonstrates that receptor clustering contributes significantly to the efficiency of signal propagation from ligand-engaged growth factor receptors.

Local polynomial estimation of heteroscedasticity in a multivariate linear regression model and its applications in economics.

PubMed

Su, Liyun; Zhao, Yanyong; Yan, Tianshun; Li, Fenglan

2012-01-01

Multivariate local polynomial fitting is applied to the multivariate linear heteroscedastic regression model. Firstly, the local polynomial fitting is applied to estimate heteroscedastic function, then the coefficients of regression model are obtained by using generalized least squares method. One noteworthy feature of our approach is that we avoid the testing for heteroscedasticity by improving the traditional two-stage method. Due to non-parametric technique of local polynomial estimation, it is unnecessary to know the form of heteroscedastic function. Therefore, we can improve the estimation precision, when the heteroscedastic function is unknown. Furthermore, we verify that the regression coefficients is asymptotic normal based on numerical simulations and normal Q-Q plots of residuals. Finally, the simulation results and the local polynomial estimation of real data indicate that our approach is surely effective in finite-sample situations.

Multivariate calibration on NIR data: development of a model for the rapid evaluation of ethanol content in bakery products.

PubMed

Bello, Alessandra; Bianchi, Federica; Careri, Maria; Giannetto, Marco; Mori, Giovanni; Musci, Marilena

2007-11-05

A new NIR method based on multivariate calibration for determination of ethanol in industrially packed wholemeal bread was developed and validated. GC-FID was used as reference method for the determination of actual ethanol concentration of different samples of wholemeal bread with proper content of added ethanol, ranging from 0 to 3.5% (w/w). Stepwise discriminant analysis was carried out on the NIR dataset, in order to reduce the number of original variables by selecting those that were able to discriminate between the samples of different ethanol concentrations. With the so selected variables a multivariate calibration model was then obtained by multiple linear regression. The prediction power of the linear model was optimized by a new "leave one out" method, so that the number of original variables resulted further reduced.

Kinetic modeling of benzodiazepine receptor binding with PET and high specific activity [(11)C]Iomazenil in healthy human subjects.

PubMed

Bremner, J D; Horti, A; Staib, L H; Zea-Ponce, Y; Soufer, R; Charney, D S; Baldwin, R

2000-01-01

Quantitation of the PET benzodiazepine receptor antagonist, [(11)C]Iomazenil, using low specific activity radioligand was recently described. The purpose of this study was to quantitate benzodiazepine receptor binding in human subjects using PET and high specific activity [(11)C]Iomazenil. Six healthy human subjects underwent PET imaging following a bolus injection of high specific activity (>100 Ci/mmol) [(11)C]iomazenil. Arterial samples were collected at multiple time points after injection for measurement of unmetabolized total and nonprotein-bound parent compound in plasma. Time activity curves of radioligand concentration in brain and plasma were analyzed using two and three compartment model. Kinetic rate constants of transfer of radioligand between plasma, nonspecifically bound brain tissue, and specifically bound brain tissue compartments were fitted to the model. Values for fitted kinetic rate constants were used in the calculation of measures of benzodiazepine receptor binding, including binding potential (the ratio of receptor density to affinity), and product of BP and the fraction of free nonprotein-bound parent compound (V(3)'). Use of the three compartment model improved the goodness of fit in comparison to the two compartment model. Values for kinetic rate constants and measures of benzodiazepine receptor binding, including BP and V(3)', were similar to results obtained with the SPECT radioligand [(123)I]iomazenil, and a prior report with low specific activity [(11)C]Iomazenil. Kinetic modeling using the three compartment model with PET and high specific activity [(11)C]Iomazenil provides a reliable measure of benzodiazepine receptor binding. Synapse 35:68-77, 2000. Published 2000 Wiley-Liss, Inc.

Ethylene Regulates Levels of Ethylene Receptor/CTR1 Signaling Complexes in Arabidopsis thaliana

DOE PAGES

Shakeel, Samina N.; Gao, Zhiyong; Amir, Madiha; ...

2015-03-26

The plant hormone ethylene is perceived by a five-member family of receptors in Arabidopsis thaliana. The receptors function in conjunction with the Raf-like kinase CTR1 to negatively regulate ethylene signal transduction. CTR1 interacts with multiple members of the receptor family based on co-purification analysis, interacting more strongly with receptors containing a receiver domain. Levels of membrane-associated CTR1 vary in response to ethylene, doing so in a post-transcriptional manner that correlates with ethylene-mediated changes in levels of the ethylene receptors ERS1, ERS2, EIN4, and ETR2. Interactions between CTR1 and the receptor ETR1 protect ETR1 from ethylene-induced turnover. Kinetic and dose-response analysesmore » support a model in which two opposing factors control levels of the ethylene receptor/CTR1 complexes. Ethylene stimulates the production of new complexes largely through transcriptional induction of the receptors. However, ethylene also induces turnover of receptors, such that levels of ethylene receptor/CTR1 complexes decrease at higher ethylene concentrations. Lastly, we discuss implications of this model for ethylene signaling.« less

Ethylene Regulates Levels of Ethylene Receptor/CTR1 Signaling Complexes in Arabidopsis thaliana*

PubMed Central

Shakeel, Samina N.; Gao, Zhiyong; Amir, Madiha; Chen, Yi-Feng; Rai, Muneeza Iqbal; Haq, Noor Ul; Schaller, G. Eric

2015-01-01

The plant hormone ethylene is perceived by a five-member family of receptors in Arabidopsis thaliana. The receptors function in conjunction with the Raf-like kinase CTR1 to negatively regulate ethylene signal transduction. CTR1 interacts with multiple members of the receptor family based on co-purification analysis, interacting more strongly with receptors containing a receiver domain. Levels of membrane-associated CTR1 vary in response to ethylene, doing so in a post-transcriptional manner that correlates with ethylene-mediated changes in levels of the ethylene receptors ERS1, ERS2, EIN4, and ETR2. Interactions between CTR1 and the receptor ETR1 protect ETR1 from ethylene-induced turnover. Kinetic and dose-response analyses support a model in which two opposing factors control levels of the ethylene receptor/CTR1 complexes. Ethylene stimulates the production of new complexes largely through transcriptional induction of the receptors. However, ethylene also induces turnover of receptors, such that levels of ethylene receptor/CTR1 complexes decrease at higher ethylene concentrations. Implications of this model for ethylene signaling are discussed. PMID:25814663

Pain-relieving prospects for adenosine receptors and ectonucleotidases

PubMed Central

Zylka, Mark J.

2010-01-01

Adenosine receptor agonists have potent antinociceptive effects in diverse preclinical models of chronic pain. In contrast, the efficacy of adenosine or adenosine receptor agonists at treating pain in humans is unclear. Two ectonucleotidases that generate adenosine in nociceptive neurons were recently identified. When injected spinally, these enzymes have long-lasting adenosine A1 receptor (A1R)-dependent antinociceptive effects in inflammatory and neuropathic pain models. Furthermore, recent findings indicate that spinal adenosine A2A receptor activation can enduringly inhibit neuropathic pain symptoms. Collectively, these studies suggest the possibility of treating chronic pain in humans by targeting specific adenosine receptor subtypes in anatomically defined regions with agonists or with ectonucleotidases that generate adenosine. PMID:21236731

Structural modeling of G-protein coupled receptors: An overview on automatic web-servers.

PubMed

Busato, Mirko; Giorgetti, Alejandro

2016-08-01

Despite the significant efforts and discoveries during the last few years in G protein-coupled receptor (GPCR) expression and crystallization, the receptors with known structures to date are limited only to a small fraction of human GPCRs. The lack of experimental three-dimensional structures of the receptors represents a strong limitation that hampers a deep understanding of their function. Computational techniques are thus a valid alternative strategy to model three-dimensional structures. Indeed, recent advances in the field, together with extraordinary developments in crystallography, in particular due to its ability to capture GPCRs in different activation states, have led to encouraging results in the generation of accurate models. This, prompted the community of modelers to render their methods publicly available through dedicated databases and web-servers. Here, we present an extensive overview on these services, focusing on their advantages, drawbacks and their role in successful applications. Future challenges in the field of GPCR modeling, such as the predictions of long loop regions and the modeling of receptor activation states are presented as well. Copyright © 2016 Elsevier Ltd. All rights reserved.

Modeling of turbulent supersonic H2-air combustion with a multivariate beta PDF

NASA Technical Reports Server (NTRS)

Baurle, R. A.; Hassan, H. A.

1993-01-01

Recent calculations of turbulent supersonic reacting shear flows using an assumed multivariate beta PDF (probability density function) resulted in reduced production rates and a delay in the onset of combustion. This result is not consistent with available measurements. The present research explores two possible reasons for this behavior: use of PDF's that do not yield Favre averaged quantities, and the gradient diffusion assumption. A new multivariate beta PDF involving species densities is introduced which makes it possible to compute Favre averaged mass fractions. However, using this PDF did not improve comparisons with experiment. A countergradient diffusion model is then introduced. Preliminary calculations suggest this to be the cause of the discrepancy.

Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies

PubMed Central

Inouye, David I.; Ravikumar, Pradeep; Dhillon, Inderjit S.

2016-01-01

We develop Square Root Graphical Models (SQR), a novel class of parametric graphical models that provides multivariate generalizations of univariate exponential family distributions. Previous multivariate graphical models (Yang et al., 2015) did not allow positive dependencies for the exponential and Poisson generalizations. However, in many real-world datasets, variables clearly have positive dependencies. For example, the airport delay time in New York—modeled as an exponential distribution—is positively related to the delay time in Boston. With this motivation, we give an example of our model class derived from the univariate exponential distribution that allows for almost arbitrary positive and negative dependencies with only a mild condition on the parameter matrix—a condition akin to the positive definiteness of the Gaussian covariance matrix. Our Poisson generalization allows for both positive and negative dependencies without any constraints on the parameter values. We also develop parameter estimation methods using node-wise regressions with ℓ1 regularization and likelihood approximation methods using sampling. Finally, we demonstrate our exponential generalization on a synthetic dataset and a real-world dataset of airport delay times. PMID:27563373

Optimal moment determination in POME-copula based hydrometeorological dependence modelling

NASA Astrophysics Data System (ADS)

Liu, Dengfeng; Wang, Dong; Singh, Vijay P.; Wang, Yuankun; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Chen, Yuanfang; Chen, Xi

2017-07-01

Copula has been commonly applied in multivariate modelling in various fields where marginal distribution inference is a key element. To develop a flexible, unbiased mathematical inference framework in hydrometeorological multivariate applications, the principle of maximum entropy (POME) is being increasingly coupled with copula. However, in previous POME-based studies, determination of optimal moment constraints has generally not been considered. The main contribution of this study is the determination of optimal moments for POME for developing a coupled optimal moment-POME-copula framework to model hydrometeorological multivariate events. In this framework, margins (marginals, or marginal distributions) are derived with the use of POME, subject to optimal moment constraints. Then, various candidate copulas are constructed according to the derived margins, and finally the most probable one is determined, based on goodness-of-fit statistics. This optimal moment-POME-copula framework is applied to model the dependence patterns of three types of hydrometeorological events: (i) single-site streamflow-water level; (ii) multi-site streamflow; and (iii) multi-site precipitation, with data collected from Yichang and Hankou in the Yangtze River basin, China. Results indicate that the optimal-moment POME is more accurate in margin fitting and the corresponding copulas reflect a good statistical performance in correlation simulation. Also, the derived copulas, capturing more patterns which traditional correlation coefficients cannot reflect, provide an efficient way in other applied scenarios concerning hydrometeorological multivariate modelling.

Modelling world gold prices and USD foreign exchange relationship using multivariate GARCH model

NASA Astrophysics Data System (ADS)

Ping, Pung Yean; Ahmad, Maizah Hura Binti

2014-12-01

World gold price is a popular investment commodity. The series have often been modeled using univariate models. The objective of this paper is to show that there is a co-movement between gold price and USD foreign exchange rate. Using the effect of the USD foreign exchange rate on the gold price, a model that can be used to forecast future gold prices is developed. For this purpose, the current paper proposes a multivariate GARCH (Bivariate GARCH) model. Using daily prices of both series from 01.01.2000 to 05.05.2014, a causal relation between the two series understudied are found and a bivariate GARCH model is produced.

Design, evaluation and test of an electronic, multivariable control for the F100 turbofan engine

NASA Technical Reports Server (NTRS)

Skira, C. A.; Dehoff, R. L.; Hall, W. E., Jr.

1980-01-01

A digital, multivariable control design procedure for the F100 turbofan engine is described. The controller is based on locally linear synthesis techniques using linear, quadratic regulator design methods. The control structure uses an explicit model reference form with proportional and integral feedback near a nominal trajectory. Modeling issues, design procedures for the control law and the estimation of poorly measured variables are presented.

Copula Multivariate analysis of Gross primary production and its hydro-environmental driver; A BIOME-BGC model applied to the Antisana páramos

NASA Astrophysics Data System (ADS)

Minaya, Veronica; Corzo, Gerald; van der Kwast, Johannes; Galarraga, Remigio; Mynett, Arthur

2014-05-01

Simulations of carbon cycling are prone to uncertainties from different sources, which in general are related to input data, parameters and the model representation capacities itself. The gross carbon uptake in the cycle is represented by the gross primary production (GPP), which deals with the spatio-temporal variability of the precipitation and the soil moisture dynamics. This variability associated with uncertainty of the parameters can be modelled by multivariate probabilistic distributions. Our study presents a novel methodology that uses multivariate Copulas analysis to assess the GPP. Multi-species and elevations variables are included in a first scenario of the analysis. Hydro-meteorological conditions that might generate a change in the next 50 or more years are included in a second scenario of this analysis. The biogeochemical model BIOME-BGC was applied in the Ecuadorian Andean region in elevations greater than 4000 masl with the presence of typical vegetation of páramo. The change of GPP over time is crucial for climate scenarios of the carbon cycling in this type of ecosystem. The results help to improve our understanding of the ecosystem function and clarify the dynamics and the relationship with the change of climate variables. Keywords: multivariate analysis, Copula, BIOME-BGC, NPP, páramos

Gaussian Mixture Models of Between-Source Variation for Likelihood Ratio Computation from Multivariate Data

PubMed Central

Franco-Pedroso, Javier; Ramos, Daniel; Gonzalez-Rodriguez, Joaquin

2016-01-01

In forensic science, trace evidence found at a crime scene and on suspect has to be evaluated from the measurements performed on them, usually in the form of multivariate data (for example, several chemical compound or physical characteristics). In order to assess the strength of that evidence, the likelihood ratio framework is being increasingly adopted. Several methods have been derived in order to obtain likelihood ratios directly from univariate or multivariate data by modelling both the variation appearing between observations (or features) coming from the same source (within-source variation) and that appearing between observations coming from different sources (between-source variation). In the widely used multivariate kernel likelihood-ratio, the within-source distribution is assumed to be normally distributed and constant among different sources and the between-source variation is modelled through a kernel density function (KDF). In order to better fit the observed distribution of the between-source variation, this paper presents a different approach in which a Gaussian mixture model (GMM) is used instead of a KDF. As it will be shown, this approach provides better-calibrated likelihood ratios as measured by the log-likelihood ratio cost (Cllr) in experiments performed on freely available forensic datasets involving different trace evidences: inks, glass fragments and car paints. PMID:26901680

Implementation Challenges for Multivariable Control: What You Did Not Learn in School

NASA Technical Reports Server (NTRS)

Garg, Sanjay

2008-01-01

Multivariable control allows controller designs that can provide decoupled command tracking and robust performance in the presence of modeling uncertainties. Although the last two decades have seen extensive development of multivariable control theory and example applications to complex systems in software/hardware simulations, there are no production flying systems aircraft or spacecraft, that use multivariable control. This is because of the tremendous challenges associated with implementation of such multivariable control designs. Unfortunately, the curriculum in schools does not provide sufficient time to be able to provide an exposure to the students in such implementation challenges. The objective of this paper is to share the lessons learned by a practitioner of multivariable control in the process of applying some of the modern control theory to the Integrated Flight Propulsion Control (IFPC) design for an advanced Short Take-Off Vertical Landing (STOVL) aircraft simulation.