Sample records for disease modelling study
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Tran, Phoebe; Waller, Lance
2015-01-01
Lyme disease has been the subject of many studies due to increasing incidence rates year after year and the severe complications that can arise in later stages of the disease. Negative binomial models have been used to model Lyme disease in the past with some success. However, there has been little focus on the reliability and consistency of these models when they are used to study Lyme disease at multiple spatial scales. This study seeks to explore how sensitive/consistent negative binomial models are when they are used to study Lyme disease at different spatial scales (at the regional and sub-regional levels). The study area includes the thirteen states in the Northeastern United States with the highest Lyme disease incidence during the 2002-2006 period. Lyme disease incidence at county level for the period of 2002-2006 was linked with several previously identified key landscape and climatic variables in a negative binomial regression model for the Northeastern region and two smaller sub-regions (the New England sub-region and the Mid-Atlantic sub-region). This study found that negative binomial models, indeed, were sensitive/inconsistent when used at different spatial scales. We discuss various plausible explanations for such behavior of negative binomial models. Further investigation of the inconsistency and sensitivity of negative binomial models when used at different spatial scales is important for not only future Lyme disease studies and Lyme disease risk assessment/management but any study that requires use of this model type in a spatial context. Copyright © 2014 Elsevier Inc. All rights reserved.
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Russell, James C; Proctor, Spencer D
2006-01-01
Cardiovascular disease, the leading cause of death in much of the modern world, is the common symptomatic end stage of a number of distinct diseases and, therefore, is multifactorial and polygenetic in character. The two major underlying causes are disorders of lipid metabolism and metabolic syndrome. The ability to develop preventative and ameliorative treatments will depend on animal models that mimic human disease processes. The focus of this review is to identify suitable animal models and insights into cardiovascular disease achieved to date using such models. The ideal animal model of cardiovascular disease will mimic the human subject metabolically and pathophysiologically, will be large enough to permit physiological and metabolic studies, and will develop end-stage disease comparable to those in humans. Given the complex multifactorial nature of cardiovascular disease, no one species will be suitable for all studies. Potential larger animal models are problematic due to cost, ethical considerations, or poor pathophysiological comparability to humans. Rabbits require high-cholesterol diets to develop cardiovascular disease, and there are no rabbit models of metabolic syndrome. Spontaneous mutations in rats provide several complementary models of obesity, hyperlipidemia, insulin resistance, and type 2 diabetes, one of which spontaneously develops cardiovascular disease and ischemic lesions. The mouse, like normal rats, is characteristically resistant to cardiovascular disease, although genetically altered strains respond to cholesterol feeding with atherosclerosis, but not with end-stage ischemic lesions. The most useful and valid species/strains for the study of cardiovascular disease appear to be small rodents, rats, and mice. This fragmented field would benefit from a consensus on well-characterized appropriate models for the study of different aspects of cardiovascular disease and a renewed emphasis on the biology of underlying diseases.
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A vector space model approach to identify genetically related diseases.
Sarkar, Indra Neil
2012-01-01
The relationship between diseases and their causative genes can be complex, especially in the case of polygenic diseases. Further exacerbating the challenges in their study is that many genes may be causally related to multiple diseases. This study explored the relationship between diseases through the adaptation of an approach pioneered in the context of information retrieval: vector space models. A vector space model approach was developed that bridges gene disease knowledge inferred across three knowledge bases: Online Mendelian Inheritance in Man, GenBank, and Medline. The approach was then used to identify potentially related diseases for two target diseases: Alzheimer disease and Prader-Willi Syndrome. In the case of both Alzheimer Disease and Prader-Willi Syndrome, a set of plausible diseases were identified that may warrant further exploration. This study furthers seminal work by Swanson, et al. that demonstrated the potential for mining literature for putative correlations. Using a vector space modeling approach, information from both biomedical literature and genomic resources (like GenBank) can be combined towards identification of putative correlations of interest. To this end, the relevance of the predicted diseases of interest in this study using the vector space modeling approach were validated based on supporting literature. The results of this study suggest that a vector space model approach may be a useful means to identify potential relationships between complex diseases, and thereby enable the coordination of gene-based findings across multiple complex diseases.
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Transgenic animal models of neurodegeneration based on human genetic studies
Richie, Christopher T.; Hoffer, Barry J.; Airavaara, Mikko
2011-01-01
The identification of genes linked to neurodegenerative diseases such as Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), Huntington's disease (HD) and Parkinson's disease (PD) has led to the development of animal models for studying mechanism and evaluating potential therapies. None of the transgenic models developed based on disease-associated genes have been able to fully recapitulate the behavioral and pathological features of the corresponding disease. However, there has been enormous progress made in identifying potential therapeutic targets and understanding some of the common mechanisms of neurodegeneration. In this review, we will discuss transgenic animal models for AD, ALS, HD and PD that are based on human genetic studies. All of the diseases discussed have active or complete clinical trials for experimental treatments that benefited from transgenic models of the disease. PMID:20931247
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Porcine models of digestive disease: the future of large animal translational research
Gonzalez, Liara M.; Moeser, Adam J.; Blikslager, Anthony T.
2015-01-01
There is increasing interest in non-rodent translational models for the study of human disease. The pig, in particular, serves as a useful animal model for the study of pathophysiological conditions relevant to the human intestine. This review assesses currently used porcine models of gastrointestinal physiology and disease and provides a rationale for the use of these models for future translational studies. The pig has proven its utility for the study of fundamental disease conditions such as ischemia/ reperfusion injury, stress-induced intestinal dysfunction, and short bowel syndrome. Pigs have also shown great promise for the study of intestinal barrier function, surgical tissue manipulation and intervention, as well as biomaterial implantation and tissue transplantation. Advantages of pig models highlighted by these studies include the physiological similarity to human intestine as well as to mechanisms of human disease. Emerging future directions for porcine models of human disease include the fields of transgenics and stem cell biology, with exciting implications for regenerative medicine. PMID:25655839
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Asaad, Mazen; Lee, Jin Hyung
2018-05-18
Alzheimer's disease is a leading healthcare challenge facing our society today. Functional magnetic resonance imaging (fMRI) of the brain has played an important role in our efforts to understand how Alzheimer's disease alters brain function. Using fMRI in animal models of Alzheimer's disease has the potential to provide us with a more comprehensive understanding of the observations made in human clinical fMRI studies. However, using fMRI in animal models of Alzheimer's disease presents some unique challenges. Here, we highlight some of these challenges and discuss potential solutions for researchers interested in performing fMRI in animal models. First, we briefly summarize our current understanding of Alzheimer's disease from a mechanistic standpoint. We then overview the wide array of animal models available for studying this disease and how to choose the most appropriate model to study, depending on which aspects of the condition researchers seek to investigate. Finally, we discuss the contributions of fMRI to our understanding of Alzheimer's disease and the issues to consider when designing fMRI studies for animal models, such as differences in brain activity based on anesthetic choice and ways to interrogate more specific questions in rodents beyond those that can be addressed in humans. The goal of this article is to provide information on the utility of fMRI, and approaches to consider when using fMRI, for studies of Alzheimer's disease in animal models. © 2018. Published by The Company of Biologists Ltd.
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A guide to using functional magnetic resonance imaging to study Alzheimer's disease in animal models
Asaad, Mazen
2018-01-01
ABSTRACT Alzheimer's disease is a leading healthcare challenge facing our society today. Functional magnetic resonance imaging (fMRI) of the brain has played an important role in our efforts to understand how Alzheimer's disease alters brain function. Using fMRI in animal models of Alzheimer's disease has the potential to provide us with a more comprehensive understanding of the observations made in human clinical fMRI studies. However, using fMRI in animal models of Alzheimer's disease presents some unique challenges. Here, we highlight some of these challenges and discuss potential solutions for researchers interested in performing fMRI in animal models. First, we briefly summarize our current understanding of Alzheimer's disease from a mechanistic standpoint. We then overview the wide array of animal models available for studying this disease and how to choose the most appropriate model to study, depending on which aspects of the condition researchers seek to investigate. Finally, we discuss the contributions of fMRI to our understanding of Alzheimer's disease and the issues to consider when designing fMRI studies for animal models, such as differences in brain activity based on anesthetic choice and ways to interrogate more specific questions in rodents beyond those that can be addressed in humans. The goal of this article is to provide information on the utility of fMRI, and approaches to consider when using fMRI, for studies of Alzheimer's disease in animal models. PMID:29784664
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Roles of amino acids in preventing and treating intestinal diseases: recent studies with pig models.
Liu, Yulan; Wang, Xiuying; Hou, Yongqing; Yin, Yulong; Qiu, Yinsheng; Wu, Guoyao; Hu, Chien-An Andy
2017-08-01
Animal models are needed to study and understand a human complex disease. Because of their similarities in anatomy, structure, physiology, and pathophysiology, the pig has proven its usefulness in studying human gastrointestinal diseases, such as inflammatory bowel disease, ischemia/reperfusion injury, diarrhea, and cancer. To understand the pathogenesis of these diseases, a number of experimental models generated in pigs are available, for example, through surgical manipulation, chemical induction, microbial infection, and genetic engineering. Our interests have been using amino acids as therapeutics in pig and human disease models. Amino acids not only play an important role in protein biosynthesis, but also exert significant physiological effects in regulating immunity, anti-oxidation, redox regulation, energy metabolism, signal transduction, and animal behavior. Recent studies in pigs have shown that specific dietary amino acids can improve intestinal integrity and function under normal and pathological conditions that protect the host from different diseases. In this review, we summarize several pig models in intestinal diseases and how amino acids can be used as therapeutics in treating pig and human diseases.
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Anand, Vibha; Rosenman, Marc B; Downs, Stephen M
2013-09-01
To develop a map of disease associations exclusively using two publicly available genetic sources: the catalog of single nucleotide polymorphisms (SNPs) from the HapMap, and the catalog of Genome Wide Association Studies (GWAS) from the NHGRI, and to evaluate it with a large, long-standing electronic medical record (EMR). A computational model, In Silico Bayesian Integration of GWAS (IsBIG), was developed to learn associations among diseases using a Bayesian network (BN) framework, using only genetic data. The IsBIG model (I-Model) was re-trained using data from our EMR (M-Model). Separately, another clinical model (C-Model) was learned from this training dataset. The I-Model was compared with both the M-Model and the C-Model for power to discriminate a disease given other diseases using a test dataset from our EMR. Area under receiver operator characteristics curve was used as a performance measure. Direct associations between diseases in the I-Model were also searched in the PubMed database and in classes of the Human Disease Network (HDN). On the basis of genetic information alone, the I-Model linked a third of diseases from our EMR. When compared to the M-Model, the I-Model predicted diseases given other diseases with 94% specificity, 33% sensitivity, and 80% positive predictive value. The I-Model contained 117 direct associations between diseases. Of those associations, 20 (17%) were absent from the searches of the PubMed database; one of these was present in the C-Model. Of the direct associations in the I-Model, 7 (35%) were absent from disease classes of HDN. Using only publicly available genetic sources we have mapped associations in GWAS to a human disease map using an in silico approach. Furthermore, we have validated this disease map using phenotypic data from our EMR. Models predicting disease associations on the basis of known genetic associations alone are specific but not sensitive. Genetic data, as it currently exists, can only explain a fraction of the risk of a disease. Our approach makes a quantitative statement about disease variation that can be explained in an EMR on the basis of genetic associations described in the GWAS. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
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Roche, Benjamin; Guégan, Jean-François; Bousquet, François
2008-10-15
Computational biology is often associated with genetic or genomic studies only. However, thanks to the increase of computational resources, computational models are appreciated as useful tools in many other scientific fields. Such modeling systems are particularly relevant for the study of complex systems, like the epidemiology of emerging infectious diseases. So far, mathematical models remain the main tool for the epidemiological and ecological analysis of infectious diseases, with SIR models could be seen as an implicit standard in epidemiology. Unfortunately, these models are based on differential equations and, therefore, can become very rapidly unmanageable due to the too many parameters which need to be taken into consideration. For instance, in the case of zoonotic and vector-borne diseases in wildlife many different potential host species could be involved in the life-cycle of disease transmission, and SIR models might not be the most suitable tool to truly capture the overall disease circulation within that environment. This limitation underlines the necessity to develop a standard spatial model that can cope with the transmission of disease in realistic ecosystems. Computational biology may prove to be flexible enough to take into account the natural complexity observed in both natural and man-made ecosystems. In this paper, we propose a new computational model to study the transmission of infectious diseases in a spatially explicit context. We developed a multi-agent system model for vector-borne disease transmission in a realistic spatial environment. Here we describe in detail the general behavior of this model that we hope will become a standard reference for the study of vector-borne disease transmission in wildlife. To conclude, we show how this simple model could be easily adapted and modified to be used as a common framework for further research developments in this field.
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KERIS: kaleidoscope of gene responses to inflammation between species
Li, Peng; Tompkins, Ronald G; Xiao, Wenzhong
2017-01-01
A cornerstone of modern biomedical research is the use of animal models to study disease mechanisms and to develop new therapeutic approaches. In order to help the research community to better explore the similarities and differences of genomic response between human inflammatory diseases and murine models, we developed KERIS: kaleidoscope of gene responses to inflammation between species (available at http://www.igenomed.org/keris/). As of June 2016, KERIS includes comparisons of the genomic response of six human inflammatory diseases (burns, trauma, infection, sepsis, endotoxin and acute respiratory distress syndrome) and matched mouse models, using 2257 curated samples from the Inflammation and the Host Response to Injury Glue Grant studies and other representative studies in Gene Expression Omnibus. A researcher can browse, query, visualize and compare the response patterns of genes, pathways and functional modules across different diseases and corresponding murine models. The database is expected to help biologists choosing models when studying the mechanisms of particular genes and pathways in a disease and prioritizing the translation of findings from disease models into clinical studies. PMID:27789704
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A conceptual disease model for adult Pompe disease.
Kanters, Tim A; Redekop, W Ken; Rutten-Van Mölken, Maureen P M H; Kruijshaar, Michelle E; Güngör, Deniz; van der Ploeg, Ans T; Hakkaart, Leona
2015-09-15
Studies in orphan diseases are, by nature, confronted with small patient populations, meaning that randomized controlled trials will have limited statistical power. In order to estimate the effectiveness of treatments in orphan diseases and extrapolate effects into the future, alternative models might be needed. The purpose of this study is to develop a conceptual disease model for Pompe disease in adults (an orphan disease). This conceptual model describes the associations between the most important levels of health concepts for Pompe disease in adults, from biological parameters via physiological parameters, symptoms and functional indicators to health perceptions and final health outcomes as measured in terms of health-related quality of life. The structure of the Wilson-Cleary health outcomes model was used as a blueprint, and filled with clinically relevant aspects for Pompe disease based on literature and expert opinion. Multiple observations per patient from a Dutch cohort study in untreated patients were used to quantify the relationships between the different levels of health concepts in the model by means of regression analyses. Enzyme activity, muscle strength, respiratory function, fatigue, level of handicap, general health perceptions, mental and physical component scales and utility described the different levels of health concepts in the Wilson-Cleary model for Pompe disease. Regression analyses showed that functional status was affected by fatigue, muscle strength and respiratory function. Health perceptions were affected by handicap. In turn, self-reported quality of life was affected by health perceptions. We conceptualized a disease model that incorporated the mechanisms believed to be responsible for impaired quality of life in Pompe disease. The model provides a comprehensive overview of various aspects of Pompe disease in adults, which can be useful for both clinicians and policymakers to support their multi-faceted decision making.
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Concise Review: Stem Cell Trials Using Companion Animal Disease Models.
Hoffman, Andrew M; Dow, Steven W
2016-07-01
Studies to evaluate the therapeutic potential of stem cells in humans would benefit from more realistic animal models. In veterinary medicine, companion animals naturally develop many diseases that resemble human conditions, therefore, representing a novel source of preclinical models. To understand how companion animal disease models are being studied for this purpose, we reviewed the literature between 2008 and 2015 for reports on stem cell therapies in dogs and cats, excluding laboratory animals, induced disease models, cancer, and case reports. Disease models included osteoarthritis, intervertebral disc degeneration, dilated cardiomyopathy, inflammatory bowel diseases, Crohn's fistulas, meningoencephalomyelitis (multiple sclerosis-like), keratoconjunctivitis sicca (Sjogren's syndrome-like), atopic dermatitis, and chronic (end-stage) kidney disease. Stem cells evaluated in these studies included mesenchymal stem-stromal cells (MSC, 17/19 trials), olfactory ensheathing cells (OEC, 1 trial), or neural lineage cells derived from bone marrow MSC (1 trial), and 16/19 studies were performed in dogs. The MSC studies (13/17) used adipose tissue-derived MSC from either allogeneic (8/13) or autologous (5/13) sources. The majority of studies were open label, uncontrolled studies. Endpoints and protocols were feasible, and the stem cell therapies were reportedly safe and elicited beneficial patient responses in all but two of the trials. In conclusion, companion animals with naturally occurring diseases analogous to human conditions can be recruited into clinical trials and provide realistic insight into feasibility, safety, and biologic activity of novel stem cell therapies. However, improvements in the rigor of manufacturing, study design, and regulatory compliance will be needed to better utilize these models. Stem Cells 2016;34:1709-1729. © 2016 AlphaMed Press.
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Comparing ESC and iPSC-Based Models for Human Genetic Disorders.
Halevy, Tomer; Urbach, Achia
2014-10-24
Traditionally, human disorders were studied using animal models or somatic cells taken from patients. Such studies enabled the analysis of the molecular mechanisms of numerous disorders, and led to the discovery of new treatments. Yet, these systems are limited or even irrelevant in modeling multiple genetic diseases. The isolation of human embryonic stem cells (ESCs) from diseased blastocysts, the derivation of induced pluripotent stem cells (iPSCs) from patients' somatic cells, and the new technologies for genome editing of pluripotent stem cells have opened a new window of opportunities in the field of disease modeling, and enabled studying diseases that couldn't be modeled in the past. Importantly, despite the high similarity between ESCs and iPSCs, there are several fundamental differences between these cells, which have important implications regarding disease modeling. In this review we compare ESC-based models to iPSC-based models, and highlight the advantages and disadvantages of each system. We further suggest a roadmap for how to choose the optimal strategy to model each specific disorder.
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Chu, Haitao; Nie, Lei; Cole, Stephen R; Poole, Charles
2009-08-15
In a meta-analysis of diagnostic accuracy studies, the sensitivities and specificities of a diagnostic test may depend on the disease prevalence since the severity and definition of disease may differ from study to study due to the design and the population considered. In this paper, we extend the bivariate nonlinear random effects model on sensitivities and specificities to jointly model the disease prevalence, sensitivities and specificities using trivariate nonlinear random-effects models. Furthermore, as an alternative parameterization, we also propose jointly modeling the test prevalence and the predictive values, which reflect the clinical utility of a diagnostic test. These models allow investigators to study the complex relationship among the disease prevalence, sensitivities and specificities; or among test prevalence and the predictive values, which can reveal hidden information about test performance. We illustrate the proposed two approaches by reanalyzing the data from a meta-analysis of radiological evaluation of lymph node metastases in patients with cervical cancer and a simulation study. The latter illustrates the importance of carefully choosing an appropriate normality assumption for the disease prevalence, sensitivities and specificities, or the test prevalence and the predictive values. In practice, it is recommended to use model selection techniques to identify a best-fitting model for making statistical inference. In summary, the proposed trivariate random effects models are novel and can be very useful in practice for meta-analysis of diagnostic accuracy studies. Copyright 2009 John Wiley & Sons, Ltd.
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Chen, Yong
2017-01-01
The expansion of shell disease is an emerging threat to the inshore lobster fisheries in the northeastern United States. The development of models to improve the efficiency and precision of existing monitoring programs is advocated as an important step in mitigating its harmful effects. The objective of this study is to construct a statistical model that could enhance the existing monitoring effort through (1) identification of potential disease-associated abiotic and biotic factors, and (2) estimation of spatial variation in disease prevalence in the lobster fishery. A delta-generalized additive modeling (GAM) approach was applied using bottom trawl survey data collected from 2001–2013 in Long Island Sound, a tidal estuary between New York and Connecticut states. Spatial distribution of shell disease prevalence was found to be strongly influenced by the interactive effects of latitude and longitude, possibly indicative of a geographic origin of shell disease. Bottom temperature, bottom salinity, and depth were also important factors affecting the spatial variability in shell disease prevalence. The delta-GAM projected high disease prevalence in non-surveyed locations. Additionally, a potential spatial discrepancy was found between modeled disease hotspots and survey-based gravity centers of disease prevalence. This study provides a modeling framework to enhance research, monitoring and management of emerging and continuing marine disease threats. PMID:28196150
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Cholongitas, E; Papatheodoridis, G V; Vangeli, M; Terreni, N; Patch, D; Burroughs, A K
2005-12-01
Prognosis in cirrhotic patients has had a resurgence of interest because of liver transplantation and new therapies for complications of end-stage cirrhosis. The model for end-stage liver disease score is now used for allocation in liver transplantation waiting lists, replacing Child-Turcotte-Pugh score. However, there is debate as whether it is better in other settings of cirrhosis. To review studies comparing the accuracy of model for end-stage liver disease score vs. Child-Turcotte-Pugh score in non-transplant settings. Transjugular intrahepatic portosystemic shunt studies (with 1360 cirrhotics) only one of five, showed model for end-stage liver disease to be superior to Child-Turcotte-Pugh to predict 3-month mortality, but not for 12-month mortality. Prognosis of cirrhosis studies (with 2569 patients) none of four showed significant differences between the two scores for either short- or long-term prognosis whereas no differences for variceal bleeding studies (with 411 cirrhotics). Modified Child-Turcotte-Pugh score, by adding creatinine, performed similarly to model for end-stage liver disease score. Hepatic encephalopathy and hyponatraemia (as an index of ascites), both components of Child-Turcotte-Pugh score, add to the prognostic performance of model for end-stage liver disease score. Based on current literature, model for end-stage liver disease score does not perform better than Child-Turcotte-Pugh score in non-transplant settings. Modified Child-Turcotte-Pugh and model for end-stage liver disease scores need further evaluation.
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An agent-based approach for modeling dynamics of contagious disease spread
Perez, Liliana; Dragicevic, Suzana
2009-01-01
Background The propagation of communicable diseases through a population is an inherent spatial and temporal process of great importance for modern society. For this reason a spatially explicit epidemiologic model of infectious disease is proposed for a greater understanding of the disease's spatial diffusion through a network of human contacts. Objective The objective of this study is to develop an agent-based modelling approach the integrates geographic information systems (GIS) to simulate the spread of a communicable disease in an urban environment, as a result of individuals' interactions in a geospatial context. Methods The methodology for simulating spatiotemporal dynamics of communicable disease propagation is presented and the model is implemented using measles outbreak in an urban environment as a case study. Individuals in a closed population are explicitly represented by agents associated to places where they interact with other agents. They are endowed with mobility, through a transportation network allowing them to move between places within the urban environment, in order to represent the spatial heterogeneity and the complexity involved in infectious diseases diffusion. The model is implemented on georeferenced land use dataset from Metro Vancouver and makes use of census data sets from Statistics Canada for the municipality of Burnaby, BC, Canada study site. Results The results provide insights into the application of the model to calculate ratios of susceptible/infected in specific time frames and urban environments, due to its ability to depict the disease progression based on individuals' interactions. It is demonstrated that the dynamic spatial interactions within the population lead to high numbers of exposed individuals who perform stationary activities in areas after they have finished commuting. As a result, the sick individuals are concentrated in geographical locations like schools and universities. Conclusion The GIS-agent based model designed for this study can be easily customized to study the disease spread dynamics of any other communicable disease by simply adjusting the modeled disease timeline and/or the infection model and modifying the transmission process. This type of simulations can help to improve comprehension of disease spread dynamics and to take better steps towards the prevention and control of an epidemic outbreak. PMID:19656403
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Deterministic SLIR model for tuberculosis disease mapping
NASA Astrophysics Data System (ADS)
Aziz, Nazrina; Diah, Ijlal Mohd; Ahmad, Nazihah; Kasim, Maznah Mat
2017-11-01
Tuberculosis (TB) occurs worldwide. It can be transmitted to others directly through air when active TB persons sneeze, cough or spit. In Malaysia, it was reported that TB cases had been recognized as one of the most infectious disease that lead to death. Disease mapping is one of the methods that can be used as the prevention strategies since it can displays clear picture for the high-low risk areas. Important thing that need to be considered when studying the disease occurrence is relative risk estimation. The transmission of TB disease is studied through mathematical model. Therefore, in this study, deterministic SLIR models are used to estimate relative risk for TB disease transmission.
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Perentos, Nicholas; Martins, Amadeu Q.; Watson, Thomas C.; Bartsch, Ullrich; Mitchell, Nadia L.; Palmer, David N.; Jones, Matthew W.
2015-01-01
Creating valid mouse models of slowly progressing human neurological diseases is challenging, not least because the short lifespan of rodents confounds realistic modelling of disease time course. With their large brains and long lives, sheep offer significant advantages for translational studies of human disease. Here we used normal and CLN5 Batten disease affected sheep to demonstrate the use of the species for studying neurological function in a model of human disease. We show that electroencephalography can be used in sheep, and that longitudinal recordings spanning many months are possible. This is the first time such an electroencephalography study has been performed in sheep. We characterized sleep in sheep, quantifying characteristic vigilance states and neurophysiological hallmarks such as sleep spindles. Mild sleep abnormalities and abnormal epileptiform waveforms were found in the electroencephalographies of Batten disease affected sheep. These abnormalities resemble the epileptiform activity seen in children with Batten disease and demonstrate the translational relevance of both the technique and the model. Given that both spontaneous and engineered sheep models of human neurodegenerative diseases already exist, sheep constitute a powerful species in which longitudinal in vivo studies can be conducted. This will advance our understanding of normal brain function and improve our capacity for translational research into neurological disorders. PMID:25724202
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Wakie, Tewodros; Kumar, Sunil; Senay, Gabriel; Takele, Abera; Lencho, Alemu
2016-01-01
A number of studies have reported the presence of wheat septoria leaf blotch (Septoria tritici; SLB) disease in Ethiopia. However, the environmental factors associated with SLB disease, and areas under risk of SLB disease, have not been studied. Here, we tested the hypothesis that environmental variables can adequately explain observed SLB disease severity levels in West Shewa, Central Ethiopia. Specifically, we identified 50 environmental variables and assessed their relationships with SLB disease severity. Geographically referenced disease severity data were obtained from the field, and linear regression and Boosted Regression Trees (BRT) modeling approaches were used for developing spatial models. Moderate-resolution imaging spectroradiometer (MODIS) derived vegetation indices and land surface temperature (LST) variables highly influenced SLB model predictions. Soil and topographic variables did not sufficiently explain observed SLB disease severity variation in this study. Our results show that wheat growing areas in Central Ethiopia, including highly productive districts, are at risk of SLB disease. The study demonstrates the integration of field data with modeling approaches such as BRT for predicting the spatial patterns of severity of a pathogenic wheat disease in Central Ethiopia. Our results can aid Ethiopia's wheat disease monitoring efforts, while our methods can be replicated for testing related hypotheses elsewhere.
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Magnetic resonance imaging of amyloid plaques in transgenic mouse models of Alzheimer's disease
Chamberlain, Ryan; Wengenack, Thomas M.; Poduslo, Joseph F.; Garwood, Michael; Jack, Clifford R.
2011-01-01
A major objective in the treatment of Alzheimer's disease is amyloid plaque reduction. Transgenic mouse models of Alzheimer's disease provide a controlled and consistent environment for studying amyloid plaque deposition in Alzheimer's disease. Magnetic resonance imaging is an attractive tool for longitudinal studies because it offers non-invasive monitoring of amyloid plaques. Recent studies have demonstrated the ability of magnetic resonance imaging to detect individual plaques in living mice. This review discusses the mouse models, MR pulse sequences, and parameters that have been used to image plaques and how they can be optimized for future studies. PMID:21499442
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Temporal and long-term trend analysis of class C notifiable diseases in China from 2009 to 2014
Zhang, Xingyu; Hou, Fengsu; Qiao, Zhijiao; Li, Xiaosong; Zhou, Lijun; Liu, Yuanyuan; Zhang, Tao
2016-01-01
Objectives Time series models are effective tools for disease forecasting. This study aims to explore the time series behaviour of 11 notifiable diseases in China and to predict their incidence through effective models. Settings and participants The Chinese Ministry of Health started to publish class C notifiable diseases in 2009. The monthly reported case time series of 11 infectious diseases from the surveillance system between 2009 and 2014 was collected. Methods We performed a descriptive and a time series study using the surveillance data. Decomposition methods were used to explore (1) their seasonality expressed in the form of seasonal indices and (2) their long-term trend in the form of a linear regression model. Autoregressive integrated moving average (ARIMA) models have been established for each disease. Results The number of cases and deaths caused by hand, foot and mouth disease ranks number 1 among the detected diseases. It occurred most often in May and July and increased, on average, by 0.14126/100 000 per month. The remaining incidence models show good fit except the influenza and hydatid disease models. Both the hydatid disease and influenza series become white noise after differencing, so no available ARIMA model can be fitted for these two diseases. Conclusion Time series analysis of effective surveillance time series is useful for better understanding the occurrence of the 11 types of infectious disease. PMID:27797981
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Non-alcoholic fatty liver disease (NAFLD) models in drug discovery.
Cole, Banumathi K; Feaver, Ryan E; Wamhoff, Brian R; Dash, Ajit
2018-02-01
The progressive disease spectrum of non-alcoholic fatty liver disease (NAFLD), which includes non-alcoholic steatohepatitis (NASH), is a rapidly emerging public health crisis with no approved therapy. The diversity of various therapies under development highlights the lack of consensus around the most effective target, underscoring the need for better translatable preclinical models to study the complex progressive disease and effective therapies. Areas covered: This article reviews published literature of various mouse models of NASH used in preclinical studies, as well as complex organotypic in vitro and ex vivo liver models being developed. It discusses translational challenges associated with both kinds of models, and describes some of the studies that validate their application in NAFLD. Expert opinion: Animal models offer advantages of understanding drug distribution and effects in a whole body context, but are limited by important species differences. Human organotypic in vitro and ex vivo models with physiological relevance and translatability need to be used in a tiered manner with simpler screens. Leveraging newer technologies, like metabolomics, proteomics, and transcriptomics, and the future development of validated disease biomarkers will allow us to fully utilize the value of these models to understand disease and evaluate novel drugs in isolation or combination.
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The progression rate of spinocerebellar ataxia type 2 changes with stage of disease.
Monte, Thais Lampert; Reckziegel, Estela da Rosa; Augustin, Marina Coutinho; Locks-Coelho, Lucas D; Santos, Amanda Senna P; Furtado, Gabriel Vasata; de Mattos, Eduardo Preusser; Pedroso, José Luiz; Barsottini, Orlando Póvoas; Vargas, Fernando Regla; Saraiva-Pereira, Maria-Luiza; Camey, Suzi Alves; Leotti, Vanessa Bielefeldt; Jardim, Laura Bannach
2018-01-25
Spinocerebellar ataxia type 2 (SCA2) affects several neurological structures, giving rise to multiple symptoms. However, only the natural history of ataxia is well known, as measured during the study duration. We aimed to describe the progression rate of ataxia, by the Scale for the Assessment and Rating of Ataxia (SARA), as well as the progression rate of the overall neurological picture, by the Neurological Examination Score for Spinocerebellar Ataxias (NESSCA), and not only during the study duration but also in a disease duration model. Comparisons between these models might allow us to explore whether progression is linear during the disease duration in SCA2; and to look for potential modifiers. Eighty-eight evaluations were prospectively done on 49 symptomatic subjects; on average (SD), study duration and disease duration models covered 13 (2.16) months and 14 (6.66) years of individuals' life, respectively. SARA progressed 1.75 (CI 95%: 0.92-2.57) versus 0.79 (95% CI 0.45 to 1.14) points/year in the study duration and disease duration models. NESSCA progressed 1.45 (CI 95%: 0.74-2.16) versus 0.41 (95% CI 0.24 to 0.59) points/year in the same models. In order to explain these discrepancies, the progression rates of the study duration model were plotted against disease duration. Then an acceleration was detected after 10 years of disease duration: SARA scores progressed 0.35 before and 2.45 points/year after this deadline (p = 0.013). Age at onset, mutation severity, and presence of amyotrophy, parkinsonism, dystonic manifestations and cognitive decline at baseline did not influence the rate of disease progression. NESSCA and SARA progression rates were not constant during disease duration in SCA2: early phases of disease were associated with slower progressions. Modelling of future clinical trials on SCA2 should take this phenomenon into account, since disease duration might impact on inclusion criteria, sample size, and study duration. Our database is available online and accessible to future studies aimed to compare the present data with other cohorts.
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Herzog, Sereina A; Blaizot, Stéphanie; Hens, Niel
2017-12-18
Mathematical models offer the possibility to investigate the infectious disease dynamics over time and may help in informing design of studies. A systematic review was performed in order to determine to what extent mathematical models have been incorporated into the process of planning studies and hence inform study design for infectious diseases transmitted between humans and/or animals. We searched Ovid Medline and two trial registry platforms (Cochrane, WHO) using search terms related to infection, mathematical model, and study design from the earliest dates to October 2016. Eligible publications and registered trials included mathematical models (compartmental, individual-based, or Markov) which were described and used to inform the design of infectious disease studies. We extracted information about the investigated infection, population, model characteristics, and study design. We identified 28 unique publications but no registered trials. Focusing on compartmental and individual-based models we found 12 observational/surveillance studies and 11 clinical trials. Infections studied were equally animal and human infectious diseases for the observational/surveillance studies, while all but one between humans for clinical trials. The mathematical models were used to inform, amongst other things, the required sample size (n = 16), the statistical power (n = 9), the frequency at which samples should be taken (n = 6), and from whom (n = 6). Despite the fact that mathematical models have been advocated to be used at the planning stage of studies or surveillance systems, they are used scarcely. With only one exception, the publications described theoretical studies, hence, not being utilised in real studies.
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A complete categorization of multiscale models of infectious disease systems.
Garira, Winston
2017-12-01
Modelling of infectious disease systems has entered a new era in which disease modellers are increasingly turning to multiscale modelling to extend traditional modelling frameworks into new application areas and to achieve higher levels of detail and accuracy in characterizing infectious disease systems. In this paper we present a categorization framework for categorizing multiscale models of infectious disease systems. The categorization framework consists of five integration frameworks and five criteria. We use the categorization framework to give a complete categorization of host-level immuno-epidemiological models (HL-IEMs). This categorization framework is also shown to be applicable in categorizing other types of multiscale models of infectious diseases beyond HL-IEMs through modifying the initial categorization framework presented in this study. Categorization of multiscale models of infectious disease systems in this way is useful in bringing some order to the discussion on the structure of these multiscale models.
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Endometriosis research: animal models for the study of a complex disease.
Tirado-González, Irene; Barrientos, Gabriela; Tariverdian, Nadja; Arck, Petra C; García, Mariana G; Klapp, Burghard F; Blois, Sandra M
2010-11-01
Endometriosis is a common gynaecological disease that is characterized and defined as the presence of endometrial tissue outside the uterus, causing painful periods and subfertility in approximately 10% of women. After more than 50 years of research, little is known about the mechanisms underlying the development and establishment of this condition. Animal models allow us to study the temporal sequence of events involved in disease establishment and progression. Also, because this disease occurs spontaneously only in humans and non-human primates and there are practical problems associated with studying the disease, animal models have been developed for the evaluation of endometriosis. This review describes the animal models for endometriosis that have been used to date, highlighting their importance for the investigation of disease mechanisms that would otherwise be more difficult to elucidate, and proposing new alternatives aimed at overcoming some of these limitations. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.
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Tappenden, Paul; Chilcott, Jim; Brennan, Alan; Squires, Hazel; Glynne-Jones, Rob; Tappenden, Janine
2013-06-01
To assess the feasibility and value of simulating whole disease and treatment pathways within a single model to provide a common economic basis for informing resource allocation decisions. A patient-level simulation model was developed with the intention of being capable of evaluating multiple topics within National Institute for Health and Clinical Excellence's colorectal cancer clinical guideline. The model simulates disease and treatment pathways from preclinical disease through to detection, diagnosis, adjuvant/neoadjuvant treatments, follow-up, curative/palliative treatments for metastases, supportive care, and eventual death. The model parameters were informed by meta-analyses, randomized trials, observational studies, health utility studies, audit data, costing sources, and expert opinion. Unobservable natural history parameters were calibrated against external data using Bayesian Markov chain Monte Carlo methods. Economic analysis was undertaken using conventional cost-utility decision rules within each guideline topic and constrained maximization rules across multiple topics. Under usual processes for guideline development, piecewise economic modeling would have been used to evaluate between one and three topics. The Whole Disease Model was capable of evaluating 11 of 15 guideline topics, ranging from alternative diagnostic technologies through to treatments for metastatic disease. The constrained maximization analysis identified a configuration of colorectal services that is expected to maximize quality-adjusted life-year gains without exceeding current expenditure levels. This study indicates that Whole Disease Model development is feasible and can allow for the economic analysis of most interventions across a disease service within a consistent conceptual and mathematical infrastructure. This disease-level modeling approach may be of particular value in providing an economic basis to support other clinical guidelines. Copyright © 2013 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.
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Nalls, Mike A; McLean, Cory Y; Rick, Jacqueline; Eberly, Shirley; Hutten, Samantha J; Gwinn, Katrina; Sutherland, Margaret; Martinez, Maria; Heutink, Peter; Williams, Nigel M; Hardy, John; Gasser, Thomas; Brice, Alexis; Price, T Ryan; Nicolas, Aude; Keller, Margaux F; Molony, Cliona; Gibbs, J Raphael; Chen-Plotkin, Alice; Suh, Eunran; Letson, Christopher; Fiandaca, Massimo S; Mapstone, Mark; Federoff, Howard J; Noyce, Alastair J; Morris, Huw; Van Deerlin, Vivianna M; Weintraub, Daniel; Zabetian, Cyrus; Hernandez, Dena G; Lesage, Suzanne; Mullins, Meghan; Conley, Emily Drabant; Northover, Carrie A M; Frasier, Mark; Marek, Ken; Day-Williams, Aaron G; Stone, David J; Ioannidis, John P A; Singleton, Andrew B
2015-10-01
Accurate diagnosis and early detection of complex diseases, such as Parkinson's disease, has the potential to be of great benefit for researchers and clinical practice. We aimed to create a non-invasive, accurate classification model for the diagnosis of Parkinson's disease, which could serve as a basis for future disease prediction studies in longitudinal cohorts. We developed a model for disease classification using data from the Parkinson's Progression Marker Initiative (PPMI) study for 367 patients with Parkinson's disease and phenotypically typical imaging data and 165 controls without neurological disease. Olfactory function, genetic risk, family history of Parkinson's disease, age, and gender were algorithmically selected by stepwise logistic regression as significant contributors to our classifying model. We then tested the model with data from 825 patients with Parkinson's disease and 261 controls from five independent cohorts with varying recruitment strategies and designs: the Parkinson's Disease Biomarkers Program (PDBP), the Parkinson's Associated Risk Study (PARS), 23andMe, the Longitudinal and Biomarker Study in PD (LABS-PD), and the Morris K Udall Parkinson's Disease Research Center of Excellence cohort (Penn-Udall). Additionally, we used our model to investigate patients who had imaging scans without evidence of dopaminergic deficit (SWEDD). In the population from PPMI, our initial model correctly distinguished patients with Parkinson's disease from controls at an area under the curve (AUC) of 0·923 (95% CI 0·900-0·946) with high sensitivity (0·834, 95% CI 0·711-0·883) and specificity (0·903, 95% CI 0·824-0·946) at its optimum AUC threshold (0·655). All Hosmer-Lemeshow simulations suggested that when parsed into random subgroups, the subgroup data matched that of the overall cohort. External validation showed good classification of Parkinson's disease, with AUCs of 0·894 (95% CI 0·867-0·921) in the PDBP cohort, 0·998 (0·992-1·000) in PARS, 0·955 (no 95% CI available) in 23andMe, 0·929 (0·896-0·962) in LABS-PD, and 0·939 (0·891-0·986) in the Penn-Udall cohort. Four of 17 SWEDD participants who our model classified as having Parkinson's disease converted to Parkinson's disease within 1 year, whereas only one of 38 SWEDD participants who were not classified as having Parkinson's disease underwent conversion (test of proportions, p=0·003). Our model provides a potential new approach to distinguish participants with Parkinson's disease from controls. If the model can also identify individuals with prodromal or preclinical Parkinson's disease in prospective cohorts, it could facilitate identification of biomarkers and interventions. National Institute on Aging, National Institute of Neurological Disorders and Stroke, and the Michael J Fox Foundation. Copyright © 2015 Elsevier Ltd. All rights reserved.
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Applications of Induced Pluripotent Stem Cells in Studying the Neurodegenerative Diseases.
Wan, Wenbin; Cao, Lan; Kalionis, Bill; Xia, Shijin; Tai, Xiantao
2015-01-01
Neurodegeneration is the umbrella term for the progressive loss of structure or function of neurons. Incurable neurodegenerative disorders such as Alzheimer's disease (AD) and Parkinson's disease (PD) show dramatic rising trends particularly in the advanced age groups. However, the underlying mechanisms are not yet fully elucidated, and to date there are no biomarkers for early detection or effective treatments for the underlying causes of these diseases. Furthermore, due to species variation and differences between animal models (e.g., mouse transgenic and knockout models) of neurodegenerative diseases, substantial debate focuses on whether animal and cell culture disease models can correctly model the condition in human patients. In 2006, Yamanaka of Kyoto University first demonstrated a novel approach for the preparation of induced pluripotent stem cells (iPSCs), which displayed similar pluripotency potential to embryonic stem cells (ESCs). Currently, iPSCs studies are permeating many sectors of disease research. Patient sample-derived iPSCs can be used to construct patient-specific disease models to elucidate the pathogenic mechanisms of disease development and to test new therapeutic strategies. Accordingly, the present review will focus on recent progress in iPSC research in the modeling of neurodegenerative disorders and in the development of novel therapeutic options.
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Advances and Limitations of Disease Biogeography Using Ecological Niche Modeling
Escobar, Luis E.; Craft, Meggan E.
2016-01-01
Mapping disease transmission risk is crucial in public and animal health for evidence based decision-making. Ecology and epidemiology are highly related disciplines that may contribute to improvements in mapping disease, which can be used to answer health related questions. Ecological niche modeling is increasingly used for understanding the biogeography of diseases in plants, animals, and humans. However, epidemiological applications of niche modeling approaches for disease mapping can fail to generate robust study designs, producing incomplete or incorrect inferences. This manuscript is an overview of the history and conceptual bases behind ecological niche modeling, specifically as applied to epidemiology and public health; it does not pretend to be an exhaustive and detailed description of ecological niche modeling literature and methods. Instead, this review includes selected state-of-the-science approaches and tools, providing a short guide to designing studies incorporating information on the type and quality of the input data (i.e., occurrences and environmental variables), identification and justification of the extent of the study area, and encourages users to explore and test diverse algorithms for more informed conclusions. We provide a friendly introduction to the field of disease biogeography presenting an updated guide for researchers looking to use ecological niche modeling for disease mapping. We anticipate that ecological niche modeling will soon be a critical tool for epidemiologists aiming to map disease transmission risk, forecast disease distribution under climate change scenarios, and identify landscape factors triggering outbreaks. PMID:27547199
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Advances and Limitations of Disease Biogeography Using Ecological Niche Modeling.
Escobar, Luis E; Craft, Meggan E
2016-01-01
Mapping disease transmission risk is crucial in public and animal health for evidence based decision-making. Ecology and epidemiology are highly related disciplines that may contribute to improvements in mapping disease, which can be used to answer health related questions. Ecological niche modeling is increasingly used for understanding the biogeography of diseases in plants, animals, and humans. However, epidemiological applications of niche modeling approaches for disease mapping can fail to generate robust study designs, producing incomplete or incorrect inferences. This manuscript is an overview of the history and conceptual bases behind ecological niche modeling, specifically as applied to epidemiology and public health; it does not pretend to be an exhaustive and detailed description of ecological niche modeling literature and methods. Instead, this review includes selected state-of-the-science approaches and tools, providing a short guide to designing studies incorporating information on the type and quality of the input data (i.e., occurrences and environmental variables), identification and justification of the extent of the study area, and encourages users to explore and test diverse algorithms for more informed conclusions. We provide a friendly introduction to the field of disease biogeography presenting an updated guide for researchers looking to use ecological niche modeling for disease mapping. We anticipate that ecological niche modeling will soon be a critical tool for epidemiologists aiming to map disease transmission risk, forecast disease distribution under climate change scenarios, and identify landscape factors triggering outbreaks.
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Nalls, Mike A.; McLean, Cory Y.; Rick, Jacqueline; Eberly, Shirley; Hutten, Samantha J.; Gwinn, Katrina; Sutherland, Margaret; Martinez, Maria; Heutink, Peter; Williams, Nigel; Hardy, John; Gasser, Thomas; Brice, Alexis; Price, T. Ryan; Nicolas, Aude; Keller, Margaux F.; Molony, Cliona; Gibbs, J. Raphael; Chen-Plotkin, Alice; Suh, Eunran; Letson, Christopher; Fiandaca, Massimo S.; Mapstone, Mark; Federoff, Howard J.; Noyce, Alastair J; Morris, Huw; Van Deerlin, Vivianna M.; Weintraub, Daniel; Zabetian, Cyrus; Hernandez, Dena G.; Lesage, Suzanne; Mullins, Meghan; Conley, Emily Drabant; Northover, Carrie; Frasier, Mark; Marek, Ken; Day-Williams, Aaron G.; Stone, David J.; Ioannidis, John P. A.; Singleton, Andrew B.
2015-01-01
Background Accurate diagnosis and early detection of complex disease has the potential to be of enormous benefit to clinical trialists, patients, and researchers alike. We sought to create a non-invasive, low-cost, and accurate classification model for diagnosing Parkinson’s disease risk to serve as a basis for future disease prediction studies in prospective longitudinal cohorts. Methods We developed a simple disease classifying model within 367 patients with Parkinson’s disease and phenotypically typical imaging data and 165 controls without neurological disease of the Parkinson’s Progression Marker Initiative (PPMI) study. Olfactory function, genetic risk, family history of PD, age and gender were algorithmically selected as significant contributors to our classifying model. This model was developed using the PPMI study then tested in 825 patients with Parkinson’s disease and 261 controls from five independent studies with varying recruitment strategies and designs including the Parkinson’s Disease Biomarkers Program (PDBP), Parkinson’s Associated Risk Study (PARS), 23andMe, Longitudinal and Biomarker Study in PD (LABS-PD), and Morris K. Udall Parkinson’s Disease Research Center of Excellence (Penn-Udall). Findings Our initial model correctly distinguished patients with Parkinson’s disease from controls at an area under the curve (AUC) of 0.923 (95% CI = 0.900 – 0.946) with high sensitivity (0.834, 95% CI = 0.711 – 0.883) and specificity (0.903, 95% CI = 0.824 – 0.946) in PPMI at its optimal AUC threshold (0.655). The model is also well-calibrated with all Hosmer-Lemeshow simulations suggesting that when parsed into random subgroups, the actual data mirrors that of the larger expected data, demonstrating that our model is robust and fits well. Likewise external validation shows excellent classification of PD with AUCs of 0.894 in PDBP, 0.998 in PARS, 0.955 in 23andMe, 0.929 in LABS-PD, and 0.939 in Penn-Udall. Additionally, when our model classifies SWEDD as PD, they convert within one year to typical PD more than would be expected by chance, with 4 out of 17 classified as PD converting to PD during brief follow-up; while SWEDD not classified as PD showed one conversion to PD out of 38 participants (test of proportions, p-value = 0.003). Interpretation This model may serve as a basis for future investigations into the classification, prediction and treatment of Parkinson’s disease, particularly those planning on attempting to identify prodromal or preclinical etiologically typical PD cases in prospective cohorts for efficient interventional and biomarker studies. Funding Please see the acknowledgements and funding section at the end of the manuscript. PMID:26271532
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Barker, Charlotte I.S.; Germovsek, Eva; Hoare, Rollo L.; Lestner, Jodi M.; Lewis, Joanna; Standing, Joseph F.
2014-01-01
Pharmacokinetic/pharmacodynamic (PKPD) modelling is used to describe and quantify dose–concentration–effect relationships. Within paediatric studies in infectious diseases and immunology these methods are often applied to developing guidance on appropriate dosing. In this paper, an introduction to the field of PKPD modelling is given, followed by a review of the PKPD studies that have been undertaken in paediatric infectious diseases and immunology. The main focus is on identifying the methodological approaches used to define the PKPD relationship in these studies. The major findings were that most studies of infectious diseases have developed a PK model and then used simulations to define a dose recommendation based on a pre-defined PD target, which may have been defined in adults or in vitro. For immunological studies much of the modelling has focused on either PK or PD, and since multiple drugs are usually used, delineating the relative contributions of each is challenging. The use of dynamical modelling of in vitro antibacterial studies, and paediatric HIV mechanistic PD models linked with the PK of all drugs, are emerging methods that should enhance PKPD-based recommendations in the future. PMID:24440429
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77 FR 24497 - Government-Owned Inventions; Availability for Licensing
Federal Register 2010, 2011, 2012, 2013, 2014
2012-04-24
... distribution and may be useful as model systems for studies of cardiovascular disease, drug metabolism and... tissue distribution and may be useful as model systems for studies of cardiovascular disease, drug..., Atherosclerosis, Metabolic Syndrome and Lipid Storage Diseases Description of Technology: Lipid droplets are key...
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Optimizing mouse models of neurodegenerative disorders: are therapeutics in sight?
Lutz, Cathleen M; Osborne, Melissa A
2013-01-01
The genomic and biologic conservation between mice and humans, along with our increasing ability to manipulate the mouse genome, places the mouse as a premier model for deciphering disease mechanisms and testing potential new therapies. Despite these advantages, mouse models of neurodegenerative disease are sometimes difficult to generate and can present challenges that must be carefully addressed when used for preclinical studies. For those models that do exist, the standardization and optimization of the models is a critical step in ensuring success in both basic research and preclinical use. This review looks back on the history of model development for neurodegenerative diseases and highlights the key strategies that have been learned in order to improve the design, development and use of mouse models in the study of neurodegenerative disease.
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Brookes, V J; Barry, S C; Hernández-Jover, M; Ward, M P
2017-04-01
The objective of this study was to trial point of truth calibration (POTCal) as a novel method for disease prioritisation. To illustrate the application of this method, we used a previously described case-study of prioritisation of exotic diseases for the pig industry in Australia. Disease scenarios were constructed from criteria which described potential impact and pig-producers were asked to score the importance of each scenario. POTCal was used to model participants' estimates of disease importance as a function of the criteria, to derive a predictive model to prioritise a range of exotic diseases. The best validation of producers' estimates was achieved using a model derived from all responses. The highest weighted criteria were attack rate, case fatality rate and market loss, and the highest priority diseases were the vesicular diseases followed by swine fevers and zoonotic encephalitides. Comparison of results with a previous study in which probabilistic inversion was used to prioritise diseases for the same group of producers highlighted differences between disease prioritisation methods. Overall, this study demonstrated that POTCal can be used for disease prioritisation. An advantage of POTCal is that valid models can be developed that reflect decision-makers' heuristics. Specifically, this evaluation of the use of POTCal in animal health illustrates how the judgements of participants can be incorporated into a decision-making process. Further research is needed to investigate the influence of scenarios presented to participants during POTCal evaluations, and the robustness of this approach applied to different disease issues (e.g. exotic versus endemic) and production types (e.g. intensive versus extensive). To our knowledge, this is the first report of the use of POTCal for disease prioritisation. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.
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Sze To, G N; Chao, C Y H
2010-02-01
Infection risk assessment is very useful in understanding the transmission dynamics of infectious diseases and in predicting the risk of these diseases to the public. Quantitative infection risk assessment can provide quantitative analysis of disease transmission and the effectiveness of infection control measures. The Wells-Riley model has been extensively used for quantitative infection risk assessment of respiratory infectious diseases in indoor premises. Some newer studies have also proposed the use of dose-response models for such purpose. This study reviews and compares these two approaches to infection risk assessment of respiratory infectious diseases. The Wells-Riley model allows quick assessment and does not require interspecies extrapolation of infectivity. Dose-response models can consider other disease transmission routes in addition to airborne route and can calculate the infectious source strength of an outbreak in terms of the quantity of the pathogen rather than a hypothetical unit. Spatial distribution of airborne pathogens is one of the most important factors in infection risk assessment of respiratory disease. Respiratory deposition of aerosol induces heterogeneous infectivity of intake pathogens and randomness on the intake dose, which are not being well accounted for in current risk models. Some suggestions for further development of the risk assessment models are proposed. This review article summarizes the strengths and limitations of the Wells-Riley and the dose-response models for risk assessment of respiratory diseases. Even with many efforts by various investigators to develop and modify the risk assessment models, some limitations still persist. This review serves as a reference for further development of infection risk assessment models of respiratory diseases. The Wells-Riley model and dose-response model offer specific advantages. Risk assessors can select the approach that is suitable to their particular conditions to perform risk assessment.
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LaDeau, Shannon L; Glass, Gregory E; Hobbs, N Thompson; Latimer, Andrew; Ostfeld, Richard S
2011-07-01
Ecologists worldwide are challenged to contribute solutions to urgent and pressing environmental problems by forecasting how populations, communities, and ecosystems will respond to global change. Rising to this challenge requires organizing ecological information derived from diverse sources and formally assimilating data with models of ecological processes. The study of infectious disease has depended on strategies for integrating patterns of observed disease incidence with mechanistic process models since John Snow first mapped cholera cases around a London water pump in 1854. Still, zoonotic and vector-borne diseases increasingly affect human populations, and methods used to successfully characterize directly transmitted diseases are often insufficient. We use four case studies to demonstrate that advances in disease forecasting require better understanding of zoonotic host and vector populations, as well of the dynamics that facilitate pathogen amplification and disease spillover into humans. In each case study, this goal is complicated by limited data, spatiotemporal variability in pathogen transmission and impact, and often, insufficient biological understanding. We present a conceptual framework for data-model fusion in infectious disease research that addresses these fundamental challenges using a hierarchical state-space structure to (1) integrate multiple data sources and spatial scales to inform latent parameters, (2) partition uncertainty in process and observation models, and (3) explicitly build upon existing ecological and epidemiological understanding. Given the constraints inherent in the study of infectious disease and the urgent need for progress, fusion of data and expertise via this type of conceptual framework should prove an indispensable tool.
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Eley, Diann S; Patterson, Elizabeth; Young, Jacqui; Fahey, Paul P; Del Mar, Chris B; Hegney, Desley G; Synnott, Robyn L; Mahomed, Rosemary; Baker, Peter G; Scuffham, Paul A
2013-01-01
The Australian government's commitment to health service reform has placed general practice at the centre of its agenda to manage chronic disease. Concerns about the capacity of GPs to meet the growing chronic disease burden has stimulated the implementation and testing of new models of care that better utilise practice nurses (PN). This paper reports on a mixed-methods study nested within a larger study that trialled the feasibility and acceptability of a new model of nurse-led chronic disease management in three general practices. Patients over 18 years of age with type 2 diabetes, hypertension or stable ischaemic heart disease were randomised into PN-led or usual GP-led care. Primary outcomes were self-reported quality of life and perceptions of the model's feasibility and acceptability from the perspective of patients and GPs. Over the 12-month study quality of life decreased but the trend between groups was not statistically different. Qualitative data indicate that the PN-led model was acceptable and feasible to GPs and patients. It is possible to extend the scope of PN care to lead the routine clinical management of patients' stable chronic diseases. All GPs identified significant advantages to the model and elected to continue with the PN-led care after our study concluded.
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Sharafi, Mehdi; Ghaem, Haleh; Tabatabaee, Hamid Reza; Faramarzi, Hossein
2017-01-01
To predict the trend of cutaneous leishmaniasis and assess the relationship between the disease trend and weather variables in south of Fars province using Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The trend of cutaneous leishmaniasis was predicted using Mini tab software and SARIMA model. Besides, information about the disease and weather conditions was collected monthly based on time series design during January 2010 to March 2016. Moreover, various SARIMA models were assessed and the best one was selected. Then, the model's fitness was evaluated based on normality of the residuals' distribution, correspondence between the fitted and real amounts, and calculation of Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC). The study results indicated that SARIMA model (4,1,4)(0,1,0) (12) in general and SARIMA model (4,1,4)(0,1,1) (12) in below and above 15 years age groups could appropriately predict the disease trend in the study area. Moreover, temperature with a three-month delay (lag3) increased the disease trend, rainfall with a four-month delay (lag4) decreased the disease trend, and rainfall with a nine-month delay (lag9) increased the disease trend. Based on the results, leishmaniasis follows a descending trend in the study area in case drought condition continues, SARIMA models can suitably measure the disease trend, and the disease follows a seasonal trend. Copyright © 2017 Hainan Medical University. Production and hosting by Elsevier B.V. All rights reserved.
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Disease management with ARIMA model in time series.
Sato, Renato Cesar
2013-01-01
The evaluation of infectious and noninfectious disease management can be done through the use of a time series analysis. In this study, we expect to measure the results and prevent intervention effects on the disease. Clinical studies have benefited from the use of these techniques, particularly for the wide applicability of the ARIMA model. This study briefly presents the process of using the ARIMA model. This analytical tool offers a great contribution for researchers and healthcare managers in the evaluation of healthcare interventions in specific populations.
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A review of presented mathematical models in Parkinson's disease: black- and gray-box models.
Sarbaz, Yashar; Pourakbari, Hakimeh
2016-06-01
Parkinson's disease (PD), one of the most common movement disorders, is caused by damage to the central nervous system. Despite all of the studies on PD, the formation mechanism of its symptoms remained unknown. It is still not obvious why damage only to the substantia nigra pars compacta, a small part of the brain, causes a wide range of symptoms. Moreover, the causes of brain damages remain to be fully elucidated. Exact understanding of the brain function seems to be impossible. On the other hand, some engineering tools are trying to understand the behavior and performance of complex systems. Modeling is one of the most important tools in this regard. Developing quantitative models for this disease has begun in recent decades. They are very effective not only in better understanding of the disease, offering new therapies, and its prediction and control, but also in its early diagnosis. Modeling studies include two main groups: black-box models and gray-box models. Generally, in the black-box modeling, regardless of the system information, the symptom is only considered as the output. Such models, besides the quantitative analysis studies, increase our knowledge of the disorders behavior and the disease symptoms. The gray-box models consider the involved structures in the symptoms appearance as well as the final disease symptoms. These models can effectively save time and be cost-effective for the researchers and help them select appropriate treatment mechanisms among all possible options. In this review paper, first, efforts are made to investigate some studies on PD quantitative analysis. Then, PD quantitative models will be reviewed. Finally, the results of using such models are presented to some extent.
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Engineering Large Animal Species to Model Human Diseases.
Rogers, Christopher S
2016-07-01
Animal models are an important resource for studying human diseases. Genetically engineered mice are the most commonly used species and have made significant contributions to our understanding of basic biology, disease mechanisms, and drug development. However, they often fail to recreate important aspects of human diseases and thus can have limited utility as translational research tools. Developing disease models in species more similar to humans may provide a better setting in which to study disease pathogenesis and test new treatments. This unit provides an overview of the history of genetically engineered large animals and the techniques that have made their development possible. Factors to consider when planning a large animal model, including choice of species, type of modification and methodology, characterization, production methods, and regulatory compliance, are also covered. © 2016 by John Wiley & Sons, Inc. Copyright © 2016 John Wiley & Sons, Inc.
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Modeling human disease using organotypic cultures.
Schweiger, Pawel J; Jensen, Kim B
2016-12-01
Reliable disease models are needed in order to improve quality of healthcare. This includes gaining better understanding of disease mechanisms, developing new therapeutic interventions and personalizing treatment. Up-to-date, the majority of our knowledge about disease states comes from in vivo animal models and in vitro cell culture systems. However, it has been exceedingly difficult to model disease at the tissue level. Since recently, the gap between cell line studies and in vivo modeling has been narrowing thanks to progress in biomaterials and stem cell research. Development of reliable 3D culture systems has enabled a rapid expansion of sophisticated in vitro models. Here we focus on some of the latest advances and future perspectives in 3D organoids for human disease modeling. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Effect of climate variables on cocoa black pod incidence in Sabah using ARIMAX model
NASA Astrophysics Data System (ADS)
Ling Sheng Chang, Albert; Ramba, Haya; Mohd. Jaaffar, Ahmad Kamil; Kim Phin, Chong; Chong Mun, Ho
2016-06-01
Cocoa black pod disease is one of the major diseases affecting the cocoa production in Malaysia and also around the world. Studies have shown that the climate variables have influenced the cocoa black pod disease incidence and it is important to quantify the black pod disease variation due to the effect of climate variables. Application of time series analysis especially auto-regressive moving average (ARIMA) model has been widely used in economics study and can be used to quantify the effect of climate variables on black pod incidence to forecast the right time to control the incidence. However, ARIMA model does not capture some turning points in cocoa black pod incidence. In order to improve forecasting performance, other explanatory variables such as climate variables should be included into ARIMA model as ARIMAX model. Therefore, this paper is to study the effect of climate variables on the cocoa black pod disease incidence using ARIMAX model. The findings of the study showed ARIMAX model using MA(1) and relative humidity at lag 7 days, RHt - 7 gave better R square value compared to ARIMA model using MA(1) which could be used to forecast the black pod incidence to assist the farmers determine timely application of fungicide spraying and culture practices to control the black pod incidence.
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Genetic variants associated with neurodegenerative Alzheimer disease in natural models.
Salazar, Claudia; Valdivia, Gonzalo; Ardiles, Álvaro O; Ewer, John; Palacios, Adrián G
2016-02-26
The use of transgenic models for the study of neurodegenerative diseases has made valuable contributions to the field. However, some important limitations, including protein overexpression and general systemic compensation for the missing genes, has caused researchers to seek natural models that show the main biomarkers of neurodegenerative diseases during aging. Here we review some of these models-most of them rodents, focusing especially on the genetic variations in biomarkers for Alzheimer diseases, in order to explain their relationships with variants associated with the occurrence of the disease in humans.
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FHSA-SED: Two-Locus Model Detection for Genome-Wide Association Study with Harmony Search Algorithm.
Tuo, Shouheng; Zhang, Junying; Yuan, Xiguo; Zhang, Yuanyuan; Liu, Zhaowen
2016-01-01
Two-locus model is a typical significant disease model to be identified in genome-wide association study (GWAS). Due to intensive computational burden and diversity of disease models, existing methods have drawbacks on low detection power, high computation cost, and preference for some types of disease models. In this study, two scoring functions (Bayesian network based K2-score and Gini-score) are used for characterizing two SNP locus as a candidate model, the two criteria are adopted simultaneously for improving identification power and tackling the preference problem to disease models. Harmony search algorithm (HSA) is improved for quickly finding the most likely candidate models among all two-locus models, in which a local search algorithm with two-dimensional tabu table is presented to avoid repeatedly evaluating some disease models that have strong marginal effect. Finally G-test statistic is used to further test the candidate models. We investigate our method named FHSA-SED on 82 simulated datasets and a real AMD dataset, and compare it with two typical methods (MACOED and CSE) which have been developed recently based on swarm intelligent search algorithm. The results of simulation experiments indicate that our method outperforms the two compared algorithms in terms of detection power, computation time, evaluation times, sensitivity (TPR), specificity (SPC), positive predictive value (PPV) and accuracy (ACC). Our method has identified two SNPs (rs3775652 and rs10511467) that may be also associated with disease in AMD dataset.
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FHSA-SED: Two-Locus Model Detection for Genome-Wide Association Study with Harmony Search Algorithm
Tuo, Shouheng; Zhang, Junying; Yuan, Xiguo; Zhang, Yuanyuan; Liu, Zhaowen
2016-01-01
Motivation Two-locus model is a typical significant disease model to be identified in genome-wide association study (GWAS). Due to intensive computational burden and diversity of disease models, existing methods have drawbacks on low detection power, high computation cost, and preference for some types of disease models. Method In this study, two scoring functions (Bayesian network based K2-score and Gini-score) are used for characterizing two SNP locus as a candidate model, the two criteria are adopted simultaneously for improving identification power and tackling the preference problem to disease models. Harmony search algorithm (HSA) is improved for quickly finding the most likely candidate models among all two-locus models, in which a local search algorithm with two-dimensional tabu table is presented to avoid repeatedly evaluating some disease models that have strong marginal effect. Finally G-test statistic is used to further test the candidate models. Results We investigate our method named FHSA-SED on 82 simulated datasets and a real AMD dataset, and compare it with two typical methods (MACOED and CSE) which have been developed recently based on swarm intelligent search algorithm. The results of simulation experiments indicate that our method outperforms the two compared algorithms in terms of detection power, computation time, evaluation times, sensitivity (TPR), specificity (SPC), positive predictive value (PPV) and accuracy (ACC). Our method has identified two SNPs (rs3775652 and rs10511467) that may be also associated with disease in AMD dataset. PMID:27014873
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A Gaussian random field model for similarity-based smoothing in Bayesian disease mapping.
Baptista, Helena; Mendes, Jorge M; MacNab, Ying C; Xavier, Miguel; Caldas-de-Almeida, José
2016-08-01
Conditionally specified Gaussian Markov random field (GMRF) models with adjacency-based neighbourhood weight matrix, commonly known as neighbourhood-based GMRF models, have been the mainstream approach to spatial smoothing in Bayesian disease mapping. In the present paper, we propose a conditionally specified Gaussian random field (GRF) model with a similarity-based non-spatial weight matrix to facilitate non-spatial smoothing in Bayesian disease mapping. The model, named similarity-based GRF, is motivated for modelling disease mapping data in situations where the underlying small area relative risks and the associated determinant factors do not vary systematically in space, and the similarity is defined by "similarity" with respect to the associated disease determinant factors. The neighbourhood-based GMRF and the similarity-based GRF are compared and accessed via a simulation study and by two case studies, using new data on alcohol abuse in Portugal collected by the World Mental Health Survey Initiative and the well-known lip cancer data in Scotland. In the presence of disease data with no evidence of positive spatial correlation, the simulation study showed a consistent gain in efficiency from the similarity-based GRF, compared with the adjacency-based GMRF with the determinant risk factors as covariate. This new approach broadens the scope of the existing conditional autocorrelation models. © The Author(s) 2016.
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Preparation of organotypic brain slice cultures for the study of Alzheimer’s disease
Croft, Cara L.; Noble, Wendy
2018-01-01
Alzheimer's disease, the most common cause of dementia, is a progressive neurodegenerative disorder characterised by amyloid-beta deposits in extracellular plaques, intracellular neurofibrillary tangles of aggregated tau, synaptic dysfunction and neuronal death. There are no cures for AD and current medications only alleviate some disease symptoms. Transgenic rodent models to study Alzheimer’s mimic features of human disease such as age-dependent accumulation of abnormal beta-amyloid and tau, synaptic dysfunction, cognitive deficits and neurodegeneration. These models have proven vital for improving our understanding of the molecular mechanisms underlying AD and for identifying promising therapeutic approaches. However, modelling neurodegenerative disease in animals commonly involves aging animals until they develop harmful phenotypes, often coupled with invasive procedures. In vivo studies are also resource, labour, time and cost intensive. We have developed a novel organotypic brain slice culture model to study Alzheimer’ disease which brings the potential of substantially reducing the number of rodents used in dementia research from an estimated 20,000 per year. We obtain 36 brain slices from each mouse pup, considerably reducing the numbers of animals required to investigate multiple stages of disease. This tractable model also allows the opportunity to modulate multiple pathways in tissues from a single animal. We believe that this model will most benefit dementia researchers in the academic and drug discovery sectors. We validated the slice culture model against aged mice, showing that the molecular phenotype closely mimics that displayed in vivo, albeit in an accelerated timescale. We showed beneficial outcomes following treatment of slices with agents previously shown to have therapeutic effects in vivo, and we also identified new mechanisms of action of other compounds. Thus, organotypic brain slice cultures from transgenic mouse models expressing Alzheimer’s disease-related genes may provide a valid and sensitive replacement for in vivo studies that do not involve behavioural analysis. PMID:29904599
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FlyBase portals to human disease research using Drosophila models
Millburn, Gillian H.; Crosby, Madeline A.; Gramates, L. Sian; Tweedie, Susan
2016-01-01
ABSTRACT The use of Drosophila melanogaster as a model for studying human disease is well established, reflected by the steady increase in both the number and proportion of fly papers describing human disease models in recent years. In this article, we highlight recent efforts to improve the availability and accessibility of the disease model information in FlyBase (http://flybase.org), the model organism database for Drosophila. FlyBase has recently introduced Human Disease Model Reports, each of which presents background information on a specific disease, a tabulation of related disease subtypes, and summaries of experimental data and results using fruit flies. Integrated presentations of relevant data and reagents described in other sections of FlyBase are incorporated into these reports, which are specifically designed to be accessible to non-fly researchers in order to promote collaboration across model organism communities working in translational science. Another key component of disease model information in FlyBase is that data are collected in a consistent format – using the evolving Disease Ontology (an open-source standardized ontology for human-disease-associated biomedical data) – to allow robust and intuitive searches. To facilitate this, FlyBase has developed a dedicated tool for querying and navigating relevant data, which include mutations that model a disease and any associated interacting modifiers. In this article, we describe how data related to fly models of human disease are presented in individual Gene Reports and in the Human Disease Model Reports. Finally, we discuss search strategies and new query tools that are available to access the disease model data in FlyBase. PMID:26935103
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Models of acute and chronic pancreatitis.
Lerch, Markus M; Gorelick, Fred S
2013-06-01
Animal models of acute and chronic pancreatitis have been created to examine mechanisms of pathogenesis, test therapeutic interventions, and study the influence of inflammation on the development of pancreatic cancer. In vitro models can be used to study early stage, short-term processes that involve acinar cell responses. Rodent models reproducibly develop mild or severe disease. One of the most commonly used pancreatitis models is created by administration of supraphysiologic concentrations of caerulein, an ortholog of cholecystokinin. Induction of chronic pancreatitis with factors thought to have a role in human disease, such as combinations of lipopolysaccharide and chronic ethanol feeding, might be relevant to human disease. Models of autoimmune chronic pancreatitis have also been developed. Most models, particularly of chronic pancreatitis, require further characterization to determine which features of human disease they include. Copyright © 2013 AGA Institute. Published by Elsevier Inc. All rights reserved.
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Samtani, Mahesh N; Raghavan, Nandini; Novak, Gerald; Nandy, Partha; Narayan, Vaibhav A
2014-01-01
Background The objective of this analysis was to develop a nonlinear disease progression model, using an expanded set of covariates that captures the longitudinal Clinical Dementia Rating Scale–Sum of Boxes (CDR–SB) scores. These were derived from the Alzheimer’s Disease Neuroimaging Initiative ADNI-1 study, of 301 Alzheimer’s disease and mild cognitive impairment patients who were followed for 2–3 years. Methods The model describes progression rate and baseline disease score as a function of covariates. The covariates that were tested fell into five groups: a) hippocampal volume; b) serum and cerebrospinal fluid (CSF) biomarkers; c) demographics and apolipoprotein Epsilon 4 (ApoE4) allele status; d) baseline cognitive tests; and e) disease state and comedications. Results Covariates associated with baseline disease severity were disease state, hippocampal volume, and comedication use. Disease progression rate was influenced by baseline CSF biomarkers, Trail-Making Test part A score, delayed logical memory test score, and current level of impairment as measured by CDR–SB. The rate of disease progression was dependent on disease severity, with intermediate scores around the inflection point score of 10 exhibiting high disease progression rate. The CDR–SB disease progression rate in a typical patient, with late mild cognitive impairment and mild Alzheimer’s disease, was estimated to be approximately 0.5 and 1.4 points/year, respectively. Conclusions In conclusion, this model describes disease progression in terms of CDR–SB changes in patients and its dependency on novel covariates. The CSF biomarkers included in the model discriminate mild cognitive impairment subjects as progressors and nonprogressors. Therefore, the model may be utilized for optimizing study designs, through patient population enrichment and clinical trial simulations. PMID:24926196
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Chu, Haitao; Zhou, Yijie; Cole, Stephen R.; Ibrahim, Joseph G.
2010-01-01
Summary To evaluate the probabilities of a disease state, ideally all subjects in a study should be diagnosed by a definitive diagnostic or gold standard test. However, since definitive diagnostic tests are often invasive and expensive, it is generally unethical to apply them to subjects whose screening tests are negative. In this article, we consider latent class models for screening studies with two imperfect binary diagnostic tests and a definitive categorical disease status measured only for those with at least one positive screening test. Specifically, we discuss a conditional independent and three homogeneous conditional dependent latent class models and assess the impact of misspecification of the dependence structure on the estimation of disease category probabilities using frequentist and Bayesian approaches. Interestingly, the three homogeneous dependent models can provide identical goodness-of-fit but substantively different estimates for a given study. However, the parametric form of the assumed dependence structure itself is not “testable” from the data, and thus the dependence structure modeling considered here can only be viewed as a sensitivity analysis concerning a more complicated non-identifiable model potentially involving heterogeneous dependence structure. Furthermore, we discuss Bayesian model averaging together with its limitations as an alternative way to partially address this particularly challenging problem. The methods are applied to two cancer screening studies, and simulations are conducted to evaluate the performance of these methods. In summary, further research is needed to reduce the impact of model misspecification on the estimation of disease prevalence in such settings. PMID:20191614
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Analyzing the impact of modeling choices and assumptions in compartmental epidemiological models
DOE Office of Scientific and Technical Information (OSTI.GOV)
Nutaro, James J.; Pullum, Laura L.; Ramanathan, Arvind
In this study, computational models have become increasingly used as part of modeling, predicting, and understanding how infectious diseases spread within large populations. These models can be broadly classified into differential equation-based models (EBM) and agent-based models (ABM). Both types of models are central in aiding public health officials design intervention strategies in case of large epidemic outbreaks. We examine these models in the context of illuminating their hidden assumptions and the impact these may have on the model outcomes. Very few ABM/EBMs are evaluated for their suitability to address a particular public health concern, and drawing relevant conclusions aboutmore » their suitability requires reliable and relevant information regarding the different modeling strategies and associated assumptions. Hence, there is a need to determine how the different modeling strategies, choices of various parameters, and the resolution of information for EBMs and ABMs affect outcomes, including predictions of disease spread. In this study, we present a quantitative analysis of how the selection of model types (i.e., EBM vs. ABM), the underlying assumptions that are enforced by model types to model the disease propagation process, and the choice of time advance (continuous vs. discrete) affect the overall outcomes of modeling disease spread. Our study reveals that the magnitude and velocity of the simulated epidemic depends critically on the selection of modeling principles, various assumptions of disease process, and the choice of time advance.« less
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Analyzing the impact of modeling choices and assumptions in compartmental epidemiological models
Nutaro, James J.; Pullum, Laura L.; Ramanathan, Arvind; ...
2016-05-01
In this study, computational models have become increasingly used as part of modeling, predicting, and understanding how infectious diseases spread within large populations. These models can be broadly classified into differential equation-based models (EBM) and agent-based models (ABM). Both types of models are central in aiding public health officials design intervention strategies in case of large epidemic outbreaks. We examine these models in the context of illuminating their hidden assumptions and the impact these may have on the model outcomes. Very few ABM/EBMs are evaluated for their suitability to address a particular public health concern, and drawing relevant conclusions aboutmore » their suitability requires reliable and relevant information regarding the different modeling strategies and associated assumptions. Hence, there is a need to determine how the different modeling strategies, choices of various parameters, and the resolution of information for EBMs and ABMs affect outcomes, including predictions of disease spread. In this study, we present a quantitative analysis of how the selection of model types (i.e., EBM vs. ABM), the underlying assumptions that are enforced by model types to model the disease propagation process, and the choice of time advance (continuous vs. discrete) affect the overall outcomes of modeling disease spread. Our study reveals that the magnitude and velocity of the simulated epidemic depends critically on the selection of modeling principles, various assumptions of disease process, and the choice of time advance.« less
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Adeno-associated virus–targeted disruption of the CFTR gene in cloned ferrets
Sun, Xingshen; Yan, Ziying; Yi, Yaling; Li, Ziyi; Lei, Diana; Rogers, Christopher S.; Chen, Juan; Zhang, Yulong; Welsh, Michael J.; Leno, Gregory H.; Engelhardt, John F.
2008-01-01
Somatic cell gene targeting combined with nuclear transfer cloning presents tremendous potential for the creation of new, large-animal models of human diseases. Mouse disease models often fail to reproduce human phenotypes, underscoring the need for the generation and study of alternative disease models. Mice deficient for CFTR have been poor models for cystic fibrosis (CF), lacking many aspects of human CF lung disease. In this study, we describe the production of a CFTR gene–deficient model in the domestic ferret using recombinant adeno-associated virus–mediated gene targeting in fibroblasts, followed by nuclear transfer cloning. As part of this approach, we developed a somatic cell rejuvenation protocol using serial nuclear transfer to produce live CFTR-deficient clones from senescent gene-targeted fibroblasts. We transferred 472 reconstructed embryos into 11 recipient jills and obtained 8 healthy male ferret clones heterozygous for a disruption in exon 10 of the CFTR gene. To our knowledge, this study represents the first description of genetically engineered ferrets and describes an approach that may be of substantial utility in modeling not only CF, but also other genetic diseases. PMID:18324338
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[The experimental models of Parkinson's disease in animals].
Grigor'ian, G A; Bazian, A S
2007-01-01
The current review describes the modem Parkinson's disease models in animals, their advantages, limitations and disadvantages. It was noted that the most widespread up-to-date models based on etiology of the Parkinson's disease. Although toxins mostly produce the Parkinson's disease, a study of involved genes allows investigating not only inherited but also sporadic (not inherited) forms of disease since the same genes are involved in both cases. Mutations of genes lead to formation of "mutant" toxic proteins, which produce a death of the specialized neurons of the nigrostriatal dopaminergic system and the development of Parkinson's disease. A significant place in the review takes adescription of characteristics of the toxic models produced by 6-OHDA, MPTP and rotenone, their similarities and differences in pathogenetic mechanisms of the Parkinson's disease development. On the basis of the considered experimental models of Parkinson's disease a conclusion has been done that none of these models may in full and adequate scale imitate the entire clinical, pathophysiological, morphological, biochemical and other aspects of the Parkinson's disease development.
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Validity of using ad hoc methods to analyze secondary traits in case-control association studies.
Yung, Godwin; Lin, Xihong
2016-12-01
Case-control association studies often collect from their subjects information on secondary phenotypes. Reusing the data and studying the association between genes and secondary phenotypes provide an attractive and cost-effective approach that can lead to discovery of new genetic associations. A number of approaches have been proposed, including simple and computationally efficient ad hoc methods that ignore ascertainment or stratify on case-control status. Justification for these approaches relies on the assumption of no covariates and the correct specification of the primary disease model as a logistic model. Both might not be true in practice, for example, in the presence of population stratification or the primary disease model following a probit model. In this paper, we investigate the validity of ad hoc methods in the presence of covariates and possible disease model misspecification. We show that in taking an ad hoc approach, it may be desirable to include covariates that affect the primary disease in the secondary phenotype model, even though these covariates are not necessarily associated with the secondary phenotype. We also show that when the disease is rare, ad hoc methods can lead to severely biased estimation and inference if the true disease model follows a probit model instead of a logistic model. Our results are justified theoretically and via simulations. Applied to real data analysis of genetic associations with cigarette smoking, ad hoc methods collectively identified as highly significant (P<10-5) single nucleotide polymorphisms from over 10 genes, genes that were identified in previous studies of smoking cessation. © 2016 WILEY PERIODICALS, INC.
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Induced pluripotent stem cell technology for modelling and therapy of cerebellar ataxia
Watson, Lauren M.; Wong, Maggie M. K.; Becker, Esther B. E.
2015-01-01
Induced pluripotent stem cell (iPSC) technology has emerged as an important tool in understanding, and potentially reversing, disease pathology. This is particularly true in the case of neurodegenerative diseases, in which the affected cell types are not readily accessible for study. Since the first descriptions of iPSC-based disease modelling, considerable advances have been made in understanding the aetiology and progression of a diverse array of neurodegenerative conditions, including Parkinson's disease and Alzheimer's disease. To date, however, relatively few studies have succeeded in using iPSCs to model the neurodegeneration observed in cerebellar ataxia. Given the distinct neurodevelopmental phenotypes associated with certain types of ataxia, iPSC-based models are likely to provide significant insights, not only into disease progression, but also to the development of early-intervention therapies. In this review, we describe the existing iPSC-based disease models of this heterogeneous group of conditions and explore the challenges associated with generating cerebellar neurons from iPSCs, which have thus far hindered the expansion of this research. PMID:26136256
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How to become a top model: impact of animal experimentation on human Salmonella disease research.
Tsolis, Renée M; Xavier, Mariana N; Santos, Renato L; Bäumler, Andreas J
2011-05-01
Salmonella serotypes are a major cause of human morbidity and mortality worldwide. Over the past decades, a series of animal models have been developed to advance vaccine development, provide insights into immunity to infection, and study the pathogenesis of human Salmonella disease. The successive introduction of new animal models, each suited to interrogate previously neglected aspects of Salmonella disease, has ushered in important conceptual advances that continue to have a strong and sustained influence on the ideas driving research on Salmonella serotypes. This article reviews important milestones in the use of animal models to study human Salmonella disease and identify research needs to guide future work.
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Cao, Lei; Tan, Lan; Jiang, Teng; Zhu, Xi-Chen; Yu, Jin-Tai
2015-08-01
Although most neurodegenerative diseases have been closely related to aberrant accumulation of aggregation-prone proteins in neurons, understanding their pathogenesis remains incomplete, and there is no treatment to delay the onset or slow the progression of many neurodegenerative diseases. The availability of induced pluripotent stem cells (iPSCs) in recapitulating the phenotypes of several late-onset neurodegenerative diseases marks the new era in in vitro modeling. The iPSC collection represents a unique and well-characterized resource to elucidate disease mechanisms in these diseases and provides a novel human stem cell platform for screening new candidate therapeutics. Modeling human diseases using iPSCs has created novel opportunities for both mechanistic studies as well as for the discovery of new disease therapies. In this review, we introduce iPSC-based disease modeling in neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis. In addition, we discuss the implementation of iPSCs in drug discovery associated with some new techniques.
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Airway segmentation and analysis for the study of mouse models of lung disease using micro-CT
NASA Astrophysics Data System (ADS)
Artaechevarria, X.; Pérez-Martín, D.; Ceresa, M.; de Biurrun, G.; Blanco, D.; Montuenga, L. M.; van Ginneken, B.; Ortiz-de-Solorzano, C.; Muñoz-Barrutia, A.
2009-11-01
Animal models of lung disease are gaining importance in understanding the underlying mechanisms of diseases such as emphysema and lung cancer. Micro-CT allows in vivo imaging of these models, thus permitting the study of the progression of the disease or the effect of therapeutic drugs in longitudinal studies. Automated analysis of micro-CT images can be helpful to understand the physiology of diseased lungs, especially when combined with measurements of respiratory system input impedance. In this work, we present a fast and robust murine airway segmentation and reconstruction algorithm. The algorithm is based on a propagating fast marching wavefront that, as it grows, divides the tree into segments. We devised a number of specific rules to guarantee that the front propagates only inside the airways and to avoid leaking into the parenchyma. The algorithm was tested on normal mice, a mouse model of chronic inflammation and a mouse model of emphysema. A comparison with manual segmentations of two independent observers shows that the specificity and sensitivity values of our method are comparable to the inter-observer variability, and radius measurements of the mainstem bronchi reveal significant differences between healthy and diseased mice. Combining measurements of the automatically segmented airways with the parameters of the constant phase model provides extra information on how disease affects lung function.
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Human Environmental Disease Network: A computational model to assess toxicology of contaminants.
Taboureau, Olivier; Audouze, Karine
2017-01-01
During the past decades, many epidemiological, toxicological and biological studies have been performed to assess the role of environmental chemicals as potential toxicants associated with diverse human disorders. However, the relationships between diseases based on chemical exposure rarely have been studied by computational biology. We developed a human environmental disease network (EDN) to explore and suggest novel disease-disease and chemical-disease relationships. The presented scored EDN model is built upon the integration of systems biology and chemical toxicology using information on chemical contaminants and their disease relationships reported in the TDDB database. The resulting human EDN takes into consideration the level of evidence of the toxicant-disease relationships, allowing inclusion of some degrees of significance in the disease-disease associations. Such a network can be used to identify uncharacterized connections between diseases. Examples are discussed for type 2 diabetes (T2D). Additionally, this computational model allows confirmation of already known links between chemicals and diseases (e.g., between bisphenol A and behavioral disorders) and also reveals unexpected associations between chemicals and diseases (e.g., between chlordane and olfactory alteration), thus predicting which chemicals may be risk factors to human health. The proposed human EDN model allows exploration of common biological mechanisms of diseases associated with chemical exposure, helping us to gain insight into disease etiology and comorbidity. This computational approach is an alternative to animal testing supporting the 3R concept.
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FlyBase portals to human disease research using Drosophila models.
Millburn, Gillian H; Crosby, Madeline A; Gramates, L Sian; Tweedie, Susan
2016-03-01
The use of Drosophila melanogaster as a model for studying human disease is well established, reflected by the steady increase in both the number and proportion of fly papers describing human disease models in recent years. In this article, we highlight recent efforts to improve the availability and accessibility of the disease model information in FlyBase (http://flybase.org), the model organism database for Drosophila. FlyBase has recently introduced Human Disease Model Reports, each of which presents background information on a specific disease, a tabulation of related disease subtypes, and summaries of experimental data and results using fruit flies. Integrated presentations of relevant data and reagents described in other sections of FlyBase are incorporated into these reports, which are specifically designed to be accessible to non-fly researchers in order to promote collaboration across model organism communities working in translational science. Another key component of disease model information in FlyBase is that data are collected in a consistent format --- using the evolving Disease Ontology (an open-source standardized ontology for human-disease-associated biomedical data) - to allow robust and intuitive searches. To facilitate this, FlyBase has developed a dedicated tool for querying and navigating relevant data, which include mutations that model a disease and any associated interacting modifiers. In this article, we describe how data related to fly models of human disease are presented in individual Gene Reports and in the Human Disease Model Reports. Finally, we discuss search strategies and new query tools that are available to access the disease model data in FlyBase. © 2016. Published by The Company of Biologists Ltd.
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Spread of Ebola disease with susceptible exposed infected isolated recovered (SEIIhR) model
NASA Astrophysics Data System (ADS)
Azizah, Afina; Widyaningsih, Purnami; Retno Sari Saputro, Dewi
2017-06-01
Ebola is a deadly infectious disease and has caused an epidemic on several countries in West Africa. Mathematical modeling to study the spread of Ebola disease has been developed, including through models susceptible infected removed (SIR) and susceptible exposed infected removed (SEIR). Furthermore, susceptible exposed infected isolated recovered (SEIIhR) model has been derived. The aims of this research are to derive SEIIhR model for Ebola disease, to determine the patterns of its spread, to determine the equilibrium point and stability of the equilibrium point using phase plane analysis, and also to apply the SEIIhR model on Ebola epidemic in Sierra Leone in 2014. The SEIIhR model is a differential equation system. Pattern of ebola disease spread with SEIIhR model is solution of the differential equation system. The equilibrium point of SEIIhR model is unique and it is a disease-free equilibrium point that stable. Application of the model is based on the data Ebola epidemic in Sierra Leone. The free-disease equilibrium point (Se; Ee; Ie; Ihe; Re )=(5743865, 0, 0, 0, 0) is stable.
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The obesity paradox and incident cardiovascular disease: A population-based study.
Chang, Virginia W; Langa, Kenneth M; Weir, David; Iwashyna, Theodore J
2017-01-01
Prior work suggests that obesity may confer a survival advantage among persons with cardiovascular disease (CVD). This obesity "paradox" is frequently studied in the context of prevalent disease, a stage in the disease process when confounding from illness-related weight loss and selective survival are especially problematic. Our objective was to examine the association of obesity with mortality among persons with incident CVD, where biases are potentially reduced, and to compare these findings with those based on prevalent disease. We used data from the Health and Retirement Study, an ongoing, nationally representative longitudinal survey of U.S. adults age 50 years and older initiated in 1992 and linked to Medicare claims. Cox proportional hazard models were used to estimate the association between weight status and mortality among persons with specific CVD diagnoses. CVD diagnoses were established by self-reported survey data as well as Medicare claims. Prevalent disease models used concurrent weight status, and incident disease models used pre-diagnosis weight status. We examined myocardial infarction, congestive heart failure, stroke, and ischemic heart disease. A strong and significant obesity paradox was consistently observed in prevalent disease models (hazard of death 18-36% lower for obese class I relative to normal weight), replicating prior findings. However, in incident disease models of the same conditions in the same dataset, there was no evidence of this survival benefit. Findings from models using survey- vs. claims-based diagnoses were largely consistent. We observed an obesity paradox in prevalent CVD, replicating prior findings in a population-based sample with longer-term follow-up. In incident CVD, however, we did not find evidence of a survival advantage for obesity. Our findings do not offer support for reevaluating clinical and public health guidelines in pursuit of a potential obesity paradox.
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Ravindran, Rekha; Sharma, Nitika; Roy, Sujata; Thakur, Ashoke R; Ganesh, Subhadra; Kumar, Sriram; Devi, Jamuna; Rajkumar, Johanna
2015-01-01
Withania somnifera commonly known as Ashwagandha in India is used in many herbal formulations to treat various cardiovascular diseases. The key metabolite of this plant, Withaferin A was analyzed for its molecular mechanism through docking studies on different targets of cardiovascular disease. Six receptor proteins associated with cardiovascular disease were selected and interaction studies were performed with Withaferin A using AutoDock Vina. CORINA was used to model the small molecules and HBAT to compute the hydrogen bonding. Among the six targets, β1- adrenergic receptors, HMG-CoA and Angiotensinogen-converting enzyme showed significant interaction with Withaferin A. Pharmacophore modeling was done using PharmaGist to understand the pharmacophoric potential of Withaferin A. Clustering of Withaferin A with different existing drug molecules for cardiovascular disease was performed with ChemMine based on structural similarity and physicochemical properties. The ability of natural active component, Withaferin A to interact with different receptors associated with cardiovascular disease was elucidated with various modeling techniques. These studies conclusively revealed Withaferin A as a potent lead compound against multiple targets associated with cardiovascular disease.
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Molecular Hydrogen as an Emerging Therapeutic Medical Gas for Neurodegenerative and Other Diseases
Ohno, Kinji; Ito, Mikako; Ichihara, Masatoshi; Ito, Masafumi
2012-01-01
Effects of molecular hydrogen on various diseases have been documented for 63 disease models and human diseases in the past four and a half years. Most studies have been performed on rodents including two models of Parkinson's disease and three models of Alzheimer's disease. Prominent effects are observed especially in oxidative stress-mediated diseases including neonatal cerebral hypoxia; Parkinson's disease; ischemia/reperfusion of spinal cord, heart, lung, liver, kidney, and intestine; transplantation of lung, heart, kidney, and intestine. Six human diseases have been studied to date: diabetes mellitus type 2, metabolic syndrome, hemodialysis, inflammatory and mitochondrial myopathies, brain stem infarction, and radiation-induced adverse effects. Two enigmas, however, remain to be solved. First, no dose-response effect is observed. Rodents and humans are able to take a small amount of hydrogen by drinking hydrogen-rich water, but marked effects are observed. Second, intestinal bacteria in humans and rodents produce a large amount of hydrogen, but an addition of a small amount of hydrogen exhibits marked effects. Further studies are required to elucidate molecular bases of prominent hydrogen effects and to determine the optimal frequency, amount, and method of hydrogen administration for each human disease. PMID:22720117
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Novakovic, A M; Krekels, E H J; Munafo, A; Ueckert, S; Karlsson, M O
2017-01-01
In this study, we report the development of the first item response theory (IRT) model within a pharmacometrics framework to characterize the disease progression in multiple sclerosis (MS), as measured by Expanded Disability Status Score (EDSS). Data were collected quarterly from a 96-week phase III clinical study by a blinder rater, involving 104,206 item-level observations from 1319 patients with relapsing-remitting MS (RRMS), treated with placebo or cladribine. Observed scores for each EDSS item were modeled describing the probability of a given score as a function of patients' (unobserved) disability using a logistic model. Longitudinal data from placebo arms were used to describe the disease progression over time, and the model was then extended to cladribine arms to characterize the drug effect. Sensitivity with respect to patient disability was calculated as Fisher information for each EDSS item, which were ranked according to the amount of information they contained. The IRT model was able to describe baseline and longitudinal EDSS data on item and total level. The final model suggested that cladribine treatment significantly slows disease-progression rate, with a 20% decrease in disease-progression rate compared to placebo, irrespective of exposure, and effects an additional exposure-dependent reduction in disability progression. Four out of eight items contained 80% of information for the given range of disabilities. This study has illustrated that IRT modeling is specifically suitable for accurate quantification of disease status and description and prediction of disease progression in phase 3 studies on RRMS, by integrating EDSS item-level data in a meaningful manner.
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Caenorhabditis elegans as a model to study renal development and disease: sexy cilia.
Barr, Maureen M
2005-02-01
The nematode Caenorhabditis elegans has no kidney per se, yet "the worm" has proved to be an excellent model to study renal-related issues, including tubulogenesis of the excretory canal, membrane transport and ion channel function, and human genetic diseases including autosomal dominant polycystic kidney disease (ADPKD). The goal of this review is to explain how C. elegans has provided insight into cilia development, cilia function, and human cystic kidney diseases.
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In Vitro Microfluidic Models for Neurodegenerative Disorders.
Osaki, Tatsuya; Shin, Yoojin; Sivathanu, Vivek; Campisi, Marco; Kamm, Roger D
2018-01-01
Microfluidic devices enable novel means of emulating neurodegenerative disease pathophysiology in vitro. These organ-on-a-chip systems can potentially reduce animal testing and substitute (or augment) simple 2D culture systems. Reconstituting critical features of neurodegenerative diseases in a biomimetic system using microfluidics can thereby accelerate drug discovery and improve our understanding of the mechanisms of several currently incurable diseases. This review describes latest advances in modeling neurodegenerative diseases in the central nervous system and the peripheral nervous system. First, this study summarizes fundamental advantages of microfluidic devices in the creation of compartmentalized cell culture microenvironments for the co-culture of neurons, glial cells, endothelial cells, and skeletal muscle cells and in their recapitulation of spatiotemporal chemical gradients and mechanical microenvironments. Then, this reviews neurodegenerative-disease-on-a-chip models focusing on Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. Finally, this study discusses about current drawbacks of these models and strategies that may overcome them. These organ-on-chip technologies can be useful to be the first line of testing line in drug development and toxicology studies, which can contribute significantly to minimize the phase of animal testing steps. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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Askari, Marjan; Westerhof, Richard; Eslami, Saied; Medlock, Stephanie; de Rooij, Sophia E; Abu-Hanna, Ameen
2013-10-01
To propose a combined disease management and process modeling approach for evaluating and improving care processes, and demonstrate its usability and usefulness in a real-world fall management case study. We identified essential disease management related concepts and mapped them into explicit questions meant to expose areas for improvement in the respective care processes. We applied the disease management oriented questions to a process model of a comprehensive real world fall prevention and treatment program covering primary and secondary care. We relied on interviews and observations to complete the process models, which were captured in UML activity diagrams. A preliminary evaluation of the usability of our approach by gauging the experience of the modeler and an external validator was conducted, and the usefulness of the method was evaluated by gathering feedback from stakeholders at an invitational conference of 75 attendees. The process model of the fall management program was organized around the clinical tasks of case finding, risk profiling, decision making, coordination and interventions. Applying the disease management questions to the process models exposed weaknesses in the process including: absence of program ownership, under-detection of falls in primary care, and lack of efficient communication among stakeholders due to missing awareness about other stakeholders' workflow. The modelers experienced the approach as usable and the attendees of the invitational conference found the analysis results to be valid. The proposed disease management view of process modeling was usable and useful for systematically identifying areas of improvement in a fall management program. Although specifically applied to fall management, we believe our case study is characteristic of various disease management settings, suggesting the wider applicability of the approach. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
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Zingg, Dana; Häsler, Stephan; Schuepbach-Regula, Gertraud; Schwermer, Heinzpeter; Dürr, Salome
2015-01-01
Foot-and-mouth disease (FMD) is a highly contagious disease that caused several large outbreaks in Europe in the last century. The last important outbreak in Switzerland took place in 1965/66 and affected more than 900 premises and more than 50,000 animals were slaughtered. Large-scale emergency vaccination of the cattle and pig population has been applied to control the epidemic. In recent years, many studies have used infectious disease models to assess the impact of different disease control measures, including models developed for diseases exotic for the specific region of interest. Often, the absence of real outbreak data makes a validation of such models impossible. This study aimed to evaluate whether a spatial, stochastic simulation model (the Davis Animal Disease Simulation model) can predict the course of a Swiss FMD epidemic based on the available historic input data on population structure, contact rates, epidemiology of the virus, and quality of the vaccine. In addition, the potential outcome of the 1965/66 FMD epidemic without application of vaccination was investigated. Comparing the model outcomes to reality, only the largest 10% of the simulated outbreaks approximated the number of animals being culled. However, the simulation model highly overestimated the number of culled premises. While the outbreak duration could not be well reproduced by the model compared to the 1965/66 epidemic, it was able to accurately estimate the size of the area infected. Without application of vaccination, the model predicted a much higher mean number of culled animals than with vaccination, demonstrating that vaccination was likely crucial in disease control for the Swiss FMD outbreak in 1965/66. The study demonstrated the feasibility to analyze historical outbreak data with modern analytical tools. However, it also confirmed that predicted epidemics from a most carefully parameterized model cannot integrate all eventualities of a real epidemic. Therefore, decision makers need to be aware that infectious disease models are useful tools to support the decision-making process but their results are not equal valuable as real observations and should always be interpreted with caution. PMID:26697436
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Aspergillus in chronic lung disease: Modeling what goes on in the airways.
Takazono, Takahiro; Sheppard, Donald C
2017-01-01
Aspergillus species cause a range of respiratory diseases in humans. While immunocompromised patients are at risk for the development of invasive infection with these opportunistic molds, patients with underlying pulmonary disease can develop chronic airway infection with Aspergillus species. These conditions span a range of inflammatory and allergic diseases including Aspergillus bronchitis, allergic bronchopulmonary aspergillosis, and severe asthma with fungal sensitization. Animal models are invaluable tools for the study of the molecular mechanism underlying the colonization of airways by Aspergillus and the host response to these non-invasive infections. In this review we summarize the state-of-the-art with respect to the available animal models of noninvasive and allergic Aspergillus airway disease; the key findings of host-pathogen interaction studies using these models; and the limitations and future directions that should guide the development and use of models for the study of these important pulmonary conditions. © The Author 2016. Published by Oxford University Press on behalf of The International Society for Human and Animal Mycology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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NASA Astrophysics Data System (ADS)
Budi Astuti, Ani; Iriawan, Nur; Irhamah; Kuswanto, Heri; Sasiarini, Laksmi
2017-10-01
Bayesian statistics proposes an approach that is very flexible in the number of samples and distribution of data. Bayesian Mixture Model (BMM) is a Bayesian approach for multimodal models. Diabetes Mellitus (DM) is more commonly known in the Indonesian community as sweet pee. This disease is one type of chronic non-communicable diseases but it is very dangerous to humans because of the effects of other diseases complications caused. WHO reports in 2013 showed DM disease was ranked 6th in the world as the leading causes of human death. In Indonesia, DM disease continues to increase over time. These research would be studied patterns and would be built the BMM models of the DM data through simulation studies where the simulation data built on cases of blood sugar levels of DM patients in RSUD Saiful Anwar Malang. The results have been successfully demonstrated pattern of distribution of the DM data which has a normal mixture distribution. The BMM models have succeed to accommodate the real condition of the DM data based on the data driven concept.
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Modeling Addictive Consumption as an Infectious Disease*
Alamar, Benjamin; Glantz, Stanton A.
2011-01-01
The dominant model of addictive consumption in economics is the theory of rational addiction. The addict in this model chooses how much they are going to consume based upon their level of addiction (past consumption), the current benefits and all future costs. Several empirical studies of cigarette sales and price data have found a correlation between future prices and consumption and current consumption. These studies have argued that the correlation validates the rational addiction model and invalidates any model in which future consumption is not considered. An alternative to the rational addiction model is one in which addiction spreads through a population as if it were an infectious disease, as supported by the large body of empirical research of addictive behaviors. In this model an individual's probability of becoming addicted to a substance is linked to the behavior of their parents, friends and society. In the infectious disease model current consumption is based only on the level of addiction and current costs. Price and consumption data from a simulation of the infectious disease model showed a qualitative match to the results of the rational addiction model. The infectious disease model can explain all of the theoretical results of the rational addiction model with the addition of explaining initial consumption of the addictive good. PMID:21339848
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Wefers, Benedikt; Meyer, Melanie; Ortiz, Oskar; Hrabé de Angelis, Martin; Hansen, Jens; Wurst, Wolfgang; Kühn, Ralf
2013-01-01
The study of genetic disease mechanisms relies mostly on targeted mouse mutants that are derived from engineered embryonic stem (ES) cells. Nevertheless, the establishment of mutant ES cells is laborious and time-consuming, restricting the study of the increasing number of human disease mutations discovered by high-throughput genomic analysis. Here, we present an advanced approach for the production of mouse disease models by microinjection of transcription activator-like effector nucleases (TALENs) and synthetic oligodeoxynucleotides into one-cell embryos. Within 2 d of embryo injection, we created and corrected chocolate missense mutations in the small GTPase RAB38; a regulator of intracellular vesicle trafficking and phenotypic model of Hermansky-Pudlak syndrome. Because ES cell cultures and targeting vectors are not required, this technology enables instant germline modifications, making heterozygous mutants available within 18 wk. The key features of direct mutagenesis by TALENs and oligodeoxynucleotides, minimal effort and high speed, catalyze the generation of future in vivo models for the study of human disease mechanisms and interventions. PMID:23426636
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Agent-Based Modeling of Chronic Diseases: A Narrative Review and Future Research Directions
Lawley, Mark A.; Siscovick, David S.; Zhang, Donglan; Pagán, José A.
2016-01-01
The United States is experiencing an epidemic of chronic disease. As the US population ages, health care providers and policy makers urgently need decision models that provide systematic, credible prediction regarding the prevention and treatment of chronic diseases to improve population health management and medical decision-making. Agent-based modeling is a promising systems science approach that can model complex interactions and processes related to chronic health conditions, such as adaptive behaviors, feedback loops, and contextual effects. This article introduces agent-based modeling by providing a narrative review of agent-based models of chronic disease and identifying the characteristics of various chronic health conditions that must be taken into account to build effective clinical- and policy-relevant models. We also identify barriers to adopting agent-based models to study chronic diseases. Finally, we discuss future research directions of agent-based modeling applied to problems related to specific chronic health conditions. PMID:27236380
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Agent-Based Modeling of Chronic Diseases: A Narrative Review and Future Research Directions.
Li, Yan; Lawley, Mark A; Siscovick, David S; Zhang, Donglan; Pagán, José A
2016-05-26
The United States is experiencing an epidemic of chronic disease. As the US population ages, health care providers and policy makers urgently need decision models that provide systematic, credible prediction regarding the prevention and treatment of chronic diseases to improve population health management and medical decision-making. Agent-based modeling is a promising systems science approach that can model complex interactions and processes related to chronic health conditions, such as adaptive behaviors, feedback loops, and contextual effects. This article introduces agent-based modeling by providing a narrative review of agent-based models of chronic disease and identifying the characteristics of various chronic health conditions that must be taken into account to build effective clinical- and policy-relevant models. We also identify barriers to adopting agent-based models to study chronic diseases. Finally, we discuss future research directions of agent-based modeling applied to problems related to specific chronic health conditions.
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Nekouei, Zohreh Khayyam; Yousefy, Alireza; Doost, Hamid Taher Neshat; Manshaee, Gholamreza; Sadeghei, Masoumeh
2014-01-01
Background: Conducted researches show that psychological factors may have a very important role in the etiology, continuity and consequences of coronary heart diseases. This study has drawn the psychological risk and protective factors and their effects in patients with coronary heart diseases (CHD) in a structural model. It aims to determine the structural relations between psychological risk and protective factors with quality of life in patients with coronary heart disease. Materials and Methods: The present cross-sectional and correlational studies were conducted using structural equation modeling. The study sample included 398 patients of coronary heart disease in the university referral Hospital, as well as other city health care centers in Isfahan city. They were selected based on random sampling method. Then, in case, they were executed the following questionnaires: Coping with stressful situations (CISS- 21), life orientation (LOT-10), general self-efficacy (GSE-10), depression, anxiety and stress (DASS-21), perceived stress (PSS-14), multidimensional social support (MSPSS-12), alexithymia (TAS-20), spiritual intelligence (SQ-23) and quality of life (WHOQOL-26). Results: The results showed that protective and risk factors could affect the quality of life in patients with CHD with factor loadings of 0.35 and −0.60, respectively. Moreover, based on the values of the framework of the model such as relative chi-square (CMIN/DF = 3.25), the Comparative Fit Index (CFI = 0.93), the Parsimony Comparative Fit Index (PCFI = 0.68), the Root Mean Square Error of Approximation (RMSEA = 0.07) and details of the model (significance of the relationships) it has been confirmed that the psychocardiological structural model of the study is the good fitting model. Conclusion: This study was among the first to research the different psychological risk and protective factors of coronary heart diseases in the form of a structural model. The results of this study have emphasized the necessity of noticing the psychological factors in primary prevention by preventive programs and in secondary prevention by rehabilitation centers to improve the quality of life of the people with heart diseases. PMID:24778660
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Modelling clinical systemic lupus erythematosus: similarities, differences and success stories
Celhar, Teja
2017-01-01
Abstract Mouse models of SLE have been indispensable tools to study disease pathogenesis, to identify genetic susceptibility loci and targets for drug development, and for preclinical testing of novel therapeutics. Recent insights into immunological mechanisms of disease progression have boosted a revival in SLE drug development. Despite promising results in mouse studies, many novel drugs have failed to meet clinical end points. This is probably because of the complexity of the disease, which is driven by polygenic predisposition and diverse environmental factors, resulting in a heterogeneous clinical presentation. Each mouse model recapitulates limited aspects of lupus, especially in terms of the mechanism underlying disease progression. The main mouse models have been fairly successful for the evaluation of broad-acting immunosuppressants. However, the advent of targeted therapeutics calls for a selection of the most appropriate model(s) for testing and, ultimately, identification of patients who will be most likely to respond. PMID:28013204
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A Fruitful Endeavor: Modeling ALS in the Fruit Fly
Casci, Ian; Pandey, Udai Bhan
2014-01-01
For over a century Drosophila melanogaster, commonly known as the fruit fly, has been instrumental in genetics research and disease modeling. In more recent years, it has been a powerful tool for modeling and studying neurodegenerative diseases, including the devastating and fatal amyotrophic lateral sclerosis (ALS). The success of this model organism in ALS research comes from the availability of tools to manipulate gene/protein expression in a number of desired cell-types, and the subsequent recapitulation of cellular and molecular phenotypic features of the disease. Several Drosophila models have now been developed for studying the roles of ALS-associated genes in disease pathogenesis that allowed us to understand the molecular pathways that lead to motor neuron degeneration in ALS patients. Our primary goal in this review is to highlight the lessons we have learned using Drosophila models pertaining to ALS research. PMID:25289585
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The rabbit as a model for studying lung disease and stem cell therapy.
Kamaruzaman, Nurfatin Asyikhin; Kardia, Egi; Kamaldin, Nurulain 'Atikah; Latahir, Ahmad Zaeri; Yahaya, Badrul Hisham
2013-01-01
No single animal model can reproduce all of the human features of both acute and chronic lung diseases. However, the rabbit is a reliable model and clinically relevant facsimile of human disease. The similarities between rabbits and humans in terms of airway anatomy and responses to inflammatory mediators highlight the value of this species in the investigation of lung disease pathophysiology and in the development of therapeutic agents. The inflammatory responses shown by the rabbit model, especially in the case of asthma, are comparable with those that occur in humans. The allergic rabbit model has been used extensively in drug screening tests, and this model and humans appear to be sensitive to similar drugs. In addition, recent studies have shown that the rabbit serves as a good platform for cell delivery for the purpose of stem-cell-based therapy.
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The Rabbit as a Model for Studying Lung Disease and Stem Cell Therapy
Kamaruzaman, Nurfatin Asyikhin; Kamaldin, Nurulain ‘Atikah; Latahir, Ahmad Zaeri; Yahaya, Badrul Hisham
2013-01-01
No single animal model can reproduce all of the human features of both acute and chronic lung diseases. However, the rabbit is a reliable model and clinically relevant facsimile of human disease. The similarities between rabbits and humans in terms of airway anatomy and responses to inflammatory mediators highlight the value of this species in the investigation of lung disease pathophysiology and in the development of therapeutic agents. The inflammatory responses shown by the rabbit model, especially in the case of asthma, are comparable with those that occur in humans. The allergic rabbit model has been used extensively in drug screening tests, and this model and humans appear to be sensitive to similar drugs. In addition, recent studies have shown that the rabbit serves as a good platform for cell delivery for the purpose of stem-cell-based therapy. PMID:23653896
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Dubé, C; Ribble, C; Kelton, D; McNab, B
2009-04-01
Livestock movements are important in spreading infectious diseases and many countries have developed regulations that require farmers to report livestock movements to authorities. This has led to the availability of large amounts of data for analysis and inclusion in computer simulation models developed to support policy formulation. Social network analysis has become increasingly popular to study and characterize the networks resulting from the movement of livestock from farm-to-farm and through other types of livestock operations. Network analysis is a powerful tool that allows one to study the relationships created among these operations, providing information on the role that they play in acquiring and spreading infectious diseases, information that is not readily available from more traditional livestock movement studies. Recent advances in the study of real-world complex networks are now being applied to veterinary epidemiology and infectious disease modelling and control. A review of the principles of network analysis and of the relevance of various complex network theories to infectious disease modelling and control is presented in this paper.
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Use of space-time models to investigate the stability of patterns of disease.
Abellan, Juan Jose; Richardson, Sylvia; Best, Nicky
2008-08-01
The use of Bayesian hierarchical spatial models has become widespread in disease mapping and ecologic studies of health-environment associations. In this type of study, the data are typically aggregated over an extensive time period, thus neglecting the time dimension. The output of purely spatial disease mapping studies is therefore the average spatial pattern of risk over the period analyzed, but the results do not inform about, for example, whether a high average risk was sustained over time or changed over time. We investigated how including the time dimension in disease-mapping models strengthens the epidemiologic interpretation of the overall pattern of risk. We discuss a class of Bayesian hierarchical models that simultaneously characterize and estimate the stable spatial and temporal patterns as well as departures from these stable components. We show how useful rules for classifying areas as stable can be constructed based on the posterior distribution of the space-time interactions. We carry out a simulation study to investigate the sensitivity and specificity of the decision rules we propose, and we illustrate our approach in a case study of congenital anomalies in England. Our results confirm that extending hierarchical disease-mapping models to models that simultaneously consider space and time leads to a number of benefits in terms of interpretation and potential for detection of localized excesses.
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Describing and Modeling Workflow and Information Flow in Chronic Disease Care
Unertl, Kim M.; Weinger, Matthew B.; Johnson, Kevin B.; Lorenzi, Nancy M.
2009-01-01
Objectives The goal of the study was to develop an in-depth understanding of work practices, workflow, and information flow in chronic disease care, to facilitate development of context-appropriate informatics tools. Design The study was conducted over a 10-month period in three ambulatory clinics providing chronic disease care. The authors iteratively collected data using direct observation and semi-structured interviews. Measurements The authors observed all aspects of care in three different chronic disease clinics for over 150 hours, including 157 patient-provider interactions. Observation focused on interactions among people, processes, and technology. Observation data were analyzed through an open coding approach. The authors then developed models of workflow and information flow using Hierarchical Task Analysis and Soft Systems Methodology. The authors also conducted nine semi-structured interviews to confirm and refine the models. Results The study had three primary outcomes: models of workflow for each clinic, models of information flow for each clinic, and an in-depth description of work practices and the role of health information technology (HIT) in the clinics. The authors identified gaps between the existing HIT functionality and the needs of chronic disease providers. Conclusions In response to the analysis of workflow and information flow, the authors developed ten guidelines for design of HIT to support chronic disease care, including recommendations to pursue modular approaches to design that would support disease-specific needs. The study demonstrates the importance of evaluating workflow and information flow in HIT design and implementation. PMID:19717802
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Rabbit and Mouse Models of HSV-1 Latency, Reactivation, and Recurrent Eye Diseases
Webre, Jody M.; Hill, James M.; Nolan, Nicole M.; Clement, Christian; McFerrin, Harris E.; Bhattacharjee, Partha S.; Hsia, Victor; Neumann, Donna M.; Foster, Timothy P.; Lukiw, Walter J.; Thompson, Hilary W.
2012-01-01
The exact mechanisms of HSV-1 establishment, maintenance, latency, reactivation, and also the courses of recurrent ocular infections remain a mystery. Comprehensive understanding of the HSV-1 disease process could lead to prevention of HSV-1 acute infection, reactivation, and more effective treatments of recurrent ocular disease. Animal models have been used for over sixty years to investigate our concepts and hypotheses of HSV-1 diseases. In this paper we present descriptions and examples of rabbit and mouse eye models of HSV-1 latency, reactivation, and recurrent diseases. We summarize studies in animal models of spontaneous and induced HSV-1 reactivation and recurrent disease. Numerous stimuli that induce reactivation in mice and rabbits are described, as well as factors that inhibit viral reactivation from latency. The key features, advantages, and disadvantages of the mouse and rabbit models in relation to the study of ocular HSV-1 are discussed. This paper is pertinent but not intended to be all inclusive. We will give examples of key papers that have reported novel discoveries related to the review topics. PMID:23091352
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Nucleotide excision repair deficient mouse models and neurological disease
Niedernhofer, Laura J.
2008-01-01
Nucleotide excision repair (NER) is a highly conserved mechanism to remove helix-distorting DNA base damage. A major substrate for NER is DNA damage caused by environmental genotoxins, most notably ultraviolet radiation. Xeroderma pigmentosum, Cockayne syndrome and trichothiodystrophy are three human diseases caused by inherited defects in NER. The symptoms and severity of these diseases vary dramatically, ranging from profound developmental delay to cancer predisposition and accelerated aging. All three syndromes include neurological disease, indicating an important role for NER in protecting against spontaneous DNA damage as well. To study the pathophysiology caused by DNA damage, numerous mouse models of NER deficiency were generated by knocking-out genes required for NER or knocking-in disease-causing human mutations. This review explores the utility of these mouse models to study neurological disease caused by NER deficiency. PMID:18272436
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Alhdiri, Maryam Ahmed; Samat, Nor Azah; Mohamed, Zulkifley
2017-03-01
Cancer is the most rapidly spreading disease in the world, especially in developing countries, including Libya. Cancer represents a significant burden on patients, families, and their societies. This disease can be controlled if detected early. Therefore, disease mapping has recently become an important method in the fields of public health research and disease epidemiology. The correct choice of statistical model is a very important step to producing a good map of a disease. Libya was selected to perform this work and to examine its geographical variation in the incidence of lung cancer. The objective of this paper is to estimate the relative risk for lung cancer. Four statistical models to estimate the relative risk for lung cancer and population censuses of the study area for the time period 2006 to 2011 were used in this work. They are initially known as Standardized Morbidity Ratio, which is the most popular statistic, which used in the field of disease mapping, Poisson-gamma model, which is one of the earliest applications of Bayesian methodology, Besag, York and Mollie (BYM) model and Mixture model. As an initial step, this study begins by providing a review of all proposed models, which we then apply to lung cancer data in Libya. Maps, tables and graph, goodness-of-fit (GOF) were used to compare and present the preliminary results. This GOF is common in statistical modelling to compare fitted models. The main general results presented in this study show that the Poisson-gamma model, BYM model, and Mixture model can overcome the problem of the first model (SMR) when there is no observed lung cancer case in certain districts. Results show that the Mixture model is most robust and provides better relative risk estimates across a range of models. Creative Commons Attribution License
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Alhdiri, Maryam Ahmed; Samat, Nor Azah; Mohamed, Zulkifley
2017-01-01
Cancer is the most rapidly spreading disease in the world, especially in developing countries, including Libya. Cancer represents a significant burden on patients, families, and their societies. This disease can be controlled if detected early. Therefore, disease mapping has recently become an important method in the fields of public health research and disease epidemiology. The correct choice of statistical model is a very important step to producing a good map of a disease. Libya was selected to perform this work and to examine its geographical variation in the incidence of lung cancer. The objective of this paper is to estimate the relative risk for lung cancer. Four statistical models to estimate the relative risk for lung cancer and population censuses of the study area for the time period 2006 to 2011 were used in this work. They are initially known as Standardized Morbidity Ratio, which is the most popular statistic, which used in the field of disease mapping, Poisson-gamma model, which is one of the earliest applications of Bayesian methodology, Besag, York and Mollie (BYM) model and Mixture model. As an initial step, this study begins by providing a review of all proposed models, which we then apply to lung cancer data in Libya. Maps, tables and graph, goodness-of-fit (GOF) were used to compare and present the preliminary results. This GOF is common in statistical modelling to compare fitted models. The main general results presented in this study show that the Poisson-gamma model, BYM model, and Mixture model can overcome the problem of the first model (SMR) when there is no observed lung cancer case in certain districts. Results show that the Mixture model is most robust and provides better relative risk estimates across a range of models. PMID:28440974
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Simulation of an epidemic model with vector transmission
NASA Astrophysics Data System (ADS)
Dickman, Adriana G.; Dickman, Ronald
2015-03-01
We study a lattice model for vector-mediated transmission of a disease in a population consisting of two species, A and B, which contract the disease from one another. Individuals of species A are sedentary, while those of species B (the vector) diffuse in space. Examples of such diseases are malaria, dengue fever, and Pierce's disease in vineyards. The model exhibits a phase transition between an absorbing (infection free) phase and an active one as parameters such as infection rates and vector density are varied. We study the static and dynamic critical behavior of the model using initial spreading, initial decay, and quasistationary simulations. Simulations are checked against mean-field analysis. Although phase transitions to an absorbing state fall generically in the directed percolation universality class, this appears not to be the case for the present model.
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Gandjour, Afschin; Tschulena, Ulrich; Steppan, Sonja; Gatti, Emanuele
2015-04-01
The aim of this paper is to develop a simulation model that analyzes cost-offsets of a hypothetical disease management program (DMP) for patients with chronic kidney disease (CKD) in Germany compared to no such program. A lifetime Markov model with simulated 65-year-old patients with CKD was developed using published data on costs and health status and simulating the progression to end-stage renal disease (ESRD), cardiovascular disease and death. A statutory health insurance perspective was adopted. This modeling study shows considerable potential for cost-offsets from a DMP for patients with CKD. The potential for cost-offsets increases with relative risk reduction by the DMP and baseline glomerular filtration rate. Results are most sensitive to the cost of dialysis treatment. This paper presents a general 'prototype' simulation model for the prevention of ESRD. The model allows for further modification and adaptation in future applications.
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Model construction for the intention to use telecare in patients with chronic diseases.
Huang, Jui-Chen; Lee, Yii-Ching
2013-01-01
Objective. This study chose patients with chronic diseases as study subjects to investigate their intention to use telecare. Methods. A large medical institute in Taiwan was used as the sample unit. Patients older than 20 years, who had chronic diseases, were sampled by convenience sampling and surveyed with a structural questionnaire, and a total of 500 valid questionnaires were collected. Model construction was based on the Health Belief Model. The reliability and validity of the measurement model were tested using confirmatory factor analysis (CFA), and the causal model was explained by structural equation modeling (SEM). Results. The priority should be on promoting the perceived benefits of telecare, with a secondary focus on the external cues to action, such as promoting the influences of important people on the patients. Conclusion. The findings demonstrated that patients with chronic diseases use telecare differently from the general public. To promote the use and acceptance of telecare in patients with chronic diseases, technology developers should prioritize the promotion of the usefulness of telecare. In addition, policy makers can strengthen the marketing from media and medical personnel, in order to increase the acceptance of telecare by patients with chronic diseases.
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Tissue Chips to aid drug development and modeling for rare diseases
Low, Lucie A.; Tagle, Danilo A.
2016-01-01
Introduction The technologies used to design, create and use microphysiological systems (MPS, “tissue chips” or “organs-on-chips”) have progressed rapidly in the last 5 years, and validation studies of the functional relevance of these platforms to human physiology, and response to drugs for individual model organ systems, are well underway. These studies are paving the way for integrated multi-organ systems that can model diseases and predict drug efficacy and toxicology of multiple organs in real-time, improving the potential for diagnostics and development of novel treatments of rare diseases in the future. Areas covered This review will briefly summarize the current state of tissue chip research and highlight model systems where these microfabricated (or bioengineered) devices are already being used to screen therapeutics, model disease states, and provide potential treatments in addition to helping elucidate the basic molecular and cellular phenotypes of rare diseases. Expert opinion Microphysiological systems hold great promise and potential for modeling rare disorders, as well as for their potential use to enhance the predictive power of new drug therapeutics, plus potentially increase the statistical power of clinical trials while removing the inherent risks of these trials in rare disease populations. PMID:28626620
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Morsink, Maarten C; Dukers, Danny F
2009-03-01
Animal models have been widely used for studying the physiology and pharmacology of psychiatric and neurological diseases. The concepts of face, construct, and predictive validity are used as indicators to estimate the extent to which the animal model mimics the disease. Currently, we used these three concepts to design a theoretical assignment to integrate the teaching of neurophysiology, neuropharmacology, and experimental design. For this purpose, seven case studies were developed in which animal models for several psychiatric and neurological diseases were described and in which neuroactive drugs used to treat or study these diseases were introduced. Groups of undergraduate students were assigned to one of these case studies and asked to give a classroom presentation in which 1) the disease and underlying pathophysiology are described, 2) face and construct validity of the animal model are discussed, and 3) a pharmacological experiment with the associated neuroactive drug to assess predictive validity is presented. After evaluation of the presentations, we found that the students had gained considerable insight into disease phenomenology, its underlying neurophysiology, and the mechanism of action of the neuroactive drug. Moreover, the assignment was very useful in the teaching of experimental design, allowing an in-depth discussion of experimental control groups and the prediction of outcomes in these groups if the animal model were to display predictive validity. Finally, the highly positive responses in the student evaluation forms indicated that the assignment was of great interest to the students. Hence, the currently developed case studies constitute a very useful tool for teaching neurophysiology, neuropharmacology, and experimental design.
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Muñoz-Soriano, Verónica; Paricio, Nuria
2011-01-01
Parkinson's disease (PD) is the second most common neurodegenerative disorder and is mainly characterized by the selective and progressive loss of dopaminergic neurons, accompanied by locomotor defects. Although most PD cases are sporadic, several genes are associated with rare familial forms of the disease. Analyses of their function have provided important insights into the disease process, demonstrating that three types of cellular defects are mainly involved in the formation and/or progression of PD: abnormal protein aggregation, oxidative damage, and mitochondrial dysfunction. These studies have been mainly performed in PD models created in mice, fruit flies, and worms. Among them, Drosophila has emerged as a very valuable model organism in the study of either toxin-induced or genetically linked PD. Indeed, many of the existing fly PD models exhibit key features of the disease and have been instrumental to discover pathways relevant for PD pathogenesis, which could facilitate the development of therapeutic strategies. PMID:21512585
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NASA Astrophysics Data System (ADS)
Le Floch, Francois; Harris, Wesley L.
2009-11-01
A novel methodology has been developed to address sickle cell disease, based on highly descriptive mathematical models for blood flow in the capillaries. Our investigations focus on the coupling between oxygen delivery and red blood cell dynamics, which is crucial to understanding sickle cell crises and is unique to this blood disease. The main part of our work is an extensive study of blood dynamics through simulations of red cells deforming within the capillary vessels, and relies on the use of a large mathematical system of equations describing oxygen transfer, blood plasma dynamics and red cell membrane mechanics. This model is expected to lead to the development of new research strategies for sickle cell disease. Our simulation model could be used not only to assess current researched remedies, but also to spur innovative research initiatives, based on our study of the physical properties coupled in sickle cell disease.
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Vaccines against viral hemorrhagic fevers: non-human primate models.
Carrion, Ricardo; Patterson, Jean L
2011-06-01
Viral hemorrhagic fevers are a group of disease syndromes caused by infection with certain RNA viruses. The disease is marked by a febrile response, malaise, coagulopathy and vascular permeability culminating in death. Case fatality rates can reach 90% depending on the etiologic agent. Currently, there is no approved antiviral treatment. Because of the high case fatality, risk of importation and the potential to use these agents as biological weapons, development of countermeasures to these agents is a high priority. The sporadic nature of disease outbreaks and the ethical issues associated with conducting a human trial for such diseases make human studies impractical; therefore, development of countermeasures must occur in relevant animal models. Non-human primates are superior models to study infectious disease because their immune system is similar to humans and they are good predictors of efficacy in vaccine development and other intervention strategies. This review article summarizes viral hemorrhagic fever non-human primate models.
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Comparative biology of cystic fibrosis animal models.
Fisher, John T; Zhang, Yulong; Engelhardt, John F
2011-01-01
Animal models of human diseases are critical for dissecting mechanisms of pathophysiology and developing therapies. In the context of cystic fibrosis (CF), mouse models have been the dominant species by which to study CF disease processes in vivo for the past two decades. Although much has been learned through these CF mouse models, limitations in the ability of this species to recapitulate spontaneous lung disease and several other organ abnormalities seen in CF humans have created a need for additional species on which to study CF. To this end, pig and ferret CF models have been generated by somatic cell nuclear transfer and are currently being characterized. These new larger animal models have phenotypes that appear to closely resemble human CF disease seen in newborns, and efforts to characterize their adult phenotypes are ongoing. This chapter will review current knowledge about comparative lung cell biology and cystic fibrosis transmembrane conductance regulator (CFTR) biology among mice, pigs, and ferrets that has implications for CF disease modeling in these species. We will focus on methods used to compare the biology and function of CFTR between these species and their relevance to phenotypes seen in the animal models. These cross-species comparisons and the development of both the pig and the ferret CF models may help elucidate pathophysiologic mechanisms of CF lung disease and lead to new therapeutic approaches.
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Training Systems Modelers through the Development of a Multi-scale Chagas Disease Risk Model
NASA Astrophysics Data System (ADS)
Hanley, J.; Stevens-Goodnight, S.; Kulkarni, S.; Bustamante, D.; Fytilis, N.; Goff, P.; Monroy, C.; Morrissey, L. A.; Orantes, L.; Stevens, L.; Dorn, P.; Lucero, D.; Rios, J.; Rizzo, D. M.
2012-12-01
The goal of our NSF-sponsored Division of Behavioral and Cognitive Sciences grant is to create a multidisciplinary approach to develop spatially explicit models of vector-borne disease risk using Chagas disease as our model. Chagas disease is a parasitic disease endemic to Latin America that afflicts an estimated 10 million people. The causative agent (Trypanosoma cruzi) is most commonly transmitted to humans by blood feeding triatomine insect vectors. Our objectives are: (1) advance knowledge on the multiple interacting factors affecting the transmission of Chagas disease, and (2) provide next generation genomic and spatial analysis tools applicable to the study of other vector-borne diseases worldwide. This funding is a collaborative effort between the RSENR (UVM), the School of Engineering (UVM), the Department of Biology (UVM), the Department of Biological Sciences (Loyola (New Orleans)) and the Laboratory of Applied Entomology and Parasitology (Universidad de San Carlos). Throughout this five-year study, multi-educational groups (i.e., high school, undergraduate, graduate, and postdoctoral) will be trained in systems modeling. This systems approach challenges students to incorporate environmental, social, and economic as well as technical aspects and enables modelers to simulate and visualize topics that would either be too expensive, complex or difficult to study directly (Yasar and Landau 2003). We launch this research by developing a set of multi-scale, epidemiological models of Chagas disease risk using STELLA® software v.9.1.3 (isee systems, inc., Lebanon, NH). We use this particular system dynamics software as a starting point because of its simple graphical user interface (e.g., behavior-over-time graphs, stock/flow diagrams, and causal loops). To date, high school and undergraduate students have created a set of multi-scale (i.e., homestead, village, and regional) disease models. Modeling the system at multiple spatial scales forces recognition that the system's structure generates its behavior; and STELLA®'s graphical interface allows researchers at multiple educational levels to observe patterns and trends as the system changes over time. Graduate students and postdoctoral researchers will utilize these initial models to more efficiently communicate and transfer knowledge across disciplines prior to generating more novel and complex disease risk models. The hope is that these models will improve causal viewpoints, understanding of the system patterns, and how to best mitigate disease risk across multiple spatial scales. Yasar O, Landau RH (2003) Elements of computational science and engineering education. Siam Review 45(4): 787-805.
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O'Keeffe, Aidan G; Tom, Brian D M; Farewell, Vernon T
2011-01-01
In psoriatic arthritis, permanent joint damage characterizes disease progression and represents a major debilitating aspect of the disease. Understanding the process of joint damage will assist in the treatment and disease management of patients. Multistate models provide a means to examine patterns of disease, such as symmetric joint damage. Additionally, the link between damage and the dynamic course of disease activity (represented by joint swelling and stress pain) at both the individual joint level and otherwise can be represented within a correlated multistate model framework. Correlation is reflected through the use of random effects for progressive models and robust variance estimation for non-progressive models. Such analyses, undertaken with data from a large psoriatic arthritis cohort, are discussed and the extent to which they permit causal reasoning is considered. For this, emphasis is given to the use of the Bradford Hill criteria for causation in observational studies and the concept of local (in)dependence to capture the dynamic nature of the relationships. PMID:22163372
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Moore, Jason H; Boczko, Erik M; Summar, Marshall L
2005-02-01
Understanding how DNA sequence variations impact human health through a hierarchy of biochemical and physiological systems is expected to improve the diagnosis, prevention, and treatment of common, complex human diseases. We have previously developed a hierarchical dynamic systems approach based on Petri nets for generating biochemical network models that are consistent with genetic models of disease susceptibility. This modeling approach uses an evolutionary computation approach called grammatical evolution as a search strategy for optimal Petri net models. We have previously demonstrated that this approach routinely identifies biochemical network models that are consistent with a variety of genetic models in which disease susceptibility is determined by nonlinear interactions between two or more DNA sequence variations. We review here this approach and then discuss how it can be used to model biochemical and metabolic data in the context of genetic studies of human disease susceptibility.
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Siegrist, J; Dragano, N
2008-03-01
Given the far-reaching changes of modern working life, psychosocial stress at work has received increased attention. Its influence on stress-related disease risks is analysed with the help of standardised measurements based on theoretical models. Two such models have gained special prominence in recent years, the demand-control model and the effort-reward imbalance model. The former model places its emphasis on a distinct combination of job characteristics, whereas the latter model's focus is on the imbalance between efforts spent and rewards received in turn. The predictive power of these models with respect to coronary or cardiovascular disease and depression was tested in a number of prospective epidemiological investigations. In summary, twofold elevated disease risks are observed. Effects on cardiovascular disease are particularly pronounced among men, whereas no gender differences are observed for depression. Additional evidence derived from experimental and ambulatory monitoring studies supplements this body of findings. Current scientific evidence justifies an increased awareness and assessment of these newly discovered occupational risks, in particular by occupational health professionals. Moreover, structural and interpersonal measures of stress prevention and health promotion at work are warranted, with special emphasis on gender differences.
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Disease modeling using human induced pluripotent stem cells: lessons from the liver.
Gieseck, Richard L; Colquhoun, Jennifer; Hannan, Nicholas R F
2015-01-01
Human pluripotent stem cells (hPSCs) have the capacity to differentiate into any of the hundreds of distinct cell types that comprise the human body. This unique characteristic has resulted in considerable interest in the field of regenerative medicine, given the potential for these cells to be used to protect, repair, or replace diseased, injured, and aged cells within the human body. In addition to their potential in therapeutics, hPSCs can be used to study the earliest stages of human development and to provide a platform for both drug screening and disease modeling using human cells. Recently, the description of human induced pluripotent stem cells (hIPSCs) has allowed the field of disease modeling to become far more accessible and physiologically relevant, as pluripotent cells can be generated from patients of any genetic background. Disease models derived from hIPSCs that manifest cellular disease phenotypes have been established to study several monogenic diseases; furthermore, hIPSCs can be used for phenotype-based drug screens to investigate complex diseases for which the underlying genetic mechanism is unknown. As a result, the use of stem cells as research tools has seen an unprecedented growth within the last decade as researchers look for in vitro disease models which closely mimic in vivo responses in humans. Here, we discuss the beginnings of hPSCs, starting with isolation of human embryonic stem cells, moving into the development and optimization of hIPSC technology, and ending with the application of hIPSCs towards disease modeling and drug screening applications, with specific examples highlighting the modeling of inherited metabolic disorders of the liver. This article is part of a Special Issue entitled Linking transcription to physiology in lipodomics. Crown Copyright © 2014. Published by Elsevier B.V. All rights reserved.
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Calahorro, Fernando; Ruiz-Rubio, Manuel
2011-12-01
The nematode Caenorhabditis elegans has a very well-defined and genetically tractable nervous system which offers an effective model to explore basic mechanistic pathways that might be underpin complex human neurological diseases. Here, the role C. elegans is playing in understanding two neurodegenerative conditions, Parkinson's and Alzheimer's disease (AD), and a complex neurological condition, autism, is used as an exemplar of the utility of this model system. C. elegans is an imperfect model of Parkinson's disease because it lacks orthologues of the human disease-related genes PARK1 and LRRK2 which are linked to the autosomal dominant form of this disease. Despite this fact, the nematode is a good model because it allows transgenic expression of these human genes and the study of the impact on dopaminergic neurons in several genetic backgrounds and environmental conditions. For AD, C. elegans has orthologues of the amyloid precursor protein and both human presenilins, PS1 and PS2. In addition, many of the neurotoxic properties linked with Aβ amyloid and tau peptides can be studied in the nematode. Autism spectrum disorder is a complex neurodevelopmental disorder characterised by impairments in human social interaction, difficulties in communication, and restrictive and repetitive behaviours. Establishing C. elegans as a model for this complex behavioural disorder is difficult; however, abnormalities in neuronal synaptic communication are implicated in the aetiology of the disorder. Numerous studies have associated autism with mutations in several genes involved in excitatory and inhibitory synapses in the mammalian brain, including neuroligin, neurexin and shank, for which there are C. elegans orthologues. Thus, several molecular pathways and behavioural phenotypes in C. elegans have been related to autism. In general, the nematode offers a series of advantages that combined with knowledge from other animal models and human research, provides a powerful complementary experimental approach for understanding the molecular mechanisms and underlying aetiology of complex neurological diseases.
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CRISPR-engineered genome editing for the next generation neurological disease modeling.
Feng, Weijun; Liu, Hai-Kun; Kawauchi, Daisuke
2018-02-02
Neurological disorders often occur because of failure of proper brain development and/or appropriate maintenance of neuronal circuits. In order to understand roles of causative factors (e.g. genes, cell types) in disease development, generation of solid animal models has been one of straight-forward approaches. Recent next generation sequencing studies on human patient-derived clinical samples have identified various types of recurrent mutations in individual neurological diseases. While these discoveries have prompted us to evaluate impact of mutated genes on these neurological diseases, a feasible but flexible genome editing tool had remained to be developed. An advance of genome editing technology using the clustered regularly interspaced short palindromic repeats (CRISPR) with the CRISPR-associated protein (Cas) offers us a tremendous potential to create a variety of mutations in the cell, leading to "next generation" disease models carrying disease-associated mutations. We will here review recent progress of CRISPR-based brain disease modeling studies and discuss future requirement to tackle current difficulties in usage of these technologies. Copyright © 2017 Elsevier Inc. All rights reserved.
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NASA Astrophysics Data System (ADS)
Kim, K. H.; Lee, S.; Han, E.; Ines, A. V. M.
2017-12-01
Climate-Agriculture-Modeling and Decision Tool (CAMDT) is a decision support system (DSS) tool that aims to facilitate translations of probabilistic seasonal climate forecasts (SCF) to crop responses such as yield and water stress. Since CAMDT is a software framework connecting different models and algorithms with SCF information, it can be easily customized for different types of agriculture models. In this study, we replaced the DSSAT-CSM-Rice model originally incorporated in CAMDT with a generic epidemiological model, EPIRICE, to generate a seasonal pest outlook. The resulting CAMDT-Disease generates potential risks for selected fungal, viral, and bacterial diseases of rice over the next months by translating SCFs into agriculturally-relevant risk information. The integrated modeling procedure of CAMDT-Disease first disaggregates a given SCF using temporal downscaling methods (predictWTD or FResampler1), runs EPIRICE with the downscaled weather inputs, and finally visualizes the EPIRICE outputs as disease risk compared to that of the previous year and the 30-year-climatological average. In addition, the easy-to-use graphical user interface adopted from CAMDT allows users to simulate "what-if" scenarios of disease risks over different planting dates with given SCFs. Our future work includes the simulation of the effect of crop disease on yields through the disease simulation models with the DSSAT-CSM-Rice model, as disease remains one of the most critical yield-reducing factors in the field.
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Contemporary Animal Models For Human Gene Therapy Applications.
Gopinath, Chitra; Nathar, Trupti Job; Ghosh, Arkasubhra; Hickstein, Dennis Durand; Nelson, Everette Jacob Remington
2015-01-01
Over the past three decades, gene therapy has been making considerable progress as an alternative strategy in the treatment of many diseases. Since 2009, several studies have been reported in humans on the successful treatment of various diseases. Animal models mimicking human disease conditions are very essential at the preclinical stage before embarking on a clinical trial. In gene therapy, for instance, they are useful in the assessment of variables related to the use of viral vectors such as safety, efficacy, dosage and localization of transgene expression. However, choosing a suitable disease-specific model is of paramount importance for successful clinical translation. This review focuses on the animal models that are most commonly used in gene therapy studies, such as murine, canine, non-human primates, rabbits, porcine, and a more recently developed humanized mice. Though small and large animals both have their own pros and cons as disease-specific models, the choice is made largely based on the type and length of study performed. While small animals with a shorter life span could be well-suited for degenerative/aging studies, large animals with longer life span could suit longitudinal studies and also help with dosage adjustments to maximize therapeutic benefit. Recently, humanized mice or mouse-human chimaeras have gained interest in the study of human tissues or cells, thereby providing a more reliable understanding of therapeutic interventions. Thus, animal models are of great importance with regard to testing new vector technologies in vivo for assessing safety and efficacy prior to a gene therapy clinical trial.
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Modeling infectious diseases dissemination through online role-playing games.
Balicer, Ran D
2007-03-01
As mathematical modeling of infectious diseases becomes increasingly important for developing public health policies, a novel platform for such studies might be considered. Millions of people worldwide play interactive online role-playing games, forming complex and rich networks among their virtual characters. An unexpected outbreak of an infective communicable disease (unplanned by the game creators) recently occurred in this virtual world. This outbreak holds surprising similarities to real-world epidemics. It is possible that these virtual environments could serve as a platform for studying the dissemination of infectious diseases, and as a testing ground for novel interventions to control emerging communicable diseases.
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Brookes, V J; Hernández-Jover, M; Neslo, R; Cowled, B; Holyoake, P; Ward, M P
2014-01-01
We describe stakeholder preference modelling using a combination of new and recently developed techniques to elicit criterion weights to incorporate into a multi-criteria decision analysis framework to prioritise exotic diseases for the pig industry in Australia. Australian pig producers were requested to rank disease scenarios comprising nine criteria in an online questionnaire. Parallel coordinate plots were used to visualise stakeholder preferences, which aided identification of two diverse groups of stakeholders - one group prioritised diseases with impacts on livestock, and the other group placed more importance on diseases with zoonotic impacts. Probabilistic inversion was used to derive weights for the criteria to reflect the values of each of these groups, modelling their choice using a weighted sum value function. Validation of weights against stakeholders' rankings for scenarios based on real diseases showed that the elicited criterion weights for the group who prioritised diseases with livestock impacts were a good reflection of their values, indicating that the producers were able to consistently infer impacts from the disease information in the scenarios presented to them. The highest weighted criteria for this group were attack rate and length of clinical disease in pigs, and market loss to the pig industry. The values of the stakeholders who prioritised zoonotic diseases were less well reflected by validation, indicating either that the criteria were inadequate to consistently describe zoonotic impacts, the weighted sum model did not describe stakeholder choice, or that preference modelling for zoonotic diseases should be undertaken separately from livestock diseases. Limitations of this study included sampling bias, as the group participating were not necessarily representative of all pig producers in Australia, and response bias within this group. The method used to elicit criterion weights in this study ensured value trade-offs between a range of potential impacts, and that the weights were implicitly related to the scale of measurement of disease criteria. Validation of the results of the criterion weights against real diseases - a step rarely used in MCDA - added scientific rigour to the process. The study demonstrated that these are useful techniques for elicitation of criterion weights for disease prioritisation by stakeholders who are not disease experts. Preference modelling for zoonotic diseases needs further characterisation in this context. Copyright © 2013 Elsevier B.V. All rights reserved.
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Urbanowicz, Ryan J; Kiralis, Jeff; Sinnott-Armstrong, Nicholas A; Heberling, Tamra; Fisher, Jonathan M; Moore, Jason H
2012-10-01
Geneticists who look beyond single locus disease associations require additional strategies for the detection of complex multi-locus effects. Epistasis, a multi-locus masking effect, presents a particular challenge, and has been the target of bioinformatic development. Thorough evaluation of new algorithms calls for simulation studies in which known disease models are sought. To date, the best methods for generating simulated multi-locus epistatic models rely on genetic algorithms. However, such methods are computationally expensive, difficult to adapt to multiple objectives, and unlikely to yield models with a precise form of epistasis which we refer to as pure and strict. Purely and strictly epistatic models constitute the worst-case in terms of detecting disease associations, since such associations may only be observed if all n-loci are included in the disease model. This makes them an attractive gold standard for simulation studies considering complex multi-locus effects. We introduce GAMETES, a user-friendly software package and algorithm which generates complex biallelic single nucleotide polymorphism (SNP) disease models for simulation studies. GAMETES rapidly and precisely generates random, pure, strict n-locus models with specified genetic constraints. These constraints include heritability, minor allele frequencies of the SNPs, and population prevalence. GAMETES also includes a simple dataset simulation strategy which may be utilized to rapidly generate an archive of simulated datasets for given genetic models. We highlight the utility and limitations of GAMETES with an example simulation study using MDR, an algorithm designed to detect epistasis. GAMETES is a fast, flexible, and precise tool for generating complex n-locus models with random architectures. While GAMETES has a limited ability to generate models with higher heritabilities, it is proficient at generating the lower heritability models typically used in simulation studies evaluating new algorithms. In addition, the GAMETES modeling strategy may be flexibly combined with any dataset simulation strategy. Beyond dataset simulation, GAMETES could be employed to pursue theoretical characterization of genetic models and epistasis.
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Nuclear Receptors in Neurodegenerative Diseases
Skerrett, Rebecca; Malm, Tarja; Landreth, Gary
2014-01-01
Nuclear receptors have generated substantial interest in the past decade as potential therapeutic targets for the treatment of neurodegenerative disorders. Despite years of effort, effective treatments for progressive neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, Huntington’s disease and ALS remain elusive, making non-classical drug targets such as nuclear receptors an attractive alternative. A substantial literature in mouse models of disease and several clinical trials have investigated the role of nuclear receptors in various neurodegenerative disorders, most prominently AD. These studies have met with mixed results, yet the majority of studies in mouse models report positive outcomes. The mechanisms by which nuclear receptor agonists affect disease pathology remain unclear. Deciphering the complex signaling underlying nuclear receptor action in neurodegenerative diseases is essential for understanding this variability in preclinical studies, and for the successful translation of nuclear receptor agonists into clinical therapies. PMID:24874548
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Disease phobia and disease conviction are separate dimensions underlying hypochondriasis.
Fergus, Thomas A; Valentiner, David P
2010-12-01
The current study uses data from a large nonclinical college student sample (N = 503) to examine a structural model of hypochondriasis (HC). This model predicts the distinctiveness of two dimensions (disease phobia and disease conviction) purported to underlie the disorder, and that these two dimensions are differentially related to variables important to health anxiety and somatoform disorders, respectively. Results were generally consistent with the hypothesized model. Specifically, (a) body perception variables (somatosensory amplification and anxiety sensitivity - physical) emerged as significant predictors of disease phobia, but not disease conviction; (b) emotion dysregulation variables (cognitive avoidance and cognitive reappraisal) emerged as significant predictors of disease conviction, but not disease phobia; and (c) both disease phobia and disease conviction independently predicted medical utilization. Further, collapsing disease phobia and disease conviction onto a single latent factor provided an inadequate fit to the data. Conceptual and therapeutic implications of these results are discussed. 2010 Elsevier Ltd. All rights reserved.
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Not lost in translation: how study of diseases in our pets can benefit them and us.
Henry, Carolyn J; Bryan, Jeffrey N
2013-01-01
Practice-changing medical discovery requires preclinical and clinical assessment be carried out using appropriate disease models. There is growing awareness of companion animals with naturally-occurring disease as such models. They offer significant advantages over more traditional in vivo models of induced disease. This review describes current efforts to promote translation of discoveries between human and veterinary medicine in order to more rapidly and efficiently make progress in improving the health of all human and animal patients.
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Evaluation of spatio-temporal Bayesian models for the spread of infectious diseases in oil palm.
Denis, Marie; Cochard, Benoît; Syahputra, Indra; de Franqueville, Hubert; Tisné, Sébastien
2018-02-01
In the field of epidemiology, studies are often focused on mapping diseases in relation to time and space. Hierarchical modeling is a common flexible and effective tool for modeling problems related to disease spread. In the context of oil palm plantations infected by the fungal pathogen Ganoderma boninense, we propose and compare two spatio-temporal hierarchical Bayesian models addressing the lack of information on propagation modes and transmission vectors. We investigate two alternative process models to study the unobserved mechanism driving the infection process. The models help gain insight into the spatio-temporal dynamic of the infection by identifying a genetic component in the disease spread and by highlighting a spatial component acting at the end of the experiment. In this challenging context, we propose models that provide assumptions on the unobserved mechanism driving the infection process while making short-term predictions using ready-to-use software. Copyright © 2018 Elsevier Ltd. All rights reserved.
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Nikoloulopoulos, Aristidis K
2017-10-01
A bivariate copula mixed model has been recently proposed to synthesize diagnostic test accuracy studies and it has been shown that it is superior to the standard generalized linear mixed model in this context. Here, we call trivariate vine copulas to extend the bivariate meta-analysis of diagnostic test accuracy studies by accounting for disease prevalence. Our vine copula mixed model includes the trivariate generalized linear mixed model as a special case and can also operate on the original scale of sensitivity, specificity, and disease prevalence. Our general methodology is illustrated by re-analyzing the data of two published meta-analyses. Our study suggests that there can be an improvement on trivariate generalized linear mixed model in fit to data and makes the argument for moving to vine copula random effects models especially because of their richness, including reflection asymmetric tail dependence, and computational feasibility despite their three dimensionality.
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Su, Yingying; Wang, Miao; Liu, Yifei; Ye, Hong; Gao, Daiquan; Chen, Weibi; Zhang, Yunzhou; Zhang, Yan
2014-12-01
This study aimed to conduct and assess a module modified acute physiology and chronic health evaluation (MM-APACHE) II model, based on disease categories modified-acute physiology and chronic health evaluation (DCM-APACHE) II model, in predicting mortality more accurately in neuro-intensive care units (N-ICUs). In total, 1686 patients entered into this prospective study. Acute physiology and chronic health evaluation (APACHE) II scores of all patients on admission and worst 24-, 48-, 72-hour scores were obtained. Neurological diagnosis on admission was classified into five categories: cerebral infarction, intracranial hemorrhage, neurological infection, spinal neuromuscular (SNM) disease, and other neurological diseases. The APACHE II scores of cerebral infarction, intracranial hemorrhage, and neurological infection patients were used for building the MM-APACHE II model. There were 1386 cases for cerebral infarction disease, intracranial hemorrhage disease, and neurological infection disease. The logistic linear regression showed that 72-hour APACHE II score (Wals = 173.04, P < 0.001) and disease classification (Wals = 12.51, P = 0.02) were of importance in forecasting hospital mortality. Module modified acute physiology and chronic health evaluation II model, built on the variables of the 72-hour APACHE II score and disease category, had good discrimination (area under the receiver operating characteristic curve (AU-ROC = 0.830)) and calibration (χ2 = 12.518, P = 0.20), and was better than the Knaus APACHE II model (AU-ROC = 0.778). The APACHE II severity of disease classification system cannot provide accurate prognosis for all kinds of the diseases. A MM-APACHE II model can accurately predict hospital mortality for cerebral infarction, intracranial hemorrhage, and neurologic infection patients in N-ICU.
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Smith, Alec S.T.; Davis, Jennifer; Lee, Gabsang; Mack, David L.
2016-01-01
Engineered in vitro models using human cells, particularly patient-derived induced pluripotent stem cells (iPSCs), offer a potential solution to issues associated with the use of animals for studying disease pathology and drug efficacy. Given the prevalence of muscle diseases in human populations, an engineered tissue model of human skeletal muscle could provide a biologically accurate platform to study basic muscle physiology, disease progression, and drug efficacy and/or toxicity. Such platforms could be used as phenotypic drug screens to identify compounds capable of alleviating or reversing congenital myopathies, such as Duchene muscular dystrophy (DMD). Here, we review current skeletal muscle modeling technologies with a specific focus on efforts to generate biomimetic systems for investigating the pathophysiology of dystrophic muscle. PMID:27109386
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NASA Astrophysics Data System (ADS)
Saptaningtyas, F. Y.; Prihantini
2018-03-01
In Indonesia there are many breeders of cattle that are actually used as a livelihood so that Indonesia is prone to the spread of anthrax disease. This disease can be transmitted through indirect contacts such as deep impurities, saliva and the like. Anthrax disease is a type of disease caused by bacteria and there is a link between livestock and humans as the host. Anthrax disease with quarantine special factors can be modelled with SEIQR where existed from susceptible, exposed, symptomatic infected, quarantine and recovered compartment with research method used that is quantitative method, so different with disease models caused by bacteria in general.In this study we will determine the qualitative analysis of the anthrax disease distribution model with goal of research are to obtain model transmission Anthrax, to find equilibrium point of model and to find the basic reproduction number R 0, where R0 aims to determine the spread of disease or the absence of disease spread through endemic equilibrium stability analysis. The goal from this research is compare stability analysis between model with quarantine and model without quarantine use Routh-Hurwitz criteria to prove that E 1 and E 2 are asymptotic stability equilibrium so from this research conclude that quarantine population can speed up recovered population to be free disease condition from Anthrax.
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Modelling the effect of urbanization on the transmission of an infectious disease.
Zhang, Ping; Atkinson, Peter M
2008-01-01
This paper models the impact of urbanization on infectious disease transmission by integrating a CA land use development model, population projection matrix model and CA epidemic model in S-Plus. The innovative feature of this model lies in both its explicit treatment of spatial land use development, demographic changes, infectious disease transmission and their combination in a dynamic, stochastic model. Heuristically-defined transition rules in cellular automata (CA) were used to capture the processes of both land use development with urban sprawl and infectious disease transmission. A population surface model and dwelling distribution surface were used to bridge the gap between urbanization and infectious disease transmission. A case study is presented involving modelling influenza transmission in Southampton, a dynamically evolving city in the UK. The simulation results for Southampton over a 30-year period show that the pattern of the average number of infection cases per day can depend on land use and demographic changes. The modelling framework presents a useful tool that may be of use in planning applications.
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Impact of delay on disease outbreak in a spatial epidemic model
NASA Astrophysics Data System (ADS)
Zhao, Xia-Xia; Wang, Jian-Zhong
2015-04-01
One of the central issues in studying epidemic spreading is the mechanism on disease outbreak. In this paper, we investigate the effects of time delay on disease outbreak in spatial epidemics based on a reaction-diffusion model. By mathematical analysis and numerical simulations, we show that when time delay is more than a critical value, the disease outbreaks. The obtained results show that the time delay is an important factor in the spread of the disease, which may provide new insights on disease control.
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Kwon, Michelle; Pavlov, Tengis S.; Nozu, Kandai; Rasmussen, Shauna A.; Ilatovskaya, Daria V.; Lerch-Gaggl, Alexandra; North, Lauren M.; Kim, Hyunho; Qian, Feng; Sweeney, William E.; Avner, Ellis D.; Blumer, Joe B.; Staruschenko, Alexander; Park, Frank
2012-01-01
Polycystic kidney diseases are the most common genetic diseases that affect the kidney. There remains a paucity of information regarding mechanisms by which G proteins are regulated in the context of polycystic kidney disease to promote abnormal epithelial cell expansion and cystogenesis. In this study, we describe a functional role for the accessory protein, G-protein signaling modulator 1 (GPSM1), also known as activator of G-protein signaling 3, to act as a modulator of cyst progression in an orthologous mouse model of autosomal dominant polycystic kidney disease (ADPKD). A complete loss of Gpsm1 in the Pkd1V/V mouse model of ADPKD, which displays a hypomorphic phenotype of polycystin-1, demonstrated increased cyst progression and reduced renal function compared with age-matched cystic Gpsm1+/+ and Gpsm1+/− mice. Electrophysiological studies identified a role by which GPSM1 increased heteromeric polycystin-1/polycystin-2 ion channel activity via Gβγ subunits. In summary, the present study demonstrates an important role for GPSM1 in controlling the dynamics of cyst progression in an orthologous mouse model of ADPKD and presents a therapeutic target for drug development in the treatment of this costly disease. PMID:23236168
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Lim, Sunghoon; Tucker, Conrad S; Kumara, Soundar
2017-02-01
The authors of this work propose an unsupervised machine learning model that has the ability to identify real-world latent infectious diseases by mining social media data. In this study, a latent infectious disease is defined as a communicable disease that has not yet been formalized by national public health institutes and explicitly communicated to the general public. Most existing approaches to modeling infectious-disease-related knowledge discovery through social media networks are top-down approaches that are based on already known information, such as the names of diseases and their symptoms. In existing top-down approaches, necessary but unknown information, such as disease names and symptoms, is mostly unidentified in social media data until national public health institutes have formalized that disease. Most of the formalizing processes for latent infectious diseases are time consuming. Therefore, this study presents a bottom-up approach for latent infectious disease discovery in a given location without prior information, such as disease names and related symptoms. Social media messages with user and temporal information are extracted during the data preprocessing stage. An unsupervised sentiment analysis model is then presented. Users' expressions about symptoms, body parts, and pain locations are also identified from social media data. Then, symptom weighting vectors for each individual and time period are created, based on their sentiment and social media expressions. Finally, latent-infectious-disease-related information is retrieved from individuals' symptom weighting vectors. Twitter data from August 2012 to May 2013 are used to validate this study. Real electronic medical records for 104 individuals, who were diagnosed with influenza in the same period, are used to serve as ground truth validation. The results are promising, with the highest precision, recall, and F 1 score values of 0.773, 0.680, and 0.724, respectively. This work uses individuals' social media messages to identify latent infectious diseases, without prior information, quicker than when the disease(s) is formalized by national public health institutes. In particular, the unsupervised machine learning model using user, textual, and temporal information in social media data, along with sentiment analysis, identifies latent infectious diseases in a given location. Copyright © 2016 Elsevier Inc. All rights reserved.
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A nonlocal spatial model for Lyme disease
NASA Astrophysics Data System (ADS)
Yu, Xiao; Zhao, Xiao-Qiang
2016-07-01
This paper is devoted to the study of a nonlocal and time-delayed reaction-diffusion model for Lyme disease with a spatially heterogeneous structure. In the case of a bounded domain, we first prove the existence of the positive steady state and a threshold type result for the disease-free system, and then establish the global dynamics for the model system in terms of the basic reproduction number. In the case of an unbound domain, we obtain the existence of the disease spreading speed and its coincidence with the minimal wave speed. At last, we use numerical simulations to verify our analytic results and investigate the influence of model parameters and spatial heterogeneity on the disease infection risk.
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Animal models for testing anti-prion drugs.
Fernández-Borges, Natalia; Elezgarai, Saioa R; Eraña, Hasier; Castilla, Joaquín
2013-01-01
Prion diseases belong to a group of fatal infectious diseases with no effective therapies available. Throughout the last 35 years, less than 50 different drugs have been tested in different experimental animal models without hopeful results. An important limitation when searching for new drugs is the existence of appropriate models of the disease. The three different possible origins of prion diseases require the existence of different animal models for testing anti-prion compounds. Wild type, over-expressing transgenic mice and other more sophisticated animal models have been used to evaluate a diversity of compounds which some of them were previously tested in different in vitro experimental models. The complexity of prion diseases will require more pre-screening studies, reliable sporadic (or spontaneous) animal models and accurate chemical modifications of the selected compounds before having an effective therapy against human prion diseases. This review is intended to put on display the more relevant animal models that have been used in the search of new antiprion therapies and describe some possible procedures when handling chemical compounds presumed to have anti-prion activity prior to testing them in animal models.
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Modeling Huntington׳s disease with patient-derived neurons.
Mattis, Virginia B; Svendsen, Clive N
2017-02-01
Huntington׳s Disease (HD) is a fatal neurodegenerative disorder caused by expanded polyglutamine repeats in the Huntingtin (HTT) gene. While the gene was identified over two decades ago, it remains poorly understood why mutant HTT (mtHTT) is initially toxic to striatal medium spiny neurons (MSNs). Models of HD using non-neuronal human patient cells and rodents exhibit some characteristic HD phenotypes. While these current models have contributed to the field, they are limited in disease manifestation and may vary in their response to treatments. As such, human HD patient MSNs for disease modeling could greatly expand the current understanding of HD and facilitate the search for a successful treatment. It is now possible to use pluripotent stem cells, which can generate any tissue type in the body, to study and potentially treat HD. This review covers disease modeling in vitro and, via chimeric animal generation, in vivo using human HD patient MSNs differentiated from embryonic stem cells or induced pluripotent stem cells. This includes an overview of the differentiation of pluripotent cells into MSNs, the established phenotypes found in cell-based models and transplantation studies using these cells. This review not only outlines the advancements in the rapidly progressing field of HD modeling using neurons derived from human pluripotent cells, but also it highlights several remaining controversial issues such as the 'ideal' series of pluripotent lines, the optimal cell types to use and the study of a primarily adult-onset disease in a developmental model. This article is part of a Special Issue entitled SI: Exploiting human neurons. Copyright © 2015 Elsevier B.V. All rights reserved.
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Dynamical analysis and simulation of a 2-dimensional disease model with convex incidence
NASA Astrophysics Data System (ADS)
Yu, Pei; Zhang, Wenjing; Wahl, Lindi M.
2016-08-01
In this paper, a previously developed 2-dimensional disease model is studied, which can be used for both epidemiologic modeling and in-host disease modeling. The main attention of this paper is focused on various dynamical behaviors of the system, including Hopf and generalized Hopf bifurcations which yield bistability and tristability, Bogdanov-Takens bifurcation, and homoclinic bifurcation. It is shown that the Bogdanov-Takens bifurcation and homoclinic bifurcation provide a new mechanism for generating disease recurrence, that is, cycles of remission and relapse such as the viral blips observed in HIV infection.
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Ryu, Do-Yeal; Rahman, Md Saidur; Pang, Myung-Geol
2017-09-06
Bisphenol-A (BPA) is a ubiquitous endocrine-disrupting chemical. Recently, many issues have arisen surrounding the disease pathogenesis of BPA. Therefore, several studies have been conducted to investigate the proteomic biomarkers of BPA that are associated with disease processes. However, studies on identifying highly sensitive biological cell model systems in determining BPA health risk are lacking. Here, we determined suitable cell model systems and potential biomarkers for predicting BPA-mediated disease using the bioinformatics tool Pathway Studio. We compiled known BPA-mediated diseases in humans, which were categorized into five major types. Subsequently, we investigated the differentially expressed proteins following BPA exposure in several cell types, and analyzed the efficacy of altered proteins to investigate their associations with BPA-mediated diseases. Our results demonstrated that colon cancer cells (SW480), mammary gland, and Sertoli cells were highly sensitive biological model systems, because of the efficacy of predicting the majority of BPA-mediated diseases. We selected glucose-6-phosphate dehydrogenase (G6PD), cytochrome b-c1 complex subunit 1 (UQCRC1), and voltage-dependent anion-selective channel protein 2 (VDAC2) as highly sensitive biomarkers to predict BPA-mediated diseases. Furthermore, we summarized proteomic studies in spermatozoa following BPA exposure, which have recently been considered as another suitable cell type for predicting BPA-mediated diseases.
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Ryu, Do-Yeal
2017-01-01
Bisphenol-A (BPA) is a ubiquitous endocrine-disrupting chemical. Recently, many issues have arisen surrounding the disease pathogenesis of BPA. Therefore, several studies have been conducted to investigate the proteomic biomarkers of BPA that are associated with disease processes. However, studies on identifying highly sensitive biological cell model systems in determining BPA health risk are lacking. Here, we determined suitable cell model systems and potential biomarkers for predicting BPA-mediated disease using the bioinformatics tool Pathway Studio. We compiled known BPA-mediated diseases in humans, which were categorized into five major types. Subsequently, we investigated the differentially expressed proteins following BPA exposure in several cell types, and analyzed the efficacy of altered proteins to investigate their associations with BPA-mediated diseases. Our results demonstrated that colon cancer cells (SW480), mammary gland, and Sertoli cells were highly sensitive biological model systems, because of the efficacy of predicting the majority of BPA-mediated diseases. We selected glucose-6-phosphate dehydrogenase (G6PD), cytochrome b-c1 complex subunit 1 (UQCRC1), and voltage-dependent anion-selective channel protein 2 (VDAC2) as highly sensitive biomarkers to predict BPA-mediated diseases. Furthermore, we summarized proteomic studies in spermatozoa following BPA exposure, which have recently been considered as another suitable cell type for predicting BPA-mediated diseases. PMID:28878155
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A proposed metabolic strategy for monitoring disease progression in Alzheimer's disease.
Greenberg, Nicola; Grassano, Antonio; Thambisetty, Madhav; Lovestone, Simon; Legido-Quigley, Cristina
2009-04-01
A specific, sensitive and essentially non-invasive assay to diagnose and monitor Alzheimer's disease (AD) would be valuable to both clinicians and medical researchers. The aim of this study was to perform a metabonomic statistical analysis on plasma fingerprints. Objectives were to investigate novel biomarkers indicative of AD, to consider the role of bile acids as AD biomarkers and to consider whether mild cognitive impairment (MCI) is a separate disease from AD. Samples were analysed by ultraperformance liquid chromatography-MS and resulting data sets were interpreted using soft-independent modelling of class analogy statistical analysis methods. PCA models did not show any grouping of subjects by disease state. Partial least-squares discriminant analysis (PLS-DS) models yielded class separation for AD. However, as with earlier studies, model validation revealed a predictive power of Q(2)<0.5 and indicating their unsuitability for predicting disease state. Three bile acids were extracted from the data and quantified, up-regulation was observed for MCI and AD patients. PLS-DA did not support MCI being considered as a separate disease from AD with MCI patient metabolic profiles being significantly closer to AD patients than controls. This study suggested that further investigation into the lipid fraction of the metabolome may yield useful biomarkers for AD and metabolomic profiles could be used to predict disease state in a clinical setting.
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Nilpotent singularities and dynamics in an SIR type of compartmental model with hospital resources
NASA Astrophysics Data System (ADS)
Shan, Chunhua; Yi, Yingfei; Zhu, Huaiping
2016-03-01
An SIR type of compartmental model with a standard incidence rate and a nonlinear recovery rate was formulated to study the impact of available resources of public health system especially the number of hospital beds. Cusp, focus and elliptic type of nilpotent singularities of codimension 3 are discovered and analyzed in this three dimensional model. Complex dynamics of disease transmission including multi-steady states and multi-periodicity are revealed by bifurcation analysis. Large-amplitude oscillations found in our model provide a more reasonable explanation for disease recurrence. With clinical data, our studies have practical implications for the prevention and control of infectious diseases.
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The power of yeast to model diseases of the powerhouse of the cell
Baile, Matthew G.; Claypool, Steven M
2013-01-01
Mitochondria participate in a variety of cellular functions. As such, mitochondrial diseases exhibit numerous clinical phenotypes. Because mitochondrial functions are highly conserved between humans and Saccharomyces cerevisiae, yeast are an excellent model to study mitochondrial disease, providing insight into both physiological and pathophysiological processes. PMID:23276920
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Sampaziotis, Fotios; de Brito, Miguel Cardoso; Madrigal, Pedro; Bertero, Alessandro; Saeb-Parsy, Kourosh; Soares, Filipa A C; Schrumpf, Elisabeth; Melum, Espen; Karlsen, Tom H; Bradley, J Andrew; Gelson, William Th; Davies, Susan; Baker, Alastair; Kaser, Arthur; Alexander, Graeme J; Hannan, Nicholas R F; Vallier, Ludovic
2015-08-01
The study of biliary disease has been constrained by a lack of primary human cholangiocytes. Here we present an efficient, serum-free protocol for directed differentiation of human induced pluripotent stem cells into cholangiocyte-like cells (CLCs). CLCs show functional characteristics of cholangiocytes, including bile acids transfer, alkaline phosphatase activity, γ-glutamyl-transpeptidase activity and physiological responses to secretin, somatostatin and vascular endothelial growth factor. We use CLCs to model in vitro key features of Alagille syndrome, polycystic liver disease and cystic fibrosis (CF)-associated cholangiopathy. Furthermore, we use CLCs generated from healthy individuals and patients with polycystic liver disease to reproduce the effects of the drugs verapamil and octreotide, and we show that the experimental CF drug VX809 rescues the disease phenotype of CF cholangiopathy in vitro. Our differentiation protocol will facilitate the study of biological mechanisms controlling biliary development, as well as disease modeling and drug screening.
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Häsler, Barbara; Alarcon, Pablo; Raboisson, Didier; Waret-Szkuta, Agnes; Rushton, Jonathan
2015-01-01
Aims and objectives The aim of the study was to investigate and compare the financial impact of Schmallenberg disease for different dairy production types in the United Kingdom and France. Materials and methods Integrated production and financial models for dairy cattle were developed and applied to Schmallenberg virus (SBV) disease in a British and French context. The five main production systems that prevail in these two countries were considered. Their respective gross margins measuring the holding's profitability were calculated based on public benchmarking, literature and expert opinion data. A partial budget analysis was performed within each production model to estimate the impact of SBV in the systems modelled. Two disease scenarios were simulated: low impact and high impact. Results The model gross margin obtained per cow space and year ranged from £1014 to £1484 for the UK and from £1037 to £1890 for France depending on the production system considered. In the UK, the net SBV disease costs in £/cow space/year for an average dairy farm with 100 milking spaces were estimated between £16.3 and £51.4 in the high-impact scenario and between £8.2 and £25.9 in the low-impact scenario. For France, the net SBV disease costs in £/cow space/year ranged from £19.6 to £48.6 in the high-impact scenario and £9.7 to £22.8 in the low-impact scenario, respectively. Conclusion The study illustrates how the combination of production and financial models allows assessing disease impact taking into account differing management and husbandry practices and associated price structures in the dairy sector. It supports decision-making of farmers and veterinarians who are considering disease control measures as it provides an approach to estimate baseline disease impact in common dairy production systems in the UK and France. PMID:26392883
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The obesity paradox and incident cardiovascular disease: A population-based study
Langa, Kenneth M.; Weir, David; Iwashyna, Theodore J.
2017-01-01
Background Prior work suggests that obesity may confer a survival advantage among persons with cardiovascular disease (CVD). This obesity “paradox” is frequently studied in the context of prevalent disease, a stage in the disease process when confounding from illness-related weight loss and selective survival are especially problematic. Our objective was to examine the association of obesity with mortality among persons with incident CVD, where biases are potentially reduced, and to compare these findings with those based on prevalent disease. Methods We used data from the Health and Retirement Study, an ongoing, nationally representative longitudinal survey of U.S. adults age 50 years and older initiated in 1992 and linked to Medicare claims. Cox proportional hazard models were used to estimate the association between weight status and mortality among persons with specific CVD diagnoses. CVD diagnoses were established by self-reported survey data as well as Medicare claims. Prevalent disease models used concurrent weight status, and incident disease models used pre-diagnosis weight status. Results We examined myocardial infarction, congestive heart failure, stroke, and ischemic heart disease. A strong and significant obesity paradox was consistently observed in prevalent disease models (hazard of death 18–36% lower for obese class I relative to normal weight), replicating prior findings. However, in incident disease models of the same conditions in the same dataset, there was no evidence of this survival benefit. Findings from models using survey- vs. claims-based diagnoses were largely consistent. Conclusion We observed an obesity paradox in prevalent CVD, replicating prior findings in a population-based sample with longer-term follow-up. In incident CVD, however, we did not find evidence of a survival advantage for obesity. Our findings do not offer support for reevaluating clinical and public health guidelines in pursuit of a potential obesity paradox. PMID:29216243
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Animal Models of Ebolavirus Infection
Claire, Marisa C St; Ragland, Dan R; Bollinger, Laura; Jahrling, Peter B
2017-01-01
Ebola virus is a highly pathogenic member of the family Filoviridae that causes a severe hemorrhagic disease in humans and NHP. The 2013–2016 West African outbreak has increased interest in the development and refinement of animal models of Ebola virus disease. These models are used to test countermeasures and vaccines, gain scientific insights into the mechanisms of disease progression and transmission, and study key correlates of immunology. Ebola virus is classified as a BSL4 pathogen and Category A agent, for which the United States government requires preparedness in case of bioterrorism. Rodents, such as Syrian golden hamsters (Mesocricetus auratus), mice (Mus musculus), and guinea pigs (Cavia porcellus), are the most common research species. However, NHP, especially macaques, are favored for Ebola virus disease research due to similarities with humans regarding the pathogenesis, clinical presentation, laboratory findings, and causes of fatality. To satisfy the regulatory requirements for approval of countermeasures against high-consequence pathogens, the FDA instituted the Animal Rule, which permits efficacy studies in animal models in place of human clinical data when such studies are not feasible or ethical. This review provides a comprehensive summary of various animal models and their use in Ebola virus disease research. PMID:28662754
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Imaging plus X: multimodal models of neurodegenerative disease.
Oxtoby, Neil P; Alexander, Daniel C
2017-08-01
This article argues that the time is approaching for data-driven disease modelling to take centre stage in the study and management of neurodegenerative disease. The snowstorm of data now available to the clinician defies qualitative evaluation; the heterogeneity of data types complicates integration through traditional statistical methods; and the large datasets becoming available remain far from the big-data sizes necessary for fully data-driven machine-learning approaches. The recent emergence of data-driven disease progression models provides a balance between imposed knowledge of disease features and patterns learned from data. The resulting models are both predictive of disease progression in individual patients and informative in terms of revealing underlying biological patterns. Largely inspired by observational models, data-driven disease progression models have emerged in the last few years as a feasible means for understanding the development of neurodegenerative diseases. These models have revealed insights into frontotemporal dementia, Huntington's disease, multiple sclerosis, Parkinson's disease and other conditions. For example, event-based models have revealed finer graded understanding of progression patterns; self-modelling regression and differential equation models have provided data-driven biomarker trajectories; spatiotemporal models have shown that brain shape changes, for example of the hippocampus, can occur before detectable neurodegeneration; and network models have provided some support for prion-like mechanistic hypotheses of disease propagation. The most mature results are in sporadic Alzheimer's disease, in large part because of the availability of the Alzheimer's disease neuroimaging initiative dataset. Results generally support the prevailing amyloid-led hypothetical model of Alzheimer's disease, while revealing finer detail and insight into disease progression. The emerging field of disease progression modelling provides a natural mechanism to integrate different kinds of information, for example from imaging, serum and cerebrospinal fluid markers and cognitive tests, to obtain new insights into progressive diseases. Such insights include fine-grained longitudinal patterns of neurodegeneration, from early stages, and the heterogeneity of these trajectories over the population. More pragmatically, such models enable finer precision in patient staging and stratification, prediction of progression rates and earlier and better identification of at-risk individuals. We argue that this will make disease progression modelling invaluable for recruitment and end-points in future clinical trials, potentially ameliorating the high failure rate in trials of, e.g., Alzheimer's disease therapies. We review the state of the art in these techniques and discuss the future steps required to translate the ideas to front-line application.
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Osteoarthritis: new insights in animal models.
Longo, Umile Giuseppe; Loppini, Mattia; Fumo, Caterina; Rizzello, Giacomo; Khan, Wasim Sardar; Maffulli, Nicola; Denaro, Vincenzo
2012-01-01
Osteoarthritis (OA) is the most frequent and symptomatic health problem in the middle-aged and elderly population, with over one-half of all people over the age of 65 showing radiographic changes in painful knees. The aim of the present study was to perform an overview on the available animal models used in the research field on the OA. Discrepancies between the animal models and the human disease are present. As regards human 'idiopathic' OA, with late onset and slow progression, it is perhaps wise not to be overly enthusiastic about animal models that show severe chondrodysplasia and very early OA. Advantage by using genetically engineered mouse models, in comparison with other surgically induced models, is that molecular etiology is known. Find potential molecular markers for the onset of the disease and pay attention to the role of gender and environmental factors should be very helpful in the study of mice that acquire premature OA. Surgically induced destabilization of joint is the most widely used induction method. These models allow the temporal control of disease induction and follow predictable progression of the disease. In animals, ACL transection and meniscectomy show a speed of onset and severity of disease higher than in humans after same injury.
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Drake, Tom L; Devine, Angela; Yeung, Shunmay; Day, Nicholas P J; White, Lisa J; Lubell, Yoel
2016-02-01
Economic evaluation using dynamic transmission models is important for capturing the indirect effects of infectious disease interventions. We examine the use of these methods in low- and middle-income countries, where infectious diseases constitute a major burden. This review is comprised of two parts: (1) a summary of dynamic transmission economic evaluations across all disease areas published between 2011 and mid-2014 and (2) an in-depth review of mosquito-borne disease studies focusing on health economic methods and reporting. Studies were identified through a systematic search of the MEDLINE database and supplemented by reference list screening. Fifty-seven studies were eligible for inclusion in the all-disease review. The most common subject disease was HIV/AIDS, followed by malaria. A diverse range of modelling methods, outcome metrics and sensitivity analyses were used, indicating little standardisation. Seventeen studies were included in the mosquito-borne disease review. With notable exceptions, most studies did not employ economic evaluation methods beyond calculating a cost-effectiveness ratio or net benefit. Many did not adhere to health care economic evaluations reporting guidelines, particularly with respect to full model reporting and uncertainty analysis. We present a summary of the state-of-the-art and offer recommendations for improved implementation and reporting of health economic methods in this crossover discipline. © 2016 The Authors. Health Economics published by John Wiley & Sons Ltd.
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Bennett, R; Christiansen, K; Clifton-Hadley, R
1999-04-09
Many 'economic' studies of livestock diseases in Great Britain have been carried out over time. Most studies have considered just one or two diseases and used a different methodology and valuation base from other studies, hampering any comparative assessment of the economic impact of diseases. A standardized methodology was applied to the estimation of the direct costs to livestock production of some 30 endemic diseases/conditions of farm animals in Great Britain. This involved identification of the livestock populations at risk, estimation of the annual incidence of each disease in these populations, identification of the range and incidence of physical effects of each disease on production, valuation of the physical effects of each disease and estimation of the financial value of output losses/resource wastage due to a disease and the costs of specific treatment and prevention measures. The wider economic impacts of disease (such as the implications for human health, animal welfare and markets) were not included in the assessments. Using this standardized methodology with common financial values, a simple spreadsheet model was constructed for each disease. Given the paucity of appropriate disease data for economic assessment, 'low' and 'high' values were used to reflect uncertainties surrounding key disease parameters. Preliminary estimates of the value of disease output losses/resource wastage, treatment and prevention costs are presented for each disease. Despite the limitations of the spreadsheet models and of the estimates derived from them, we conclude that the models represent a useful start in developing a system for the comparative economic assessment of livestock diseases in Great Britain.
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Modeling Alzheimer’s disease with human induced pluripotent stem (iPS) cells
Mungenast, Alison E.; Siegert, Sandra; Tsai, Li-Huei
2018-01-01
In the last decade, induced pluripotent stem (iPS) cells have revolutionized the utility of human in vitro models of neurological disease. The iPS-derived and differentiated cells allow researchers to study the impact of a distinct cell type in health and disease as well as performing therapeutic drug screens on a human genetic background. In particular, clinical trials for Alzheimer’s disease (AD) have been often failing. Two of the potential reasons are first, the species gap involved in proceeding from initial discoveries in rodent models to human studies, and second, an unsatisfying patient stratification, meaning subgrouping patients based on the disease severity due to the lack of phenotypic and genetic markers. iPS cells overcome this obstacles and will improve our understanding of disease subtypes in AD. They allow researchers conducting in depth characterization of neural cells from both familial and sporadic AD patients as well as preclinical screens on human cells. In this review, we briefly outline the status quo of iPS cell research in neurological diseases along with the general advantages and pitfalls of these models. We summarize how genome-editing techniques such as CRISPR/Cas will allow researchers to reduce the problem of genomic variability inherent to human studies, followed by recent iPS cell studies relevant to AD. We then focus on current techniques for the differentiation of iPS cells into neural cell types that are relevant to AD research. Finally, we discuss how the generation of three-dimensional cell culture systems will be important for understanding AD phenotypes in a complex cellular milieu, and how both two- and three-dimensional iPS cell models can provide platforms for drug discovery and translational studies into the treatment of AD. PMID:26657644
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Modeling Alzheimer's disease with human induced pluripotent stem (iPS) cells.
Mungenast, Alison E; Siegert, Sandra; Tsai, Li-Huei
2016-06-01
In the last decade, induced pluripotent stem (iPS) cells have revolutionized the utility of human in vitro models of neurological disease. The iPS-derived and differentiated cells allow researchers to study the impact of a distinct cell type in health and disease as well as performing therapeutic drug screens on a human genetic background. In particular, clinical trials for Alzheimer's disease (AD) have been failing. Two of the potential reasons are first, the species gap involved in proceeding from initial discoveries in rodent models to human studies, and second, an unsatisfying patient stratification, meaning subgrouping patients based on the disease severity due to the lack of phenotypic and genetic markers. iPS cells overcome this obstacles and will improve our understanding of disease subtypes in AD. They allow researchers conducting in depth characterization of neural cells from both familial and sporadic AD patients as well as preclinical screens on human cells. In this review, we briefly outline the status quo of iPS cell research in neurological diseases along with the general advantages and pitfalls of these models. We summarize how genome-editing techniques such as CRISPR/Cas9 will allow researchers to reduce the problem of genomic variability inherent to human studies, followed by recent iPS cell studies relevant to AD. We then focus on current techniques for the differentiation of iPS cells into neural cell types that are relevant to AD research. Finally, we discuss how the generation of three-dimensional cell culture systems will be important for understanding AD phenotypes in a complex cellular milieu, and how both two- and three-dimensional iPS cell models can provide platforms for drug discovery and translational studies into the treatment of AD. Copyright © 2015 Elsevier Inc. All rights reserved.
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Roy, Sandip; McElwain, Terry F; Wan, Yan
2011-10-01
Developing control policies for zoonotic diseases is challenging, both because of the complex spread dynamics exhibited by these diseases, and because of the need for implementing complex multi-species surveillance and control efforts using limited resources. Mathematical models, and in particular network models, of disease spread are promising as tools for control-policy design, because they can provide comprehensive quantitative representations of disease transmission. A layered dynamical network model for the transmission and control of zoonotic diseases is introduced as a tool for analyzing disease spread and designing cost-effective surveillance and control. The model development is achieved using brucellosis transmission among wildlife, cattle herds, and human sub-populations in an agricultural system as a case study. Precisely, a model that tracks infection counts in interacting animal herds of multiple species (e.g., cattle herds and groups of wildlife for brucellosis) and in human subpopulations is introduced. The model is then abstracted to a form that permits comprehensive targeted design of multiple control capabilities as well as model identification from data. Next, techniques are developed for such quantitative design of control policies (that are directed to both the animal and human populations), and for model identification from snapshot and time-course data, by drawing on recent results in the network control community. The modeling approach is shown to provide quantitative insight into comprehensive control policies for zoonotic diseases, and in turn to permit policy design for mitigation of these diseases. For the brucellosis-transmission example in particular, numerous insights are obtained regarding the optimal distribution of resources among available control capabilities (e.g., vaccination, surveillance and culling, pasteurization of milk) and points in the spread network (e.g., transhumance vs. sedentary herds). In addition, a preliminary identification of the network model for brucellosis is achieved using historical data, and the robustness of the obtained model is demonstrated. As a whole, our results indicate that network modeling can aid in designing control policies for zoonotic diseases.
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Roy, Sandip; McElwain, Terry F.; Wan, Yan
2011-01-01
Background Developing control policies for zoonotic diseases is challenging, both because of the complex spread dynamics exhibited by these diseases, and because of the need for implementing complex multi-species surveillance and control efforts using limited resources. Mathematical models, and in particular network models, of disease spread are promising as tools for control-policy design, because they can provide comprehensive quantitative representations of disease transmission. Methodology/Principal Findings A layered dynamical network model for the transmission and control of zoonotic diseases is introduced as a tool for analyzing disease spread and designing cost-effective surveillance and control. The model development is achieved using brucellosis transmission among wildlife, cattle herds, and human sub-populations in an agricultural system as a case study. Precisely, a model that tracks infection counts in interacting animal herds of multiple species (e.g., cattle herds and groups of wildlife for brucellosis) and in human subpopulations is introduced. The model is then abstracted to a form that permits comprehensive targeted design of multiple control capabilities as well as model identification from data. Next, techniques are developed for such quantitative design of control policies (that are directed to both the animal and human populations), and for model identification from snapshot and time-course data, by drawing on recent results in the network control community. Conclusions/Significance The modeling approach is shown to provide quantitative insight into comprehensive control policies for zoonotic diseases, and in turn to permit policy design for mitigation of these diseases. For the brucellosis-transmission example in particular, numerous insights are obtained regarding the optimal distribution of resources among available control capabilities (e.g., vaccination, surveillance and culling, pasteurization of milk) and points in the spread network (e.g., transhumance vs. sedentary herds). In addition, a preliminary identification of the network model for brucellosis is achieved using historical data, and the robustness of the obtained model is demonstrated. As a whole, our results indicate that network modeling can aid in designing control policies for zoonotic diseases. PMID:22022621
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A systematic review of 3-D printing in cardiovascular and cerebrovascular diseases
Sun, Zhonghua; Lee, Shen-Yuan
2017-01-01
Objective: The application of 3-D printing has been increasingly used in medicine, with research showing many applications in cardiovascular disease. This systematic review analyzes those studies published about the applications of 3-D printed, patient-specific models in cardiovascular and cerebrovascular diseases. Methods: A search of PubMed/Medline and Scopus databases was performed to identify studies investigating the 3-D printing in cardiovascular and cerebrovascular diseases. Only studies based on patient’s medical images were eligible for review, while reports on in vitro phantom or review articles were excluded. Results: A total of 48 studies met selection criteria for inclusion in the review. A range of patient-specific 3-D printed models of different cardiovascular and cerebrovascular diseases were generated in these studies with most of them being developed using cardiac CT and MRI data, less commonly with 3-D invasive angiographic or echocardiographic images. The review of these studies showed high accuracy of 3-D printed, patient-specific models to represent complex anatomy of the cardiovascular and cerebrovascular system and depict various abnormalities, especially congenital heart diseases and valvular pathologies. Further, 3-D printing can serve as a useful education tool for both parents and clinicians, and a valuable tool for pre-surgical planning and simulation. Conclusion: This systematic review shows that 3-D printed models based on medical imaging modalities can accurately replicate complex anatomical structures and pathologies of the cardiovascular and cerebrovascular system. 3-D printing is a useful tool for both education and surgical planning in these diseases. PMID:28430115
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Baker, Jannah; White, Nicole; Mengersen, Kerrie; Rolfe, Margaret; Morgan, Geoffrey G
2017-01-01
Three variant formulations of a spatiotemporal shared component model are proposed that allow examination of changes in shared underlying factors over time. Models are evaluated within the context of a case study examining hospitalisation rates for five chronic diseases for residents of a regional area in New South Wales: type II diabetes mellitus (DMII), chronic obstructive pulmonary disease (COPD), coronary arterial disease (CAD), hypertension (HT) and congestive heart failure (CHF) between 2001-2006. These represent ambulatory care sensitive (ACS) conditions, often used as a proxy for avoidable hospitalisations. Using a selected model, the effects of socio-economic status (SES) as a shared component are estimated and temporal patterns in the influence of the residual shared spatial component are examined. Choice of model depends upon the application. In the featured application, a model allowing for changing influence of the shared spatial component over time was found to have the best fit and was selected for further analyses. Hospitalisation rates were found to be increasing for COPD and DMII, decreasing for CHF and stable for CAD and HT. SES was substantively associated with hospitalisation rates, with differing degrees of influence for each disease. In general, most of the spatial variation in hospitalisation rates was explained by disease-specific spatial components, followed by the residual shared spatial component. Appropriate selection of a joint disease model allows for the examination of temporal patterns of disease outcomes and shared underlying spatial factors, and distinction between different shared spatial factors.
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Wang, Zhi-Bo; Zhang, Xiaoqing; Li, Xue-Jun
2013-01-01
Establishing human cell models of spinal muscular atrophy (SMA) to mimic motor neuron-specific phenotypes holds the key to understanding the pathogenesis of this devastating disease. Here, we developed a closely representative cell model of SMA by knocking down the disease-determining gene, survival motor neuron (SMN), in human embryonic stem cells (hESCs). Our study with this cell model demonstrated that knocking down of SMN does not interfere with neural induction or the initial specification of spinal motor neurons. Notably, the axonal outgrowth of spinal motor neurons was significantly impaired and these disease-mimicking neurons subsequently degenerated. Furthermore, these disease phenotypes were caused by SMN-full length (SMN-FL) but not SMN-Δ7 (lacking exon 7) knockdown, and were specific to spinal motor neurons. Restoring the expression of SMN-FL completely ameliorated all of the disease phenotypes, including specific axonal defects and motor neuron loss. Finally, knockdown of SMN-FL led to excessive mitochondrial oxidative stress in human motor neuron progenitors. The involvement of oxidative stress in the degeneration of spinal motor neurons in the SMA cell model was further confirmed by the administration of N-acetylcysteine, a potent antioxidant, which prevented disease-related apoptosis and subsequent motor neuron death. Thus, we report here the successful establishment of an hESC-based SMA model, which exhibits disease gene isoform specificity, cell type specificity, and phenotype reversibility. Our model provides a unique paradigm for studying how motor neurons specifically degenerate and highlights the potential importance of antioxidants for the treatment of SMA. PMID:23208423
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Modeling human mobility responses to the large-scale spreading of infectious diseases.
Meloni, Sandro; Perra, Nicola; Arenas, Alex; Gómez, Sergio; Moreno, Yamir; Vespignani, Alessandro
2011-01-01
Current modeling of infectious diseases allows for the study of realistic scenarios that include population heterogeneity, social structures, and mobility processes down to the individual level. The advances in the realism of epidemic description call for the explicit modeling of individual behavioral responses to the presence of disease within modeling frameworks. Here we formulate and analyze a metapopulation model that incorporates several scenarios of self-initiated behavioral changes into the mobility patterns of individuals. We find that prevalence-based travel limitations do not alter the epidemic invasion threshold. Strikingly, we observe in both synthetic and data-driven numerical simulations that when travelers decide to avoid locations with high levels of prevalence, this self-initiated behavioral change may enhance disease spreading. Our results point out that the real-time availability of information on the disease and the ensuing behavioral changes in the population may produce a negative impact on disease containment and mitigation.
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LITTLE FISH, BIG DATA: ZEBRAFISH AS A MODEL FOR CARDIOVASCULAR AND METABOLIC DISEASE.
Gut, Philipp; Reischauer, Sven; Stainier, Didier Y R; Arnaout, Rima
2017-07-01
The burden of cardiovascular and metabolic diseases worldwide is staggering. The emergence of systems approaches in biology promises new therapies, faster and cheaper diagnostics, and personalized medicine. However, a profound understanding of pathogenic mechanisms at the cellular and molecular levels remains a fundamental requirement for discovery and therapeutics. Animal models of human disease are cornerstones of drug discovery as they allow identification of novel pharmacological targets by linking gene function with pathogenesis. The zebrafish model has been used for decades to study development and pathophysiology. More than ever, the specific strengths of the zebrafish model make it a prime partner in an age of discovery transformed by big-data approaches to genomics and disease. Zebrafish share a largely conserved physiology and anatomy with mammals. They allow a wide range of genetic manipulations, including the latest genome engineering approaches. They can be bred and studied with remarkable speed, enabling a range of large-scale phenotypic screens. Finally, zebrafish demonstrate an impressive regenerative capacity scientists hope to unlock in humans. Here, we provide a comprehensive guide on applications of zebrafish to investigate cardiovascular and metabolic diseases. We delineate advantages and limitations of zebrafish models of human disease and summarize their most significant contributions to understanding disease progression to date. Copyright © 2017 the American Physiological Society.
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[Molecular pathogenesis and therapeutic approach of GM2 gangliosidosis].
Tsuji, Daisuke
2013-01-01
Tay-Sachs and Sandhoff diseases (GM2 gangliosidoses) are autosomal recessive lysosomal storage diseases caused by gene mutations in HEXA and HEXB, each encoding human lysosomal β-hexosaminidase α-subunits and β-subunits, respectively. In Tay-Sachs disease, excessive accumulation of GM2 ganglioside (GM2), mainly in the central nervous system, is caused by a deficiency of the HexA isozyme (αβ heterodimer), resulting in progressive neurologic disorders. In Sandhoff disease, combined deficiencies of HexA and HexB (ββ homodimer) cause not only the accumulation of GM2 but also of oligosaccharides carrying terminal N-acetylhexosamine residues (GlcNAc-oligosaccharides), resulting in systemic manifestations including hepatosplenomegaly as well as neurologic symptoms. Hence there is little clinically effective treatment for these GM2 gangliosidoses. Recent studies on the molecular pathogenesis in Sandhoff disease patients and disease model mice have shown the involvement of microglial activation and chemokine induction in neuroinflammation and neurodegeneration in this disease. Experimental and therapeutic approaches, including recombinant enzyme replacement, have been performed using Sandhoff disease model mice, suggesting the future application of novel techniques to treat GM2 gangliosidoses (Hex deficiencies), including Sandhoff disease as well as Tay-Sachs disease. In this study, we isolated astrocytes and microglia from the neonatal brain of Sandhoff disease model mice and demonstrated abnormalities of glial cells. Moreover, we demonstrated the therapeutic effect of an intracerebroventricular administration of novel recombinant human HexA carrying a high content of M6P residue in Sandhoff disease model mice.
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Long, Yan; Xu, Miao; Li, Rong; Dai, Sheng; Beers, Jeanette; Chen, Guokai; Soheilian, Ferri; Baxa, Ulrich; Wang, Mengqiao; Marugan, Juan J; Muro, Silvia; Li, Zhiyuan; Brady, Roscoe; Zheng, Wei
2016-12-01
: Niemann-Pick disease type A (NPA) is a lysosomal storage disease caused by mutations in the SMPD1 gene that encodes acid sphingomyelinase (ASM). Deficiency in ASM function results in lysosomal accumulation of sphingomyelin and neurodegeneration. Currently, there is no effective treatment for NPA. To accelerate drug discovery for treatment of NPA, we generated induced pluripotent stem cells from two patient dermal fibroblast lines and differentiated them into neural stem cells. The NPA neural stem cells exhibit a disease phenotype of lysosomal sphingomyelin accumulation and enlarged lysosomes. By using this disease model, we also evaluated three compounds that reportedly reduced lysosomal lipid accumulation in Niemann-Pick disease type C as well as enzyme replacement therapy with ASM. We found that α-tocopherol, δ-tocopherol, hydroxypropyl-β-cyclodextrin, and ASM reduced sphingomyelin accumulation and enlarged lysosomes in NPA neural stem cells. Therefore, the NPA neural stem cells possess the characteristic NPA disease phenotype that can be ameliorated by tocopherols, cyclodextrin, and ASM. Our results demonstrate the efficacies of cyclodextrin and tocopherols in the NPA cell-based model. Our data also indicate that the NPA neural stem cells can be used as a new cell-based disease model for further study of disease pathophysiology and for high-throughput screening to identify new lead compounds for drug development. Currently, there is no effective treatment for Niemann-Pick disease type A (NPA). To accelerate drug discovery for treatment of NPA, NPA-induced pluripotent stem cells were generated from patient dermal fibroblasts and differentiated into neural stem cells. By using the differentiated NPA neuronal cells as a cell-based disease model system, α-tocopherol, δ-tocopherol, and hydroxypropyl-β-cyclodextrin significantly reduced sphingomyelin accumulation in these NPA neuronal cells. Therefore, this cell-based NPA model can be used for further study of disease pathophysiology and for high-throughput screening of compound libraries to identify lead compounds for drug development. ©AlphaMed Press.
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How predictive quantitative modelling of tissue organisation can inform liver disease pathogenesis.
Drasdo, Dirk; Hoehme, Stefan; Hengstler, Jan G
2014-10-01
From the more than 100 liver diseases described, many of those with high incidence rates manifest themselves by histopathological changes, such as hepatitis, alcoholic liver disease, fatty liver disease, fibrosis, and, in its later stages, cirrhosis, hepatocellular carcinoma, primary biliary cirrhosis and other disorders. Studies of disease pathogeneses are largely based on integrating -omics data pooled from cells at different locations with spatial information from stained liver structures in animal models. Even though this has led to significant insights, the complexity of interactions as well as the involvement of processes at many different time and length scales constrains the possibility to condense disease processes in illustrations, schemes and tables. The combination of modern imaging modalities with image processing and analysis, and mathematical models opens up a promising new approach towards a quantitative understanding of pathologies and of disease processes. This strategy is discussed for two examples, ammonia metabolism after drug-induced acute liver damage, and the recovery of liver mass as well as architecture during the subsequent regeneration process. This interdisciplinary approach permits integration of biological mechanisms and models of processes contributing to disease progression at various scales into mathematical models. These can be used to perform in silico simulations to promote unravelling the relation between architecture and function as below illustrated for liver regeneration, and bridging from the in vitro situation and animal models to humans. In the near future novel mechanisms will usually not be directly elucidated by modelling. However, models will falsify hypotheses and guide towards the most informative experimental design. Copyright © 2014 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.
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Animal Models of Barrett's Esophagus and Esophageal Adenocarcinoma-Past, Present, and Future.
Kapoor, Harit; Lohani, Kush Raj; Lee, Tommy H; Agrawal, Devendra K; Mittal, Sumeet K
2015-12-01
Esophageal adenocarcinoma is the fastest rising cancer in the United States. It develops from long-standing gastroesophageal reflux disease which affects >20% of the general population. It carries a very poor prognosis with 5-year survival <20%. The disease is known to sequentially progress from reflux esophagitis to a metaplastic precursor, Barrett's esophagus and then onto dysplasia and esophageal adenocarcinoma. However, only few patients with reflux develop Barrett's esophagus and only a minority of these turn malignant. The reason for this heterogeneity in clinical progression is unknown. To improve patient management, molecular changes which facilitate disease progression must be identified. Animal models can provide a comprehensive functional and anatomic platform for such a study. Rats and mice have been the most widely studied but disease homology with humans has been questioned. No animal model naturally simulates the inflammation to adenocarcinoma progression as in humans, with all models requiring surgical bypass or destruction of existing antireflux mechanisms. Valuable properties of individual models could be utilized to holistically evaluate disease progression. In this review paper, we critically examined the current animal models of Barrett's esophagus, their differences and homologies with human disease and how they have shaped our current understanding of Barrett's carcinogenesis. © 2015 Wiley Periodicals, Inc.
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Hsieh, Pei-Lun; Chen, Ching-Min
2016-08-01
Longer average life expectancies have caused the rapid growth of the elderly as a percentage of Taiwan's population and, as a result of the number of elders with chronic diseases and disability. Providing continuing-care services in community settings for elderly with multiple chronic conditions has become an urgent need. To review the nurse-led care models that are currently practiced among elders with chronic disease in the community and to further examine the effectiveness and essential components of these models using a systematic review method. Twelve original articles on chronic disease-care planning for the elderly or on nurse-led care management interventions that were published between 2000 and 2015 in any of five electronic databases: MEDLINE, PubMed, CINAHL (Cumulative Index to Nursing and Allied Health Literature) Plus with Full Text, Cochrane Library, and CEPS (Chinese Electronic Periodicals Service）were selected and analyzed systematically. Four types of nurse-led community care models, including primary healthcare, secondary prevention care, cross-boundary models, and case management, were identified. Chronic disease-care planning, case management, and disease self-management were found to be the essential components of the services that were provided. The care models used systematic processes to conduct assessment, planning, implementation, coordination, and follow-up activities as well as to deliver services and to evaluate disease status. The results revealed that providing continuing-care services through the nurse-led community chronic disease-care model and cross-boundary model enhanced the ability of the elderly to self-manage their chronic diseases, improved healthcare referrals, provided holistic care, and maximized resource utilization efficacy. The present study cross-referenced all reviewed articles in terms of target clients, content, intervention, measurements, and outcome indicators. Study results may be referenced in future implementations of nurse-led community care models as well as in future research.
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Experimental anti-GBM disease as a tool for studying spontaneous lupus nephritis.
Fu, Yuyang; Du, Yong; Mohan, Chandra
2007-08-01
Lupus nephritis is an immune-mediated disease, where antibodies and T cells both play pathogenic roles. Since spontaneous lupus nephritis in mouse models takes 6-12 months to manifest, there is an urgent need for a mouse model that can be used to delineate the pathogenic processes that lead to immune nephritis, over a quicker time frame. We propose that the experimental anti-glomerular basement membrane (GBM) disease model might be a suitable tool for uncovering some of the molecular steps underlying lupus nephritis. This article reviews the current evidence that supports the use of the experimental anti-GBM nephritis model for studying spontaneous lupus nephritis. Importantly, out of about 25 different molecules that have been specifically examined in the experimental anti-GBM model and also spontaneous lupus nephritis, all influence both diseases concordantly, suggesting that the experimental model might be a useful tool for unraveling the molecular basis of spontaneous lupus nephritis. This has important clinical implications, both from the perspective of genetic susceptibility as well as clinical therapeutics.
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Model Organisms Facilitate Rare Disease Diagnosis and Therapeutic Research
Wangler, Michael F.; Yamamoto, Shinya; Chao, Hsiao-Tuan; Posey, Jennifer E.; Westerfield, Monte; Postlethwait, John; Hieter, Philip; Boycott, Kym M.; Campeau, Philippe M.; Bellen, Hugo J.
2017-01-01
Efforts to identify the genetic underpinnings of rare undiagnosed diseases increasingly involve the use of next-generation sequencing and comparative genomic hybridization methods. These efforts are limited by a lack of knowledge regarding gene function, and an inability to predict the impact of genetic variation on the encoded protein function. Diagnostic challenges posed by undiagnosed diseases have solutions in model organism research, which provides a wealth of detailed biological information. Model organism geneticists are by necessity experts in particular genes, gene families, specific organs, and biological functions. Here, we review the current state of research into undiagnosed diseases, highlighting large efforts in North America and internationally, including the Undiagnosed Diseases Network (UDN) (Supplemental Material, File S1) and UDN International (UDNI), the Centers for Mendelian Genomics (CMG), and the Canadian Rare Diseases Models and Mechanisms Network (RDMM). We discuss how merging human genetics with model organism research guides experimental studies to solve these medical mysteries, gain new insights into disease pathogenesis, and uncover new therapeutic strategies. PMID:28874452
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Dower, Ken; Zhao, Shanrong; Schlerman, Franklin J; Savary, Leigh; Campanholle, Gabriela; Johnson, Bryce G; Xi, Li; Nguyen, Vuong; Zhan, Yutian; Lech, Matthew P; Wang, Ju; Nie, Qing; Karsdal, Morten A; Genovese, Federica; Boucher, Germaine; Brown, Thomas P; Zhang, Baohong; Homer, Bruce L; Martinez, Robert V
2017-01-01
ZSF1 rats exhibit spontaneous nephropathy secondary to obesity, hypertension, and diabetes, and have gained interest as a model system with potentially high translational value to progressive human disease. To thoroughly characterize this model, and to better understand how closely it recapitulates human disease, we performed a high resolution longitudinal analysis of renal disease progression in ZSF1 rats spanning from early disease to end stage renal disease. Analyses included metabolic endpoints, renal histology and ultrastructure, evaluation of a urinary biomarker of fibrosis, and transcriptome analysis of glomerular-enriched tissue over the course of disease. Our findings support the translational value of the ZSF1 rat model, and are provided here to assist researchers in the determination of the model's suitability for testing a particular mechanism of interest, the design of therapeutic intervention studies, and the identification of new targets and biomarkers for type 2 diabetic nephropathy.
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Feeding difficulties, a key feature of the Drosophila NDUFS4 mitochondrial disease model
Foriel, Sarah; Eidhof, Ilse
2018-01-01
ABSTRACT Mitochondrial diseases are associated with a wide variety of clinical symptoms and variable degrees of severity. Patients with such diseases generally have a poor prognosis and often an early fatal disease outcome. With an incidence of 1 in 5000 live births and no curative treatments available, relevant animal models to evaluate new therapeutic regimes for mitochondrial diseases are urgently needed. By knocking down ND-18, the unique Drosophila ortholog of NDUFS4, an accessory subunit of the NADH:ubiquinone oxidoreductase (Complex I), we developed and characterized several dNDUFS4 models that recapitulate key features of mitochondrial disease. Like in humans, the dNDUFS4 KD flies display severe feeding difficulties, an aspect of mitochondrial disorders that has so far been largely ignored in animal models. The impact of this finding, and an approach to overcome it, will be discussed in the context of interpreting disease model characterization and intervention studies. This article has an associated First Person interview with the first author of the paper. PMID:29590638
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Alzheimer's disease: insights from Drosophila melanogaster models
Moloney, Aileen; Sattelle, David B.; Lomas, David A.; Crowther, Damian C.
2010-01-01
The power of fruit fly genetics is being deployed against some of the most intractable and economically significant problems in modern medicine, the neurodegenerative diseases. Fly models of Alzheimer's disease can be exposed to the rich diversity of biological techniques that are available to the community and are providing new insights into disease mechanisms, and assisting in the identification of novel targets for therapy. Similar approaches might also help us to interpret the results of genome-wide association studies of human neurodegenerative diseases by allowing us to triage gene “hits” according to whether a candidate risk factor gene has a modifying effect on the disease phenotypes in fly model systems. PMID:20036556
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Fruitful research: drug target discovery for neurodegenerative diseases in Drosophila.
Konsolaki, Mary
2013-12-01
Although vertebrate model systems have obvious advantages in the study of human disease, invertebrate organisms have contributed enormously to this field as well. The conservation of genome structure and physiology among organisms poses unexpected peculiarities, and the redundancy in certain gene families or the presence of polymorphisms that can slightly alter gene expression can, in certain instances, bring invertebrate systems, such as Drosophila, closer to humans than mice and vice versa. This necessitates the analysis of disease pathways in multiple model organisms. The author highlights findings from Drosophila models of neurodegenerative diseases that have occurred in the past few years. She also highlights and discusses various molecular, genetic and genomic tools used in flies, as well as methods for generating disease models. Finally, the author describes Drosophila models of Alzheimer's, Parkinson's tri-nucleotide repeat diseases, and Fragile X syndrome and summarizes insights in disease mechanisms that have been discovered directly in fly models. Full genome genetic screens in Drosophila can lead to the rapid identification of drug target candidates that can be subsequently validated in a vertebrate system. In addition, the Drosophila models of neurodegeneration may often show disease phenotypes that are absent in equivalent mouse models. The author believes that the extensive contribution of Drosophila to both new disease drug target discovery, in addition to target validation, makes them indispensible to drug discovery and development.
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2009-01-01
Background Natural history models of human papillomavirus (HPV) infection and disease have been used in a number of policy evaluations of technologies to prevent and screen for HPV disease (e.g., cervical cancer, anogenital warts), sometimes with wide variation in values for epidemiologic and clinical inputs. The objectives of this study are to: (1) Provide an updated critical and systematic review of the evidence base to support epidemiologic and clinical modeling of key HPV disease-related parameters in the context of an HPV multi-type disease transmission model which we have applied within a U.S. population context; (2) Identify areas where additional studies are particularly needed. Methods Consistent with our and other prior HPV natural history models, the literature review was confined to cervical disease and genital warts. Between October 2005 and January 2006, data were gathered from the published English language medical literature through a search of the PubMed database and references were examined from prior HPV natural history models and review papers. Study design and data quality from individual studies were compared and analyses meeting pre-defined criteria were selected. Results Published data meeting review eligibility criteria were most plentiful for natural history parameters relating to the progression and regression of cervical intraepithelial neoplasia (CIN) without HPV typing, and data concerning the natural history of HPV disease due to specific HPV types were often lacking. Epidemiologic evidence to support age-dependency in the risk of progression and regression of HPV disease was found to be weak, and an alternative hypothesis concerning the time-dependence of transition rates is explored. No data were found on the duration of immunity following HPV infection. In the area of clinical management, data were observed to be lacking on the proportion of clinically manifest anogenital warts that are treated and the proportion of cervical cancer cases that become symptomatic by stage. Conclusion Knowledge of the natural history of HPV disease has been considerably enhanced over the past two decades, through the publication of an increasing number of relevant studies. However, considerable opportunity remains for advancing our understanding of HPV natural history and the quality of associated models, particularly with respect to examining HPV age- and type-specific outcomes, and acquired immunity following infection. PMID:19640281
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Mining geographic variations of Plasmodium vivax for active surveillance: a case study in China.
Shi, Benyun; Tan, Qi; Zhou, Xiao-Nong; Liu, Jiming
2015-05-27
Geographic variations of an infectious disease characterize the spatial differentiation of disease incidences caused by various impact factors, such as environmental, demographic, and socioeconomic factors. Some factors may directly determine the force of infection of the disease (namely, explicit factors), while many other factors may indirectly affect the number of disease incidences via certain unmeasurable processes (namely, implicit factors). In this study, the impact of heterogeneous factors on geographic variations of Plasmodium vivax incidences is systematically investigate in Tengchong, Yunnan province, China. A space-time model that resembles a P. vivax transmission model and a hidden time-dependent process, is presented by taking into consideration both explicit and implicit factors. Specifically, the transmission model is built upon relevant demographic, environmental, and biophysical factors to describe the local infections of P. vivax. While the hidden time-dependent process is assessed by several socioeconomic factors to account for the imported cases of P. vivax. To quantitatively assess the impact of heterogeneous factors on geographic variations of P. vivax infections, a Markov chain Monte Carlo (MCMC) simulation method is developed to estimate the model parameters by fitting the space-time model to the reported spatial-temporal disease incidences. Since there is no ground-truth information available, the performance of the MCMC method is first evaluated against a synthetic dataset. The results show that the model parameters can be well estimated using the proposed MCMC method. Then, the proposed model is applied to investigate the geographic variations of P. vivax incidences among all 18 towns in Tengchong, Yunnan province, China. Based on the geographic variations, the 18 towns can be further classify into five groups with similar socioeconomic causality for P. vivax incidences. Although this study focuses mainly on the transmission of P. vivax, the proposed space-time model is general and can readily be extended to investigate geographic variations of other diseases. Practically, such a computational model will offer new insights into active surveillance and strategic planning for disease surveillance and control.
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Guo, Xiaojun; Liu, Sifeng; Wu, Lifeng; Tang, Lingling
2014-01-01
Objective In this study, a novel grey self-memory coupling model was developed to forecast the incidence rates of two notifiable infectious diseases (dysentery and gonorrhea); the effectiveness and applicability of this model was assessed based on its ability to predict the epidemiological trend of infectious diseases in China. Methods The linear model, the conventional GM(1,1) model and the GM(1,1) model with self-memory principle (SMGM(1,1) model) were used to predict the incidence rates of the two notifiable infectious diseases based on statistical incidence data. Both simulation accuracy and prediction accuracy were assessed to compare the predictive performances of the three models. The best-fit model was applied to predict future incidence rates. Results Simulation results show that the SMGM(1,1) model can take full advantage of the systematic multi-time historical data and possesses superior predictive performance compared with the linear model and the conventional GM(1,1) model. By applying the novel SMGM(1,1) model, we obtained the possible incidence rates of the two representative notifiable infectious diseases in China. Conclusion The disadvantages of the conventional grey prediction model, such as sensitivity to initial value, can be overcome by the self-memory principle. The novel grey self-memory coupling model can predict the incidence rates of infectious diseases more accurately than the conventional model, and may provide useful references for making decisions involving infectious disease prevention and control. PMID:25546054
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Guo, Xiaojun; Liu, Sifeng; Wu, Lifeng; Tang, Lingling
2014-01-01
In this study, a novel grey self-memory coupling model was developed to forecast the incidence rates of two notifiable infectious diseases (dysentery and gonorrhea); the effectiveness and applicability of this model was assessed based on its ability to predict the epidemiological trend of infectious diseases in China. The linear model, the conventional GM(1,1) model and the GM(1,1) model with self-memory principle (SMGM(1,1) model) were used to predict the incidence rates of the two notifiable infectious diseases based on statistical incidence data. Both simulation accuracy and prediction accuracy were assessed to compare the predictive performances of the three models. The best-fit model was applied to predict future incidence rates. Simulation results show that the SMGM(1,1) model can take full advantage of the systematic multi-time historical data and possesses superior predictive performance compared with the linear model and the conventional GM(1,1) model. By applying the novel SMGM(1,1) model, we obtained the possible incidence rates of the two representative notifiable infectious diseases in China. The disadvantages of the conventional grey prediction model, such as sensitivity to initial value, can be overcome by the self-memory principle. The novel grey self-memory coupling model can predict the incidence rates of infectious diseases more accurately than the conventional model, and may provide useful references for making decisions involving infectious disease prevention and control.
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Gene Therapy Models of Alzheimer’s Disease and Other Dementias
Combs, Benjamin; Kneynsberg, Andrew; Kanaan, Nicholas M.
2016-01-01
Dementias are among the most common neurological disorders, and Alzheimer’s disease (AD) is the most common cause of dementia worldwide. AD remains a looming health crisis despite great efforts to learn the mechanisms surrounding the neuron dysfunction and neurodegeneration that accompanies AD primarily in the medial temporal lobe. In addition to AD, a group of diseases known as frontotemporal dementias (FTDs) are degenerative diseases involving atrophy and degeneration in the frontal and temporal lobe regions. Importantly, AD and a number of FTDs are collectively known as tauopathies due to the abundant accumulation of pathological tau inclusions in the brain. The precise role tau plays in disease pathogenesis remains an area of strong research focus. A critical component to effectively study any human disease is the availability of models that recapitulate key features of the disease. Accordingly, a number of animal models are currently being pursued to fill the current gaps in our knowledge of the causes of dementias and to develop effective therapeutics. Recent developments in gene therapy-based approaches, particularly in recombinant adeno-associated viruses (rAAVs), have provided new tools to study AD and other related neurodegenerative disorders. Additionally, gene therapy approaches have emerged as an intriguing possibility for treating these diseases in humans. This chapter explores the current state of rAAV models of AD and other dementias, discuss recent efforts to improve these models, and describe current and future possibilities in the use of rAAVs and other viruses in treatments of disease. PMID:26611599
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Okugn, Akoma; Woldeyohannes, Demelash
2018-01-01
In developing country most of human infectious diseases are caused by eating contaminated food. Estimated nine out ten of the diarrheal disease is attributable to the environment and associated with risk factors of poor food hygiene practice. Understanding the risk of eating unsafe food is the major concern to prevent and control food borne diseases. The main goal of this study was to assessing food hygiene practices and its associated factors among model and non model households at Abobo district. This study was conducted from 18 October 2013 to 13 June 2014. A community-based comparative cross-sectional study design was used. Pretested structured questionnaire was used to collect data. A total of 1247 households (417 model and 830 non model households) were included in the study from Abobo district. Bivariate and multivariate logistic regression analysis was used to identify factors associated with outcome variable. The study revealed that good food hygiene practice was 51%, of which 79% were model and 36.70% were non model households. Type of household [AOR: 2.07, 95% CI: (1.32-3.39)], sex of household head [AOR: 1.63, 95% CI: (1.06-2.48)], Availability of liquid wastes disposal pit [AOR: 2.23, 95% CI: (1.39,3.63)], Knowledge of liquid waste to cause diseases [AOR: 1.95, 95% (1.23,3.08)], and availability of functional hand washing facility [AOR: 3.61, 95% CI: (1.86-7.02)] were the factors associated with food handling practices. This study revealed that good food handling practice is low among model and non model households. While type of household (model versus non model households), sex, knowledge of solid waste to cause diseases, availability of functional hand washing facility, and availability of liquid wastes disposal pit were the factors associated with outcome variable. Health extension workers should play a great role in educating households regarding food hygiene practices to improve their knowledge and practices of the food hygiene.
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van Kempen, Bob J H; Ferket, Bart S; Hofman, Albert; Steyerberg, Ewout W; Colkesen, Ersen B; Boekholdt, S Matthijs; Wareham, Nicholas J; Khaw, Kay-Tee; Hunink, M G Myriam
2012-12-06
We developed a Monte Carlo Markov model designed to investigate the effects of modifying cardiovascular disease (CVD) risk factors on the burden of CVD. Internal, predictive, and external validity of the model have not yet been established. The Rotterdam Ischemic Heart Disease and Stroke Computer Simulation (RISC) model was developed using data covering 5 years of follow-up from the Rotterdam Study. To prove 1) internal and 2) predictive validity, the incidences of coronary heart disease (CHD), stroke, CVD death, and non-CVD death simulated by the model over a 13-year period were compared with those recorded for 3,478 participants in the Rotterdam Study with at least 13 years of follow-up. 3) External validity was verified using 10 years of follow-up data from the European Prospective Investigation of Cancer (EPIC)-Norfolk study of 25,492 participants, for whom CVD and non-CVD mortality was compared. At year 5, the observed incidences (with simulated incidences in brackets) of CHD, stroke, and CVD and non-CVD mortality for the 3,478 Rotterdam Study participants were 5.30% (4.68%), 3.60% (3.23%), 4.70% (4.80%), and 7.50% (7.96%), respectively. At year 13, these percentages were 10.60% (10.91%), 9.90% (9.13%), 14.20% (15.12%), and 24.30% (23.42%). After recalibrating the model for the EPIC-Norfolk population, the 10-year observed (simulated) incidences of CVD and non-CVD mortality were 3.70% (4.95%) and 6.50% (6.29%). All observed incidences fell well within the 95% credibility intervals of the simulated incidences. We have confirmed the internal, predictive, and external validity of the RISC model. These findings provide a basis for analyzing the effects of modifying cardiovascular disease risk factors on the burden of CVD with the RISC model.
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Li, Guanghui; Luo, Jiawei; Xiao, Qiu; Liang, Cheng; Ding, Pingjian
2018-05-12
Interactions between microRNAs (miRNAs) and diseases can yield important information for uncovering novel prognostic markers. Since experimental determination of disease-miRNA associations is time-consuming and costly, attention has been given to designing efficient and robust computational techniques for identifying undiscovered interactions. In this study, we present a label propagation model with linear neighborhood similarity, called LPLNS, to predict unobserved miRNA-disease associations. Additionally, a preprocessing step is performed to derive new interaction likelihood profiles that will contribute to the prediction since new miRNAs and diseases lack known associations. Our results demonstrate that the LPLNS model based on the known disease-miRNA associations could achieve impressive performance with an AUC of 0.9034. Furthermore, we observed that the LPLNS model based on new interaction likelihood profiles could improve the performance to an AUC of 0.9127. This was better than other comparable methods. In addition, case studies also demonstrated our method's outstanding performance for inferring undiscovered interactions between miRNAs and diseases, especially for novel diseases. Copyright © 2018. Published by Elsevier Inc.
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De Wilde, Bram; Beckers, Anneleen; Lindner, Sven; Kristina, Althoff; De Preter, Katleen; Depuydt, Pauline; Mestdagh, Pieter; Sante, Tom; Lefever, Steve; Hertwig, Falk; Peng, Zhiyu; Shi, Le-Ming; Lee, Sangkyun; Vandermarliere, Elien; Martens, Lennart; Menten, Björn; Schramm, Alexander; Fischer, Matthias; Schulte, Johannes; Vandesompele, Jo; Speleman, Frank
2018-02-02
Genetically engineered mouse models have proven to be essential tools for unraveling fundamental aspects of cancer biology and for testing novel therapeutic strategies. To optimally serve these goals, it is essential that the mouse model faithfully recapitulates the human disease. Recently, novel mouse models for neuroblastoma have been developed. Here, we report on the further genomic characterization through exome sequencing and DNA copy number analysis of four of the currently available murine neuroblastoma model systems ( ALK, Th- MYCN, Dbh- MYCN and Lin28b ). The murine tumors revealed a low number of genomic alterations - in keeping with human neuroblastoma - and a positive correlation of the number of genetic lesions with the time to onset of tumor formation was observed. Gene copy number alterations are the hallmark of both murine and human disease and frequently affect syntenic genomic regions. Despite low mutational load, the genes mutated in murine disease were found to be enriched for genes mutated in human disease. Taken together, our study further supports the validity of the tested mouse models for mechanistic and preclinical studies of human neuroblastoma.
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Ren, Meng; Li, Na; Wang, Zhan; Liu, Yisi; Chen, Xi; Chu, Yuanyuan; Li, Xiangyu; Zhu, Zhongmin; Tian, Liqiao; Xiang, Hao
2017-01-01
Few studies have compared different methods when exploring the short-term effects of air pollutants on respiratory disease mortality in Wuhan, China. This study assesses the association between air pollutants and respiratory disease mortality with both time-series and time-stratified–case-crossover designs. The generalized additive model (GAM) and the conditional logistic regression model were used to assess the short-term effects of air pollutants on respiratory disease mortality. Stratified analyses were performed by age, sex, and diseases. A 10 μg/m3 increment in SO2 level was associated with an increase in relative risk for all respiratory disease mortality of 2.4% and 1.9% in the case-crossover and time-series analyses in single pollutant models, respectively. Strong evidence of an association between NO2 and daily respiratory disease mortality among men or people older than 65 years was found in the case-crossover study. There was a positive association between air pollutants and respiratory disease mortality in Wuhan, China. Both time-series and case-crossover analyses consistently reveal the association between three air pollutants and respiratory disease mortality. The estimates of association between air pollution and respiratory disease mortality from the case–crossover analysis displayed greater variation than that from the time-series analysis. PMID:28084399
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Ren, Meng; Li, Na; Wang, Zhan; Liu, Yisi; Chen, Xi; Chu, Yuanyuan; Li, Xiangyu; Zhu, Zhongmin; Tian, Liqiao; Xiang, Hao
2017-01-13
Few studies have compared different methods when exploring the short-term effects of air pollutants on respiratory disease mortality in Wuhan, China. This study assesses the association between air pollutants and respiratory disease mortality with both time-series and time-stratified-case-crossover designs. The generalized additive model (GAM) and the conditional logistic regression model were used to assess the short-term effects of air pollutants on respiratory disease mortality. Stratified analyses were performed by age, sex, and diseases. A 10 μg/m 3 increment in SO 2 level was associated with an increase in relative risk for all respiratory disease mortality of 2.4% and 1.9% in the case-crossover and time-series analyses in single pollutant models, respectively. Strong evidence of an association between NO 2 and daily respiratory disease mortality among men or people older than 65 years was found in the case-crossover study. There was a positive association between air pollutants and respiratory disease mortality in Wuhan, China. Both time-series and case-crossover analyses consistently reveal the association between three air pollutants and respiratory disease mortality. The estimates of association between air pollution and respiratory disease mortality from the case-crossover analysis displayed greater variation than that from the time-series analysis.
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NASA Astrophysics Data System (ADS)
Ren, Meng; Li, Na; Wang, Zhan; Liu, Yisi; Chen, Xi; Chu, Yuanyuan; Li, Xiangyu; Zhu, Zhongmin; Tian, Liqiao; Xiang, Hao
2017-01-01
Few studies have compared different methods when exploring the short-term effects of air pollutants on respiratory disease mortality in Wuhan, China. This study assesses the association between air pollutants and respiratory disease mortality with both time-series and time-stratified-case-crossover designs. The generalized additive model (GAM) and the conditional logistic regression model were used to assess the short-term effects of air pollutants on respiratory disease mortality. Stratified analyses were performed by age, sex, and diseases. A 10 μg/m3 increment in SO2 level was associated with an increase in relative risk for all respiratory disease mortality of 2.4% and 1.9% in the case-crossover and time-series analyses in single pollutant models, respectively. Strong evidence of an association between NO2 and daily respiratory disease mortality among men or people older than 65 years was found in the case-crossover study. There was a positive association between air pollutants and respiratory disease mortality in Wuhan, China. Both time-series and case-crossover analyses consistently reveal the association between three air pollutants and respiratory disease mortality. The estimates of association between air pollution and respiratory disease mortality from the case-crossover analysis displayed greater variation than that from the time-series analysis.
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Modeling Mosquito-Borne Disease Spread in U.S. Urbanized Areas: The Case of Dengue in Miami
Robert, Michael A.; Christofferson, Rebecca C.; Silva, Noah J. B.; Vasquez, Chalmers; Mores, Christopher N.; Wearing, Helen J.
2016-01-01
Expansion of mosquito-borne pathogens into more temperate regions of the world necessitates tools such as mathematical models for understanding the factors that contribute to the introduction and emergence of a disease in populations naïve to the disease. Often, these models are not developed and analyzed until after a pathogen is detected in a population. In this study, we develop a spatially explicit stochastic model parameterized with publicly available U.S. Census data for studying the potential for disease spread in Urbanized Areas of the United States. To illustrate the utility of the model, we specifically study the potential for introductions of dengue to lead to autochthonous transmission and outbreaks in a population representative of the Miami Urbanized Area, where introductions of dengue have occurred frequently in recent years. We describe seasonal fluctuations in mosquito populations by fitting a population model to trap data provided by the Miami-Dade Mosquito Control Division. We show that the timing and location of introduced cases could play an important role in determining both the probability that local transmission occurs as well as the total number of cases throughout the entire region following introduction. We show that at low rates of clinical presentation, small outbreaks of dengue could go completely undetected during a season, which may confound mitigation efforts that rely upon detection. We discuss the sensitivity of the model to several critical parameter values that are currently poorly characterized and motivate the collection of additional data to strengthen the predictive power of this and similar models. Finally, we emphasize the utility of the general structure of this model in studying mosquito-borne diseases such as chikungunya and Zika virus in other regions. PMID:27532496
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Gryphon: A Hybrid Agent-Based Modeling and Simulation Platform for Infectious Diseases
NASA Astrophysics Data System (ADS)
Yu, Bin; Wang, Jijun; McGowan, Michael; Vaidyanathan, Ganesh; Younger, Kristofer
In this paper we present Gryphon, a hybrid agent-based stochastic modeling and simulation platform developed for characterizing the geographic spread of infectious diseases and the effects of interventions. We study both local and non-local transmission dynamics of stochastic simulations based on the published parameters and data for SARS. The results suggest that the expected numbers of infections and the timeline of control strategies predicted by our stochastic model are in reasonably good agreement with previous studies. These preliminary results indicate that Gryphon is able to characterize other future infectious diseases and identify endangered regions in advance.
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Transgenic mouse models of Parkinson's disease and Huntington's disease.
Skaper, Stephen D; Giusti, Pietro
2010-08-01
Parkinson's disease (PD) is a chronic progressive neurodegenerative movement disorder characterized by a profound and selective loss of nigrostriatal dopaminergic neurons. Another neurodegenerative disorder, Huntington's disease (HD), is characterized by striking movement abnormalities and the loss of medium-sized spiny neurons in the striatum. Current medications only provide symptomatic relief and fail to halt the death of neurons in these disorders. A major hurdle in the development of neuroprotective therapies is due to limited understanding of disease processes leading to the death of neurons. The etiology of dopaminergic neuronal demise in PD is elusive, but a combination of genetic and environmental factors seems to play a critical role. The majority of PD cases are sporadic; however, the discovery of genes linked to rare familial forms of disease and studies from experimental animal models has provided crucial insights into molecular mechanisms of disease pathogenesis. HD, on the other hand, is one of the few neurodegenerative diseases with a known genetic cause, namely an expanded CAG repeat mutation, extending a polyglutamine tract in the huntingtin protein. One of the most important advances in HD research has been the generation of various mouse models that enable the exploration of early pathological, molecular, and cellular abnormalities produced by the mutation. In addition, these models for both HD and PD have made possible the testing of different pharmacological approaches to delay the onset or slow the progression of disease. This article will provide an overview of the genetics underlying PD and HD, the animal models developed, and their potential utility to the study of disease pathophysiology.
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Misbehaving macrophages in the pathogenesis of psoriasis.
Clark, Rachael A; Kupper, Thomas S
2006-08-01
Psoriasis is a chronic inflammatory skin disease unique to humans. In this issue of the JCI, 2 studies of very different mouse models of psoriasis both report that macrophages play a key role in inducing psoriasis-like skin disease. Psoriasis is clearly a polygenic, inherited disease of uncontrolled cutaneous inflammation. The debate that currently rages in the field is whether psoriasis is a disease of autoreactive T cells or whether it reflects an intrinsic defect within the skin--or both. However, these questions have proven difficult to dissect using molecular genetic tools. In the current studies, the authors have used 2 different animal models to address the role of macrophages in disease pathogenesis: Wang et al. use a mouse model in which inflammation is T cell dependent, whereas the model used by Stratis et al. is T cell independent (see the related articles beginning on pages 2105 and 2094, respectively). Strikingly, both groups report an important contribution by macrophages, implying that macrophages can contribute to both epithelial-based and T cell-mediated pathways of inflammation.
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Bruner-Tran, Kaylon L.; Mokshagundam, Shilpa; Herington, Jennifer L.; Ding, Tianbing; Osteen, Kevin G.
2018-01-01
Background: Although it has been more than a century since endometriosis was initially described in the literature, understanding the etiology and natural history of the disease has been challenging. However, the broad utility of murine and rat models of experimental endometriosis has enabled the elucidation of a number of potentially targetable processes which may otherwise promote this disease. Objective: To review a variety of studies utilizing rodent models of endometriosis to illustrate their utility in examining mechanisms associated with development and progression of this disease. Results: Use of rodent models of endometriosis has provided a much broader understanding of the risk factors for the initial development of endometriosis, the cellular pathology of the disease and the identification of potential therapeutic targets. Conclusion: Although there are limitations with any animal model, the variety of experimental endometriosis models that have been developed has enabled investigation into numerous aspects of this disease. Thanks to these models, our under-standing of the early processes of disease development, the role of steroid responsiveness, inflammatory processes and the peritoneal environment has been advanced. More recent models have begun to shed light on how epigenetic alterations con-tribute to the molecular basis of this disease as well as the multiple comorbidities which plague many patients. Continued de-velopments of animal models which aid in unraveling the mechanisms of endometriosis development provide the best oppor-tunity to identify therapeutic strategies to prevent or regress this enigmatic disease.
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Concise Review: Cardiac Disease Modeling Using Induced Pluripotent Stem Cells.
Yang, Chunbo; Al-Aama, Jumana; Stojkovic, Miodrag; Keavney, Bernard; Trafford, Andrew; Lako, Majlinda; Armstrong, Lyle
2015-09-01
Genetic cardiac diseases are major causes of morbidity and mortality. Although animal models have been created to provide some useful insights into the pathogenesis of genetic cardiac diseases, the significant species differences and the lack of genetic information for complex genetic diseases markedly attenuate the application values of such data. Generation of induced pluripotent stem cells (iPSCs) from patient-specific specimens and subsequent derivation of cardiomyocytes offer novel avenues to study the mechanisms underlying cardiac diseases, to identify new causative genes, and to provide insights into the disease aetiology. In recent years, the list of human iPSC-based models for genetic cardiac diseases has been expanding rapidly, although there are still remaining concerns on the level of functionality of iPSC-derived cardiomyocytes and their ability to be used for modeling complex cardiac diseases in adults. This review focuses on the development of cardiomyocyte induction from pluripotent stem cells, the recent progress in heart disease modeling using iPSC-derived cardiomyocytes, and the challenges associated with understanding complex genetic diseases. To address these issues, we examine the similarity between iPSC-derived cardiomyocytes and their ex vivo counterparts and how this relates to the method used to differentiate the pluripotent stem cells into a cardiomyocyte phenotype. We progress to examine categories of congenital cardiac abnormalities that are suitable for iPSC-based disease modeling. © AlphaMed Press.
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A model of self-regulation for control of chronic disease.
Clark, Noreen M; Gong, Molly; Kaciroti, Niko
2014-10-01
Chronic disease poses increasing threat to individual and community health. The day-to-day manager of disease is the patient who undertakes actions with the guidance of a clinician. The ability of the patient to control the illness through an effective therapeutic plan is significantly influenced by social and behavioral factors. This article presents a model of patient management of chronic disease that accounts for intrapersonal and external influences on management and emphasizes the central role of self-regulatory processes in disease control. Asthma serves as a case for exploration of the model. Findings from a 5-year study of 637 children with asthma and their care-taking parents supported that the self-regulation elements of the model were reasonably stable over time and baseline values were predictive of important disease management outcomes. © 2014 Society for Public Health Education.
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Schöllnberger, Helmut; Eidemüller, Markus; Cullings, Harry M; Simonetto, Cristoforo; Neff, Frauke; Kaiser, Jan Christian
2018-03-01
The scientific community faces important discussions on the validity of the linear no-threshold (LNT) model for radiation-associated cardiovascular diseases at low and moderate doses. In the present study, mortalities from cerebrovascular diseases (CeVD) and heart diseases from the latest data on atomic bomb survivors were analyzed. The analysis was performed with several radio-biologically motivated linear and nonlinear dose-response models. For each detrimental health outcome one set of models was identified that all fitted the data about equally well. This set was used for multi-model inference (MMI), a statistical method of superposing different models to allow risk estimates to be based on several plausible dose-response models rather than just relying on a single model of choice. MMI provides a more accurate determination of the dose response and a more comprehensive characterization of uncertainties. It was found that for CeVD, the dose-response curve from MMI is located below the linear no-threshold model at low and medium doses (0-1.4 Gy). At higher doses MMI predicts a higher risk compared to the LNT model. A sublinear dose-response was also found for heart diseases (0-3 Gy). The analyses provide no conclusive answer to the question whether there is a radiation risk below 0.75 Gy for CeVD and 2.6 Gy for heart diseases. MMI suggests that the dose-response curves for CeVD and heart diseases in the Lifespan Study are sublinear at low and moderate doses. This has relevance for radiotherapy treatment planning and for international radiation protection practices in general.
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A Tol2 Gateway-Compatible Toolbox for the Study of the Nervous System and Neurodegenerative Disease.
Don, Emily K; Formella, Isabel; Badrock, Andrew P; Hall, Thomas E; Morsch, Marco; Hortle, Elinor; Hogan, Alison; Chow, Sharron; Gwee, Serene S L; Stoddart, Jack J; Nicholson, Garth; Chung, Roger; Cole, Nicholas J
2017-02-01
Currently there is a lack in fundamental understanding of disease progression of most neurodegenerative diseases, and, therefore, treatments and preventative measures are limited. Consequently, there is a great need for adaptable, yet robust model systems to both investigate elementary disease mechanisms and discover effective therapeutics. We have generated a Tol2 Gateway-compatible toolbox to study neurodegenerative disorders in zebrafish, which includes promoters for astrocytes, microglia and motor neurons, multiple fluorophores, and compatibility for the introduction of genes of interest or disease-linked genes. This toolbox will advance the rapid and flexible generation of zebrafish models to discover the biology of the nervous system and the disease processes that lead to neurodegeneration.
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Modeling Niemann Pick type C1 using human embryonic and induced pluripotent stem cells.
Ordoñez, M Paulina; Steele, John W
2017-02-01
Data generated in Niemann Pick type C1 (NPC1) human embryonic and human induced pluripotent stem cell derived neurons complement on-going studies in animal models and provide the first example, in disease-relevant human cells, of processes that underlie preferential neuronal defects in a NPC1. Our work and that of other investigators in human neurons derived from stem cells highlight the importance of performing rigorous mechanistic studies in relevant cell types to guide drug discovery and therapeutic development, alongside of existing animal models. Through the use of human stem cell-derived models of disease, we can identify and discover or repurpose drugs that revert early events that lead to neuronal failure in NPC1. Together with the study of disease pathogenesis and efficacy of therapies in animal models, these strategies will fulfill the promise of stem cell technology in the development of new treatments for human diseases. This article is part of a Special Issue entitled SI: Exploiting human neurons. Copyright © 2016 Elsevier B.V. All rights reserved.
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Shtandel', S A; Gopkalova, I V; Khaziev, V V; Dubovik, V N; Svetlova-Kovalenko, E A
2009-01-01
On genealogic data about 242 Gravers disease patients, fertility parameters of 2105 healthy and 369 Grave's disease women peculiarities of genetic determination and natural selection of disease were studied. Results of the genetic analysis have revealed conformity of Grave's disease inheritance to alternative model parameters. Homozygote penetrance within the framework of this model was 78.8%, heterozygote--17.3%. For one generation in the Kharkov area population frequency of a gene to Grave's disease predisposition increases 0.8%.
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Huang, Da-Cang; Wang, Jin-Feng
2018-01-15
Hand, foot and mouth disease (HFMD) has been recognized as a significant public health threat and poses a tremendous challenge to disease control departments. To date, the relationship between meteorological factors and HFMD has been documented, and public interest of disease has been proven to be trackable from the Internet. However, no study has explored the combination of these two factors in the monitoring of HFMD. Therefore, the main aim of this study was to develop an effective monitoring model of HFMD in Guangzhou, China by utilizing historical HFMD cases, Internet-based search engine query data and meteorological factors. To this end, a case study was conducted in Guangzhou, using a network-based generalized additive model (GAM) including all factors related to HFMD. Three other models were also constructed using some of the variables for comparison. The results suggested that the model showed the best estimating ability when considering all of the related factors. Copyright © 2017 Elsevier B.V. All rights reserved.
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Nipah Virus C and W Proteins Contribute to Respiratory Disease in Ferrets
Satterfield, Benjamin A.; Cross, Robert W.; Fenton, Karla A.; Borisevich, Viktoriya; Agans, Krystle N.; Deer, Daniel J.; Graber, Jessica; Basler, Christopher F.; Mire, Chad E.
2016-01-01
ABSTRACT Nipah virus (NiV) is a highly lethal paramyxovirus that recently emerged as a causative agent of febrile encephalitis and severe respiratory disease in humans. The ferret model has emerged as the preferred small-animal model with which to study NiV disease, but much is still unknown about the viral determinants of NiV pathogenesis, including the contribution of the C protein in ferrets. Additionally, studies have yet to examine the synergistic effects of the various P gene products on pathogenesis in animal models. Using recombinant NiVs (rNiVs), we examine the sole contribution of the NiV C protein and the combined contributions of the C and W proteins in the ferret model of NiV pathogenesis. We show that an rNiV void of C expression resulted in 100% mortality, though with limited respiratory disease, like our previously reported rNiV void of W expression; this finding is in stark contrast to the attenuated phenotype observed in previous hamster studies utilizing rNiVs void of C expression. We also observed that an rNiV void of both C and W expression resulted in limited respiratory disease; however, there was severe neurological disease leading to 60% mortality, and the surviving ferrets demonstrated sequelae similar to those for human survivors of NiV encephalitis. IMPORTANCE Nipah virus (NiV) is a human pathogen capable of causing lethal respiratory and neurological disease. Many human survivors have long-lasting neurological impairment. Using a ferret model, this study demonstrated the roles of the NiV C and W proteins in pathogenesis, where lack of either the C or the W protein independently decreased the severity of clinical respiratory disease but did not decrease lethality. Abolishing both C and W expression, however, dramatically decreased the severity of respiratory disease and the level of destruction of splenic germinal centers. These ferrets still suffered severe neurological disease: 60% succumbed to disease, and the survivors experienced long-term neurological impairment, such as that seen in human survivors. This new ferret NiV C and W knockout model may allow, for the first time, the examination of interventions to prevent or mitigate the neurological damage and sequelae experienced by human survivors. PMID:27147733
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Nipah Virus C and W Proteins Contribute to Respiratory Disease in Ferrets.
Satterfield, Benjamin A; Cross, Robert W; Fenton, Karla A; Borisevich, Viktoriya; Agans, Krystle N; Deer, Daniel J; Graber, Jessica; Basler, Christopher F; Geisbert, Thomas W; Mire, Chad E
2016-07-15
Nipah virus (NiV) is a highly lethal paramyxovirus that recently emerged as a causative agent of febrile encephalitis and severe respiratory disease in humans. The ferret model has emerged as the preferred small-animal model with which to study NiV disease, but much is still unknown about the viral determinants of NiV pathogenesis, including the contribution of the C protein in ferrets. Additionally, studies have yet to examine the synergistic effects of the various P gene products on pathogenesis in animal models. Using recombinant NiVs (rNiVs), we examine the sole contribution of the NiV C protein and the combined contributions of the C and W proteins in the ferret model of NiV pathogenesis. We show that an rNiV void of C expression resulted in 100% mortality, though with limited respiratory disease, like our previously reported rNiV void of W expression; this finding is in stark contrast to the attenuated phenotype observed in previous hamster studies utilizing rNiVs void of C expression. We also observed that an rNiV void of both C and W expression resulted in limited respiratory disease; however, there was severe neurological disease leading to 60% mortality, and the surviving ferrets demonstrated sequelae similar to those for human survivors of NiV encephalitis. Nipah virus (NiV) is a human pathogen capable of causing lethal respiratory and neurological disease. Many human survivors have long-lasting neurological impairment. Using a ferret model, this study demonstrated the roles of the NiV C and W proteins in pathogenesis, where lack of either the C or the W protein independently decreased the severity of clinical respiratory disease but did not decrease lethality. Abolishing both C and W expression, however, dramatically decreased the severity of respiratory disease and the level of destruction of splenic germinal centers. These ferrets still suffered severe neurological disease: 60% succumbed to disease, and the survivors experienced long-term neurological impairment, such as that seen in human survivors. This new ferret NiV C and W knockout model may allow, for the first time, the examination of interventions to prevent or mitigate the neurological damage and sequelae experienced by human survivors. Copyright © 2016, American Society for Microbiology. All Rights Reserved.
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Santos, Guido; Díaz, Mario; Torres, Néstor V.
2016-01-01
A connection between lipid rafts and Alzheimer's disease has been studied during the last decades. Mathematical modeling approaches have recently been used to correlate the effects of lipid composition changes in the physicochemical properties of raft-like membranes. Here we propose an agent based model to assess the effect of lipid changes in lipid rafts on the evolution and progression of Alzheimer's disease using lipid profile data obtained in an established model of familial Alzheimer's disease. We have observed that lipid raft size and lipid mobility in non-raft domains are two main factors that increase during age and are accelerated in the transgenic Alzheimer's disease mouse model. The consequences of these changes are discussed in the context of neurotoxic amyloid β production. Our agent based model predicts that increasing sterols (mainly cholesterol) and long-chain polyunsaturated fatty acids (LCPUFA) (mainly DHA, docosahexaenoic acid) proportions in the membrane composition might delay the onset and progression of the disease. PMID:27014089
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Applicability of the SMART Model of Transition Readiness for Sickle-Cell Disease
Valenzuela, Jessica M.; Crosby, Lori E.; Diaz Pow Sang, Claudia
2016-01-01
Objectives This study aimed to examine the applicability of the Social-ecological Model of Adolescent and Young Adult Readiness to Transition (SMART) model for adolescents and young adults (AYA) with sickle-cell disease (SCD). Methods 14 AYA with SCD (14–24 years old) and 10 clinical experts (6–20 years of experience) completed semi-structured interviews. AYA completed brief questionnaires. Interviews were coded for themes, which were reviewed to determine their fit within the SMART model. Results Overall, most themes were consistent with the model (e.g., sociodemographics/culture, neurocognition/IQ, etc.). Factors related to race/culture, pain management, health-care navigation skills, societal stigma, and lack of awareness about SCD were salient for AYA with SCD. Conclusions Findings suggest the SMART model may be appropriate in SCD with the consideration of disease-related stigma. This study is a step toward developing a disease-specific model of transition readiness for SCD. Future directions include the development of a measure of transition readiness for this population. PMID:26717957
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Proposal for an integrated evaluation model for the study of whole systems health care in cancer.
Jonas, Wayne B; Beckner, William; Coulter, Ian
2006-12-01
For more than 200 years, biomedicine has approached the treatment of disease by studying disease processes (patho-genesis), inferring causal connections and developing specific approaches for therapeutically interfering with those processes. This pathogenic approach has been highly successful in acute and traumatic disease but less successful in chronic disease, primarily because of the complex, multi-factorial nature of most chronic disease, which does not allow for simple causal inference or for simple therapeutic interventions. This article suggests that chronic disease is best approached by enhancing healing processes (salutogenesis) as a whole system. Because of the nature of complex systems in chronic disease, an evaluation model based on integrative medicine is felt to be more appropriate than a disease model. The authors propose and describe an integrated model for the evaluation of healing (IMEH) that collects multilevel "thick case" observational data in assessing complex practices for chronic disease. If successful, this approach could become a blueprint for studying healing capacity in whole medical systems, including complementary medicine, traditional medicine, and conventional primary care. In addition, streamlining data collection and applying rapid informatics management might allow for such data to be used in guiding clinical practice. The IMEH involves collection, integration, and potentially feedback of relevant variables in the following areas: (1) sociocultural, (2) psychological and behavioral, (3) clinical (diagnosis based), and (4) biological. Evaluation and integration of these components would involve specialized research teams that feed their data into a single data management and information analysis center. These data can then be subjected to descriptive and pathway analysis providing "bench and bedside" information.
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Cardiovascular disease (CVD) models are used for identification of mechanisms of susceptibility to air pollution. We hypothesized that baseline systemic biomarkers and cardiac gene expression in CVD rat models will have influence on their ozone-induced lung inflammation. Male 12-...
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Animal models: an important tool in mycology.
Capilla, Javier; Clemons, Karl V; Stevens, David A
2007-12-01
Animal models of fungal infections are, and will remain, a key tool in the advancement of the medical mycology. Many different types of animal models of fungal infection have been developed, with murine models the most frequently used, for studies of pathogenesis, virulence, immunology, diagnosis, and therapy. The ability to control numerous variables in performing the model allows us to mimic human disease states and quantitatively monitor the course of the disease. However, no single model can answer all questions and different animal species or different routes of infection can show somewhat different results. Thus, the choice of which animal model to use must be made carefully, addressing issues of the type of human disease to mimic, the parameters to follow and collection of the appropriate data to answer those questions being asked. This review addresses a variety of uses for animal models in medical mycology. It focuses on the most clinically important diseases affecting humans and cites various examples of the different types of studies that have been performed. Overall, animal models of fungal infection will continue to be valuable tools in addressing questions concerning fungal infections and contribute to our deeper understanding of how these infections occur, progress and can be controlled and eliminated.
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Piro, Fredrik Niclas; Madsen, Christian; Næss, Øyvind; Nafstad, Per; Claussen, Bjørgulf
2008-01-01
Objective To explore various contributors to people's reporting of self reported air pollution problems in area of living, including GIS-modeled air pollution, and to investigate whether those with respiratory or other chronic diseases tend to over-report air pollution problems, compared to healthy people. Methods Cross-sectional data from the Oslo Health Study (2000–2001) were linked with GIS-modeled air pollution data from the Norwegian Institute of Air Research. Multivariate regression analyses were performed. 14 294 persons aged 30, 40, 45, 60 or 75 years old with complete information on modeled and self reported air pollution were included. Results People who reported air pollution problems were exposed to significantly higher GIS-modeled air pollution levels than those who did not report such problems. People with chronic disease, reported significantly more air pollution problems after adjustment for modeled levels of nitrogen dioxides, socio-demographic variables, smoking, depression, dwelling conditions and an area deprivation index, even if they had a non-respiratory disease. No diseases, however, were significantly associated with levels of nitrogen dioxides. Conclusion Self reported air pollution problems in area of living are strongly associated with increased levels of GIS-modeled air pollution. Over and above this, those who report to have a chronic disease tend to report more air pollution problems in area of living, despite no significant difference in air pollution exposure compared to healthy people, and no associations between these diseases and NO2. Studies on the association between self reported air pollution problems and health should be aware of the possibility that disease itself may influence the reporting of air pollution. PMID:18307757
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Fishing for causes and cures of motor neuron disorders
Patten, Shunmoogum A.; Armstrong, Gary A. B.; Lissouba, Alexandra; Kabashi, Edor; Parker, J. Alex; Drapeau, Pierre
2014-01-01
Motor neuron disorders (MNDs) are a clinically heterogeneous group of neurological diseases characterized by progressive degeneration of motor neurons, and share some common pathological pathways. Despite remarkable advances in our understanding of these diseases, no curative treatment for MNDs exists. To better understand the pathogenesis of MNDs and to help develop new treatments, the establishment of animal models that can be studied efficiently and thoroughly is paramount. The zebrafish (Danio rerio) is increasingly becoming a valuable model for studying human diseases and in screening for potential therapeutics. In this Review, we highlight recent progress in using zebrafish to study the pathology of the most common MNDs: spinal muscular atrophy (SMA), amyotrophic lateral sclerosis (ALS) and hereditary spastic paraplegia (HSP). These studies indicate the power of zebrafish as a model to study the consequences of disease-related genes, because zebrafish homologues of human genes have conserved functions with respect to the aetiology of MNDs. Zebrafish also complement other animal models for the study of pathological mechanisms of MNDs and are particularly advantageous for the screening of compounds with therapeutic potential. We present an overview of their potential usefulness in MND drug discovery, which is just beginning and holds much promise for future therapeutic development. PMID:24973750
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Silkworm: A Promising Model Organism in Life Science.
Meng, Xu; Zhu, Feifei; Chen, Keping
2017-09-01
As an important economic insect, silkworm Bombyx mori (L.) (Lepidoptera: Bombycidae) has numerous advantages in life science, such as low breeding cost, large progeny size, short generation time, and clear genetic background. Additionally, there are rich genetic resources associated with silkworms. The completion of the silkworm genome has further accelerated it to be a modern model organism in life science. Genomic studies showed that some silkworm genes are highly homologous to certain genes related to human hereditary disease and, therefore, are a candidate model for studying human disease. In this article, we provided a review of silkworm as an important model in various research areas, including human disease, screening of antimicrobial agents, environmental safety monitoring, and antitumor studies. In addition, the application potentiality of silkworm model in life sciences was discussed. © The Author 2017. Published by Oxford University Press on behalf of Entomological Society of America.
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Beyond factor analysis: Multidimensionality and the Parkinson's Disease Sleep Scale-Revised.
Pushpanathan, Maria E; Loftus, Andrea M; Gasson, Natalie; Thomas, Meghan G; Timms, Caitlin F; Olaithe, Michelle; Bucks, Romola S
2018-01-01
Many studies have sought to describe the relationship between sleep disturbance and cognition in Parkinson's disease (PD). The Parkinson's Disease Sleep Scale (PDSS) and its variants (the Parkinson's disease Sleep Scale-Revised; PDSS-R, and the Parkinson's Disease Sleep Scale-2; PDSS-2) quantify a range of symptoms impacting sleep in only 15 items. However, data from these scales may be problematic as included items have considerable conceptual breadth, and there may be overlap in the constructs assessed. Multidimensional measurement models, accounting for the tendency for items to measure multiple constructs, may be useful more accurately to model variance than traditional confirmatory factor analysis. In the present study, we tested the hypothesis that a multidimensional model (a bifactor model) is more appropriate than traditional factor analysis for data generated by these types of scales, using data collected using the PDSS-R as an exemplar. 166 participants diagnosed with idiopathic PD participated in this study. Using PDSS-R data, we compared three models: a unidimensional model; a 3-factor model consisting of sub-factors measuring insomnia, motor symptoms and obstructive sleep apnoea (OSA) and REM sleep behaviour disorder (RBD) symptoms; and, a confirmatory bifactor model with both a general factor and the same three sub-factors. Only the confirmatory bifactor model achieved satisfactory model fit, suggesting that PDSS-R data are multidimensional. There were differential associations between factor scores and patient characteristics, suggesting that some PDSS-R items, but not others, are influenced by mood and personality in addition to sleep symptoms. Multidimensional measurement models may also be a helpful tool in the PDSS and the PDSS-2 scales and may improve the sensitivity of these instruments.
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Human airway epithelial cell cultures for modeling respiratory syncytial virus infection.
Pickles, Raymond J
2013-01-01
Respiratory syncytial virus (RSV) is an important human respiratory pathogen with narrow species tropism. Limited availability of human pathologic specimens during early RSV-induced lung disease and ethical restrictions for RSV challenge studies in the lower airways of human volunteers has slowed our understanding of how RSV causes airway disease and greatly limited the development of therapeutic strategies for reducing RSV disease burden. Our current knowledge of RSV infection and pathology is largely based on in vitro studies using nonpolarized epithelial cell-lines grown on plastic or in vivo studies using animal models semipermissive for RSV infection. Although these models have revealed important aspects of RSV infection, replication, and associated inflammatory responses, these models do not broadly recapitulate the early interactions and potential consequences of RSV infection of the human columnar airway epithelium in vivo. In this chapter, the pro et contra of in vitro models of human columnar airway epithelium and their usefulness in respiratory virus pathogenesis and vaccine development studies will be discussed. The use of such culture models to predict characteristics of RSV infection and the correlation of these findings to the human in vivo situation will likely accelerate our understanding of RSV pathogenesis potentially identifying novel strategies for limiting the severity of RSV-associated airway disease.
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A study on specialist or special disease clinics based on big data.
Fang, Zhuyuan; Fan, Xiaowei; Chen, Gong
2014-09-01
Correlation analysis and processing of massive medical information can be implemented through big data technology to find the relevance of different factors in the life cycle of a disease and to provide the basis for scientific research and clinical practice. This paper explores the concept of constructing a big medical data platform and introduces the clinical model construction. Medical data can be collected and consolidated by distributed computing technology. Through analysis technology, such as artificial neural network and grey model, a medical model can be built. Big data analysis, such as Hadoop, can be used to construct early prediction and intervention models as well as clinical decision-making model for specialist and special disease clinics. It establishes a new model for common clinical research for specialist and special disease clinics.
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Using induced pluripotent stem cells derived neurons to model brain diseases.
McKinney, Cindy E
2017-07-01
The ability to use induced pluripotent stem cells (iPSC) to model brain diseases is a powerful tool for unraveling mechanistic alterations in these disorders. Rodent models of brain diseases have spurred understanding of pathology but the concern arises that they may not recapitulate the full spectrum of neuron disruptions associated with human neuropathology. iPSC derived neurons, or other neural cell types, provide the ability to access pathology in cells derived directly from a patient's blood sample or skin biopsy where availability of brain tissue is limiting. Thus, utilization of iPSC to study brain diseases provides an unlimited resource for disease modelling but may also be used for drug screening for effective therapies and may potentially be used to regenerate aged or damaged cells in the future. Many brain diseases across the spectrum of neurodevelopment, neurodegenerative and neuropsychiatric are being approached by iPSC models. The goal of an iPSC based disease model is to identify a cellular phenotype that discriminates the disease-bearing cells from the control cells. In this mini-review, the importance of iPSC cell models validated for pluripotency, germline competency and function assessments is discussed. Selected examples for the variety of brain diseases that are being approached by iPSC technology to discover or establish the molecular basis of the neuropathology are discussed.
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Agent-Based vs. Equation-based Epidemiological Models:A Model Selection Case Study
DOE Office of Scientific and Technical Information (OSTI.GOV)
Sukumar, Sreenivas R; Nutaro, James J
This paper is motivated by the need to design model validation strategies for epidemiological disease-spread models. We consider both agent-based and equation-based models of pandemic disease spread and study the nuances and complexities one has to consider from the perspective of model validation. For this purpose, we instantiate an equation based model and an agent based model of the 1918 Spanish flu and we leverage data published in the literature for our case- study. We present our observations from the perspective of each implementation and discuss the application of model-selection criteria to compare the risk in choosing one modeling paradigmmore » to another. We conclude with a discussion of our experience and document future ideas for a model validation framework.« less
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Based on these data and preliminary studies, this proposal will be composed of a multiscale source-to-dose analysis approach for assessing the exposure interactions of environmental and biological systems. Once the entire modeling system is validated, it will run f...
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New Lives: Some Case Studies in Minamata.
ERIC Educational Resources Information Center
Tsurumi, Kazuko
Three case studies of young Japanese adults who fell ill with Minamata disease (a form of methyl-mercury poisoning) are presented and the adjustment of the individuals to the disease is analyzed in terms of a model of creativity. The model distinguishes three types of creativity: identificational (in which one identifies with old ideas and…
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The development of a simulation model of primary prevention strategies for coronary heart disease.
Babad, Hannah; Sanderson, Colin; Naidoo, Bhash; White, Ian; Wang, Duolao
2002-11-01
This paper describes the present state of development of a discrete-event micro-simulation model for coronary heart disease prevention. The model is intended to support health policy makers in assessing the impacts on health care resources of different primary prevention strategies. For each person, a set of times to disease events, conditional on the individual's risk factor profile, is sampled from a set of probability distributions that are derived from a new analysis of the Framingham cohort study on coronary heart disease. Methods used to model changes in behavioural and physiological risk factors are discussed and a description of the simulation logic is given. The model incorporates POST (Patient Oriented Simulation Technique) simulation routines.
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Kwakwa, Kristin A; Vanderburgh, Joseph P; Guelcher, Scott A; Sterling, Julie A
2017-08-01
Bone is a structurally unique microenvironment that presents many challenges for the development of 3D models for studying bone physiology and diseases, including cancer. As researchers continue to investigate the interactions within the bone microenvironment, the development of 3D models of bone has become critical. 3D models have been developed that replicate some properties of bone, but have not fully reproduced the complex structural and cellular composition of the bone microenvironment. This review will discuss 3D models including polyurethane, silk, and collagen scaffolds that have been developed to study tumor-induced bone disease. In addition, we discuss 3D printing techniques used to better replicate the structure of bone. 3D models that better replicate the bone microenvironment will help researchers better understand the dynamic interactions between tumors and the bone microenvironment, ultimately leading to better models for testing therapeutics and predicting patient outcomes.
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Spatial modelling of disease using data- and knowledge-driven approaches.
Stevens, Kim B; Pfeiffer, Dirk U
2011-09-01
The purpose of spatial modelling in animal and public health is three-fold: describing existing spatial patterns of risk, attempting to understand the biological mechanisms that lead to disease occurrence and predicting what will happen in the medium to long-term future (temporal prediction) or in different geographical areas (spatial prediction). Traditional methods for temporal and spatial predictions include general and generalized linear models (GLM), generalized additive models (GAM) and Bayesian estimation methods. However, such models require both disease presence and absence data which are not always easy to obtain. Novel spatial modelling methods such as maximum entropy (MAXENT) and the genetic algorithm for rule set production (GARP) require only disease presence data and have been used extensively in the fields of ecology and conservation, to model species distribution and habitat suitability. Other methods, such as multicriteria decision analysis (MCDA), use knowledge of the causal factors of disease occurrence to identify areas potentially suitable for disease. In addition to their less restrictive data requirements, some of these novel methods have been shown to outperform traditional statistical methods in predictive ability (Elith et al., 2006). This review paper provides details of some of these novel methods for mapping disease distribution, highlights their advantages and limitations, and identifies studies which have used the methods to model various aspects of disease distribution. Copyright © 2011. Published by Elsevier Ltd.
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Mouse Model for the Preclinical Study of Metastatic Disease | NCI Technology Transfer Center | TTC
The Laboratory of Cancer Biology and Genetics, National Cancer Institute seeks partners for collaborative research to co-develop a mouse model that shows preclinical therapeutic response of residual metastatic disease.
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A Systematic Review of Health Economics Simulation Models of Chronic Obstructive Pulmonary Disease.
Zafari, Zafar; Bryan, Stirling; Sin, Don D; Conte, Tania; Khakban, Rahman; Sadatsafavi, Mohsen
2017-01-01
Many decision-analytic models with varying structures have been developed to inform resource allocation in chronic obstructive pulmonary disease (COPD). To review COPD models for their adherence to the best practice modeling recommendations and their assumptions regarding important aspects of the natural history of COPD. A systematic search of English articles reporting on the development or application of a decision-analytic model in COPD was performed in MEDLINE, Embase, and citations within reviewed articles. Studies were summarized and evaluated on the basis of their adherence to the Consolidated Health Economic Evaluation Reporting Standards. They were also evaluated for the underlying assumptions about disease progression, heterogeneity, comorbidity, and treatment effects. Forty-nine models of COPD were included. Decision trees and Markov models were the most popular techniques (43 studies). Quality of reporting and adherence to the guidelines were generally high, especially in more recent publications. Disease progression was modeled through clinical staging in most studies. Although most studies (n = 43) had incorporated some aspects of COPD heterogeneity, only 8 reported the results across subgroups. Only 2 evaluations explicitly considered the impact of comorbidities. Treatment effect had been mostly modeled (20) as both reduction in exacerbation rate and improvement in lung function. Many COPD models have been developed, generally with similar structural elements. COPD is highly heterogeneous, and comorbid conditions play an important role in its burden. These important aspects, however, have not been adequately addressed in most of the published models. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.
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Green, Colin; Shearer, James; Ritchie, Craig W; Zajicek, John P
2011-01-01
To consider the methods available to model Alzheimer's disease (AD) progression over time to inform on the structure and development of model-based evaluations, and the future direction of modelling methods in AD. A systematic search of the health care literature was undertaken to identify methods to model disease progression in AD. Modelling methods are presented in a descriptive review. The literature search identified 42 studies presenting methods or applications of methods to model AD progression over time. The review identified 10 general modelling frameworks available to empirically model the progression of AD as part of a model-based evaluation. Seven of these general models are statistical models predicting progression of AD using a measure of cognitive function. The main concerns with models are on model structure, around the limited characterization of disease progression, and on the use of a limited number of health states to capture events related to disease progression over time. None of the available models have been able to present a comprehensive model of the natural history of AD. Although helpful, there are serious limitations in the methods available to model progression of AD over time. Advances are needed to better model the progression of AD and the effects of the disease on peoples' lives. Recent evidence supports the need for a multivariable approach to the modelling of AD progression, and indicates that a latent variable analytic approach to characterising AD progression is a promising avenue for advances in the statistical development of modelling methods. Copyright © 2011 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.
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Reverse engineering human neurodegenerative disease using pluripotent stem cell technology.
Liu, Ying; Deng, Wenbin
2016-05-01
With the technology of reprogramming somatic cells by introducing defined transcription factors that enables the generation of "induced pluripotent stem cells (iPSCs)" with pluripotency comparable to that of embryonic stem cells (ESCs), it has become possible to use this technology to produce various cells and tissues that have been difficult to obtain from living bodies. This advancement is bringing forth rapid progress in iPSC-based disease modeling, drug screening, and regenerative medicine. More and more studies have demonstrated that phenotypes of adult-onset neurodegenerative disorders could be rather faithfully recapitulated in iPSC-derived neural cell cultures. Moreover, despite the adult-onset nature of the diseases, pathogenic phenotypes and cellular abnormalities often exist in early developmental stages, providing new "windows of opportunity" for understanding mechanisms underlying neurodegenerative disorders and for discovering new medicines. The cell reprogramming technology enables a reverse engineering approach for modeling the cellular degenerative phenotypes of a wide range of human disorders. An excellent example is the study of the human neurodegenerative disease amyotrophic lateral sclerosis (ALS) using iPSCs. ALS is a progressive neurodegenerative disease characterized by the loss of upper and lower motor neurons (MNs), culminating in muscle wasting and death from respiratory failure. The iPSC approach provides innovative cell culture platforms to serve as ALS patient-derived model systems. Researchers have converted iPSCs derived from ALS patients into MNs and various types of glial cells, all of which are involved in ALS, to study the disease. The iPSC technology could be used to determine the role of specific genetic factors to track down what's wrong in the neurodegenerative disease process in the "disease-in-a-dish" model. Meanwhile, parallel experiments of targeting the same specific genes in human ESCs could also be performed to control and to complement the iPSC-based approach for ALS disease modeling studies. Much knowledge has been generated from the study of both ALS iPSCs and ESCs. As these methods have advantages and disadvantages that should be balanced on experimental design in order for them to complement one another, combining the diverse methods would help build an expanded knowledge of ALS pathophysiology. The goals are to reverse engineer the human disease using ESCs and iPSCs, generate lineage reporter lines and in vitro disease models, target disease related genes, in order to better understand the molecular and cellular mechanisms of differentiation regulation along neural (neuronal versus glial) lineages, to unravel the pathogenesis of the neurodegenerative disease, and to provide appropriate cell sources for replacement therapy. This article is part of a Special Issue entitled SI: PSC and the brain. Copyright © 2015 Elsevier B.V. All rights reserved.
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Olivier, Alicia K.; Gibson-Corley, Katherine N.
2015-01-01
Multiple organ systems, including the gastrointestinal tract, pancreas, and hepatobiliary systems, are affected by cystic fibrosis (CF). Many of these changes begin early in life and are difficult to study in young CF patients. Recent development of novel CF animal models has expanded opportunities in the field to better understand CF pathogenesis and evaluate traditional and innovative therapeutics. In this review, we discuss manifestations of CF disease in gastrointestinal, pancreatic, and hepatobiliary systems of humans and animal models. We also compare the similarities and limitations of animal models and discuss future directions for modeling CF. PMID:25591863
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An object simulation model for modeling hypothetical disease epidemics – EpiFlex
Hanley, Brian
2006-01-01
Background EpiFlex is a flexible, easy to use computer model for a single computer, intended to be operated by one user who need not be an expert. Its purpose is to study in-silico the epidemic behavior of a wide variety of diseases, both known and theoretical, by simulating their spread at the level of individuals contracting and infecting others. To understand the system fully, this paper must be read together in conjunction with study of the software and its results. EpiFlex is evaluated using results from modeling influenza A epidemics and comparing them with a variety of field data sources and other types of modeling. EpiFlex is an object-oriented Monte Carlo system, allocating entities to correspond to individuals, disease vectors, diseases, and the locations that hosts may inhabit. EpiFlex defines eight different contact types available for a disease. Contacts occur inside locations within the model. Populations are composed of demographic groups, each of which has a cycle of movement between locations. Within locations, superspreading is defined by skewing of contact distributions. Results EpiFlex indicates three phenomena of interest for public health: (1) R0 is variable, and the smaller the population, the larger the infected fraction within that population will be; (2) significant compression/synchronization between cities by a factor of roughly 2 occurs between the early incubation phase of a multi-city epidemic and the major manifestation phase; (3) if better true morbidity data were available, more asymptomatic hosts would be seen to spread disease than we currently believe is the case for influenza. These results suggest that field research to study such phenomena, while expensive, should be worthwhile. Conclusion Since EpiFlex shows all stages of disease progression, detailed insight into the progress of epidemics is possible. EpiFlex shows the characteristic multimodality and apparently random variation characteristic of real world data, but does so as an emergent property of a carefully constructed model of disease dynamics and is not simply a stochastic system. EpiFlex can provide a better understanding of infectious diseases and strategies for response. PMID:16928271
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2009-01-01
Background Isoproterenol-induced cardiac hypertrophy in mice has been used in a number of studies to model human cardiac disease. In this study, we compared the transcriptional response of the heart in this model to other animal models of heart failure, as well as to the transcriptional response of human hearts suffering heart failure. Results We performed microarray analyses on RNA from mice with isoproterenol-induced cardiac hypertrophy and mice with exercise-induced physiological hypertrophy and identified 865 and 2,534 genes that were significantly altered in pathological and physiological cardiac hypertrophy models, respectively. We compared our results to 18 different microarray data sets (318 individual arrays) representing various other animal models and four human cardiac diseases and identified a canonical set of 64 genes that are generally altered in failing hearts. We also produced a pairwise similarity matrix to illustrate relatedness of animal models with human heart disease and identified ischemia as the human condition that most resembles isoproterenol treatment. Conclusion The overall patterns of gene expression are consistent with observed structural and molecular differences between normal and maladaptive cardiac hypertrophy and support a role for the immune system (or immune cell infiltration) in the pathology of stress-induced hypertrophy. Cross-study comparisons such as the results presented here provide targets for further research of cardiac disease that might generally apply to maladaptive cardiac stresses and are also a means of identifying which animal models best recapitulate human disease at the transcriptional level. PMID:20003209
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Experimental anti-GBM nephritis as an analytical tool for studying spontaneous lupus nephritis.
Du, Yong; Fu, Yuyang; Mohan, Chandra
2008-01-01
Systemic lupus erythematosus (SLE) is an autoimmune disease that results in immune-mediated damage to multiple organs. Among these, kidney involvement is the most common and fatal. Spontaneous lupus nephritis (SLN) in mouse models has provided valuable insights into the underlying mechanisms of human lupus nephritis. However, SLN in mouse models takes 6-12 months to manifest; hence there is clearly the need for a mouse model that can be used to unveil the pathogenic processes that lead to immune nephritis over a shorter time frame. In this article more than 25 different molecules are reviewed that have been studied both in the anti-glomerular basement membrane (anti-GBM) model and in SLN and it was found that these molecules influence both diseases in a parallel fashion, suggesting that the two disease settings share common molecular mechanisms. Based on these observations, the authors believe the experimental anti-GBM disease model might be one of the best tools currently available for uncovering the downstream molecular mechanisms leading to SLN.
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Genetically manipulated mouse models of lung disease: potential and pitfalls
Choi, Alexander J. S.; Owen, Caroline A.; Choi, Augustine M. K.
2012-01-01
Gene targeting in mice (transgenic and knockout) has provided investigators with an unparalleled armamentarium in recent decades to dissect the cellular and molecular basis of critical pathophysiological states. Fruitful information has been derived from studies using these genetically engineered mice with significant impact on our understanding, not only of specific biological processes spanning cell proliferation to cell death, but also of critical molecular events involved in the pathogenesis of human disease. This review will focus on the use of gene-targeted mice to study various models of lung disease including airways diseases such as asthma and chronic obstructive pulmonary disease, and parenchymal lung diseases including idiopathic pulmonary fibrosis, pulmonary hypertension, pneumonia, and acute lung injury. We will attempt to review the current technological approaches of generating gene-targeted mice and the enormous dataset derived from these studies, providing a template for lung investigators. PMID:22198907
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Modelling the influence of human behaviour on the spread of infectious diseases: a review.
Funk, Sebastian; Salathé, Marcel; Jansen, Vincent A A
2010-09-06
Human behaviour plays an important role in the spread of infectious diseases, and understanding the influence of behaviour on the spread of diseases can be key to improving control efforts. While behavioural responses to the spread of a disease have often been reported anecdotally, there has been relatively little systematic investigation into how behavioural changes can affect disease dynamics. Mathematical models for the spread of infectious diseases are an important tool for investigating and quantifying such effects, not least because the spread of a disease among humans is not amenable to direct experimental study. Here, we review recent efforts to incorporate human behaviour into disease models, and propose that such models can be broadly classified according to the type and source of information which individuals are assumed to base their behaviour on, and according to the assumed effects of such behaviour. We highlight recent advances as well as gaps in our understanding of the interplay between infectious disease dynamics and human behaviour, and suggest what kind of data taking efforts would be helpful in filling these gaps.
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Modelling the influence of human behaviour on the spread of infectious diseases: a review
Funk, Sebastian; Salathé, Marcel; Jansen, Vincent A. A.
2010-01-01
Human behaviour plays an important role in the spread of infectious diseases, and understanding the influence of behaviour on the spread of diseases can be key to improving control efforts. While behavioural responses to the spread of a disease have often been reported anecdotally, there has been relatively little systematic investigation into how behavioural changes can affect disease dynamics. Mathematical models for the spread of infectious diseases are an important tool for investigating and quantifying such effects, not least because the spread of a disease among humans is not amenable to direct experimental study. Here, we review recent efforts to incorporate human behaviour into disease models, and propose that such models can be broadly classified according to the type and source of information which individuals are assumed to base their behaviour on, and according to the assumed effects of such behaviour. We highlight recent advances as well as gaps in our understanding of the interplay between infectious disease dynamics and human behaviour, and suggest what kind of data taking efforts would be helpful in filling these gaps. PMID:20504800
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Bounded rationality alters the dynamics of paediatric immunization acceptance.
Oraby, Tamer; Bauch, Chris T
2015-06-02
Interactions between disease dynamics and vaccinating behavior have been explored in many coupled behavior-disease models. Cognitive effects such as risk perception, framing, and subjective probabilities of adverse events can be important determinants of the vaccinating behaviour, and represent departures from the pure "rational" decision model that are often described as "bounded rationality". However, the impact of such cognitive effects in the context of paediatric infectious disease vaccines has received relatively little attention. Here, we develop a disease-behavior model that accounts for bounded rationality through prospect theory. We analyze the model and compare its predictions to a reduced model that lacks bounded rationality. We find that, in general, introducing bounded rationality increases the dynamical richness of the model and makes it harder to eliminate a paediatric infectious disease. In contrast, in other cases, a low cost, highly efficacious vaccine can be refused, even when the rational decision model predicts acceptance. Injunctive social norms can prevent vaccine refusal, if vaccine acceptance is sufficiently high in the beginning of the vaccination campaign. Cognitive processes can have major impacts on the predictions of behaviour-disease models, and further study of such processes in the context of vaccination is thus warranted.
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Bounded rationality alters the dynamics of paediatric immunization acceptance
Oraby, Tamer; Bauch, Chris T.
2015-01-01
Interactions between disease dynamics and vaccinating behavior have been explored in many coupled behavior-disease models. Cognitive effects such as risk perception, framing, and subjective probabilities of adverse events can be important determinants of the vaccinating behaviour, and represent departures from the pure “rational” decision model that are often described as “bounded rationality”. However, the impact of such cognitive effects in the context of paediatric infectious disease vaccines has received relatively little attention. Here, we develop a disease-behavior model that accounts for bounded rationality through prospect theory. We analyze the model and compare its predictions to a reduced model that lacks bounded rationality. We find that, in general, introducing bounded rationality increases the dynamical richness of the model and makes it harder to eliminate a paediatric infectious disease. In contrast, in other cases, a low cost, highly efficacious vaccine can be refused, even when the rational decision model predicts acceptance. Injunctive social norms can prevent vaccine refusal, if vaccine acceptance is sufficiently high in the beginning of the vaccination campaign. Cognitive processes can have major impacts on the predictions of behaviour-disease models, and further study of such processes in the context of vaccination is thus warranted. PMID:26035413
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The SIR model of Zika virus disease outbreak in Brazil at year 2015
NASA Astrophysics Data System (ADS)
Aik, Lim Eng; Kiang, Lam Chee; Hong, Tan Wei; Abu, Mohd Syafarudy
2017-05-01
This research study demonstrates a numerical model intended for comprehension the spread of the year 2015 Zika virus disease utilizing the standard SIR framework. In modeling virulent disease dynamics, it is important to explore whether the illness spread could accomplish a pandemic level or it could be eradicated. Information from the year 2015 Zika virus disease event is utilized and Brazil where the event began is considered in this research study. A three dimensional nonlinear differential equation is formulated and solved numerically utilizing the Euler's method in MS excel. It is appeared from the research study that, with health intercessions of public, the viable regenerative number can be decreased making it feasible for the event to cease to exist. It is additionally indicated numerically that the pandemic can just cease to exist when there are no new infected people in the populace.
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Lee, Geun-Shik; Jeong, Yeon Woo; Kim, Joung Joo; Park, Sun Woo; Ko, Kyeong Hee; Kang, Mina; Kim, Yu Kyung; Jung, Eui-Man; Moon, Changjong; Hyun, Sang Hwan; Hwang, Kyu-Chan; Kim, Nam-Hyung; Shin, Taeyoung; Jeung, Eui-Bae; Hwang, Woo Suk
2014-04-01
Canines are considered the most authentic model for studying multifactorial human diseases, as these animals typically share a common environment with man. Somatic cell nuclear transfer (SCNT) technology along with genetic engineering of nuclear donor cells provides a unique opportunity for examining human diseases using transgenic canines. In the present study, we generated transgenic canines that overexpressed the human amyloid precursor protein (APP) gene containing well-characterized familial Alzheimer's disease (AD) mutations. We successfully obtained five out of six live puppies by SCNT. This was confirmed by observing the expression of green fluorescence protein in the body as a visual transgenic marker and the overexpression of the mutated APP gene in the brain. The transgenic canines developed AD-like symptoms, such as enlarged ventricles, an atrophied hippocampus, and β-amyloid plaques in the brain. Thus, the transgenic canines we created can serve as a novel animal model for studying human AD.
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Retinoids and Retinal Diseases
Kiser, Philip D.; Palczewski, Krzysztof
2016-01-01
Recent progress in molecular understanding of the retinoid cycle in mammalian retina stems from painstaking biochemical reconstitution studies supported by natural or engineered animal models with known genetic lesions and studies of humans with specific genetic blinding diseases. Structural and membrane biology have been used to detect critical retinal enzymes and proteins and their substrates and ligands, placing them in a cellular context. These studies have been supplemented by analytical chemistry methods that have identified small molecules by their spectral characteristics, often in conjunction with the evaluation of models of animal retinal disease. It is from this background that rational therapeutic interventions to correct genetic defects or environmental insults are identified. Thus, most presently accepted modulators of the retinoid cycle already have demonstrated promising results in animal models of retinal degeneration. These encouraging signs indicate that some human blinding diseases can be alleviated by pharmacological interventions. PMID:27917399
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Anesthetic effects in Alzheimer transgenic mouse models.
Tang, Junxia X; Eckenhoff, Maryellen F
2013-12-02
Research has improved the diagnosis of Alzheimer's disease, and at earlier stages, but effective therapy continues to be elusive. Current effort is focused on delay. Environmental factors are thought to interact with genetics to modulate the progression of the disease, and one such environmental factor is exposure to general anesthetics. The possibility that some anesthetic effects have long-term consequences is of general interest and concern. The difficulty of studying a chronic, age-related disease in humans combined with the fact that anesthetics are rarely given without surgery, has led to a focus on animal models. Transgenic mouse models have been developed to mimic the hallmarks of Alzheimer's disease, including amyloid beta accumulation (plaque), neurofibrillary tangles, and cognitive dysfunction. While none of the models recapitulate the human disease with high fidelity, they allow a first look at anesthetic-Alzheimer interactions in a reasonable time frame. In studies found to date, none have concluded that anesthetics alone cause a significant change in cognitive decline, but rather an acceleration in Alzheimer neuropathology. Further studies are required to define the best anesthetic paradigm for our elderly population to mitigate changes in neuropathology and potentially cognition. Copyright © 2012 Elsevier Inc. All rights reserved.
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Use of genome editing tools in human stem cell-based disease modeling and precision medicine.
Wei, Yu-da; Li, Shuang; Liu, Gai-gai; Zhang, Yong-xian; Ding, Qiu-rong
2015-10-01
Precision medicine emerges as a new approach that takes into account individual variability. The successful conduct of precision medicine requires the use of precise disease models. Human pluripotent stem cells (hPSCs), as well as adult stem cells, can be differentiated into a variety of human somatic cell types that can be used for research and drug screening. The development of genome editing technology over the past few years, especially the CRISPR/Cas system, has made it feasible to precisely and efficiently edit the genetic background. Therefore, disease modeling by using a combination of human stem cells and genome editing technology has offered a new platform to generate " personalized " disease models, which allow the study of the contribution of individual genetic variabilities to disease progression and the development of precise treatments. In this review, recent advances in the use of genome editing in human stem cells and the generation of stem cell models for rare diseases and cancers are discussed.
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Advances in the Study of Heart Development and Disease Using Zebrafish
Brown, Daniel R.; Samsa, Leigh Ann; Qian, Li; Liu, Jiandong
2016-01-01
Animal models of cardiovascular disease are key players in the translational medicine pipeline used to define the conserved genetic and molecular basis of disease. Congenital heart diseases (CHDs) are the most common type of human birth defect and feature structural abnormalities that arise during cardiac development and maturation. The zebrafish, Danio rerio, is a valuable vertebrate model organism, offering advantages over traditional mammalian models. These advantages include the rapid, stereotyped and external development of transparent embryos produced in large numbers from inexpensively housed adults, vast capacity for genetic manipulation, and amenability to high-throughput screening. With the help of modern genetics and a sequenced genome, zebrafish have led to insights in cardiovascular diseases ranging from CHDs to arrhythmia and cardiomyopathy. Here, we discuss the utility of zebrafish as a model system and summarize zebrafish cardiac morphogenesis with emphasis on parallels to human heart diseases. Additionally, we discuss the specific tools and experimental platforms utilized in the zebrafish model including forward screens, functional characterization of candidate genes, and high throughput applications. PMID:27335817
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Wang, Xulong; Philip, Vivek M.; Ananda, Guruprasad; White, Charles C.; Malhotra, Ankit; Michalski, Paul J.; Karuturi, Krishna R. Murthy; Chintalapudi, Sumana R.; Acklin, Casey; Sasner, Michael; Bennett, David A.; De Jager, Philip L.; Howell, Gareth R.; Carter, Gregory W.
2018-01-01
Recent technical and methodological advances have greatly enhanced genome-wide association studies (GWAS). The advent of low-cost, whole-genome sequencing facilitates high-resolution variant identification, and the development of linear mixed models (LMM) allows improved identification of putatively causal variants. While essential for correcting false positive associations due to sample relatedness and population stratification, LMMs have commonly been restricted to quantitative variables. However, phenotypic traits in association studies are often categorical, coded as binary case-control or ordered variables describing disease stages. To address these issues, we have devised a method for genomic association studies that implements a generalized LMM (GLMM) in a Bayesian framework, called Bayes-GLMM. Bayes-GLMM has four major features: (1) support of categorical, binary, and quantitative variables; (2) cohesive integration of previous GWAS results for related traits; (3) correction for sample relatedness by mixed modeling; and (4) model estimation by both Markov chain Monte Carlo sampling and maximal likelihood estimation. We applied Bayes-GLMM to the whole-genome sequencing cohort of the Alzheimer’s Disease Sequencing Project. This study contains 570 individuals from 111 families, each with Alzheimer’s disease diagnosed at one of four confidence levels. Using Bayes-GLMM we identified four variants in three loci significantly associated with Alzheimer’s disease. Two variants, rs140233081 and rs149372995, lie between PRKAR1B and PDGFA. The coded proteins are localized to the glial-vascular unit, and PDGFA transcript levels are associated with Alzheimer’s disease-related neuropathology. In summary, this work provides implementation of a flexible, generalized mixed-model approach in a Bayesian framework for association studies. PMID:29507048
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Barnett, Anthony I; Hall, Wayne; Fry, Craig L; Dilkes-Frayne, Ella; Carter, Adrian
2017-12-14
Addiction treatment providers' views about the disease model of addiction (DMA), and their contemporary views about the brain disease model of addiction (BDMA), remain an understudied area. We systematically reviewed treatment providers' attitudes about the DMA/BDMA, examined factors associated with positive or negative attitudes and assessed their views on the potential clinical impact of both models. Pubmed, EMBASE, PsycINFO, CINAHL Plus and Sociological Abstracts were systematically searched. Original papers on treatment providers' views about the DMA/BDMA and its clinical impact were included. Studies focussing on tobacco, behavioural addictions or non-Western populations were excluded. The 34 included studies were predominantly quantitative and conducted in the USA. Among mixed findings of treatment providers' support for the DMA, strong validity studies indicated treatment providers supported the disease concept and moral, free-will or social models simultaneously. Support for the DMA was positively associated with treatment providers' age, year of qualification, certification status, religious beliefs, being in recovery and Alcoholics Anonymous attendance. Greater education was negatively associated with DMA support. Treatment providers identified potential positive (e.g. reduced stigma) and negative (e.g. increased sense of helplessness) impacts of the DMA on client behaviour. The review suggests treatment providers may endorse disease and other models while strategically deploying the DMA for presumed therapeutic benefits. Varying DMA support across workforces indicated service users may experience multiple and potentially contradictory explanations of addiction. Future policy development will benefit by considering how treatment providers adopt disease concepts in practice. © 2017 Australasian Professional Society on Alcohol and other Drugs.
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Adaptation and Evaluation of a Multi-Criteria Decision Analysis Model for Lyme Disease Prevention
Aenishaenslin, Cécile; Gern, Lise; Michel, Pascal; Ravel, André; Hongoh, Valérie; Waaub, Jean-Philippe; Milord, François; Bélanger, Denise
2015-01-01
Designing preventive programs relevant to vector-borne diseases such as Lyme disease (LD) can be complex given the need to include multiple issues and perspectives into prioritizing public health actions. A multi-criteria decision aid (MCDA) model was previously used to rank interventions for LD prevention in Quebec, Canada, where the disease is emerging. The aim of the current study was to adapt and evaluate the decision model constructed in Quebec under a different epidemiological context, in Switzerland, where LD has been endemic for the last thirty years. The model adaptation was undertaken with a group of Swiss stakeholders using a participatory approach. The PROMETHEE method was used for multi-criteria analysis. Key elements and results of the MCDA model are described and contrasted with the Quebec model. All criteria and most interventions of the MCDA model developed for LD prevention in Quebec were directly transferable to the Swiss context. Four new decision criteria were added, and the list of proposed interventions was modified. Based on the overall group ranking, interventions targeting human populations were prioritized in the Swiss model, with the top ranked action being the implementation of a large communication campaign. The addition of criteria did not significantly alter the intervention rankings, but increased the capacity of the model to discriminate between highest and lowest ranked interventions. The current study suggests that beyond the specificity of the MCDA models developed for Quebec and Switzerland, their general structure captures the fundamental and common issues that characterize the complexity of vector-borne disease prevention. These results should encourage public health organizations to adapt, use and share MCDA models as an effective and functional approach to enable the integration of multiple perspectives and considerations in the prevention and control of complex public health issues such as Lyme disease or other vector-borne and zoonotic diseases. PMID:26295344
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Adaptation and Evaluation of a Multi-Criteria Decision Analysis Model for Lyme Disease Prevention.
Aenishaenslin, Cécile; Gern, Lise; Michel, Pascal; Ravel, André; Hongoh, Valérie; Waaub, Jean-Philippe; Milord, François; Bélanger, Denise
2015-01-01
Designing preventive programs relevant to vector-borne diseases such as Lyme disease (LD) can be complex given the need to include multiple issues and perspectives into prioritizing public health actions. A multi-criteria decision aid (MCDA) model was previously used to rank interventions for LD prevention in Quebec, Canada, where the disease is emerging. The aim of the current study was to adapt and evaluate the decision model constructed in Quebec under a different epidemiological context, in Switzerland, where LD has been endemic for the last thirty years. The model adaptation was undertaken with a group of Swiss stakeholders using a participatory approach. The PROMETHEE method was used for multi-criteria analysis. Key elements and results of the MCDA model are described and contrasted with the Quebec model. All criteria and most interventions of the MCDA model developed for LD prevention in Quebec were directly transferable to the Swiss context. Four new decision criteria were added, and the list of proposed interventions was modified. Based on the overall group ranking, interventions targeting human populations were prioritized in the Swiss model, with the top ranked action being the implementation of a large communication campaign. The addition of criteria did not significantly alter the intervention rankings, but increased the capacity of the model to discriminate between highest and lowest ranked interventions. The current study suggests that beyond the specificity of the MCDA models developed for Quebec and Switzerland, their general structure captures the fundamental and common issues that characterize the complexity of vector-borne disease prevention. These results should encourage public health organizations to adapt, use and share MCDA models as an effective and functional approach to enable the integration of multiple perspectives and considerations in the prevention and control of complex public health issues such as Lyme disease or other vector-borne and zoonotic diseases.
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Galleria mellonella larvae as an infection model for group A streptococcus
Loh, Jacelyn MS; Adenwalla, Nazneen; Wiles, Siouxsie; Proft, Thomas
2013-01-01
Group A streptococcus is a strict human pathogen that can cause a wide range of diseases, such as tonsillitis, impetigo, necrotizing fasciitis, toxic shock, and acute rheumatic fever. Modeling human diseases in animals is complicated, and rapid, simple, and cost-effective in vivo models of GAS infection are clearly lacking. Recently, the use of non-mammalian models to model human disease is starting to re-attract attention. Galleria mellonella larvae, also known as wax worms, have been investigated for modeling a number of bacterial pathogens, and have been shown to be a useful model to study pathogenesis of the M3 serotype of GAS. In this study we provide further evidence of the validity of the wax worm model by testing different GAS M-types, as well as investigating the effect of bacterial growth phase and incubation temperature on GAS virulence in this model. In contrast to previous studies, we show that the M-protein, among others, is an important virulence factor that can be effectively modeled in the wax worm. We also highlight the need for a more in-depth investigation of the effects of experimental design and wax worm supply before we can properly vindicate the wax worm model for studying GAS pathogenesis. PMID:23652836
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Modeling ALS and FTD with iPSC-derived Neurons
Lee, Sebum; Huang, Eric J.
2015-01-01
Recent advances in genetics and neuropathology support the idea that amyotrophic lateral sclerosis (ALS) and frontotemporal lobar dementia (FTD) are two ends of a disease spectrum. Although several animal models have been developed to investigate the pathogenesis and disease progression in ALS and FTD, there are significant limitations that hamper our ability to connect these models with the neurodegenerative processes in human diseases. With the technical breakthrough in reprogramming biology, it is now possible to generate patient-specific induced pluripotent stem cells (iPSCs) and disease-relevant neuron subtypes. This review provides a comprehensive summary of studies that use iPSC-derived neurons to model ALS and FTD. We discuss the unique capabilities of iPSC-derived neurons that capture some key features of ALS and FTD, and underscore their potential roles in drug discovery. There are, however, several critical caveats that require improvements before iPSC-derived neurons can become highly effective disease models. PMID:26462653
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Exploring Human Diseases and Biological Mechanisms by Protein Structure Prediction and Modeling.
Wang, Juexin; Luttrell, Joseph; Zhang, Ning; Khan, Saad; Shi, NianQing; Wang, Michael X; Kang, Jing-Qiong; Wang, Zheng; Xu, Dong
2016-01-01
Protein structure prediction and modeling provide a tool for understanding protein functions by computationally constructing protein structures from amino acid sequences and analyzing them. With help from protein prediction tools and web servers, users can obtain the three-dimensional protein structure models and gain knowledge of functions from the proteins. In this chapter, we will provide several examples of such studies. As an example, structure modeling methods were used to investigate the relation between mutation-caused misfolding of protein and human diseases including epilepsy and leukemia. Protein structure prediction and modeling were also applied in nucleotide-gated channels and their interaction interfaces to investigate their roles in brain and heart cells. In molecular mechanism studies of plants, rice salinity tolerance mechanism was studied via structure modeling on crucial proteins identified by systems biology analysis; trait-associated protein-protein interactions were modeled, which sheds some light on the roles of mutations in soybean oil/protein content. In the age of precision medicine, we believe protein structure prediction and modeling will play more and more important roles in investigating biomedical mechanism of diseases and drug design.
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Schuster, Christina; Elamin, Marwa; Hardiman, Orla; Bede, Peter
2015-10-01
Recent quantitative neuroimaging studies have been successful in capturing phenotype and genotype-specific changes in dementia syndromes, amyotrophic lateral sclerosis, Parkinson's disease and other neurodegenerative conditions. However, the majority of imaging studies are cross-sectional, despite the obvious superiority of longitudinal study designs in characterising disease trajectories, response to therapy, progression rates and evaluating the presymptomatic phase of neurodegenerative conditions. The aim of this work is to perform a systematic review of longitudinal imaging initiatives in neurodegeneration focusing on methodology, optimal statistical models, follow-up intervals, attrition rates, primary study outcomes and presymptomatic studies. Longitudinal imaging studies were identified from 'PubMed' and reviewed from 1990 to 2014. The search terms 'longitudinal', 'MRI', 'presymptomatic' and 'imaging' were utilised in combination with one of the following degenerative conditions; Alzheimer's disease, amyotrophic lateral sclerosis/motor neuron disease, frontotemporal dementia, Huntington's disease, multiple sclerosis, Parkinson's disease, ataxia, HIV, alcohol abuse/dependence. A total of 423 longitudinal imaging papers and 103 genotype-based presymptomatic studies were identified and systematically reviewed. Imaging techniques, follow-up intervals and attrition rates showed significant variation depending on the primary diagnosis. Commonly used statistical models included analysis of annualised percentage change, mixed and random effect models, and non-linear cumulative models with acceleration-deceleration components. Although longitudinal imaging studies have the potential to provide crucial insights into the presymptomatic phase and natural trajectory of neurodegenerative processes a standardised design is required to enable meaningful data interpretation. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
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Using the Gravity Model to Estimate the Spatial Spread of Vector-Borne Diseases
Barrios, José Miguel; Verstraeten, Willem W.; Maes, Piet; Aerts, Jean-Marie; Farifteh, Jamshid; Coppin, Pol
2012-01-01
The gravity models are commonly used spatial interaction models. They have been widely applied in a large set of domains dealing with interactions amongst spatial entities. The spread of vector-borne diseases is also related to the intensity of interaction between spatial entities, namely, the physical habitat of pathogens’ vectors and/or hosts, and urban areas, thus humans. This study implements the concept behind gravity models in the spatial spread of two vector-borne diseases, nephropathia epidemica and Lyme borreliosis, based on current knowledge on the transmission mechanism of these diseases. Two sources of information on vegetated systems were tested: the CORINE land cover map and MODIS NDVI. The size of vegetated areas near urban centers and a local indicator of occupation-related exposure were found significant predictors of disease risk. Both the land cover map and the space-borne dataset were suited yet not equivalent input sources to locate and measure vegetated areas of importance for disease spread. The overall results point at the compatibility of the gravity model concept and the spatial spread of vector-borne diseases. PMID:23202882
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Using the gravity model to estimate the spatial spread of vector-borne diseases.
Barrios, José Miguel; Verstraeten, Willem W; Maes, Piet; Aerts, Jean-Marie; Farifteh, Jamshid; Coppin, Pol
2012-11-30
The gravity models are commonly used spatial interaction models. They have been widely applied in a large set of domains dealing with interactions amongst spatial entities. The spread of vector-borne diseases is also related to the intensity of interaction between spatial entities, namely, the physical habitat of pathogens’ vectors and/or hosts, and urban areas, thus humans. This study implements the concept behind gravity models in the spatial spread of two vector-borne diseases, nephropathia epidemica and Lyme borreliosis, based on current knowledge on the transmission mechanism of these diseases. Two sources of information on vegetated systems were tested: the CORINE land cover map and MODIS NDVI. The size of vegetated areas near urban centers and a local indicator of occupation-related exposure were found significant predictors of disease risk. Both the land cover map and the space-borne dataset were suited yet not equivalent input sources to locate and measure vegetated areas of importance for disease spread. The overall results point at the compatibility of the gravity model concept and the spatial spread of vector-borne diseases.
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Advances in Reprogramming-Based Study of Neurologic Disorders
Baldwin, Kristin K.
2015-01-01
The technology to convert adult human non-neural cells into neural lineages, through induced pluripotent stem cells (iPSCs), somatic cell nuclear transfer, and direct lineage reprogramming or transdifferentiation has progressed tremendously in recent years. Reprogramming-based approaches aimed at manipulating cellular identity have enormous potential for disease modeling, high-throughput drug screening, cell therapy, and personalized medicine. Human iPSC (hiPSC)-based cellular disease models have provided proof of principle evidence of the validity of this system. However, several challenges remain before patient-specific neurons produced by reprogramming can provide reliable insights into disease mechanisms or be efficiently applied to drug discovery and transplantation therapy. This review will first discuss limitations of currently available reprogramming-based methods in faithfully and reproducibly recapitulating disease pathology. Specifically, we will address issues such as culture heterogeneity, interline and inter-individual variability, and limitations of two-dimensional differentiation paradigms. Second, we will assess recent progress and the future prospects of reprogramming-based neurologic disease modeling. This includes three-dimensional disease modeling, advances in reprogramming technology, prescreening of hiPSCs and creating isogenic disease models using gene editing. PMID:25749371
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Estimating and modeling the cure fraction in population-based cancer survival analysis.
Lambert, Paul C; Thompson, John R; Weston, Claire L; Dickman, Paul W
2007-07-01
In population-based cancer studies, cure is said to occur when the mortality (hazard) rate in the diseased group of individuals returns to the same level as that expected in the general population. The cure fraction (the proportion of patients cured of disease) is of interest to patients and is a useful measure to monitor trends in survival of curable disease. There are 2 main types of cure fraction model, the mixture cure fraction model and the non-mixture cure fraction model, with most previous work concentrating on the mixture cure fraction model. In this paper, we extend the parametric non-mixture cure fraction model to incorporate background mortality, thus providing estimates of the cure fraction in population-based cancer studies. We compare the estimates of relative survival and the cure fraction between the 2 types of model and also investigate the importance of modeling the ancillary parameters in the selected parametric distribution for both types of model.
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Novel approaches to models of Alzheimer's disease pathology for drug screening and development.
Shaughnessy, Laura; Chamblin, Beth; McMahon, Lori; Nair, Ayyappan; Thomas, Mary Beth; Wakefield, John; Koentgen, Frank; Ramabhadran, Ram
2004-01-01
Development of therapeutics for Alzheimer's disease (AD) requires appropriate cell culture models that reflect the errant biochemical pathways and animal models that reflect the pathological hallmarks of the disease as well as the clinical manifestations. In the past two decades AD research has benefited significantly from the use of genetically engineered cell lines expressing components of the amyloid-generating pathway, as well as from the study of transgenic mice that develop the pathological hallmarks of the disease, mainly neuritic plaques. The choice of certain cell types and the choice of mouse as the model organism have been mandated by the feasibility of introduction and expression of foreign genes into these model systems. We describe a universal and efficient gene-delivery system, using lentiviral vectors, that permits the development of relevant cell biological systems using neuronal cells, including primary neurons and animal models in mammalian species best suited for the study of AD. In addition, lentiviral gene delivery provides avenues for creation of novel models by direct and prolonged expression of genes in the brain in any vertebrate animal. TranzVector is a lentiviral vector optimized for efficiency and safety that delivers genes to cells in culture, in tissue explants, and in live animals regardless of the dividing or differentiated status of the cells. Genes can also be delivered efficiently to fertilized single-cell-stage embryos of a wide range of mammalian species, broadening the range of the model organism (from rats to nonhuman primates) for the study of disease mechanism as well as for development of therapeutics. Copyright 2004 Humana Press Inc.
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Curtis, Lauren M; Datiles, Manuel B; Steinberg, Seth M; Mitchell, Sandra A; Bishop, Rachel J; Cowen, Edward W; Mays, Jacqueline; McCarty, John M; Kuzmina, Zoya; Pirsl, Filip; Fowler, Daniel H; Gress, Ronald E; Pavletic, Steven Z
2015-09-01
Ocular chronic graft-versus-host disease is one of the most bothersome common complications following allogeneic hematopoietic stem cell transplantation. The National Institutes of Health Chronic Graft-versus-Host Disease Consensus Project provided expert recommendations for diagnosis and organ severity scoring. However, ocular chronic graft-versus-host disease can be diagnosed only after examination by an ophthalmologist. There are no currently accepted definitions of ocular chronic graft-versus-host disease activity. The goal of this study was to identify predictive models of diagnosis and activity for use in clinical transplant practice. A total of 210 patients with moderate or severe chronic graft-versus-host disease were enrolled in a prospective, cross-sectional, observational study (clinicaltrials.gov identifier: 00092235). Experienced ophthalmologists determined presence of ocular chronic graft-versus-host disease, diagnosis and activity. Measures gathered by the transplant clinician included Schirmer's tear test and National Institutes of Health 0-3 Eye Score. Patient-reported outcome measures were the ocular subscale of the Lee Chronic Graft-versus-Host Disease Symptom Scale and Chief Eye Symptom Intensity Score. Altogether, 157 (75%) patients were diagnosed with ocular chronic graft-versus-host disease; 133 of 157 patients (85%) had active disease. In a multivariable model, the National Institutes of Health Eye Score (P<0.0001) and Schirmer's tear test (P<0.0001) were independent predictors of ocular chronic graft-versus-host disease (sensitivity 93.0%, specificity 92.2%). The Lee ocular subscale was the strongest predictor of active ocular chronic graft-versus-host disease (P<0.0001) (sensitivity 68.5%, specificity 82.6%). Ophthalmology specialist measures that were most strongly predictive of diagnosis in a multivariate model were Oxford grand total staining (P<0.0001) and meibomian score (P=0.027). These results support the use of selected transplant clinician- and patient-reported outcome measures for ocular chronic graft-versus-host disease screening when providing care to allogeneic hematopoietic stem cell transplantation survivors with moderate to severe chronic graft-versus-host disease. Prospective studies are needed to determine if the Lee ocular subscale demonstrates adequate responsiveness as a disease activity outcome measure. Copyright© Ferrata Storti Foundation.
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Curtis, Lauren M.; Datiles, Manuel B.; Steinberg, Seth M.; Mitchell, Sandra A.; Bishop, Rachel J.; Cowen, Edward W.; Mays, Jacqueline; McCarty, John M.; Kuzmina, Zoya; Pirsl, Filip; Fowler, Daniel H.; Gress, Ronald E.; Pavletic, Steven Z.
2015-01-01
Ocular chronic graft-versus-host disease is one of the most bothersome common complications following allogeneic hematopoietic stem cell transplantation. The National Institutes of Health Chronic Graft-versus-Host Disease Consensus Project provided expert recommendations for diagnosis and organ severity scoring. However, ocular chronic graft-versus-host disease can be diagnosed only after examination by an ophthalmologist. There are no currently accepted definitions of ocular chronic graft-versus-host disease activity. The goal of this study was to identify predictive models of diagnosis and activity for use in clinical transplant practice. A total of 210 patients with moderate or severe chronic graft-versus-host disease were enrolled in a prospective, cross-sectional, observational study (clinicaltrials.gov identifier: 00092235). Experienced ophthalmologists determined presence of ocular chronic graft-versus-host disease, diagnosis and activity. Measures gathered by the transplant clinician included Schirmer’s tear test and National Institutes of Health 0–3 Eye Score. Patient-reported outcome measures were the ocular subscale of the Lee Chronic Graft-versus-Host Disease Symptom Scale and Chief Eye Symptom Intensity Score. Altogether, 157 (75%) patients were diagnosed with ocular chronic graft-versus-host disease; 133 of 157 patients (85%) had active disease. In a multivariable model, the National Institutes of Health Eye Score (P<0.0001) and Schirmer’s tear test (P<0.0001) were independent predictors of ocular chronic graft-versus-host disease (sensitivity 93.0%, specificity 92.2%). The Lee ocular subscale was the strongest predictor of active ocular chronic graft-versus-host disease (P<0.0001) (sensitivity 68.5%, specificity 82.6%). Ophthalmology specialist measures that were most strongly predictive of diagnosis in a multivariate model were Oxford grand total staining (P<0.0001) and meibomian score (P=0.027). These results support the use of selected transplant clinician- and patient-reported outcome measures for ocular chronic graft-versus-host disease screening when providing care to allogeneic hematopoietic stem cell transplantation survivors with moderate to severe chronic graft-versus-host disease. Prospective studies are needed to determine if the Lee ocular subscale demonstrates adequate responsiveness as a disease activity outcome measure. PMID:26088932
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Trevisan, Marta; Sinigaglia, Alessandro; Desole, Giovanna; Berto, Alessandro; Pacenti, Monia; Palù, Giorgio; Barzon, Luisa
2015-07-13
The recent biotechnology breakthrough of cell reprogramming and generation of induced pluripotent stem cells (iPSCs), which has revolutionized the approaches to study the mechanisms of human diseases and to test new drugs, can be exploited to generate patient-specific models for the investigation of host-pathogen interactions and to develop new antimicrobial and antiviral therapies. Applications of iPSC technology to the study of viral infections in humans have included in vitro modeling of viral infections of neural, liver, and cardiac cells; modeling of human genetic susceptibility to severe viral infectious diseases, such as encephalitis and severe influenza; genetic engineering and genome editing of patient-specific iPSC-derived cells to confer antiviral resistance.
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Blackburn, Jason K; Kracalik, Ian T; Fair, Jeanne Marie
2015-01-01
The ongoing Ebola outbreak in West Africa and the current saiga antelope die off in Kazakhstan each represent very real and difficult to manage public or veterinary health crises. They also illustrate the importance of stable and funded surveillance and sound policy for intervention or disease control. While these two events highlight extreme cases of infectious disease (Ebola) or (possible) environmental exposure (saiga), diseases such as anthrax, brucellosis, tularemia, and plague are all zoonoses that pose risks and present surveillance challenges at the wildlife-livestock-human interfaces. These four diseases are also considered important actors in the threat of biological terror activities and have a long history as legacy biowarfare pathogens. This paper reviews recent studies done cooperatively between American and institutions within nations of the Former Soviet Union (FSU) focused on spatiotemporal, epidemiological, and ecological patterns of these four zoonoses. We examine recent studies and discuss the possible ways in which techniques, including ecological niche modeling, disease risk modeling, and spatiotemporal cluster analysis, can inform disease surveillance, control efforts, and impact policy. Our focus is to posit ways to apply science to disease management policy and actual management or mitigation practices. Across these examples, we illustrate the value of cooperative studies that bring together modern geospatial and epidemiological analyses to improve our understanding of the distribution of pathogens and diseases in livestock, wildlife, and humans. For example, ecological niche modeling can provide national level maps of pathogen distributions for surveillance planning, while space-time models can identify the timing and location of significant outbreak events for defining active control strategies. We advocate for the need to bring the results and the researchers from cooperative studies into the meeting rooms where policy is negotiated and use these results to inform future disease surveillance and control or eradication campaigns.
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Nijhuis, Rogier L; Stijnen, Theo; Peeters, Anna; Witteman, Jacqueline C M; Hofman, Albert; Hunink, M G Myriam
2006-01-01
To determine the apparent and internal validity of the Rotterdam Ischemic heart disease & Stroke Computer (RISC) model, a Monte Carlo-Markov model, designed to evaluate the impact of cardiovascular disease (CVD) risk factors and their modification on life expectancy (LE) and cardiovascular disease-free LE (DFLE) in a general population (hereinafter, these will be referred to together as (DF)LE). The model is based on data from the Rotterdam Study, a cohort follow-up study of 6871 subjects aged 55 years and older who visited the research center for risk factor assessment at baseline (1990-1993) and completed a follow-up visit 7 years later (original cohort). The transition probabilities and risk factor trends used in the RISC model were based on data from 3501 subjects (the study cohort). To validate the RISC model, the number of simulated CVD events during 7 years' follow-up were compared with the observed number of events in the study cohort and the original cohort, respectively, and simulated (DF)LEs were compared with the (DF)LEs calculated from multistate life tables. Both in the study cohort and in the original cohort, the simulated distribution of CVD events was consistent with the observed number of events (CVD deaths: 7.1% v. 6.6% and 7.4% v. 7.6%, respectively; non-CVD deaths: 11.2% v. 11.5% and 12.9% v. 13.0%, respectively). The distribution of (DF)LEs estimated with the RISC model consistently encompassed the (DF)LEs calculated with multistate life tables. The simulated events and (DF)LE estimates from the RISC model are consistent with observed data from a cohort follow-up study.
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Studying the Brain in a Dish: 3D Cell Culture Models of Human Brain Development and Disease.
Brown, Juliana; Quadrato, Giorgia; Arlotta, Paola
2018-01-01
The study of the cellular and molecular processes of the developing human brain has been hindered by access to suitable models of living human brain tissue. Recently developed 3D cell culture models offer the promise of studying fundamental brain processes in the context of human genetic background and species-specific developmental mechanisms. Here, we review the current state of 3D human brain organoid models and consider their potential to enable investigation of complex aspects of human brain development and the underpinning of human neurological disease. © 2018 Elsevier Inc. All rights reserved.
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White, Lauren A; Forester, James D; Craft, Meggan E
2018-05-01
Individual differences in contact rate can arise from host, group and landscape heterogeneity and can result in different patterns of spatial spread for diseases in wildlife populations with concomitant implications for disease control in wildlife of conservation concern, livestock and humans. While dynamic disease models can provide a better understanding of the drivers of spatial spread, the effects of landscape heterogeneity have only been modelled in a few well-studied wildlife systems such as rabies and bovine tuberculosis. Such spatial models tend to be either purely theoretical with intrinsic limiting assumptions or individual-based models that are often highly species- and system-specific, limiting the breadth of their utility. Our goal was to review studies that have utilized dynamic, spatial models to answer questions about pathogen transmission in wildlife and identify key gaps in the literature. We begin by providing an overview of the main types of dynamic, spatial models (e.g., metapopulation, network, lattice, cellular automata, individual-based and continuous-space) and their relation to each other. We investigate different types of ecological questions that these models have been used to explore: pathogen invasion dynamics and range expansion, spatial heterogeneity and pathogen persistence, the implications of management and intervention strategies and the role of evolution in host-pathogen dynamics. We reviewed 168 studies that consider pathogen transmission in free-ranging wildlife and classify them by the model type employed, the focal host-pathogen system, and their overall research themes and motivation. We observed a significant focus on mammalian hosts, a few well-studied or purely theoretical pathogen systems, and a lack of studies occurring at the wildlife-public health or wildlife-livestock interfaces. Finally, we discuss challenges and future directions in the context of unprecedented human-mediated environmental change. Spatial models may provide new insights into understanding, for example, how global warming and habitat disturbance contribute to disease maintenance and emergence. Moving forward, better integration of dynamic, spatial disease models with approaches from movement ecology, landscape genetics/genomics and ecoimmunology may provide new avenues for investigation and aid in the control of zoonotic and emerging infectious diseases. © 2017 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society.
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Retinal Remodeling in the Tg P347L Rabbit, a Large-Eye Model of Retinal Degeneration
Jones, Bryan William; Kondo, Mineo; Terasaki, Hiroko; Watt, Carl Brock; Rapp, Kevin; Anderson, James; Lin, Yanhua; Shaw, Marguerite Victoria; Yang, Jia-Hui; Marc, Robert Edward
2013-01-01
Retinitis pigmentosa (RP) is an inherited blinding disease characterized by progressive loss of retinal photo-receptors. There are numerous rodent models of retinal degeneration, but most are poor platforms for interventions that will translate into clinical practice. The rabbit possesses a number of desirable qualities for a model of retinal disease including a large eye and an existing and substantial knowledge base in retinal circuitry, anatomy, and ophthalmology. We have analyzed degeneration, remodeling, and reprogramming in a rabbit model of retinal degeneration, expressing a rhodopsin proline 347 to leucine transgene in a TgP347L rabbit as a powerful model to study the pathophysiology and treatment of retinal degeneration. We show that disease progression in the TgP347L rabbit closely tracks human cone-sparing RP, including the cone-associated preservation of bipolar cell signaling and triggering of reprogramming. The relatively fast disease progression makes the TgP347L rabbit an excellent model for gene therapy, cell biological intervention, progenitor cell transplantation, surgical interventions, and bionic prosthetic studies. PMID:21681749
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Zhao, Shanrong; Schlerman, Franklin J.; Savary, Leigh; Campanholle, Gabriela; Johnson, Bryce G.; Xi, Li; Nguyen, Vuong; Zhan, Yutian; Lech, Matthew P.; Wang, Ju; Nie, Qing; Karsdal, Morten A.; Genovese, Federica; Boucher, Germaine; Brown, Thomas P.; Zhang, Baohong; Homer, Bruce L.; Martinez, Robert V.
2017-01-01
ZSF1 rats exhibit spontaneous nephropathy secondary to obesity, hypertension, and diabetes, and have gained interest as a model system with potentially high translational value to progressive human disease. To thoroughly characterize this model, and to better understand how closely it recapitulates human disease, we performed a high resolution longitudinal analysis of renal disease progression in ZSF1 rats spanning from early disease to end stage renal disease. Analyses included metabolic endpoints, renal histology and ultrastructure, evaluation of a urinary biomarker of fibrosis, and transcriptome analysis of glomerular-enriched tissue over the course of disease. Our findings support the translational value of the ZSF1 rat model, and are provided here to assist researchers in the determination of the model’s suitability for testing a particular mechanism of interest, the design of therapeutic intervention studies, and the identification of new targets and biomarkers for type 2 diabetic nephropathy. PMID:28746409
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Consequences of Landscape Fragmentation on Lyme Disease Risk: A Cellular Automata Approach
Li, Sen; Hartemink, Nienke; Speybroeck, Niko; Vanwambeke, Sophie O.
2012-01-01
The abundance of infected Ixodid ticks is an important component of human risk of Lyme disease, and various empirical studies have shown that this is associated, at least in part, to landscape fragmentation. In this study, we aimed at exploring how varying woodland fragmentation patterns affect the risk of Lyme disease, through infected tick abundance. A cellular automata model was developed, incorporating a heterogeneous landscape with three interactive components: an age-structured tick population, a classical disease transmission function, and hosts. A set of simplifying assumptions were adopted with respect to the study objective and field data limitations. In the model, the landscape influences both tick survival and host movement. The validation of the model was performed with an empirical study. Scenarios of various landscape configurations (focusing on woodland fragmentation) were simulated and compared. Lyme disease risk indices (density and infection prevalence of nymphs) differed considerably between scenarios: (i) the risk could be higher in highly fragmented woodlands, which is supported by a number of recently published empirical studies, and (ii) grassland could reduce the risk in adjacent woodland, which suggests landscape fragmentation studies of zoonotic diseases should not focus on the patch-level woodland patterns only, but also on landscape-level adjacent land cover patterns. Further analysis of the simulation results indicated strong correlations between Lyme disease risk indices and the density, shape and aggregation level of woodland patches. These findings highlight the strong effect of the spatial patterns of local host population and movement on the spatial dynamics of Lyme disease risks, which can be shaped by woodland fragmentation. In conclusion, using a cellular automata approach is beneficial for modelling complex zoonotic transmission systems as it can be combined with either real world landscapes for exploring direct spatial effects or artificial representations for outlining possible empirical investigations. PMID:22761842
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USDA-ARS?s Scientific Manuscript database
Necrotic enteritis is an enteric disease of poultry resulting from infection by Clostridium perfringens with co-infection by Eimeria spp. constituting a major risk factor for disease pathogenesis. This study compared three commercial broiler chicken lines using an experimental model of necrotic ente...
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Sapak, Z; Salam, M U; Minchinton, E J; MacManus, G P V; Joyce, D C; Galea, V J
2017-09-01
A weather-based simulation model, called Powdery Mildew of Cucurbits Simulation (POMICS), was constructed to predict fungicide application scheduling to manage powdery mildew of cucurbits. The model was developed on the principle that conditions favorable for Podosphaera xanthii, a causal pathogen of this crop disease, generate a number of infection cycles in a single growing season. The model consists of two components that (i) simulate the disease progression of P. xanthii in secondary infection cycles under natural conditions and (ii) predict the disease severity with application of fungicides at any recurrent disease cycles. The underlying environmental factors associated with P. xanthii infection were quantified from laboratory and field studies, and also gathered from literature. The performance of the POMICS model when validated with two datasets of uncontrolled natural infection was good (the mean difference between simulated and observed disease severity on a scale of 0 to 5 was 0.02 and 0.05). In simulations, POMICS was able to predict high- and low-risk disease alerts. Furthermore, the predicted disease severity was responsive to the number of fungicide applications. Such responsiveness indicates that the model has the potential to be used as a tool to guide the scheduling of judicious fungicide applications.
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Diabetic aggravation of stroke and animal models
Rehni, Ashish K.; Liu, Allen; Perez-Pinzon, Miguel A.; Dave, Kunjan R.
2017-01-01
Cerebral ischemia in diabetics results in severe brain damage. Different animal models of cerebral ischemia have been used to study the aggravation of ischemic brain damage in the diabetic condition. Since different disease conditions such as diabetes differently affect outcome following cerebral ischemia, the Stroke Therapy Academic Industry Roundtable (STAIR) guidelines recommends use of diseased animals for evaluating neuroprotective therapies targeted to reduce cerebral ischemic damage. The goal of this review is to discuss the technicalities and pros/cons of various animal models of cerebral ischemia currently being employed to study diabetes-related ischemic brain damage. The rational use of such animal systems in studying the disease condition may better help evaluate novel therapeutic approaches for diabetes related exacerbation of ischemic brain damage. PMID:28274862
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Cardiac image modelling: Breadth and depth in heart disease.
Suinesiaputra, Avan; McCulloch, Andrew D; Nash, Martyn P; Pontre, Beau; Young, Alistair A
2016-10-01
With the advent of large-scale imaging studies and big health data, and the corresponding growth in analytics, machine learning and computational image analysis methods, there are now exciting opportunities for deepening our understanding of the mechanisms and characteristics of heart disease. Two emerging fields are computational analysis of cardiac remodelling (shape and motion changes due to disease) and computational analysis of physiology and mechanics to estimate biophysical properties from non-invasive imaging. Many large cohort studies now underway around the world have been specifically designed based on non-invasive imaging technologies in order to gain new information about the development of heart disease from asymptomatic to clinical manifestations. These give an unprecedented breadth to the quantification of population variation and disease development. Also, for the individual patient, it is now possible to determine biophysical properties of myocardial tissue in health and disease by interpreting detailed imaging data using computational modelling. For these population and patient-specific computational modelling methods to develop further, we need open benchmarks for algorithm comparison and validation, open sharing of data and algorithms, and demonstration of clinical efficacy in patient management and care. The combination of population and patient-specific modelling will give new insights into the mechanisms of cardiac disease, in particular the development of heart failure, congenital heart disease, myocardial infarction, contractile dysfunction and diastolic dysfunction. Copyright © 2016. Published by Elsevier B.V.
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Role of L-arginine in the pathogenesis and treatment of renal disease.
Cherla, Gautam; Jaimes, Edgar A
2004-10-01
L-arginine is a semi essential amino acid and also a substrate for the synthesis of nitric oxide (NO), polyamines, and agmatine. These L-arginine metabolites may participate in the pathogenesis of renal disease and constitute the rationale for manipulating L-arginine metabolism as a strategy to ameliorate kidney disease. Modification of dietary L-arginine intake in experimental models of kidney diseases has been shown to have both beneficial as well as deleterious effects depending on the specific model studied. L-arginine supplementation in animal models of glomerulonephritis has been shown to be detrimental, probably by increasing the production of NO from increased local expression of inducible NO synthase (iNOS). L-arginine supplementation does not modify the course of renal disease in humans with chronic glomerular diseases. However, beneficial effects of L-arginine supplementation have been reported in several models of chronic kidney disease including renal ablation, ureteral obstruction, nephropathy secondary to diabetes, and salt-sensitive hypertension. L-arginine is reduced in preeclampsia and recent experimental studies indicate that L-arginine supplementation may be beneficial in attenuating the symptoms of preeclampsia. Administration of exogenous L-arginine has been shown to be protective in ischemic acute renal failure. In summary, the role of L-arginine in the pathogenesis and treatment of renal disease is not completely understood and remains to be established.
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Ross, Elsie Gyang; Shah, Nigam H; Dalman, Ronald L; Nead, Kevin T; Cooke, John P; Leeper, Nicholas J
2016-11-01
A key aspect of the precision medicine effort is the development of informatics tools that can analyze and interpret "big data" sets in an automated and adaptive fashion while providing accurate and actionable clinical information. The aims of this study were to develop machine learning algorithms for the identification of disease and the prognostication of mortality risk and to determine whether such models perform better than classical statistical analyses. Focusing on peripheral artery disease (PAD), patient data were derived from a prospective, observational study of 1755 patients who presented for elective coronary angiography. We employed multiple supervised machine learning algorithms and used diverse clinical, demographic, imaging, and genomic information in a hypothesis-free manner to build models that could identify patients with PAD and predict future mortality. Comparison was made to standard stepwise linear regression models. Our machine-learned models outperformed stepwise logistic regression models both for the identification of patients with PAD (area under the curve, 0.87 vs 0.76, respectively; P = .03) and for the prediction of future mortality (area under the curve, 0.76 vs 0.65, respectively; P = .10). Both machine-learned models were markedly better calibrated than the stepwise logistic regression models, thus providing more accurate disease and mortality risk estimates. Machine learning approaches can produce more accurate disease classification and prediction models. These tools may prove clinically useful for the automated identification of patients with highly morbid diseases for which aggressive risk factor management can improve outcomes. Copyright © 2016 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.
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Bhuia, Mohammad Romel; Nwaru, Bright I; Weir, Christopher J; Sheikh, Aziz
2017-05-17
Models that have so far been used to estimate and project the prevalence and disease burden of asthma are in most cases inadequately described and irreproducible. We aim systematically to describe and critique the existing models in relation to their strengths, limitations and reproducibility, and to determine the appropriate models for estimating and projecting the prevalence and disease burden of asthma. We will search the following electronic databases to identify relevant literature published from 1980 to 2017: Medline, Embase, WHO Library and Information Services and Web of Science Core Collection. We will identify additional studies by searching the reference list of all the retrieved papers and contacting experts. We will include observational studies that used models for estimating and/or projecting prevalence and disease burden of asthma regarding human population of any age and sex. Two independent reviewers will assess the studies for inclusion and extract data from included papers. Data items will include authors' names, publication year, study aims, data source and time period, study population, asthma outcomes, study methodology, model type, model settings, study variables, methods of model derivation, methods of parameter estimation and/or projection, model fit information, key findings and identified research gaps. A detailed critical narrative synthesis of the models will be undertaken in relation to their strengths, limitations and reproducibility. A quality assessment checklist and scoring framework will be used to determine the appropriate models for estimating and projecting the prevalence anddiseaseburden of asthma. We will not collect any primary data for this review, and hence there is no need for formal National Health Services Research Ethics Committee approval. We will present our findings at scientific conferences and publish the findings in the peer-reviewed scientific journal. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.
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Stratton, Margaret D.; Ehrlich, Hanna Y.; Mor, Siobhan M.; Naumova, Elena N.
2017-01-01
Ross River virus (RRV), Barmah Forest virus (BFV), and dengue are three common mosquito-borne diseases in Australia that display notable seasonal patterns. Although all three diseases have been modeled on localized scales, no previous study has used harmonic models to compare seasonality of mosquito-borne diseases on a continent-wide scale. We fit Poisson harmonic regression models to surveillance data on RRV, BFV, and dengue (from 1993, 1995 and 1991, respectively, through 2015) incorporating seasonal, trend, and climate (temperature and rainfall) parameters. The models captured an average of 50–65% variability of the data. Disease incidence for all three diseases generally peaked in January or February, but peak timing was most variable for dengue. The most significant predictor parameters were trend and inter-annual periodicity for BFV, intra-annual periodicity for RRV, and trend for dengue. We found that a Temperature Suitability Index (TSI), designed to reclassify climate data relative to optimal conditions for vector establishment, could be applied to this context. Finally, we extrapolated our models to estimate the impact of a false-positive BFV epidemic in 2013. Creating these models and comparing variations in periodicities may provide insight into historical outbreaks as well as future patterns of mosquito-borne diseases. PMID:28071683
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Stratton, Margaret D; Ehrlich, Hanna Y; Mor, Siobhan M; Naumova, Elena N
2017-01-10
Ross River virus (RRV), Barmah Forest virus (BFV), and dengue are three common mosquito-borne diseases in Australia that display notable seasonal patterns. Although all three diseases have been modeled on localized scales, no previous study has used harmonic models to compare seasonality of mosquito-borne diseases on a continent-wide scale. We fit Poisson harmonic regression models to surveillance data on RRV, BFV, and dengue (from 1993, 1995 and 1991, respectively, through 2015) incorporating seasonal, trend, and climate (temperature and rainfall) parameters. The models captured an average of 50-65% variability of the data. Disease incidence for all three diseases generally peaked in January or February, but peak timing was most variable for dengue. The most significant predictor parameters were trend and inter-annual periodicity for BFV, intra-annual periodicity for RRV, and trend for dengue. We found that a Temperature Suitability Index (TSI), designed to reclassify climate data relative to optimal conditions for vector establishment, could be applied to this context. Finally, we extrapolated our models to estimate the impact of a false-positive BFV epidemic in 2013. Creating these models and comparing variations in periodicities may provide insight into historical outbreaks as well as future patterns of mosquito-borne diseases.
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NASA Astrophysics Data System (ADS)
Stratton, Margaret D.; Ehrlich, Hanna Y.; Mor, Siobhan M.; Naumova, Elena N.
2017-01-01
Ross River virus (RRV), Barmah Forest virus (BFV), and dengue are three common mosquito-borne diseases in Australia that display notable seasonal patterns. Although all three diseases have been modeled on localized scales, no previous study has used harmonic models to compare seasonality of mosquito-borne diseases on a continent-wide scale. We fit Poisson harmonic regression models to surveillance data on RRV, BFV, and dengue (from 1993, 1995 and 1991, respectively, through 2015) incorporating seasonal, trend, and climate (temperature and rainfall) parameters. The models captured an average of 50-65% variability of the data. Disease incidence for all three diseases generally peaked in January or February, but peak timing was most variable for dengue. The most significant predictor parameters were trend and inter-annual periodicity for BFV, intra-annual periodicity for RRV, and trend for dengue. We found that a Temperature Suitability Index (TSI), designed to reclassify climate data relative to optimal conditions for vector establishment, could be applied to this context. Finally, we extrapolated our models to estimate the impact of a false-positive BFV epidemic in 2013. Creating these models and comparing variations in periodicities may provide insight into historical outbreaks as well as future patterns of mosquito-borne diseases.
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Exploring human disease using the Rat Genome Database.
Shimoyama, Mary; Laulederkind, Stanley J F; De Pons, Jeff; Nigam, Rajni; Smith, Jennifer R; Tutaj, Marek; Petri, Victoria; Hayman, G Thomas; Wang, Shur-Jen; Ghiasvand, Omid; Thota, Jyothi; Dwinell, Melinda R
2016-10-01
Rattus norvegicus, the laboratory rat, has been a crucial model for studies of the environmental and genetic factors associated with human diseases for over 150 years. It is the primary model organism for toxicology and pharmacology studies, and has features that make it the model of choice in many complex-disease studies. Since 1999, the Rat Genome Database (RGD; http://rgd.mcw.edu) has been the premier resource for genomic, genetic, phenotype and strain data for the laboratory rat. The primary role of RGD is to curate rat data and validate orthologous relationships with human and mouse genes, and make these data available for incorporation into other major databases such as NCBI, Ensembl and UniProt. RGD also provides official nomenclature for rat genes, quantitative trait loci, strains and genetic markers, as well as unique identifiers. The RGD team adds enormous value to these basic data elements through functional and disease annotations, the analysis and visual presentation of pathways, and the integration of phenotype measurement data for strains used as disease models. Because much of the rat research community focuses on understanding human diseases, RGD provides a number of datasets and software tools that allow users to easily explore and make disease-related connections among these datasets. RGD also provides comprehensive human and mouse data for comparative purposes, illustrating the value of the rat in translational research. This article introduces RGD and its suite of tools and datasets to researchers - within and beyond the rat community - who are particularly interested in leveraging rat-based insights to understand human diseases. © 2016. Published by The Company of Biologists Ltd.
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Animal models of GM2 gangliosidosis: utility and limitations.
Lawson, Cheryl A; Martin, Douglas R
2016-01-01
GM2 gangliosidosis, a subset of lysosomal storage disorders, is caused by a deficiency of the glycohydrolase, β-N-acetylhexosaminidase, and includes the closely related Tay-Sachs and Sandhoff diseases. The enzyme deficiency prevents the normal, stepwise degradation of ganglioside, which accumulates unchecked within the cellular lysosome, particularly in neurons. As a result, individuals with GM2 gangliosidosis experience progressive neurological diseases including motor deficits, progressive weakness and hypotonia, decreased responsiveness, vision deterioration, and seizures. Mice and cats are well-established animal models for Sandhoff disease, whereas Jacob sheep are the only known laboratory animal model of Tay-Sachs disease to exhibit clinical symptoms. Since the human diseases are relatively rare, animal models are indispensable tools for further study of pathogenesis and for development of potential treatments. Though no effective treatments for gangliosidoses currently exist, animal models have been used to test promising experimental therapies. Herein, the utility and limitations of gangliosidosis animal models and how they have contributed to the development of potential new treatments are described.
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Animal models of GM2 gangliosidosis: utility and limitations
Lawson, Cheryl A; Martin, Douglas R
2016-01-01
GM2 gangliosidosis, a subset of lysosomal storage disorders, is caused by a deficiency of the glycohydrolase, β-N-acetylhexosaminidase, and includes the closely related Tay–Sachs and Sandhoff diseases. The enzyme deficiency prevents the normal, stepwise degradation of ganglioside, which accumulates unchecked within the cellular lysosome, particularly in neurons. As a result, individuals with GM2 gangliosidosis experience progressive neurological diseases including motor deficits, progressive weakness and hypotonia, decreased responsiveness, vision deterioration, and seizures. Mice and cats are well-established animal models for Sandhoff disease, whereas Jacob sheep are the only known laboratory animal model of Tay–Sachs disease to exhibit clinical symptoms. Since the human diseases are relatively rare, animal models are indispensable tools for further study of pathogenesis and for development of potential treatments. Though no effective treatments for gangliosidoses currently exist, animal models have been used to test promising experimental therapies. Herein, the utility and limitations of gangliosidosis animal models and how they have contributed to the development of potential new treatments are described. PMID:27499644
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Erlandsson, Lena; Nääv, Åsa; Hennessy, Annemarie; Vaiman, Daniel; Gram, Magnus; Åkerström, Bo; Hansson, Stefan R
2016-03-01
Preeclampsia is a pregnancy-related disease afflicting 3-7% of pregnancies worldwide and leads to maternal and infant morbidity and mortality. The disease is of placental origin and is commonly described as a disease of two stages. A variety of preeclampsia animal models have been proposed, but all of them have limitations in fully recapitulating the human disease. Based on the research question at hand, different or multiple models might be suitable. Multiple animal models in combination with in vitro or ex vivo studies on human placenta together offer a synergistic platform to further our understanding of the etiology of preeclampsia and potential therapeutic interventions. The described animal models of preeclampsia divide into four categories (i) spontaneous, (ii) surgically induced, (iii) pharmacologically/substance induced, and (iv) transgenic. This review aims at providing an inventory of novel models addressing etiology of the disease and or therapeutic/intervention opportunities. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
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Human immune system mouse models of Ebola virus infection.
Spengler, Jessica R; Prescott, Joseph; Feldmann, Heinz; Spiropoulou, Christina F
2017-08-01
Human immune system (HIS) mice, immunodeficient mice engrafted with human cells (with or without donor-matched tissue), offer a unique opportunity to study pathogens that cause disease predominantly or exclusively in humans. Several HIS mouse models have recently been used to study Ebola virus (EBOV) infection and disease. The results of these studies are encouraging and support further development and use of these models in Ebola research. HIS mice provide a small animal model to study EBOV isolates, investigate early viral interactions with human immune cells, screen vaccines and therapeutics that modulate the immune system, and investigate sequelae in survivors. Here we review existing models, discuss their use in pathogenesis studies and therapeutic screening, and highlight considerations for study design and analysis. Finally, we point out caveats to current models, and recommend future efforts for modeling EBOV infection in HIS mice. Published by Elsevier B.V.
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McDonald, Scott A; Devleesschauwer, Brecht; Wallinga, Jacco
2016-12-08
Disease burden is not evenly distributed within a population; this uneven distribution can be due to individual heterogeneity in progression rates between disease stages. Composite measures of disease burden that are based on disease progression models, such as the disability-adjusted life year (DALY), are widely used to quantify the current and future burden of infectious diseases. Our goal was to investigate to what extent ignoring the presence of heterogeneity could bias DALY computation. Simulations using individual-based models for hypothetical infectious diseases with short and long natural histories were run assuming either "population-averaged" progression probabilities between disease stages, or progression probabilities that were influenced by an a priori defined individual-level frailty (i.e., heterogeneity in disease risk) distribution, and DALYs were calculated. Under the assumption of heterogeneity in transition rates and increasing frailty with age, the short natural history disease model predicted 14% fewer DALYs compared with the homogenous population assumption. Simulations of a long natural history disease indicated that assuming homogeneity in transition rates when heterogeneity was present could overestimate total DALYs, in the present case by 4% (95% quantile interval: 1-8%). The consequences of ignoring population heterogeneity should be considered when defining transition parameters for natural history models and when interpreting the resulting disease burden estimates.
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Bose, Tanima; Diedrichs-Möhring, Maria; Wildner, Gerhild
2016-12-01
Understanding the immunopathogenesis of autoimmune and inflammatory diseases is a prerequisite for specific and effective therapeutical intervention. This review focuses on animal models of two common ocular inflammatory diseases, dry eye disease (DED), affecting the ocular surface, and uveitis with inflammation of the inner eye. In both diseases autoimmunity plays an important role, in idiopathic uveitis immune reactivity to intraocular autoantigens is pivotal, while in dry eye disease autoimmunity seems to play a role in one subtype of disease, Sjögren' syndrome (SjS). Comparing the immune mechanisms underlying both eye diseases reveals similarities, and significant differences. Studies have shown genetic predispositions, T and B cell involvement, cytokine and chemokine signatures and signaling pathways as well as environmental influences in both DED and uveitis. Uveitis and DED are heterogeneous diseases and there is no single animal model, which adequately represents both diseases. However, there is evidence to suggest that certain T cell-targeting therapies can be used to treat both, dry eye disease and uveitis. Animal models are essential to autoimmunity research, from the basic understanding of immune mechanisms to the pre-clinical testing of potential new therapies. Copyright © 2016 Elsevier B.V. All rights reserved.
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Exuzides, Alex; Colby, Chris; Briggs, Andrew H; Lomas, David A; Rutten-van Mölken, Maureen P M H; Tabberer, Maggie; Chambers, Mike; Muellerova, Hana; Locantore, Nicholas; Risebrough, Nancy A; Ismaila, Afisi S; Gonzalez-McQuire, Sebastian
2017-05-01
To develop statistical models predicting disease progression and outcomes in chronic obstructive pulmonary disease (COPD), using data from ECLIPSE, a large, observational study of current and former smokers with COPD. Based on a conceptual model of COPD disease progression and data from 2164 patients, associations were made between baseline characteristics, COPD disease progression attributes (exacerbations, lung function, exercise capacity, and symptoms), health-related quality of life (HRQoL), and survival. Linear and nonlinear functional forms of random intercept models were used to characterize these relationships. Endogeneity was addressed by time-lagging variables in the regression models. At the 5% significance level, an exacerbation history in the year before baseline was associated with increased risk of future exacerbations (moderate: +125.8%; severe: +89.2%) and decline in lung function (forced expiratory volume in 1 second [FEV 1 ]) (-94.20 mL per year). Each 1% increase in FEV 1 % predicted was associated with decreased risk of exacerbations (moderate: -1.1%; severe: -3.0%) and increased 6-minute walk test distance (6MWD) (+1.5 m). Increases in baseline exercise capacity (6MWD, per meter) were associated with slightly increased risk of moderate exacerbations (+0.04%) and increased FEV 1 (+0.62 mL). Symptoms (dyspnea, cough, and/or sputum) were associated with an increased risk of moderate exacerbations (+13.4% to +31.1%), and baseline dyspnea (modified Medical Research Council score ≥2 v. <2) was associated with lower FEV 1 (-112.3 mL). A series of linked statistical regression equations have been developed to express associations between indicators of COPD disease severity and HRQoL and survival. These can be used to represent disease progression, for example, in new economic models of COPD.
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Lower vertebrate and invertebrate models of Alzheimer's disease - A review.
Sharma, Neha; Khurana, Navneet; Muthuraman, Arunachalam
2017-11-15
Alzheimer's disease is a common neurodegenerative disorder which is characterized by the presence of beta- amyloid protein and neurofibrillary tangles (NFTs) in the brain. Till now, various higher vertebrate models have been in use to study the pathophysiology of this disease. But, these models possess some limitations like ethical restrictions, high cost, difficult maintenance of large quantity and lesser reproducibility. Besides, various lower chordate animals like Danio rerio, Drosophila melanogaster, Caenorhabditis elegans and Ciona intestinalis have been proved to be an important model for the in vivo determination of targets of drugs with least limitations. In this article, we reviewed different studies conducted on theses models for the better understanding of the pathophysiology of AD and their subsequent application as a potential tool in the preclinical evaluation of new drugs. Copyright © 2017 Elsevier B.V. All rights reserved.
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Persistent estrus rat models of polycystic ovary disease: an update.
Singh, Krishna B
2005-10-01
To critically review published articles on polycystic ovary (PCO) disease in rat models, with a focus on delineating its pathophysiology. Review of the English-language literature published from 1966 to March 2005 was performed through PubMed search. Keywords or phrases used were persistent estrus, chronic anovulation, polycystic ovary, polycystic ovary disease, and polycystic ovary syndrome. Articles were also located via bibliographies of published literature. University Health Sciences Center. Articles on persistent estrus and PCO in rats were selected and reviewed regarding the methods for induction of PCO disease. Changes in the reproductive cycle, ovarian morphology, hormonal parameters, and factors associated with the development of PCO disease in rat models were analyzed. Principal methods for inducing PCO in the rat include exposure to constant light, anterior hypothalamic and amygdaloidal lesions, and the use of androgens, estrogens, antiprogestin, and mifepristone. The validated rat PCO models provide useful information on morphologic and hormonal disturbances in the pathogenesis of chronic anovulation in this condition. These studies have aimed to replicate the morphologic and hormonal characteristics observed in the human PCO syndrome. The implications of these studies to human condition are discussed.
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Misbehaving macrophages in the pathogenesis of psoriasis
Clark, Rachael A.; Kupper, Thomas S.
2006-01-01
Psoriasis is a chronic inflammatory skin disease unique to humans. In this issue of the JCI, 2 studies of very different mouse models of psoriasis both report that macrophages play a key role in inducing psoriasis-like skin disease. Psoriasis is clearly a polygenic, inherited disease of uncontrolled cutaneous inflammation. The debate that currently rages in the field is whether psoriasis is a disease of autoreactive T cells or whether it reflects an intrinsic defect within the skin — or both. However, these questions have proven difficult to dissect using molecular genetic tools. In the current studies, the authors have used 2 different animal models to address the role of macrophages in disease pathogenesis: Wang et al. use a mouse model in which inflammation is T cell dependent, whereas the model used by Stratis et al. is T cell independent (see the related articles beginning on pages 2105 and 2094, respectively). Strikingly, both groups report an important contribution by macrophages, implying that macrophages can contribute to both epithelial-based and T cell–mediated pathways of inflammation. PMID:16886055
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Therapeutic effect of the natural compounds baicalein and baicalin on autoimmune diseases.
Xu, Jian; Liu, Jinlong; Yue, Guolin; Sun, Mingqiang; Li, Jinliang; Xiu, Xia; Gao, Zhenzhong
2018-05-23
A series of natural compounds have been implicated to be useful in regulating the pathogenesis of various autoimmune diseases. The present study demonstrated that the Scutellariae radix compounds baicalein and baicalin may serve as drugs for the treatment of autoimmune diseases, including rheumatoid arthritis and inflammatory bowel disease. Following the administration of baicalein and baicalin in vivo, T cell‑mediated autoimmune diseases in the mouse model were profoundly ameliorated: In the collagen‑induced arthritis model (CIA), the severity of the disease was reduced by baicalein and, consistently, baicalein was demonstrated to suppress T cell proliferation in CIA mice. In the dextran sodium sulfate (DSS)‑induced colitis model, the disease was attenuated by baicalin, and baicalin promoted colon epithelial cell (CEC) proliferation in vitro. The present study further revealed that the mRNA expression of signal transducer and activator of transcription (STAT)3 and STAT4 in the tyrosine‑protein kinase JAK‑STAT signaling pathway in T cells was downregulated by baicalein, contributing to its regulation of T cell proliferation. However, in the DSS model, the STAT4 transcription in CECs, which are the target cells of activated T cells in the gut, was downregulated by baicalin, suggesting that baicalein and baicalin mediated similar STAT expression in different cell types in autoimmune diseases. In conclusion, the similarly structured compounds baicalein and baicalin selectively exhibited therapeutic effects on autoimmune diseases by regulating cell proliferation and STAT gene expression, albeit in different cell types.
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Animal models of Parkinson's disease: limits and relevance to neuroprotection studies.
Bezard, Erwan; Yue, Zhenyu; Kirik, Deniz; Spillantini, Maria Grazia
2013-01-01
Over the last two decades, significant strides has been made toward acquiring a better knowledge of both the etiology and pathogenesis of Parkinson's disease (PD). Experimental models are of paramount importance to obtain greater insights into the pathogenesis of the disease. Thus far, neurotoxin-based animal models have been the most popular tools employed to produce selective neuronal death in both in vitro and in vivo systems. These models have been commonly referred to as the pathogenic models. The current trend in modeling PD revolves around what can be called the disease gene-based models or etiologic models. The value of utilizing multiple models with a different mechanism of insult rests on the premise that dopamine-producing neurons die by stereotyped cascades that can be activated by a range of insults, from neurotoxins to downregulation and overexpression of disease-related genes. In this position article, we present the relevance of both pathogenic and etiologic models as well as the concept of clinically relevant designs that, we argue, should be utilized in the preclinical development phase of new neuroprotective therapies before embarking into clinical trials. Copyright © 2013 Movement Disorders Society.
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Rumination time as a potential predictor of common diseases in high-productive Holstein dairy cows.
Moretti, Riccardo; Biffani, Stefano; Tiezzi, Francesco; Maltecca, Christian; Chessa, Stefania; Bozzi, Riccardo
2017-11-01
We examined the hypothesis that rumination time (RT) could serve as a useful predictor of various common diseases of high producing dairy cows and hence improve herd management and animal wellbeing. We measured the changes in rumination time (RT) in the days before the recording of diseases (specifically: mastitis, reproductive system diseases, locomotor system issues, and gastroenteric diseases). We built predictive models to assess the association between RT and these diseases, using the former as the outcome variable, and to study the effects of the latter on the former. The average Pseudo-R 2 of the fitted models was moderate to low, and this could be due to the fact that RT is influenced by other additional factors which have a greater effect than the predictors used here. Although remaining in a moderate-to-low range, the average Pseudo-R 2 of the models regarding locomotion issues and gastroenteric diseases was higher than the others, suggesting the greater effect of these diseases on RT. The results are encouraging, but further work is needed if these models are to become useful predictors.
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A One Health Approach to Hypertrophic Cardiomyopathy
Ueda, Yu; Stern, Joshua A.
2017-01-01
Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease in humans and results in significant morbidity and mortality. Research over the past 25 years has contributed enormous insight into this inherited disease particularly in the areas of genetics, molecular mechanisms, and pathophysiology. Our understanding continues to be limited by the heterogeneity of clinical presentations with various genetic mutations associated with HCM. Transgenic mouse models have been utilized especially studying the genotypic and phenotypic interactions. However, mice possess intrinsic cardiac and hemodynamic differences compared to humans and have limitations preventing their direct translation. Other animal models of HCM have been studied or generated in part to overcome these limitations. HCM in cats shows strikingly similar molecular, histopathological, and genetic similarities to human HCM, and offers an important translational opportunity for the study of this disease. Recently, inherited left ventricular hypertrophy in rhesus macaques was identified and collaborative investigations have been conducted to begin to develop a non-human primate HCM model. These naturally-occurring large-animal models may aid in advancing our understanding of HCM and developing novel therapeutic approaches to this disease. This review will highlight the features of HCM in humans and the relevant available and developing animal models of this condition. PMID:28955182
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Economic Modeling Considerations for Rare Diseases.
Pearson, Isobel; Rothwell, Ben; Olaye, Andrew; Knight, Christopher
2018-05-01
To identify challenges that affect the feasibility and rigor of economic models in rare diseases and strategies that manufacturers have employed in health technology assessment submissions to demonstrate the value of new orphan products that have limited study data. Targeted reviews of PubMed, the National Institute for Health and Care Excellence's (NICE's) Highly Specialised Technologies (HST), and the Scottish Medicines Consortium's (SMC's) ultra-orphan submissions were performed. A total of 19 PubMed studies, 3 published NICE HSTs, and 11 ultra-orphan SMC submissions were eligible for inclusion. In rare diseases, a number of different factors may affect the model's ability to comply with good practice recommendations. Many products for the treatment of rare diseases have an incomplete efficacy and safety profile at product launch. In addition, there is often limited available natural history and epidemiology data. Information on the direct and indirect cost burden of an orphan disease also may be limited, making it difficult to estimate the potential economic benefit of treatment. These challenges can prevent accurate estimation of a new product's benefits in relation to costs. Approaches that can address such challenges include using patient and/or clinician feedback to inform model assumptions; data from disease analogues; epidemiological techniques, such as matching-adjusted indirect comparison; and long-term data collection. Modeling in rare diseases is often challenging; however, a number of approaches are available to support the development of model structures and the collation of input parameters and to manage uncertainty. Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.
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Modeling of Wildlife-Associated Zoonoses: Applications and Caveats
Lewis, Bryan L.; Marathe, Madhav; Eubank, Stephen; Blackburn, Jason K.
2012-01-01
Abstract Wildlife species are identified as an important source of emerging zoonotic disease. Accordingly, public health programs have attempted to expand in scope to include a greater focus on wildlife and its role in zoonotic disease outbreaks. Zoonotic disease transmission dynamics involving wildlife are complex and nonlinear, presenting a number of challenges. First, empirical characterization of wildlife host species and pathogen systems are often lacking, and insight into one system may have little application to another involving the same host species and pathogen. Pathogen transmission characterization is difficult due to the changing nature of population size and density associated with wildlife hosts. Infectious disease itself may influence wildlife population demographics through compensatory responses that may evolve, such as decreased age to reproduction. Furthermore, wildlife reservoir dynamics can be complex, involving various host species and populations that may vary in their contribution to pathogen transmission and persistence over space and time. Mathematical models can provide an important tool to engage these complex systems, and there is an urgent need for increased computational focus on the coupled dynamics that underlie pathogen spillover at the human–wildlife interface. Often, however, scientists conducting empirical studies on emerging zoonotic disease do not have the necessary skill base to choose, develop, and apply models to evaluate these complex systems. How do modeling frameworks differ and what considerations are important when applying modeling tools to the study of zoonotic disease? Using zoonotic disease examples, we provide an overview of several common approaches and general considerations important in the modeling of wildlife-associated zoonoses. PMID:23199265
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Progress and Prospects for Genetic Modification of Nonhuman Primate Models in Biomedical Research
Chan, Anthony W. S.
2013-01-01
The growing interest of modeling human diseases using genetically modified (transgenic) nonhuman primates (NHPs) is a direct result of NHPs (rhesus macaque, etc.) close relation to humans. NHPs share similar developmental paths with humans in their anatomy, physiology, genetics, and neural functions; and in their cognition, emotion, and social behavior. The NHP model within biomedical research has played an important role in the development of vaccines, assisted reproductive technologies, and new therapies for many diseases. Biomedical research has not been the primary role of NHPs. They have mainly been used for safety evaluation and pharmacokinetics studies, rather than determining therapeutic efficacy. The development of the first transgenic rhesus macaque (2001) revolutionized the role of NHP models in biomedicine. Development of the transgenic NHP model of Huntington's disease (2008), with distinctive clinical features, further suggested the uniqueness of the model system; and the potential role of the NHP model for human genetic disorders. Modeling human genetic diseases using NHPs will continue to thrive because of the latest advances in molecular, genetic, and embryo technologies. NHPs rising role in biomedical research, specifically pre-clinical studies, is foreseeable. The path toward the development of transgenic NHPs and the prospect of transgenic NHPs in their new role in future biomedicine needs to be reviewed. This article will focus on the advancement of transgenic NHPs in the past decade, including transgenic technologies and disease modeling. It will outline new technologies that may have significant impact in future NHP modeling and will conclude with a discussion of the future prospects of the transgenic NHP model. PMID:24174443
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Novel Methods in Disease Biogeography: A Case Study with Heterosporosis
Escobar, Luis E.; Qiao, Huijie; Lee, Christine; Phelps, Nicholas B. D.
2017-01-01
Disease biogeography is currently a promising field to complement epidemiology, and ecological niche modeling theory and methods are a key component. Therefore, applying the concepts and tools from ecological niche modeling to disease biogeography and epidemiology will provide biologically sound and analytically robust descriptive and predictive analyses of disease distributions. As a case study, we explored the ecologically important fish disease Heterosporosis, a relatively poorly understood disease caused by the intracellular microsporidian parasite Heterosporis sutherlandae. We explored two novel ecological niche modeling methods, the minimum-volume ellipsoid (MVE) and the Marble algorithm, which were used to reconstruct the fundamental and the realized ecological niche of H. sutherlandae, respectively. Additionally, we assessed how the management of occurrence reports can impact the output of the models. Ecological niche models were able to reconstruct a proxy of the fundamental and realized niche for this aquatic parasite, identifying specific areas suitable for Heterosporosis. We found that the conceptual and methodological advances in ecological niche modeling provide accessible tools to update the current practices of spatial epidemiology. However, careful data curation and a detailed understanding of the algorithm employed are critical for a clear definition of the assumptions implicit in the modeling process and to ensure biologically sound forecasts. In this paper, we show how sensitive MVE is to the input data, while Marble algorithm may provide detailed forecasts with a minimum of parameters. We showed that exploring algorithms of different natures such as environmental clusters, climatic envelopes, and logistic regressions (e.g., Marble, MVE, and Maxent) provide different scenarios of potential distribution. Thus, no single algorithm should be used for disease mapping. Instead, different algorithms should be employed for a more informed and complete understanding of the pathogen or parasite in question. PMID:28770215
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Erokwu, Bernadette O; Anderson, Christian E; Flask, Chris A; Dell, Katherine M
2018-05-01
BackgroundAutosomal recessive polycystic kidney disease (ARPKD) is associated with significant mortality and morbidity, and currently, there are no disease-specific treatments available for ARPKD patients. One major limitation in establishing new therapies for ARPKD is a lack of sensitive measures of kidney disease progression. Magnetic resonance imaging (MRI) can provide multiple quantitative assessments of the disease.MethodsWe applied quantitative image analysis of high-resolution (noncontrast) T2-weighted MRI techniques to study cystic kidney disease progression and response to therapy in the PCK rat model of ARPKD.ResultsSerial imaging over a 2-month period demonstrated that renal cystic burden (RCB, %)=[total cyst volume (TCV)/total kidney volume (TKV) × 100], TCV, and, to a lesser extent, TKV detected cystic kidney disease progression, as well as the therapeutic effect of octreotide, a clinically available medication shown previously to slow both kidney and liver disease progression in this model. All three MRI measures correlated significantly with histologic measures of renal cystic area, although the correlation of RCB and TCV was stronger than that of TKV.ConclusionThese preclinical MRI results provide a basis for applying these quantitative MRI techniques in clinical studies, to stage and measure progression in human ARPKD kidney disease.
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Clinical profiles associated with influenza disease in the ferret model.
Stark, Gregory V; Long, James P; Ortiz, Diana I; Gainey, Melicia; Carper, Benjamin A; Feng, Jingyu; Miller, Stephen M; Bigger, John E; Vela, Eric M
2013-01-01
Influenza A viruses continue to pose a threat to human health; thus, various vaccines and prophylaxis continue to be developed. Testing of these products requires various animal models including mice, guinea pigs, and ferrets. However, because ferrets are naturally susceptible to infection with human influenza viruses and because the disease state resembles that of human influenza, these animals have been widely used as a model to study influenza virus pathogenesis. In this report, a statistical analysis was performed to evaluate data involving 269 ferrets infected with seasonal influenza, swine influenza, and highly pathogenic avian influenza (HPAI) from 16 different studies over a five year period. The aim of the analyses was to better qualify the ferret model by identifying relationships among important animal model parameters (endpoints) and variables of interest, which include survival, time-to-death, changes in body temperature and weight, and nasal wash samples containing virus, in addition to significant changes from baseline in selected hematology and clinical chemistry parameters. The results demonstrate that a disease clinical profile, consisting of various changes in the biological parameters tested, is associated with various influenza A infections in ferrets. Additionally, the analysis yielded correlates of protection associated with HPAI disease in ferrets. In all, the results from this study further validate the use of the ferret as a model to study influenza A pathology and to evaluate product efficacy.
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Preventive role of Indian black pepper in animal models of Alzheimer's disease.
Subedee, Lokraj; Suresh, R N; Mk, Jayanthi; Hl, Kalabharathi; Am, Satish; Vh, Pushpa
2015-04-01
Dementia is the clinical symptom of alzheimer's disease. Brain cholinesterase levels and behavioural changes are the markers for Alzheimer's disease and aluminium chloride is one causative agent for polymerization of tau protein and amyloid plaque formation in Alzheimer's disease. Effect of piper nigrum and its role in prevention of alzhimer's disease and symptoms are well linked in this study. To study the effect of piper nigrum for the prevention of alzheimer's associated histopathological, biochemical and behaviour changes in rat model. Twenty four rats were taken in this study. Their baseline behavioural parameters were noted and group was separated randomly in four. Rats were pretreated with piper nigrum and Alzheimer's disease was induced. Biochemical and histopathological changes were noted at the end of experiment. There was marked decrease in cholinesterase level, amyloidal plaque formation in rats brain who were pretreated with piper nigrum. At the same time there was decrease in escape latency time (ELT) and increase in memory in piper treated rats. Piper nigrum prove to be effective for prevention of Alzheimer's disease. This finding has to be confirmed with studies including larger population. Further research on cholinesterase inhibitors, role of flavonoids on prevention of neurodegeneration in Alzheimer's disease can be encouraged.
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Tsai, Chu-Lin; Camargo, Carlos A
2009-09-01
Acute exacerbations of chronic disease are ubiquitous in clinical medicine, and thus far, there has been a paucity of integrated methodological discussion on this phenomenon. We use acute exacerbations of chronic obstructive pulmonary disease as an example to emphasize key epidemiological and statistical issues for this understudied field in clinical epidemiology. Directed acyclic graphs are a useful epidemiological tool to explain the differential effects of risk factor on health outcomes in studies of acute and chronic phases of disease. To study the pathogenesis of acute exacerbations of chronic disease, case-crossover design and time-series analysis are well-suited study designs to differentiate acute and chronic effect. Modeling changes over time and setting appropriate thresholds are important steps to separate acute from chronic phases of disease in serial measurements. In statistical analysis, acute exacerbations are recurrent events, and some individuals are more prone to recurrences than others. Therefore, appropriate statistical modeling should take into account intraindividual dependence. Finally, we recommend the use of "event-based" number needed to treat (NNT) to prevent a single exacerbation instead of traditional patient-based NNT. Addressing these methodological challenges will advance research quality in acute on chronic disease epidemiology.
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Orru, Kati; Nordin, Steven; Harzia, Hedi; Orru, Hans
2018-07-01
Adverse health impact of air pollution on health may not only be associated with the level of exposure, but rather mediated by perception of the pollution and by top-down processing (e.g. beliefs of the exposure being hazardous), especially in areas with relatively low levels of pollutants. The aim of this study was to test a model that describes interrelations between air pollution (particles < 10 [Formula: see text]m, PM 10 ), perceived pollution, health risk perception, health symptoms and diseases. A population-based questionnaire study was conducted among 1000 Estonian residents (sample was stratified by age, sex, and geographical location) about health risk perception and coping. The PM 10 levels were modelled in 1 × 1 km grids using a Eulerian air quality dispersion model. Respondents were ascribed their annual mean PM 10 exposure according to their home address. Path analysis was performed to test the validity of the model. The data refute the model proposing that exposure level significantly influences symptoms and disease. Instead, the perceived exposure influences symptoms and the effect of perceived exposure on disease is mediated by health risk perception. This relationship is more pronounced in large cities compared to smaller towns or rural areas. Perceived pollution and health risk perception, in particular in large cities, play important roles in understanding and predicting environmentally induced symptoms and diseases at relatively low levels of air pollution.
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Islam, Jyoti; Zhang, Yanqing
2018-05-31
Alzheimer's disease is an incurable, progressive neurological brain disorder. Earlier detection of Alzheimer's disease can help with proper treatment and prevent brain tissue damage. Several statistical and machine learning models have been exploited by researchers for Alzheimer's disease diagnosis. Analyzing magnetic resonance imaging (MRI) is a common practice for Alzheimer's disease diagnosis in clinical research. Detection of Alzheimer's disease is exacting due to the similarity in Alzheimer's disease MRI data and standard healthy MRI data of older people. Recently, advanced deep learning techniques have successfully demonstrated human-level performance in numerous fields including medical image analysis. We propose a deep convolutional neural network for Alzheimer's disease diagnosis using brain MRI data analysis. While most of the existing approaches perform binary classification, our model can identify different stages of Alzheimer's disease and obtains superior performance for early-stage diagnosis. We conducted ample experiments to demonstrate that our proposed model outperformed comparative baselines on the Open Access Series of Imaging Studies dataset.
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NASA Astrophysics Data System (ADS)
Goff, P.; Hulse, A.; Harder, H. R.; Pierce, L. A.; Rizzo, D.; Hanley, J.; Orantes, L.; Stevens, L.; Justi, S.; Monroy, C.
2015-12-01
A computational simulation has been designed as an investigative case study by high school students to introduce system dynamics modeling into high school curriculum. This case study approach leads users through the forensics necessary to diagnose an unknown disease in a Central American village. This disease, Chagas, is endemic to 21 Latin American countries. The CDC estimates that of the 110 million people living in areas with the disease, 8 million are infected, with as many as 300,000 US cases. Chagas is caused by the protozoan parasite, Trypanosoma cruzi, and is spread via blood feeding insect (vectors), that feed on vertebrates and live in crevasses in the walls and roofs of adobe homes. One-third of the infected people will develop chronic Chagas who are asymptomatic for years before their heart or GI tract become enlarged resulting in death. The case study has three parts. Students play the role of WHO field investigators and work collaboratively to: 1) use genetics to identify the host(s) and vector of the disease 2) use a STELLA™ SIR (Susceptible, Infected, Recovered) system dynamics model to study Chagas at the village scale and 3) develop management strategies. The simulations identify mitigation strategies known as Ecohealth Interventions (e.g., home improvements using local materials) to help stakeholders test and compare multiple optima. High school students collaborated with researchers from the University of Vermont, Loyola University and Universidad de San Carlos, Guatemala, working in labs, interviewing researchers, and incorporating mulitple field data as part of a NSF-funded multiyear grant. The model displays stable equilibria of hosts, vectors, and disease-states. Sensitivity analyses show measures of household condition and presence of vertebrates were significant leverage points, supporting other findings by the University research team. The village-scale model explores multiple solutions to disease mitigation for the purpose of producing students who can think long-term, better understand feedbacks, and anticipate unexpected consequences associated with non-linear systems. This case study enables high school teachers to incorporate ongoing research, systems modeling, and engineering design, three core goals Next Generation Science Standards and STEM initiatives.
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Fishing for causes and cures of motor neuron disorders.
Patten, Shunmoogum A; Armstrong, Gary A B; Lissouba, Alexandra; Kabashi, Edor; Parker, J Alex; Drapeau, Pierre
2014-07-01
Motor neuron disorders (MNDs) are a clinically heterogeneous group of neurological diseases characterized by progressive degeneration of motor neurons, and share some common pathological pathways. Despite remarkable advances in our understanding of these diseases, no curative treatment for MNDs exists. To better understand the pathogenesis of MNDs and to help develop new treatments, the establishment of animal models that can be studied efficiently and thoroughly is paramount. The zebrafish (Danio rerio) is increasingly becoming a valuable model for studying human diseases and in screening for potential therapeutics. In this Review, we highlight recent progress in using zebrafish to study the pathology of the most common MNDs: spinal muscular atrophy (SMA), amyotrophic lateral sclerosis (ALS) and hereditary spastic paraplegia (HSP). These studies indicate the power of zebrafish as a model to study the consequences of disease-related genes, because zebrafish homologues of human genes have conserved functions with respect to the aetiology of MNDs. Zebrafish also complement other animal models for the study of pathological mechanisms of MNDs and are particularly advantageous for the screening of compounds with therapeutic potential. We present an overview of their potential usefulness in MND drug discovery, which is just beginning and holds much promise for future therapeutic development. © 2014. Published by The Company of Biologists Ltd.
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Smidowicz, Angelika; Regula, Julita
2015-11-01
The inflammatory process plays an important role in the pathogenesis of many chronic diseases, such as cardiovascular diseases, diabetes mellitus type 2, and metabolic syndrome. Serum C-reactive protein (CRP) and interleukin-6 (IL-6) are widely tested inflammatory markers involved in the development of these diseases. Several studies indicate a relation between nutritional status and the concentrations of human high-sensitivity CRP and IL-6. Similarly, the role of diet in reducing inflammation and thereby modulating the risk of non-communicable diseases is supported by numerous studies. This review focuses on the effects of the selected nutrition models in humans on the concentrations of CRP and IL-6. It seems that the Mediterranean diet model is most effective in inhibiting inflammation. The Dietary Approaches to Stop Hypertension model and the plant nutrition model also have proven to be beneficial. The data on low-fat and low-carbohydrate diets are inconclusive. Comprehensive studies are necessary, taking into account the cumulative effect of dietary and other factors on the inflammatory process. © 2015 American Society for Nutrition.
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Recent technological advances in using mouse models to study ovarian cancer.
House, Carrie Danielle; Hernandez, Lidia; Annunziata, Christina Messineo
2014-01-01
Serous epithelial ovarian cancer (SEOC) is the most lethal gynecological cancer in the United States with disease recurrence being the major cause of morbidity and mortality. Despite recent advances in our understanding of the molecular mechanisms responsible for the development of SEOC, the survival rate for women with this disease has remained relatively unchanged in the last two decades. Preclinical mouse models of ovarian cancer, including xenograft, syngeneic, and genetically engineered mice, have been developed to provide a mechanism for studying the development and progression of SEOC. Such models strive to increase our understanding of the etiology and dissemination of ovarian cancer in order to overcome barriers to early detection and resistance to standard chemotherapy. Although there is not a single model that is most suitable for studying ovarian cancer, improvements have led to current models that more closely mimic human disease in their genotype and phenotype. Other advances in the field, such as live animal imaging techniques, allow effective monitoring of the microenvironment and therapeutic efficacy. New and improved preclinical mouse models, combined with technological advances to study such models, will undoubtedly render success of future human clinical trials for patients with SEOC.
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Recent Technological Advances in Using Mouse Models to Study Ovarian Cancer
House, Carrie Danielle; Hernandez, Lidia; Annunziata, Christina Messineo
2014-01-01
Serous epithelial ovarian cancer (SEOC) is the most lethal gynecological cancer in the United States with disease recurrence being the major cause of morbidity and mortality. Despite recent advances in our understanding of the molecular mechanisms responsible for the development of SEOC, the survival rate for women with this disease has remained relatively unchanged in the last two decades. Preclinical mouse models of ovarian cancer, including xenograft, syngeneic, and genetically engineered mice, have been developed to provide a mechanism for studying the development and progression of SEOC. Such models strive to increase our understanding of the etiology and dissemination of ovarian cancer in order to overcome barriers to early detection and resistance to standard chemotherapy. Although there is not a single model that is most suitable for studying ovarian cancer, improvements have led to current models that more closely mimic human disease in their genotype and phenotype. Other advances in the field, such as live animal imaging techniques, allow effective monitoring of the microenvironment and therapeutic efficacy. New and improved preclinical mouse models, combined with technological advances to study such models, will undoubtedly render success of future human clinical trials for patients with SEOC. PMID:24592355
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Managing Chronic Disease in Ontario Primary Care: The Impact of Organizational Factors
Russell, Grant M.; Dahrouge, Simone; Hogg, William; Geneau, Robert; Muldoon, Laura; Tuna, Meltem
2009-01-01
PURPOSE New approaches to chronic disease management emphasize the need to improve the delivery of primary care services to meet the needs of chronically ill patients. This study (1) assessed whether chronic disease management differed among 4 models of primary health care delivery and (2) identified which practice organizational factors were independently associated with high-quality care. METHODS We undertook a cross-sectional survey with nested qualitative case studies (2 practices per model) in 137 randomly selected primary care practices from 4 delivery models in Ontario Canada: fee for service, capitation, blended payment, and community health centers (CHCs). Practice and clinician surveys were based on the Primary Care Assessment Tool. A chart audit assessed evidence-based care delivery for patients with diabetes, congestive heart failure, and coronary artery disease. Intermediate outcomes were calculated for patients with diabetes and hypertension. Multiple linear regression identified those organizational factors independently associated with chronic disease management. RESULTS Chronic disease management was superior in CHCs. Clinicians in CHCs found it easier than those in the other models to promote high-quality care through longer consultations and interprofessional collaboration. Across the whole sample and independent of model, high-quality chronic disease management was associated with the presence of a nurse-practitioner. It was also associated with lower patient-family physician ratios and when practices had 4 or fewer full-time-equivalent family physicians. CONCLUSIONS The study adds to the literature supporting the value of nurse-practitioners within primary care teams and validates the contributions of Ontario’s CHCs. Our observation that quality of care decreased in larger, busier practices suggests that moves toward larger practices and greater patient-physician ratios may have unanticipated negative effects on processes of care quality. PMID:19597168
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Particle-to-PFU ratio of Ebola virus influences disease course and survival in cynomolgus macaques.
Alfson, Kendra J; Avena, Laura E; Beadles, Michael W; Staples, Hilary; Nunneley, Jerritt W; Ticer, Anysha; Dick, Edward J; Owston, Michael A; Reed, Christopher; Patterson, Jean L; Carrion, Ricardo; Griffiths, Anthony
2015-07-01
This study addresses the role of Ebola virus (EBOV) specific infectivity in virulence. Filoviruses are highly lethal, enveloped, single-stranded negative-sense RNA viruses that can cause hemorrhagic fever. No approved vaccines or therapies exist for filovirus infections, and infectious virus must be handled in maximum containment. Efficacy testing of countermeasures, in addition to investigations of pathogenicity and immune response, often requires a well-characterized animal model. For EBOV, an obstacle in performing accurate disease modeling is a poor understanding of what constitutes an infectious dose in animal models. One well-recognized consequence of viral passage in cell culture is a change in specific infectivity, often measured as a particle-to-PFU ratio. Here, we report that serial passages of EBOV in cell culture resulted in a decrease in particle-to-PFU ratio. Notably, this correlated with decreased potency in a lethal cynomolgus macaque (Macaca fascicularis) model of infection; animals were infected with the same viral dose as determined by plaque assay, but animals that received more virus particles exhibited increased disease. This suggests that some particles are unable to form a plaque in a cell culture assay but are able to result in lethal disease in vivo. These results have a significant impact on how future studies are designed to model EBOV disease and test countermeasures. Ebola virus (EBOV) can cause severe hemorrhagic disease with a high case-fatality rate, and there are no approved vaccines or therapies. Specific infectivity can be considered the total number of viral particles per PFU, and its impact on disease is poorly understood. In stocks of most mammalian viruses, there are particles that are unable to complete an infectious cycle or unable to cause cell pathology in cultured cells. We asked if these particles cause disease in nonhuman primates by infecting monkeys with equal infectious doses of genetically identical stocks possessing either high or low specific infectivities. Interestingly, some particles that did not yield plaques in cell culture assays were able to result in lethal disease in vivo. Furthermore, the number of PFU needed to induce lethal disease in animals was very low. Our results have a significant impact on how future studies are designed to model EBOV disease and test countermeasures.
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Dou, Kaili; Yu, Ping; Liu, Fang; Guan, YingPing; Li, Zhenye; Ji, Yumeng; Du, Ningkai; Lu, Xudong; Duan, Huilong
2017-01-01
Background Chronic disease patients often face multiple challenges from difficult comorbidities. Smartphone health technology can be used to help them manage their conditions only if they accept and use the technology. Objective The aim of this study was to develop and test a theoretical model to predict and explain the factors influencing patients’ acceptance of smartphone health technology for chronic disease management. Methods Multiple theories and factors that may influence patients’ acceptance of smartphone health technology have been reviewed. A hybrid theoretical model was built based on the technology acceptance model, dual-factor model, health belief model, and the factors identified from interviews that might influence patients’ acceptance of smartphone health technology for chronic disease management. Data were collected from patient questionnaire surveys and computer log records about 157 hypertensive patients’ actual use of a smartphone health app. The partial least square method was used to test the theoretical model. Results The model accounted for .412 of the variance in patients’ intention to adopt the smartphone health technology. Intention to use accounted for .111 of the variance in actual use and had a significant weak relationship with the latter. Perceived ease of use was affected by patients’ smartphone usage experience, relationship with doctor, and self-efficacy. Although without a significant effect on intention to use, perceived ease of use had a significant positive influence on perceived usefulness. Relationship with doctor and perceived health threat had significant positive effects on perceived usefulness, countering the negative influence of resistance to change. Perceived usefulness, perceived health threat, and resistance to change significantly predicted patients’ intentions to use the technology. Age and gender had no significant influence on patients’ acceptance of smartphone technology. The study also confirmed the positive relationship between intention to use and actual use of smartphone health apps for chronic disease management. Conclusions This study developed a theoretical model to predict patients’ acceptance of smartphone health technology for chronic disease management. Although resistance to change is a significant barrier to technology acceptance, careful management of doctor-patient relationship, and raising patients’ awareness of the negative effect of chronic disease can negate the effect of resistance and encourage acceptance and use of smartphone health technology to support chronic disease management for patients in the community. PMID:29212629
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Veigh, Clare Mc; Reid, Joanne; Larkin, Philip; Porter, Sam; Hudson, Peter
2017-07-11
Previous research and key guidelines have suggested potential models of palliative care for patients with COPD and interstitial lung disease. However, these recommendations are often not effectively implemented in clinical practice and are void of guidance regarding palliative care for patients with bronchiectasis, another form of non-malignant respiratory disease. The aim of this research was to explore generalist and specialist palliative care service provision for people with non-malignant respiratory disease in the North and Republic of Ireland. Qualitative study involving a convenience sample of 17 bereaved carers and 18 healthcare professionals recruited from 2 rural and 2 urban sites on the Island of Ireland. Data collection consisted of semi-structured interviews with carers of patients with COPD, interstitial lung disease or bronchiectasis who had died 3-18 months previously; and 4 focus groups with healthcare professionals. Data analysed using thematic analysis. Findings highlighted the lack of a clear model of holistic care delivery for patients with non-malignant respiratory disease and illuminated the varying levels of palliative care provision this client group experienced. Additionally, ambiguity amongst healthcare professionals regarding prognostication illuminated the importance of the provision of palliative care being based on patient need, not prognosis. This research developed a potential model of palliative care which may help healthcare professionals introduce palliative care, and specialist respiratory care, early in the disease trajectory of non-malignant respiratory disease, whilst also encouraging the involvement of specialist palliative care for complex symptom management. This research provides an important insight into a potential model of palliative care for people with non-malignant respiratory disease, inclusive of bronchiectasis. However, the feasibility of integrating this model into clinical practice requires further exploration.
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ERIC Educational Resources Information Center
Morsink, Maarten C.; Dukers, Danny F.
2009-01-01
Animal models have been widely used for studying the physiology and pharmacology of psychiatric and neurological diseases. The concepts of face, construct, and predictive validity are used as indicators to estimate the extent to which the animal model mimics the disease. Currently, we used these three concepts to design a theoretical assignment to…
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TOWARDS LANDSCAPE DESIGN GUIDELINES FOR REDUCING LYME DISEASE RISK
Incidence of Lyme disease in the United States continues to grow. Low-density development is also increasing in endemic regions, raising questions about the relationship between development pattern and disease. This study sought to model Lyme disease incidence rate using quanti...
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Integration of Spatial and Social Network Analysis in Disease Transmission Studies.
Emch, Michael; Root, Elisabeth D; Giebultowicz, Sophia; Ali, Mohammad; Perez-Heydrich, Carolina; Yunus, Mohammad
2012-01-01
This study presents a case study of how social network and spatial analytical methods can be used simultaneously for disease transmission modeling. The paper first reviews strategies employed in previous studies and then offers the example of transmission of two bacterial diarrheal diseases in rural Bangladesh. The goal is to understand how diseases vary socially above and beyond the effects of the local neighborhood context. Patterns of cholera and shigellosis incidence are analyzed in space and within kinship-based social networks in Matlab, Bangladesh. Data include a spatially referenced longitudinal demographic database which consists of approximately 200,000 people and laboratory-confirmed cholera and shigellosis cases from 1983 to 2003. Matrices are created of kinship ties between households using a complete network design and distance matrices are also created to model spatial relationships. Moran's I statistics are calculated to measure clustering within both social and spatial matrices. Combined spatial effects-spatial disturbance models are built to simultaneously analyze spatial and social effects while controlling for local environmental context. Results indicate that cholera and shigellosis always clusters in space and only sometimes within social networks. This suggests that the local environment is most important for understanding transmission of both diseases however kinship-based social networks also influence their transmission. Simultaneous spatial and social network analysis can help us better understand disease transmission and this study has offered several strategies on how.
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Integration of Spatial and Social Network Analysis in Disease Transmission Studies
Root, Elisabeth D; Giebultowicz, Sophia; Ali, Mohammad; Perez-Heydrich, Carolina; Yunus, Mohammad
2013-01-01
This study presents a case study of how social network and spatial analytical methods can be used simultaneously for disease transmission modeling. The paper first reviews strategies employed in previous studies and then offers the example of transmission of two bacterial diarrheal diseases in rural Bangladesh. The goal is to understand how diseases vary socially above and beyond the effects of the local neighborhood context. Patterns of cholera and shigellosis incidence are analyzed in space and within kinship-based social networks in Matlab, Bangladesh. Data include a spatially referenced longitudinal demographic database which consists of approximately 200,000 people and laboratory-confirmed cholera and shigellosis cases from 1983 to 2003. Matrices are created of kinship ties between households using a complete network design and distance matrices are also created to model spatial relationships. Moran's I statistics are calculated to measure clustering within both social and spatial matrices. Combined spatial effects-spatial disturbance models are built to simultaneously analyze spatial and social effects while controlling for local environmental context. Results indicate that cholera and shigellosis always clusters in space and only sometimes within social networks. This suggests that the local environment is most important for understanding transmission of both diseases however kinship-based social networks also influence their transmission. Simultaneous spatial and social network analysis can help us better understand disease transmission and this study has offered several strategies on how. PMID:24163443
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Cheung, Li C; Pan, Qing; Hyun, Noorie; Schiffman, Mark; Fetterman, Barbara; Castle, Philip E; Lorey, Thomas; Katki, Hormuzd A
2017-09-30
For cost-effectiveness and efficiency, many large-scale general-purpose cohort studies are being assembled within large health-care providers who use electronic health records. Two key features of such data are that incident disease is interval-censored between irregular visits and there can be pre-existing (prevalent) disease. Because prevalent disease is not always immediately diagnosed, some disease diagnosed at later visits are actually undiagnosed prevalent disease. We consider prevalent disease as a point mass at time zero for clinical applications where there is no interest in time of prevalent disease onset. We demonstrate that the naive Kaplan-Meier cumulative risk estimator underestimates risks at early time points and overestimates later risks. We propose a general family of mixture models for undiagnosed prevalent disease and interval-censored incident disease that we call prevalence-incidence models. Parameters for parametric prevalence-incidence models, such as the logistic regression and Weibull survival (logistic-Weibull) model, are estimated by direct likelihood maximization or by EM algorithm. Non-parametric methods are proposed to calculate cumulative risks for cases without covariates. We compare naive Kaplan-Meier, logistic-Weibull, and non-parametric estimates of cumulative risk in the cervical cancer screening program at Kaiser Permanente Northern California. Kaplan-Meier provided poor estimates while the logistic-Weibull model was a close fit to the non-parametric. Our findings support our use of logistic-Weibull models to develop the risk estimates that underlie current US risk-based cervical cancer screening guidelines. Published 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA. Published 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA.
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ERIC Educational Resources Information Center
Gardiner, Katheleen
2009-01-01
Mouse models are a standard tool in the study of many human diseases, providing insights into the normal functions of a gene, how these are altered in disease and how they contribute to a disease process, as well as information on drug action, efficacy and side effects. Our knowledge of human genes, their genetics, functions, interactions and…
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Although cardiovascular disease (CVD) is considered a risk factor for the exacerbation of air pollution health effects, no studies have been done assessing the influence of the disease on the development of lung injury induced by asbestos exposure. In this study we examined lung ...
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The background puzzle: how identical mutations in the same gene lead to different disease symptoms.
Kammenga, Jan E
2017-10-01
Identical disease-causing mutations can lead to different symptoms in different people. The reason for this has been a puzzling problem for geneticists. Differential penetrance and expressivity of mutations has been observed within individuals with different and similar genetic backgrounds. Attempts have been made to uncover the underlying mechanisms that determine differential phenotypic effects of identical mutations through studies of model organisms. From these studies evidence is accumulating that to understand disease mechanism or predict disease prevalence, an understanding of the influence of genetic background is as important as the putative disease-causing mutations of relatively large effect. This review highlights current insights into phenotypic variation due to gene interactions, epigenetics and stochasticity in model organisms, and discusses their importance for understanding the mutational effect on disease symptoms. © 2017 Federation of European Biochemical Societies.
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Yoon, S-J; Oh, I-H; Seo, H-Y; Kim, E-J
2014-08-01
Climate change influences human health in various ways, and quantitative assessments of the effect of climate change on health at national level are becoming essential for environmental health management. This study quantified the burden of disease attributable to climate change in Korea using disability-adjusted life years (DALY), and projected how this would change over time. Diseases related to climate change in Korea were selected, and meteorological data for each risk factor of climate change were collected. Mortality was calculated, and a database of incidence and prevalence was established. After measuring the burden of each disease, the total burden of disease related to climate change was assessed by multiplying population-attributable fractions. Finally, an estimation model for the burden of disease was built based on Korean climate data. The total burden of disease related to climate change in Korea was 6.85 DALY/1000 population in 2008. Cerebrovascular diseases induced by heat waves accounted for 72.1% of the total burden of disease (hypertensive disease 1.82 DALY/1000 population, ischaemic heart disease 1.56 DALY/1000 population, cerebrovascular disease 1.56 DALY/1000 population). According to the estimation model, the total burden of disease will be 11.48 DALY/1000 population in 2100, which is twice the total burden of disease in 2008. This study quantified the burden of disease caused by climate change in Korea, and provides valuable information for determining the priorities of environmental health policy in East Asian countries with similar climates. Copyright © 2014 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
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Miyamoto, Tadayoshi; Manabe, Kou; Ueda, Shinya; Nakahara, Hidehiro
2018-05-01
What is the central question of this study? The lack of useful small-animal models for studying exercise hyperpnoea makes it difficult to investigate the underlying mechanisms of exercise-induced ventilatory abnormalities in various disease states. What is the main finding and its importance? We developed an anaesthetized-rat model for studying exercise hyperpnoea, using a respiratory equilibrium diagram for quantitative characterization of the respiratory chemoreflex feedback system. This experimental model will provide an opportunity to clarify the major determinant mechanisms of exercise hyperpnoea, and will be useful for understanding the mechanisms responsible for abnormal ventilatory responses to exercise in disease models. Exercise-induced ventilatory abnormalities in various disease states seem to arise from pathological changes of respiratory regulation. Although experimental studies in small animals are essential to investigate the pathophysiological basis of various disease models, the lack of an integrated framework for quantitatively characterizing respiratory regulation during exercise prevents us from resolving these problems. The purpose of this study was to develop an anaesthetized-rat model for studying exercise hyperpnoea for quantitative characterization of the respiratory chemoreflex feedback system. In 24 anaesthetized rats, we induced muscle contraction by stimulating bilateral distal sciatic nerves at low and high voltage to mimic exercise. We recorded breath-by-breath respiratory gas analysis data and cardiorespiratory responses while running two protocols to characterize the controller and plant of the respiratory chemoreflex. The controller was characterized by determining the linear relationship between end-tidal CO 2 pressure (P ETC O2) and minute ventilation (V̇E), and the plant by the hyperbolic relationship between V̇E and P ETC O2. During exercise, the controller curve shifted upward without change in controller gain, accompanying increased oxygen uptake. The hyperbolic plant curve shifted rightward and downward depending on exercise intensity as predicted by increased metabolism. Exercise intensity-dependent changes in operating points (V̇E and P ETC O2) were estimated by integrating the controller and plant curves in a respiratory equilibrium diagram. In conclusion, we developed an anaesthetized-rat model for studying exercise hyperpnoea, using systems analysis for quantitative characterization of the respiratory system. This novel experimental model will be useful for understanding the mechanisms responsible for abnormal ventilatory responses to exercise in disease models. © 2018 Morinomiya University of Medical Sciences. Experimental Physiology © 2018 The Physiological Society.
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Song, Peipei; He, Jiangjiang; Li, Fen; Jin, Chunlin
2017-02-01
China is facing the great challenge of treating the world's largest rare disease population, an estimated 16 million patients with rare diseases. One effort offering promise has been a pilot national project that was launched in 2013 and that focused on 20 representative rare diseases. Another government-supported special research program on rare diseases - the "Rare Diseases Clinical Cohort Study" - was launched in December 2016. According to the plan for this research project, the unified National Rare Diseases Registry System of China will be established as of 2020, and a large-scale cohort study will be conducted from 2016 to 2020. The project plans to develop 109 technical standards, to establish and improve 2 national databases of rare diseases - a multi-center clinical database and a biological sample library, and to conduct studies on more than 50,000 registered cases of 50 different rare diseases. More importantly, this study will be combined with the concept of precision medicine. Chinese population-specific basic information on rare diseases, clinical information, and genomic information will be integrated to create a comprehensive predictive model with a follow-up database system and a model to evaluate prognosis. This will provide the evidence for accurate classification, diagnosis, treatment, and estimation of prognosis for rare diseases in China. Numerous challenges including data standardization, protecting patient privacy, big data processing, and interpretation of genetic information still need to be overcome, but research prospects offer great promise.
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Data-Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer's Disease.
Oxtoby, Neil P; Garbarino, Sara; Firth, Nicholas C; Warren, Jason D; Schott, Jonathan M; Alexander, Daniel C
2017-01-01
Model-based investigations of transneuronal spreading mechanisms in neurodegenerative diseases relate the pattern of pathology severity to the brain's connectivity matrix, which reveals information about how pathology propagates through the connectivity network. Such network models typically use networks based on functional or structural connectivity in young and healthy individuals, and only end-stage patterns of pathology, thereby ignoring/excluding the effects of normal aging and disease progression. Here, we examine the sequence of changes in the elderly brain's anatomical connectivity over the course of a neurodegenerative disease. We do this in a data-driven manner that is not dependent upon clinical disease stage, by using event-based disease progression modeling. Using data from the Alzheimer's Disease Neuroimaging Initiative dataset, we sequence the progressive decline of anatomical connectivity, as quantified by graph-theory metrics, in the Alzheimer's disease brain. Ours is the first single model to contribute to understanding all three of the nature, the location, and the sequence of changes to anatomical connectivity in the human brain due to Alzheimer's disease. Our experimental results reveal new insights into Alzheimer's disease: that degeneration of anatomical connectivity in the brain may be a viable, even early, biomarker and should be considered when studying such neurodegenerative diseases.
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Deeg, Cornelia A; Thurau, Stephan R; Gerhards, Hartmut; Ehrenhofer, Marion; Wildner, Gerhild; Kaspers, Bernd
2002-09-01
Equine recurrent uveitis (ERU) is an inflammatory eye disease with high similarity to uveitis in man. It is the only spontaneous animal model for uveitis and the most frequent eye disease in horses affecting up to 10% of the population. To further investigate the pathophysiology of ERU we now report the establishment of an inducible uveitis model in horses. An ERU-like disease was elicited in seven out of seven horses by injection of interphotoreceptor retinoid-binding protein (IRBP) in complete Freund's adjuvant. Control horses did not develop uveitis. The disease model is characterized by a highly reproducible disease course and recurrent episodes with an identical time course elicited in all horses by repeated IRBP injections. The histology revealed the formation of lymphoid follicle-like structures in the eyes and an intraocular infiltration dominated by CD3(+) lymphocytes, morphological patterns typical for the spontaneous disease. Antigen-specific T cell proliferation of PBL was monitored prior to clinical uveitis and during disease episodes. An initial T cell response to IRBP-derived peptides was followed by epitope spreading to S-antigen-derived peptides in response to subsequent immunizations. Thus, horse experimental uveitis represents a valuable disease model for comparative studies with the spontaneous disease and the investigation of immunomodulatory therapeutic approaches after onset of the disease.
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Model for Postgraduate Medical Education: Study of Crohn's Disease in New Mexico
ERIC Educational Resources Information Center
Gregory, Daniel H.; And Others
1974-01-01
The purpose of this study is to introduce a model system for the continuous retrieval, storage, and dissemination of relevant clinical information that has proven to be an effective resource of real-life data. Patients with Crohn's disease from a geographic area served as the population base for 2 groups of physicians, one group practicing in the…
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Predictive modeling of mosquito abundance and dengue transmission in Kenya
NASA Astrophysics Data System (ADS)
Caldwell, J.; Krystosik, A.; Mutuku, F.; Ndenga, B.; LaBeaud, D.; Mordecai, E.
2017-12-01
Approximately 390 million people are exposed to dengue virus every year, and with no widely available treatments or vaccines, predictive models of disease risk are valuable tools for vector control and disease prevention. The aim of this study was to modify and improve climate-driven predictive models of dengue vector abundance (Aedes spp. mosquitoes) and viral transmission to people in Kenya. We simulated disease transmission using a temperature-driven mechanistic model and compared model predictions with vector trap data for larvae, pupae, and adult mosquitoes collected between 2014 and 2017 at four sites across urban and rural villages in Kenya. We tested predictive capacity of our models using four temperature measurements (minimum, maximum, range, and anomalies) across daily, weekly, and monthly time scales. Our results indicate seasonal temperature variation is a key driving factor of Aedes mosquito abundance and disease transmission. These models can help vector control programs target specific locations and times when vectors are likely to be present, and can be modified for other Aedes-transmitted diseases and arboviral endemic regions around the world.
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Mouse models to study the effect of cardiovascular risk factors on brain structure and cognition
Bink, Diewertje I; Ritz, Katja; Aronica, Eleonora; van der Weerd, Louise; Daemen, Mat JAP
2013-01-01
Recent clinical data indicates that hemodynamic changes caused by cardiovascular diseases such as atherosclerosis, heart failure, and hypertension affect cognition. Yet, the underlying mechanisms of the resulting vascular cognitive impairment (VCI) are poorly understood. One reason for the lack of mechanistic insights in VCI is that research in dementia primarily focused on Alzheimer's disease models. To fill in this gap, we critically reviewed the published data and various models of VCI. Typical findings in VCI include reduced cerebral perfusion, blood–brain barrier alterations, white matter lesions, and cognitive deficits, which have also been reported in different cardiovascular mouse models. However, the tests performed are incomplete and differ between models, hampering a direct comparison between models and studies. Nevertheless, from the currently available data we conclude that a few existing surgical animal models show the key features of vascular cognitive decline, with the bilateral common carotid artery stenosis hypoperfusion mouse model as the most promising model. The transverse aortic constriction and myocardial infarction models may be good alternatives, but these models are as yet less characterized regarding the possible cerebral changes. Mixed models could be used to study the combined effects of different cardiovascular diseases on the deterioration of cognition during aging. PMID:23963364
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Epidemics Modelings: Some New Challenges
NASA Astrophysics Data System (ADS)
Boatto, Stefanella; Khouri, Renata Stella; Solerman, Lucas; Codeço, Claudia; Bonnet, Catherine
2010-09-01
Epidemics modeling has been particularly growing in the past years. In epidemics studies, mathematical modeling is used in particular to reach a better understanding of some neglected diseases (dengue, malaria, …) and of new emerging ones (SARS, influenza A,….) of big agglomerates. Such studies offer new challenges both from the modeling point of view (searching for simple models which capture the main characteristics of the disease spreading), data analysis and mathematical complexity. We are facing often with complex networks especially when modeling the city dynamics. Such networks can be static (in first approximation) and homogeneous, static and not homogeneous and/or not static (when taking into account the city structure, micro-climates, people circulation, etc.). The objective being studying epidemics dynamics and being able to predict its spreading.
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Interprofessional Collaborative Practice Models in Chronic Disease Management.
Southerland, Janet H; Webster-Cyriaque, Jennifer; Bednarsh, Helene; Mouton, Charles P
2016-10-01
Interprofessional collaboration in health has become essential to providing high-quality care, decreased costs, and improved outcomes. Patient-centered care requires synthesis of all the components of primary and specialty medicine to address patient needs. For individuals living with chronic diseases, this model is even more critical to obtain better health outcomes. Studies have shown shown that oral health and systemic disease are correlated as it relates to disease development and progression. Thus, inclusion of oral health in many of the existing and new collaborative models could result in better management of chronic illnesses and improve overall health outcomes. Copyright © 2016 Elsevier Inc. All rights reserved.
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Ng, Victoria; Sargeant, Jan M.
2013-01-01
Background Currently, zoonoses account for 58% to 61% of all communicable diseases causing illness in humans globally and up to 75% of emerging human pathogens. Although the impact of zoonoses on animal health and public health in North America is significant, there has been no published research involving health professionals on the prioritization of zoonoses in this region. Methodology/Principal Findings We used conjoint analysis (CA), a well-established quantitative method in market research, to identify the relative importance of 21 key characteristics of zoonotic diseases for their prioritization in Canada and the US. Relative importance weights from the CA were used to develop a point-scoring system to derive a recommended list of zoonoses for prioritization in Canada and the US. Study participants with a background in epidemiology, public health, medical sciences, veterinary sciences and infectious disease research were recruited to complete the online survey (707 from Canada and 764 from the US). Hierarchical Bayes models were fitted to the survey data to derive CA-weighted scores for disease criteria. Scores were applied to 62 zoonotic diseases to rank diseases in order of priority. Conclusions/Significance We present the first zoonoses prioritization exercise involving health professionals in North America. Our previous study indicated individuals with no prior knowledge in infectious diseases were capable of producing meaningful results with acceptable model fits (79.4%). This study suggests health professionals with some knowledge in infectious diseases were capable of producing meaningful results with better-fitted models than the general public (83.7% and 84.2%). Despite more similarities in demographics and model fit between the combined public and combined professional groups, there was more uniformity across priority lists between the Canadian public and Canadian professionals and between the US public and US professionals. Our study suggests that CA can be used as a potential tool for the prioritization of zoonoses. PMID:23991057
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A cross-species analysis method to analyze animal models' similarity to human's disease state
2012-01-01
Background Animal models are indispensable tools in studying the cause of human diseases and searching for the treatments. The scientific value of an animal model depends on the accurate mimicry of human diseases. The primary goal of the current study was to develop a cross-species method by using the animal models' expression data to evaluate the similarity to human diseases' and assess drug molecules' efficiency in drug research. Therefore, we hoped to reveal that it is feasible and useful to compare gene expression profiles across species in the studies of pathology, toxicology, drug repositioning, and drug action mechanism. Results We developed a cross-species analysis method to analyze animal models' similarity to human diseases and effectiveness in drug research by utilizing the existing animal gene expression data in the public database, and mined some meaningful information to help drug research, such as potential drug candidates, possible drug repositioning, side effects and analysis in pharmacology. New animal models could be evaluated by our method before they are used in drug discovery. We applied the method to several cases of known animal model expression profiles and obtained some useful information to help drug research. We found that trichostatin A and some other HDACs could have very similar response across cell lines and species at gene expression level. Mouse hypoxia model could accurately mimic the human hypoxia, while mouse diabetes drug model might have some limitation. The transgenic mouse of Alzheimer was a useful model and we deeply analyzed the biological mechanisms of some drugs in this case. In addition, all the cases could provide some ideas for drug discovery and drug repositioning. Conclusions We developed a new cross-species gene expression module comparison method to use animal models' expression data to analyse the effectiveness of animal models in drug research. Moreover, through data integration, our method could be applied for drug research, such as potential drug candidates, possible drug repositioning, side effects and information about pharmacology. PMID:23282076
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A cross-species analysis method to analyze animal models' similarity to human's disease state.
Yu, Shuhao; Zheng, Lulu; Li, Yun; Li, Chunyan; Ma, Chenchen; Li, Yixue; Li, Xuan; Hao, Pei
2012-01-01
Animal models are indispensable tools in studying the cause of human diseases and searching for the treatments. The scientific value of an animal model depends on the accurate mimicry of human diseases. The primary goal of the current study was to develop a cross-species method by using the animal models' expression data to evaluate the similarity to human diseases' and assess drug molecules' efficiency in drug research. Therefore, we hoped to reveal that it is feasible and useful to compare gene expression profiles across species in the studies of pathology, toxicology, drug repositioning, and drug action mechanism. We developed a cross-species analysis method to analyze animal models' similarity to human diseases and effectiveness in drug research by utilizing the existing animal gene expression data in the public database, and mined some meaningful information to help drug research, such as potential drug candidates, possible drug repositioning, side effects and analysis in pharmacology. New animal models could be evaluated by our method before they are used in drug discovery. We applied the method to several cases of known animal model expression profiles and obtained some useful information to help drug research. We found that trichostatin A and some other HDACs could have very similar response across cell lines and species at gene expression level. Mouse hypoxia model could accurately mimic the human hypoxia, while mouse diabetes drug model might have some limitation. The transgenic mouse of Alzheimer was a useful model and we deeply analyzed the biological mechanisms of some drugs in this case. In addition, all the cases could provide some ideas for drug discovery and drug repositioning. We developed a new cross-species gene expression module comparison method to use animal models' expression data to analyse the effectiveness of animal models in drug research. Moreover, through data integration, our method could be applied for drug research, such as potential drug candidates, possible drug repositioning, side effects and information about pharmacology.
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Ogada, Pamella Akoth; Moualeu, Dany Pascal; Poehling, Hans-Michael
2016-01-01
Several models have been studied on predictive epidemics of arthropod vectored plant viruses in an attempt to bring understanding to the complex but specific relationship between the three cornered pathosystem (virus, vector and host plant), as well as their interactions with the environment. A large body of studies mainly focuses on weather based models as management tool for monitoring pests and diseases, with very few incorporating the contribution of vector’s life processes in the disease dynamics, which is an essential aspect when mitigating virus incidences in a crop stand. In this study, we hypothesized that the multiplication and spread of tomato spotted wilt virus (TSWV) in a crop stand is strongly related to its influences on Frankliniella occidentalis preferential behavior and life expectancy. Model dynamics of important aspects in disease development within TSWV-F. occidentalis-host plant interactions were developed, focusing on F. occidentalis’ life processes as influenced by TSWV. The results show that the influence of TSWV on F. occidentalis preferential behaviour leads to an estimated increase in relative acquisition rate of the virus, and up to 33% increase in transmission rate to healthy plants. Also, increased life expectancy; which relates to improved fitness, is dependent on the virus induced preferential behaviour, consequently promoting multiplication and spread of the virus in a crop stand. The development of vector–based models could further help in elucidating the role of tri-trophic interactions in agricultural disease systems. Use of the model to examine the components of the disease process could also boost our understanding on how specific epidemiological characteristics interact to cause diseases in crops. With this level of understanding we can efficiently develop more precise control strategies for the virus and the vector. PMID:27159134
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Ogada, Pamella Akoth; Moualeu, Dany Pascal; Poehling, Hans-Michael
2016-01-01
Several models have been studied on predictive epidemics of arthropod vectored plant viruses in an attempt to bring understanding to the complex but specific relationship between the three cornered pathosystem (virus, vector and host plant), as well as their interactions with the environment. A large body of studies mainly focuses on weather based models as management tool for monitoring pests and diseases, with very few incorporating the contribution of vector's life processes in the disease dynamics, which is an essential aspect when mitigating virus incidences in a crop stand. In this study, we hypothesized that the multiplication and spread of tomato spotted wilt virus (TSWV) in a crop stand is strongly related to its influences on Frankliniella occidentalis preferential behavior and life expectancy. Model dynamics of important aspects in disease development within TSWV-F. occidentalis-host plant interactions were developed, focusing on F. occidentalis' life processes as influenced by TSWV. The results show that the influence of TSWV on F. occidentalis preferential behaviour leads to an estimated increase in relative acquisition rate of the virus, and up to 33% increase in transmission rate to healthy plants. Also, increased life expectancy; which relates to improved fitness, is dependent on the virus induced preferential behaviour, consequently promoting multiplication and spread of the virus in a crop stand. The development of vector-based models could further help in elucidating the role of tri-trophic interactions in agricultural disease systems. Use of the model to examine the components of the disease process could also boost our understanding on how specific epidemiological characteristics interact to cause diseases in crops. With this level of understanding we can efficiently develop more precise control strategies for the virus and the vector.
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Workshop Report: The Medaka Model for Comparative Assessment of Human Disease Mechanisms
Obara, Tomoko
2015-01-01
Results of recent studies showing the utility of medaka as a model of various human disease states were presented at the 7th Aquatic Models of Human Disease Conference (December 13–18, 2014, Austin, TX). This conference brought together many of the most highly regarded national and international scientists that employ the medaka model in their investigations. To take advantage of this opportunity, a cohort of established medaka researchers were asked to stay an extra day and represent the medaka scientific community in a workshop entitled “The Medaka Model for Comparative Assessment of Human Disease Mechanisms”. The central purpose of this medaka workshop was to assess current use and project the future resource needs of the American medaka research community. The workshop sought to spur discussions of issues that would promote more informative comparative disease model studies. Finally, workshop attendees met together to propose, discuss, and agree on recommendations regarding the most effective research resources needed to enable US scientists to perform experiments leading to impacting experimental results that directly translate to human disease. Consistent with this central purpose, the workshop was divided into two sessions of invited speakers having expertise and experience in the session topics. The workshop hosted 20 scientific participants (Appendices 1 and 2) and of these, nine scientists presented formal talks. Here, we present a summary report stemming from workshop presentations and subsequent round table discussions, and forward recommendations from this group that we believe represent views of the overall medaka research community. PMID:26099189
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Daut, Elizabeth F.; Lahodny, Glenn; Peterson, Markus J.; Ivanek, Renata
2016-01-01
Illegal wildlife-pet trade can threaten wildlife populations directly from overharvest, but also indirectly as a pathway for introduction of infectious diseases. This study evaluated consequences of a hypothetical introduction of Newcastle disease (ND) into a wild population of Peru’s most trafficked psittacine, the white-winged parakeet (Brotogeris versicolurus), through release of infected confiscated individuals. We developed two mathematical models that describe ND transmission and the influence of illegal harvest in a homogeneous (model 1) and age-structured population of parakeets (model 2). Infection transmission dynamics and harvest were consistent for all individuals in model 1, which rendered it mathematically more tractable compared to the more complex, age-structured model 2 that separated the host population into juveniles and adults. We evaluated the interaction of ND transmission and harvest through changes in the basic reproduction number (R0) and short-term host population dynamics. Our findings demonstrated that ND introduction would likely provoke considerable disease-related mortality, up to 24% population decline in two years, but high harvest rates would dampen the magnitude of the outbreak. Model 2 produced moderate differences in disease dynamics compared to model 1 (R0 = 3.63 and 2.66, respectively), but highlighted the importance of adult disease dynamics in diminishing the epidemic potential. Therefore, we suggest that future studies should use a more realistic, age-structured model. Finally, for the presumptive risk that illegal trade of white-winged parakeets could introduce ND into wild populations, our results suggest that while high harvest rates may have a protective effect on the population by reducing virus transmission, the combined effects of high harvest and disease-induced mortality may threaten population survival. These results capture the complexity and consequences of the interaction between ND transmission and harvest in a wild parrot population and highlight the importance of preventing illegal trade. PMID:26816214
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CONTROL FUNCTION ASSISTED IPW ESTIMATION WITH A SECONDARY OUTCOME IN CASE-CONTROL STUDIES.
Sofer, Tamar; Cornelis, Marilyn C; Kraft, Peter; Tchetgen Tchetgen, Eric J
2017-04-01
Case-control studies are designed towards studying associations between risk factors and a single, primary outcome. Information about additional, secondary outcomes is also collected, but association studies targeting such secondary outcomes should account for the case-control sampling scheme, or otherwise results may be biased. Often, one uses inverse probability weighted (IPW) estimators to estimate population effects in such studies. IPW estimators are robust, as they only require correct specification of the mean regression model of the secondary outcome on covariates, and knowledge of the disease prevalence. However, IPW estimators are inefficient relative to estimators that make additional assumptions about the data generating mechanism. We propose a class of estimators for the effect of risk factors on a secondary outcome in case-control studies that combine IPW with an additional modeling assumption: specification of the disease outcome probability model. We incorporate this model via a mean zero control function. We derive the class of all regular and asymptotically linear estimators corresponding to our modeling assumption, when the secondary outcome mean is modeled using either the identity or the log link. We find the efficient estimator in our class of estimators and show that it reduces to standard IPW when the model for the primary disease outcome is unrestricted, and is more efficient than standard IPW when the model is either parametric or semiparametric.
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Lee, Yujeong; Chun, Hye Jeong; Lee, Kyung Moon; Jung, Young-Suk; Lee, Jaewon
2015-11-19
Parkinson's disease (PD) is the second-most common neurodegenerative disease after Alzheimer's disease, and is characterized by dopaminergic neuronal loss in midbrain. The MPTP-induced PD model has been well characterized by motor deficits and selective dopaminergic neuronal death accompanied by glial activation. Silibinin is a constituent of silymarin, an extract of milk thistle seeds, and has been proposed to have hepatoprotective, anti-cancer, anti-oxidative, and neuroprotective effects. In the present study, the authors studied the neuroprotective effects of silibinin in an acute MPTP model of PD. Silibinin was administered for 2 weeks, and then MPTP was administered to mice over 1 day (acute MPTP induced PD). Silibinin pretreatment effectively ameliorated motor dysfunction, dopaminergic neuronal loss, and glial activations caused by MPTP. In addition, an in vitro study demonstrated that silibinin suppressed astroglial activation and ERK and JNK phosphorylation in primary astrocytes in response to MPP(+) treatment. These findings show silibinin protected dopaminergic neurons in an acute MPTP-induced mouse model of PD, and suggest its neuroprotective effects might be mediated by the suppression of astrocyte activation via the inhibition of ERK and JNK phosphorylation. In conclusion, the study indicates silibinin should be viewed as a potential treatment for PD and other neurodegenerative diseases associated with neuroinflammation. Copyright © 2015 Elsevier B.V. All rights reserved.
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A discrete epidemic model for bovine Babesiosis disease and tick populations
NASA Astrophysics Data System (ADS)
Aranda, Diego F.; Trejos, Deccy Y.; Valverde, Jose C.
2017-06-01
In this paper, we provide and study a discrete model for the transmission of Babesiosis disease in bovine and tick populations. This model supposes a discretization of the continuous-time model developed by us previously. The results, here obtained by discrete methods as opposed to continuous ones, show that similar conclusions can be obtained for the discrete model subject to the assumption of some parametric constraints which were not necessary in the continuous case. We prove that these parametric constraints are not artificial and, in fact, they can be deduced from the biological significance of the model. Finally, some numerical simulations are given to validate the model and verify our theoretical study.
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Clemensson, Erik Karl Håkan; Clemensson, Laura Emily; Riess, Olaf; Nguyen, Huu Phuc
2017-01-01
The BACHD rat is a recently developed transgenic animal model of Huntington disease, a progressive neurodegenerative disorder characterized by extensive loss of striatal neurons. Cognitive impairments are common among patients, and characterization of similar deficits in animal models of the disease is therefore of interest. The present study assessed the BACHD rats' performance in the delayed alternation and the delayed non-matching to position test, two Skinner box-based tests of short-term memory function. The transgenic rats showed impaired performance in both tests, indicating general problems with handling basic aspects of the tests, while short-term memory appeared to be intact. Similar phenotypes have been found in rats with fronto-striatal lesions, suggesting that Huntington disease-related neuropathology might be present in the BACHD rats. Further analyses indicated that the performance deficit in the delayed alternation test might be due to impaired inhibitory control, which has also been implicated in Huntington disease patients. The study ultimately suggests that the BACHD rats might suffer from neuropathology and cognitive impairments reminiscent of those of Huntington disease patients.
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Application of an OCT data-based mathematical model of the foveal pit in Parkinson disease.
Ding, Yin; Spund, Brian; Glazman, Sofya; Shrier, Eric M; Miri, Shahnaz; Selesnick, Ivan; Bodis-Wollner, Ivan
2014-11-01
Spectral-domain Optical coherence tomography (OCT) has shown remarkable utility in the study of retinal disease and has helped to characterize the fovea in Parkinson disease (PD) patients. We developed a detailed mathematical model based on raw OCT data to allow differentiation of foveae of PD patients from healthy controls. Of the various models we tested, a difference of a Gaussian and a polynomial was found to have "the best fit". Decision was based on mathematical evaluation of the fit of the model to the data of 45 control eyes versus 50 PD eyes. We compared the model parameters in the two groups using receiver-operating characteristics (ROC). A single parameter discriminated 70 % of PD eyes from controls, while using seven of the eight parameters of the model allowed 76 % to be discriminated. The future clinical utility of mathematical modeling in study of diffuse neurodegenerative conditions that also affect the fovea is discussed.
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Kwakwa, Kristin A.; Vanderburgh, Joseph P.; Guelcher, Scott A.
2018-01-01
Purpose of Review Bone is a structurally unique microenvironment that presents many challenges for the development of 3D models for studying bone physiology and diseases, including cancer. As researchers continue to investigate the interactions within the bone microenvironment, the development of 3D models of bone has become critical. Recent Findings 3D models have been developed that replicate some properties of bone, but have not fully reproduced the complex structural and cellular composition of the bone microenvironment. This review will discuss 3D models including polyurethane, silk, and collagen scaffolds that have been developed to study tumor-induced bone disease. In addition, we discuss 3D printing techniques used to better replicate the structure of bone. Summary 3D models that better replicate the bone microenvironment will help researchers better understand the dynamic interactions between tumors and the bone microenvironment, ultimately leading to better models for testing therapeutics and predicting patient outcomes. PMID:28646444
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SEIIrR: Drug abuse model with rehabilitation
NASA Astrophysics Data System (ADS)
Sutanto, Azizah, Afina; Widyaningsih, Purnami; Saputro, Dewi Retno Sari
2017-05-01
Drug abuse in the world quite astonish and tend to increase. The increase and decrease on the number of drug abusers showed a pattern of spread that had the same characteristics with patterns of spread of infectious disease. The susceptible infected removed (SIR) and susceptible exposed infected removed (SEIR) epidemic models for infectious disease was developed to study social epidemic. In this paper, SEIR model for disease epidemic was developed to study drug abuse epidemic with rehabilitation treatment. The aims of this paper were to analogize susceptible exposed infected isolated recovered (SEIIrR) model on the drug abusers, to determine solutions of the model, to determine equilibrium point, and to do simulation on β. The solutions of SEIIrR model was determined by using fourth order of Runge-Kutta algorithm, equilibrium point obtained was free-drug equilibrium point. Solutions of SEIIrR showed that the model was able to suppress the spread of drug abuse. The increasing value of contact rate was not affect the number of infected individuals due to rehabilitation treatment.
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Modeling the Western Diet for Preclinical Investigations.
Hintze, Korry J; Benninghoff, Abby D; Cho, Clara E; Ward, Robert E
2018-05-01
Rodent models have been invaluable for biomedical research. Preclinical investigations with rodents allow researchers to investigate diseases by using study designs that are not suitable for human subjects. The primary criticism of preclinical animal models is that results are not always translatable to humans. Some of this lack of translation is due to inherent differences between species. However, rodent models have been refined over time, and translatability to humans has improved. Transgenic animals have greatly aided our understanding of interactions between genes and disease and have narrowed the translation gap between humans and model animals. Despite the technological innovations of animal models through advances in genetics, relatively little attention has been given to animal diets. Namely, developing diets that replicate what humans eat will help make animal models more relevant to human populations. This review focuses on commonly used rodent diets that are used to emulate the Western dietary pattern in preclinical studies of obesity and type 2 diabetes, nonalcoholic liver disease, maternal nutrition, and colorectal cancer.
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Nsoesie, Elaine O.; Butler, Patrick; Ramakrishnan, Naren; Mekaru, Sumiko R.; Brownstein, John S.
2015-01-01
Challenges with alternative data sources for disease surveillance include differentiating the signal from the noise, and obtaining information from data constrained settings. For the latter, events such as increases in hospital traffic could serve as early indicators of social disruption resulting from disease. In this study, we evaluate the feasibility of using hospital parking lot traffic data extracted from high-resolution satellite imagery to augment public health disease surveillance in Chile, Argentina and Mexico. We used archived satellite imagery collected from January 2010 to May 2013 and data on the incidence of respiratory virus illnesses from the Pan American Health Organization as a reference. We developed dynamical Elastic Net multivariable linear regression models to estimate the incidence of respiratory virus illnesses using hospital traffic and assessed how to minimize the effects of noise on the models. We noted that predictions based on models fitted using a sample of observations were better. The results were consistent across countries with selected models having reasonably low normalized root-mean-squared errors and high correlations for both the fits and predictions. The observations from this study suggest that if properly procured and combined with other information, this data source could be useful for monitoring disease trends. PMID:25765943
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Understanding the aetiology and resolution of chronic otitis media from animal and human studies
Thornton, Ruth B.; Kirkham, Lea-Ann S.; Kerschner, Joseph E.; Cheeseman, Michael T.
2017-01-01
ABSTRACT Inflammation of the middle ear, known clinically as chronic otitis media, presents in different forms, such as chronic otitis media with effusion (COME; glue ear) and chronic suppurative otitis media (CSOM). These are highly prevalent diseases, especially in childhood, and lead to significant morbidity worldwide. However, much remains unclear about this disease, including its aetiology, initiation and perpetuation, and the relative roles of mucosal and leukocyte biology, pathogens, and Eustachian tube function. Chronic otitis media is commonly modelled in mice but most existing models only partially mimic human disease and many are syndromic. Nevertheless, these models have provided insights into potential disease mechanisms, and have implicated altered immune signalling, mucociliary function and Eustachian tube function as potential predisposing mechanisms. Clinical studies of chronic otitis media have yet to implicate a particular molecular pathway or mechanism, and current human genetic studies are underpowered. We also do not fully understand how existing interventions, such as tympanic membrane repair, work, nor how chronic otitis media spontaneously resolves. This Clinical Puzzle article describes our current knowledge of chronic otitis media and the existing research models for this condition. It also identifies unanswered questions about its pathogenesis and treatment, with the goal of advancing our understanding of this disease to aid the development of novel therapeutic interventions. PMID:29125825
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Nsoesie, Elaine O; Butler, Patrick; Ramakrishnan, Naren; Mekaru, Sumiko R; Brownstein, John S
2015-03-13
Challenges with alternative data sources for disease surveillance include differentiating the signal from the noise, and obtaining information from data constrained settings. For the latter, events such as increases in hospital traffic could serve as early indicators of social disruption resulting from disease. In this study, we evaluate the feasibility of using hospital parking lot traffic data extracted from high-resolution satellite imagery to augment public health disease surveillance in Chile, Argentina and Mexico. We used archived satellite imagery collected from January 2010 to May 2013 and data on the incidence of respiratory virus illnesses from the Pan American Health Organization as a reference. We developed dynamical Elastic Net multivariable linear regression models to estimate the incidence of respiratory virus illnesses using hospital traffic and assessed how to minimize the effects of noise on the models. We noted that predictions based on models fitted using a sample of observations were better. The results were consistent across countries with selected models having reasonably low normalized root-mean-squared errors and high correlations for both the fits and predictions. The observations from this study suggest that if properly procured and combined with other information, this data source could be useful for monitoring disease trends.
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Paterson, Euan N; Neville, Charlotte E; Silvestri, Giuliana; Montgomery, Shannon; Moore, Evelyn; Silvestri, Vittorio; Cardwell, Christopher R; MacGillivray, Tom J; Maxwell, Alexander P; Woodside, Jayne V; McKay, Gareth J
2018-04-27
Associations between dietary patterns and chronic kidney disease are not well established, especially in European populations. We conducted a cross-sectional study of 1033 older Irish women (age range 56-100 years) with a restricted lifestyle. Dietary intake was assessed using a food frequency questionnaire. Renal function was determined by estimated glomerular filtration rate. Two dietary patterns were identified within the study population using factor analysis. A significant negative association was found between unhealthy dietary pattern adherence and renal function in both unadjusted and adjusted models controlling for potential confounding variables (p for trend <0.001), with a mean difference in estimated glomerular filtration rate of -6 ml/min/1.73 m 2 between those in the highest fifth of adherence to the unhealthy dietary pattern compared to the lowest, in the fully adjusted model. Chronic kidney disease risk was significantly greater for the highest fifth, compared to the lowest fifth of unhealthy dietary pattern adherence in adjusted models (adjusted odds ratio = 2.62, p < 0.001). Adherence to the healthy dietary pattern was not associated with renal function or chronic kidney disease in adjusted models. In this cohort, an unhealthy dietary pattern was associated with lower renal function and greater prevalence of chronic kidney disease.
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Ironside, J W; Bell, J E
2007-12-01
A wide range of infectious diseases can result in dementia, although the identity and nature of these diseases has changed over time. Two of the most significant current groups in terms of scientific complexity are HIV/AIDS and prion diseases. In these disorders, dementia occurs either as a consequence of targeting the brain and selectively damaging neurones, or by an indirect effect of neuroinflammation. In prion diseases, both direct neurotoxicity and neuroinflammation may act to result in neuronal damage. In HIV encephalitis, the progression of the dementia is slower, perhaps reflecting indirect damage that appears to result from neuroinflammation as a main cause of neuronal death. An ever-increasing range of model systems is now available to study the neuronal damage in infectious dementias, ranging from cell culture systems to animal models, some of which, particularly in the case of prion diseases, are very well characterised and amenable to controlled manipulation in terms of both host and agent parameters. As valuable as these experimental models are, they do not allow a direct approach to an understanding of dementia, the complexities of which cannot readily be studied in vitro or in animal models, but they do allow studies of interventions and therapeutic strategies. This review summarises the current state of knowledge regarding the major infective dementias.
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Understanding scale dependency of climatic processes with diarrheal disease
NASA Astrophysics Data System (ADS)
Nasr Azadani, F.; Jutla, A.; Akanda, A. S. S.; Colwell, R. R.
2015-12-01
The issue of scales in linking climatic processes with diarrheal diseases is perhaps one of the most challenging aspect to develop any predictive algorithm for outbreaks and to understand impacts of changing climate. Majority of diarrheal diseases have shown to be strongly associated with climate modulated environmental processes where pathogens survive. Using cholera as an example of characteristic diarrheal diseases, this study will provide methodological insights on dominant scale variability in climatic processes that are linked with trigger and transmission of disease. Cholera based epidemiological models use human to human interaction as a main transmission mechanism, however, environmental conditions for creating seasonality in outbreaks is not explicitly modeled. For example, existing models cannot create seasonality, unless some of the model parameters are a-priori chosen to vary seasonally. A systems based feedback approach will be presented to understand role of climatic processes on trigger and transmission disease. In order to investigate effect of changing climate on cholera, a downscaling approach using support vector machine will be used. Our preliminary results using three climate models, ECHAM5, GFDL, and HADCM show that varying modalities in future cholera outbreaks.
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NASA Astrophysics Data System (ADS)
Meijster, Tim; Warren, Nick; Heederik, Dick; Tielemans, Erik
2009-02-01
Recently a dynamic population model was developed that simulates a population of bakery workers longitudinally through time and tracks the development of work-related sensitisation and respiratory symptoms in each worker. Input for this model comes from cross-sectional and longitudinal epidemiological studies which allowed estimation of exposure response relationships and disease transition probabilities This model allows us to study the development of diseases and transitions between disease states over time in relation to determinants of disease including flour dust and/or allergen exposure. Furthermore it enables more realistic modelling of the health impact of different intervention strategies at the workplace (e.g. changes in exposure may take several years to impact on ill-health and often occur as a gradual trend). A large dataset of individual full-shift exposure measurements and real-time exposure measurements were used to obtain detailed insight into the effectiveness of control measures and other determinants of exposure. Given this information a population wide reduction of the median exposure with 50% was evaluated in this paper.
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Zhang, Xiaotian; Yin, Jian; Zhang, Xu
2018-03-02
Increasing evidence suggests that dysregulation of microRNAs (miRNAs) may lead to a variety of diseases. Therefore, identifying disease-related miRNAs is a crucial problem. Currently, many computational approaches have been proposed to predict binary miRNA-disease associations. In this study, in order to predict underlying miRNA-disease association types, a semi-supervised model called the network-based label propagation algorithm is proposed to infer multiple types of miRNA-disease associations (NLPMMDA) by mutual information derived from the heterogeneous network. The NLPMMDA method integrates disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity information of miRNAs and diseases to construct a heterogeneous network. NLPMMDA is a semi-supervised model which does not require verified negative samples. Leave-one-out cross validation (LOOCV) was implemented for four known types of miRNA-disease associations and demonstrated the reliable performance of our method. Moreover, case studies of lung cancer and breast cancer confirmed effective performance of NLPMMDA to predict novel miRNA-disease associations and their association types.
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Cannibalistic Predator-Prey Model with Disease in Predator — A Delay Model
NASA Astrophysics Data System (ADS)
Biswas, Santosh; Samanta, Sudip; Chattopadhyay, Joydev
In this paper, we propose and analyze a cannibalistic predator-prey model with a transmissible disease in the predator population. The disease can be transmitted through contacts with infected individuals as well as the cannibalism of an infected predator. We also consider incubation delay in disease transmission, where the incubation period represents the time in which the infectious agent develops in the host. Local stability analysis of the system around the biologically feasible equilibria is studied. Bifurcation analysis of the system around interior equilibrium is also studied. Applying the normal form theory and central manifold theorem, the direction of Hopf bifurcation, the stability and the period of bifurcating periodic solutions are derived. Under appropriate conditions, the permanence of the system with time delay is proved. Our results suggest that incubation delay destabilizes the system and can produce chaos. We also observe that cannibalism can control disease and population oscillations. Extensive numerical simulations are performed to support our analytical results.
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Mucker, Eric M; Chapman, Jennifer; Huzella, Louis M; Huggins, John W; Shamblin, Joshua; Robinson, Camenzind G; Hensley, Lisa E
2015-01-01
Although current nonhuman primate models of monkeypox and smallpox diseases provide some insight into disease pathogenesis, they require a high titer inoculum, use an unnatural route of infection, and/or do not accurately represent the entire disease course. This is a concern when developing smallpox and/or monkeypox countermeasures or trying to understand host pathogen relationships. In our studies, we altered half of the test system by using a New World nonhuman primate host, the common marmoset. Based on dose finding studies, we found that marmosets are susceptible to monkeypox virus infection, produce a high viremia, and have pathological features consistent with smallpox and monkeypox in humans. The low dose (48 plaque forming units) required to elicit a uniformly lethal disease and the extended incubation (preclinical signs) are unique features among nonhuman primate models utilizing monkeypox virus. The uniform lethality, hemorrhagic rash, high viremia, decrease in platelets, pathology, and abbreviated acute phase are reflective of early-type hemorrhagic smallpox.
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Mucker, Eric M.; Chapman, Jennifer; Huzella, Louis M.; Huggins, John W.; Shamblin, Joshua; Robinson, Camenzind G.; Hensley, Lisa E.
2015-01-01
Although current nonhuman primate models of monkeypox and smallpox diseases provide some insight into disease pathogenesis, they require a high titer inoculum, use an unnatural route of infection, and/or do not accurately represent the entire disease course. This is a concern when developing smallpox and/or monkeypox countermeasures or trying to understand host pathogen relationships. In our studies, we altered half of the test system by using a New World nonhuman primate host, the common marmoset. Based on dose finding studies, we found that marmosets are susceptible to monkeypox virus infection, produce a high viremia, and have pathological features consistent with smallpox and monkeypox in humans. The low dose (48 plaque forming units) required to elicit a uniformly lethal disease and the extended incubation (preclinical signs) are unique features among nonhuman primate models utilizing monkeypox virus. The uniform lethality, hemorrhagic rash, high viremia, decrease in platelets, pathology, and abbreviated acute phase are reflective of early-type hemorrhagic smallpox. PMID:26147658
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Beyond the zebrafish: diverse fish species for modeling human disease
Schartl, Manfred
2014-01-01
ABSTRACT In recent years, zebrafish, and to a lesser extent medaka, have become widely used small animal models for human diseases. These organisms have convincingly demonstrated the usefulness of fish for improving our understanding of the molecular and cellular mechanisms leading to pathological conditions, and for the development of new diagnostic and therapeutic tools. Despite the usefulness of zebrafish and medaka in the investigation of a wide spectrum of traits, there is evidence to suggest that other fish species could be better suited for more targeted questions. With the emergence of new, improved sequencing technologies that enable genomic resources to be generated with increasing efficiency and speed, the potential of non-mainstream fish species as disease models can now be explored. A key feature of these fish species is that the pathological condition that they model is often related to specific evolutionary adaptations. By exploring these adaptations, new disease-causing and disease-modifier genes might be identified; thus, diverse fish species could be exploited to better understand the complexity of disease processes. In addition, non-mainstream fish models could allow us to study the impact of environmental factors, as well as genetic variation, on complex disease phenotypes. This Review will discuss the opportunities that such fish models offer for current and future biomedical research. PMID:24271780
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Medicare capitation model, functional status, and multiple comorbidities: model accuracy
Noyes, Katia; Liu, Hangsheng; Temkin-Greener, Helena
2012-01-01
Objective This study examined financial implications of CMS-Hierarchical Condition Categories (HCC) risk-adjustment model on Medicare payments for individuals with comorbid chronic conditions. Study Design The study used 1992-2000 data from the Medicare Current Beneficiary Survey and corresponding Medicare claims. The pairs of comorbidities were formed based on the prior evidence about possible synergy between these conditions and activities of daily living (ADL) deficiencies and included heart disease and cancer, lung disease and cancer, stroke and hypertension, stroke and arthritis, congestive heart failure (CHF) and osteoporosis, diabetes and coronary artery disease, CHF and dementia. Methods For each beneficiary, we calculated the actual Medicare cost ratio as the ratio of the individual’s annualized costs to the mean annual Medicare cost of all people in the study. The actual Medicare cost ratios, by ADLs, were compared to the HCC ratios under the CMS-HCC payment model. Using multivariate regression models, we tested whether having the identified pairs of comorbidities affects the accuracy of CMS-HCC model predictions. Results The CMS-HCC model underpredicted Medicare capitation payments for patients with hypertension, lung disease, congestive heart failure and dementia. The difference between the actual costs and predicted payments was partially explained by beneficiary functional status and less than optimal adjustment for these chronic conditions. Conclusions Information about beneficiary functional status should be incorporated in reimbursement models since underpaying providers for caring for population with multiple comorbidities may provide severe disincentives for managed care plans to enroll such individuals and to appropriately manage their complex and costly conditions. PMID:18837646
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Chauderlier, Alban; Delattre, Lucie; Buée, Luc; Galas, Marie-Christine
2017-01-01
Oxidative damage is an early event in neurodegenerative disorders such as Alzheimer disease. To increase oxidative stress in AD-related mouse models is essential to study early mechanisms involved in the physiopathology of these diseases. In this chapter, we describe an experimental mouse model of transient and acute hyperthermic stress to induce in vivo an increase of oxidative stress in the brain of any kind of wild-type or transgenic mouse.
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Koon, Alex C.; Chan, Ho Yin Edwin
2017-01-01
For nearly a century, the fruit fly, Drosophila melanogaster, has proven to be a valuable tool in our understanding of fundamental biological processes, and has empowered our discoveries, particularly in the field of neuroscience. In recent years, Drosophila has emerged as a model organism for human neurodegenerative and neuromuscular disorders. In this review, we highlight a number of recent studies that utilized the Drosophila model to study repeat-expansion associated diseases (READs), such as polyglutamine diseases, fragile X-associated tremor/ataxia syndrome (FXTAS), myotonic dystrophy type 1 (DM1) and type 2 (DM2), and C9ORF72-associated amyotrophic lateral sclerosis/frontotemporal dementia (C9-ALS/FTD). Discoveries regarding the possible mechanisms of RNA toxicity will be focused here. These studies demonstrate Drosophila as an excellent in vivo model system that can reveal novel mechanistic insights into human disorders, providing the foundation for translational research and therapeutic development. PMID:28377694
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NASA Astrophysics Data System (ADS)
Liu, Qun; Jiang, Daqing; Shi, Ningzhong; Hayat, Tasawar
2018-02-01
In this paper, we study the dynamics of a stochastic delayed SIR epidemic model with vaccination and double diseases which make the research more complex. The environment variability in this paper is characterized by white noise and Lévy noise. We establish sufficient conditions for extinction and persistence in the mean of the two epidemic diseases. It is shown that: (i) time delay and Lévy noise have important effects on the persistence and extinction of epidemic diseases; (ii) two diseases can coexist under certain conditions.
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Grell, Kathrine; Diggle, Peter J; Frederiksen, Kirsten; Schüz, Joachim; Cardis, Elisabeth; Andersen, Per K
2015-10-15
We study methods for how to include the spatial distribution of tumours when investigating the relation between brain tumours and the exposure from radio frequency electromagnetic fields caused by mobile phone use. Our suggested point process model is adapted from studies investigating spatial aggregation of a disease around a source of potential hazard in environmental epidemiology, where now the source is the preferred ear of each phone user. In this context, the spatial distribution is a distribution over a sample of patients rather than over multiple disease cases within one geographical area. We show how the distance relation between tumour and phone can be modelled nonparametrically and, with various parametric functions, how covariates can be included in the model and how to test for the effect of distance. To illustrate the models, we apply them to a subset of the data from the Interphone Study, a large multinational case-control study on the association between brain tumours and mobile phone use. Copyright © 2015 John Wiley & Sons, Ltd.
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Patyk, Kelly A; Helm, Julie; Martin, Michael K; Forde-Folle, Kimberly N; Olea-Popelka, Francisco J; Hokanson, John E; Fingerlin, Tasha; Reeves, Aaron
2013-07-01
Epidemiologic simulation modeling of highly pathogenic avian influenza (HPAI) outbreaks provides a useful conceptual framework with which to estimate the consequences of HPAI outbreaks and to evaluate disease control strategies. The purposes of this study were to establish detailed and informed input parameters for an epidemiologic simulation model of the H5N1 strain of HPAI among commercial and backyard poultry in the state of South Carolina in the United States using a highly realistic representation of this poultry population; to estimate the consequences of an outbreak of HPAI in this population with a model constructed from these parameters; and to briefly evaluate the sensitivity of model outcomes to several parameters. Parameters describing disease state durations; disease transmission via direct contact, indirect contact, and local-area spread; and disease detection, surveillance, and control were established through consultation with subject matter experts, a review of the current literature, and the use of several computational tools. The stochastic model constructed from these parameters produced simulated outbreaks ranging from 2 to 111 days in duration (median 25 days), during which 1 to 514 flocks were infected (median 28 flocks). Model results were particularly sensitive to the rate of indirect contact that occurs among flocks. The baseline model established in this study can be used in the future to evaluate various control strategies, as a tool for emergency preparedness and response planning, and to assess the costs associated with disease control and the economic consequences of a disease outbreak. Published by Elsevier B.V.
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Bladder cancer mapping in Libya based on standardized morbidity ratio and log-normal model
NASA Astrophysics Data System (ADS)
Alhdiri, Maryam Ahmed; Samat, Nor Azah; Mohamed, Zulkifley
2017-05-01
Disease mapping contains a set of statistical techniques that detail maps of rates based on estimated mortality, morbidity, and prevalence. A traditional approach to measure the relative risk of the disease is called Standardized Morbidity Ratio (SMR). It is the ratio of an observed and expected number of accounts in an area, which has the greatest uncertainty if the disease is rare or if geographical area is small. Therefore, Bayesian models or statistical smoothing based on Log-normal model are introduced which might solve SMR problem. This study estimates the relative risk for bladder cancer incidence in Libya from 2006 to 2007 based on the SMR and log-normal model, which were fitted to data using WinBUGS software. This study starts with a brief review of these models, starting with the SMR method and followed by the log-normal model, which is then applied to bladder cancer incidence in Libya. All results are compared using maps and tables. The study concludes that the log-normal model gives better relative risk estimates compared to the classical method. The log-normal model has can overcome the SMR problem when there is no observed bladder cancer in an area.
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Developing an active implementation model for a chronic disease management program.
Smidth, Margrethe; Christensen, Morten Bondo; Olesen, Frede; Vedsted, Peter
2013-04-01
Introduction and diffusion of new disease management programs in healthcare is usually slow, but active theory-driven implementation seems to outperform other implementation strategies. However, we have only scarce evidence on the feasibility and real effect of such strategies in complex primary care settings where municipalities, general practitioners and hospitals should work together. The Central Denmark Region recently implemented a disease management program for chronic obstructive pulmonary disease (COPD) which presented an opportunity to test an active implementation model against the usual implementation model. The aim of the present paper is to describe the development of an active implementation model using the Medical Research Council's model for complex interventions and the Chronic Care Model. We used the Medical Research Council's five-stage model for developing complex interventions to design an implementation model for a disease management program for COPD. First, literature on implementing change in general practice was scrutinised and empirical knowledge was assessed for suitability. In phase I, the intervention was developed; and in phases II and III, it was tested in a block- and cluster-randomised study. In phase IV, we evaluated the feasibility for others to use our active implementation model. The Chronic Care Model was identified as a model for designing efficient implementation elements. These elements were combined into a multifaceted intervention, and a timeline for the trial in a randomised study was decided upon in accordance with the five stages in the Medical Research Council's model; this was captured in a PaTPlot, which allowed us to focus on the structure and the timing of the intervention. The implementation strategies identified as efficient were use of the Breakthrough Series, academic detailing, provision of patient material and meetings between providers. The active implementation model was tested in a randomised trial (results reported elsewhere). The combination of the theoretical model for complex interventions and the Chronic Care Model and the chosen specific implementation strategies proved feasible for a practice-based active implementation model for a chronic-disease-management-program for COPD. Using the Medical Research Council's model added transparency to the design phase which further facilitated the process of implementing the program. http://www.clinicaltrials.gov/(NCT01228708).
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Cell models of arrhythmogenic cardiomyopathy: advances and opportunities
Stadiotti, Ilaria; Perrucci, Gianluca L.; Tondo, Claudio; Pompilio, Giulio
2017-01-01
ABSTRACT Arrhythmogenic cardiomyopathy is a rare genetic disease that is mostly inherited as an autosomal dominant trait. It is associated predominantly with mutations in desmosomal genes and is characterized by the replacement of the ventricular myocardium with fibrous fatty deposits, arrhythmias and a high risk of sudden death. In vitro studies have contributed to our understanding of the pathogenic mechanisms underlying this disease, including its genetic determinants, as well as its cellular, signaling and molecular defects. Here, we review what is currently known about the pathogenesis of arrhythmogenic cardiomyopathy and focus on the in vitro models that have advanced our understanding of the disease. Finally, we assess the potential of established and innovative cell platforms for elucidating unknown aspects of this disease, and for screening new potential therapeutic agents. This appraisal of in vitro models of arrhythmogenic cardiomyopathy highlights the discoveries made about this disease and the uses of these models for future basic and therapeutic research. PMID:28679668
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Genetic enhancement of macroautophagy in vertebrate models of neurodegenerative diseases.
Ejlerskov, Patrick; Ashkenazi, Avraham; Rubinsztein, David C
2018-04-03
Most of the neurodegenerative diseases that afflict humans manifest with the intraneuronal accumulation of toxic proteins that are aggregate-prone. Extensive data in cell and neuronal models support the concept that such proteins, like mutant huntingtin or alpha-synuclein, are substrates for macroautophagy (hereafter autophagy). Furthermore, autophagy-inducing compounds lower the levels of such proteins and ameliorate their toxicity in diverse animal models of neurodegenerative diseases. However, most of these compounds also have autophagy-independent effects and it is important to understand if similar benefits are seen with genetic strategies that upregulate autophagy, as this strengthens the validity of this strategy in such diseases. Here we review studies in vertebrate models using genetic manipulations of core autophagy genes and describe how these improve pathology and neurodegeneration, supporting the validity of autophagy upregulation as a target for certain neurodegenerative diseases. Copyright © 2018 Elsevier Inc. All rights reserved.
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2010-01-01
Changes to the glycosylation profile on HIV gp120 can influence viral pathogenesis and alter AIDS disease progression. The characterization of glycosylation differences at the sequence level is inadequate as the placement of carbohydrates is structurally complex. However, no structural framework is available to date for the study of HIV disease progression. In this study, we propose a novel machine-learning based framework for the prediction of AIDS disease progression in three stages (RP, SP, and LTNP) using the HIV structural gp120 profile. This new intelligent framework proves to be accurate and provides an important benchmark for predicting AIDS disease progression computationally. The model is trained using a novel HIV gp120 glycosylation structural profile to detect possible stages of AIDS disease progression for the target sequences of HIV+ individuals. The performance of the proposed model was compared to seven existing different machine-learning models on newly proposed gp120-Benchmark_1 dataset in terms of error-rate (MSE), accuracy (CCI), stability (STD), and complexity (TBM). The novel framework showed better predictive performance with 67.82% CCI, 30.21 MSE, 0.8 STD, and 2.62 TBM on the three stages of AIDS disease progression of 50 HIV+ individuals. This framework is an invaluable bioinformatics tool that will be useful to the clinical assessment of viral pathogenesis. PMID:21143806
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Use of the Zebrafish Larvae as a Model to Study Cigarette Smoke Condensate Toxicity
Ellis, Lee D.; Soo, Evelyn C.; Achenbach, John C.; Morash, Michael G.; Soanes, Kelly H.
2014-01-01
The smoking of tobacco continues to be the leading cause of premature death worldwide and is linked to the development of a number of serious illnesses including heart disease, respiratory diseases, stroke and cancer. Currently, cell line based toxicity assays are typically used to gain information on the general toxicity of cigarettes and other tobacco products. However, they provide little information regarding the complex disease-related changes that have been linked to smoking. The ethical concerns and high cost associated with mammalian studies have limited their widespread use for in vivo toxicological studies of tobacco. The zebrafish has emerged as a low-cost, high-throughput, in vivo model in the study of toxicology. In this study, smoke condensates from 2 reference cigarettes and 6 Canadian brands of cigarettes with different design features were assessed for acute, developmental, cardiac, and behavioural toxicity (neurotoxicity) in zebrafish larvae. By making use of this multifaceted approach we have developed an in vivo model with which to compare the toxicity profiles of smoke condensates from cigarettes with different design features. This model system may provide insights into the development of smoking related disease and could provide a cost-effective, high-throughput platform for the future evaluation of tobacco products. PMID:25526262
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Use of the zebrafish larvae as a model to study cigarette smoke condensate toxicity.
Ellis, Lee D; Soo, Evelyn C; Achenbach, John C; Morash, Michael G; Soanes, Kelly H
2014-01-01
The smoking of tobacco continues to be the leading cause of premature death worldwide and is linked to the development of a number of serious illnesses including heart disease, respiratory diseases, stroke and cancer. Currently, cell line based toxicity assays are typically used to gain information on the general toxicity of cigarettes and other tobacco products. However, they provide little information regarding the complex disease-related changes that have been linked to smoking. The ethical concerns and high cost associated with mammalian studies have limited their widespread use for in vivo toxicological studies of tobacco. The zebrafish has emerged as a low-cost, high-throughput, in vivo model in the study of toxicology. In this study, smoke condensates from 2 reference cigarettes and 6 Canadian brands of cigarettes with different design features were assessed for acute, developmental, cardiac, and behavioural toxicity (neurotoxicity) in zebrafish larvae. By making use of this multifaceted approach we have developed an in vivo model with which to compare the toxicity profiles of smoke condensates from cigarettes with different design features. This model system may provide insights into the development of smoking related disease and could provide a cost-effective, high-throughput platform for the future evaluation of tobacco products.
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Cong, Yu; Lentz, Margaret R; Lara, Abigail; Alexander, Isis; Bartos, Christopher; Bohannon, J Kyle; Hammoud, Dima; Huzella, Louis; Jahrling, Peter B; Janosko, Krisztina; Jett, Catherine; Kollins, Erin; Lackemeyer, Matthew; Mollura, Daniel; Ragland, Dan; Rojas, Oscar; Solomon, Jeffrey; Xu, Ziyue; Munster, Vincent; Holbrook, Michael R
2017-04-01
Nipah virus (NiV) is a paramyxovirus (genus Henipavirus) that emerged in the late 1990s in Malaysia and has since been identified as the cause of sporadic outbreaks of severe febrile disease in Bangladesh and India. NiV infection is frequently associated with severe respiratory or neurological disease in infected humans with transmission to humans through inhalation, contact or consumption of NiV contaminated foods. In the work presented here, the development of disease was investigated in the African Green Monkey (AGM) model following intratracheal (IT) and, for the first time, small-particle aerosol administration of NiV. This study utilized computed tomography (CT) and magnetic resonance imaging (MRI) to temporally assess disease progression. The host immune response and changes in immune cell populations over the course of disease were also evaluated. This study found that IT and small-particle administration of NiV caused similar disease progression, but that IT inoculation induced significant congestion in the lungs while disease following small-particle aerosol inoculation was largely confined to the lower respiratory tract. Quantitative assessment of changes in lung volume found up to a 45% loss in IT inoculated animals. None of the subjects in this study developed overt neurological disease, a finding that was supported by MRI analysis. The development of neutralizing antibodies was not apparent over the 8-10 day course of disease, but changes in cytokine response in all animals and activated CD8+ T cell numbers suggest the onset of cell-mediated immunity. These studies demonstrate that IT and small-particle aerosol infection with NiV in the AGM model leads to a severe respiratory disease devoid of neurological indications. This work also suggests that extending the disease course or minimizing the impact of the respiratory component is critical to developing a model that has a neurological component and more accurately reflects the human condition.
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Cong, Yu; Lentz, Margaret R.; Lara, Abigail; Alexander, Isis; Bartos, Christopher; Bohannon, J. Kyle; Hammoud, Dima; Huzella, Louis; Jahrling, Peter B.; Janosko, Krisztina; Jett, Catherine; Kollins, Erin; Lackemeyer, Matthew; Mollura, Daniel; Ragland, Dan; Rojas, Oscar; Solomon, Jeffrey; Xu, Ziyue; Munster, Vincent
2017-01-01
Nipah virus (NiV) is a paramyxovirus (genus Henipavirus) that emerged in the late 1990s in Malaysia and has since been identified as the cause of sporadic outbreaks of severe febrile disease in Bangladesh and India. NiV infection is frequently associated with severe respiratory or neurological disease in infected humans with transmission to humans through inhalation, contact or consumption of NiV contaminated foods. In the work presented here, the development of disease was investigated in the African Green Monkey (AGM) model following intratracheal (IT) and, for the first time, small-particle aerosol administration of NiV. This study utilized computed tomography (CT) and magnetic resonance imaging (MRI) to temporally assess disease progression. The host immune response and changes in immune cell populations over the course of disease were also evaluated. This study found that IT and small-particle administration of NiV caused similar disease progression, but that IT inoculation induced significant congestion in the lungs while disease following small-particle aerosol inoculation was largely confined to the lower respiratory tract. Quantitative assessment of changes in lung volume found up to a 45% loss in IT inoculated animals. None of the subjects in this study developed overt neurological disease, a finding that was supported by MRI analysis. The development of neutralizing antibodies was not apparent over the 8–10 day course of disease, but changes in cytokine response in all animals and activated CD8+ T cell numbers suggest the onset of cell-mediated immunity. These studies demonstrate that IT and small-particle aerosol infection with NiV in the AGM model leads to a severe respiratory disease devoid of neurological indications. This work also suggests that extending the disease course or minimizing the impact of the respiratory component is critical to developing a model that has a neurological component and more accurately reflects the human condition. PMID:28388650
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Wilson, Patricia Mary; Brooks, Fiona; Procter, Susan; Kendall, Sally
2012-01-01
The global response to the rise in prevalence of chronic disease is a focus on the way services are managed and delivered, in which nurses are seen as central in shaping patient experience. However, there is relatively little known on how patients perceive the changes to service delivery envisaged by chronic care models. The PEARLE project aimed to explore, identify and characterise the origins, processes and outcomes of effective chronic disease management models and the nursing contributions to the models. Design, settings and participants Case study design of seven sites in England and Wales ensuring a range of chronic disease management models. Participants included over ninety patients and family carers ranging in age from children to older people with conditions such as diabetes, respiratory disease, epilepsy, or coronary heart disease. Semi-structured interviews with patients and family carers. Focus groups were conducted with adolescents and children. A whole systems approach guided data collection and data were thematically analysed. Despite nurses' role and skill development and the shift away from the acute care model, the results suggested that patients had a persisting belief in the monopoly of expertise continuing to exist in the acute care setting. Patients were more satisfied if they saw the nurse as diagnostician, prescriber and medical manager of the condition. Patients were less satisfied when they had been transferred from an established doctor-led to nurse-led service. While nurses within the study were highly skilled, patient perception was guided by the familiar rather than most appropriate service delivery. Most patients saw chronic disease management as a medicalised approach and the nursing contribution was most valued when emulating it. Patients' preferences and expectations of chronic disease management were framed by a strongly biomedical discourse. Perceptions of nurse-led chronic disease management were often shaped by what was previously familiar to the patient. At a strategic level, autonomous nursing practice requires support and further promotion to wider society if there is to be a shift in societal expectation and trust in the nurse's role in chronic disease management. Copyright © 2011 Elsevier Ltd. All rights reserved.
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Randles, Lucy G; Dawes, Gwen J S; Wensley, Beth G; Steward, Annette; Nickson, Adrian A; Clarke, Jane
2013-01-01
Studying the effects of pathogenic mutations is more complex in multidomain proteins when compared with single domains: mutations occurring at domain boundaries may have a large effect on a neighbouring domain that will not be detected in a single-domain system. To demonstrate this, we present a study that utilizes well-characterized model protein domains from human spectrin to investigate the effect of disease-and non-disease-causing single point mutations occurring at the boundaries of human spectrin repeats. Our results show that mutations in the single domains have no clear correlation with stability and disease; however, when studied in a tandem model system, the disease-causing mutations are shown to disrupt stabilizing interactions that exist between domains. This results in a much larger decrease in stability than would otherwise have been predicted, and demonstrates the importance of studying such mutations in the correct protein context. PMID:23241237
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Satellite SST-Based Coral Disease Outbreak Predictions for the Hawaiian Archipelago.
Caldwell, Jamie M; Heron, Scott F; Eakin, C Mark; Donahue, Megan J
2016-02-01
Predicting wildlife disease risk is essential for effective monitoring and management, especially for geographically expansive ecosystems such as coral reefs in the Hawaiian archipelago. Warming ocean temperature has increased coral disease outbreaks contributing to declines in coral cover worldwide. In this study we investigated seasonal effects of thermal stress on the prevalence of the three most widespread coral diseases in Hawai'i: Montipora white syndrome, Porites growth anomalies and Porites tissue loss syndrome. To predict outbreak likelihood we compared disease prevalence from surveys conducted between 2004 and 2015 from 18 Hawaiian Islands and atolls with biotic (e.g., coral density) and abiotic (satellite-derived sea surface temperature metrics) variables using boosted regression trees. To date, the only coral disease forecast models available were developed for Acropora white syndrome on the Great Barrier Reef (GBR). Given the complexities of disease etiology, differences in host demography and environmental conditions across reef regions, it is important to refine and adapt such models for different diseases and geographic regions of interest. Similar to the Acropora white syndrome models, anomalously warm conditions were important for predicting Montipora white syndrome, possibly due to a relationship between thermal stress and a compromised host immune system. However, coral density and winter conditions were the most important predictors of all three coral diseases in this study, enabling development of a forecasting system that can predict regions of elevated disease risk up to six months before an expected outbreak. Our research indicates satellite-derived systems for forecasting disease outbreaks can be appropriately adapted from the GBR tools and applied for a variety of diseases in a new region. These models can be used to enhance management capacity to prepare for and respond to emerging coral diseases throughout Hawai'i and can be modified for other diseases and regions around the world.
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Satellite SST-Based Coral Disease Outbreak Predictions for the Hawaiian Archipelago
Caldwell, Jamie M.; Heron, Scott F.; Eakin, C. Mark; Donahue, Megan J.
2017-01-01
Predicting wildlife disease risk is essential for effective monitoring and management, especially for geographically expansive ecosystems such as coral reefs in the Hawaiian archipelago. Warming ocean temperature has increased coral disease outbreaks contributing to declines in coral cover worldwide. In this study we investigated seasonal effects of thermal stress on the prevalence of the three most widespread coral diseases in Hawai’i: Montipora white syndrome, Porites growth anomalies and Porites tissue loss syndrome. To predict outbreak likelihood we compared disease prevalence from surveys conducted between 2004 and 2015 from 18 Hawaiian Islands and atolls with biotic (e.g., coral density) and abiotic (satellite-derived sea surface temperature metrics) variables using boosted regression trees. To date, the only coral disease forecast models available were developed for Acropora white syndrome on the Great Barrier Reef (GBR). Given the complexities of disease etiology, differences in host demography and environmental conditions across reef regions, it is important to refine and adapt such models for different diseases and geographic regions of interest. Similar to the Acropora white syndrome models, anomalously warm conditions were important for predicting Montipora white syndrome, possibly due to a relationship between thermal stress and a compromised host immune system. However, coral density and winter conditions were the most important predictors of all three coral diseases in this study, enabling development of a forecasting system that can predict regions of elevated disease risk up to six months before an expected outbreak. Our research indicates satellite-derived systems for forecasting disease outbreaks can be appropriately adapted from the GBR tools and applied for a variety of diseases in a new region. These models can be used to enhance management capacity to prepare for and respond to emerging coral diseases throughout Hawai’i and can be modified for other diseases and regions around the world. PMID:29071133
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Lowery, Julie; Hopp, Faith; Subramanian, Usha; Wiitala, Wyndy; Welsh, Deborah E; Larkin, Angela; Stemmer, Karen; Zak, Cassandra; Vaitkevicius, Peter
2012-01-01
While disease management appears to be effective in selected, small groups of CHF patients from randomized controlled trials, its effectiveness in a broader CHF patient population is not known. This prospective, quasi-experimental study compared patient outcomes under a nurse practitioner-led disease management model (intervention group) with outcomes under usual care (control group) in both primary and tertiary medical centers. The study included 969 veterans (458 intervention, 511 control) treated for CHF at six VA medical centers. Intervention patients had significantly fewer (p<0.05) CHF and all-cause admissions at one-year follow-up, and lower mortality at both one- and two-year follow-up. These data provide support for the potential effectiveness of the intervention, and suggest that the evidence from RCTs of disease management models for CHF can be translated into clinical practice, even without the benefits of a selected patient population and dedicated resources often found in RCTs. © 2011 Wiley Periodicals, Inc.
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Bayesian Covariate Selection in Mixed-Effects Models For Longitudinal Shape Analysis
Muralidharan, Prasanna; Fishbaugh, James; Kim, Eun Young; Johnson, Hans J.; Paulsen, Jane S.; Gerig, Guido; Fletcher, P. Thomas
2016-01-01
The goal of longitudinal shape analysis is to understand how anatomical shape changes over time, in response to biological processes, including growth, aging, or disease. In many imaging studies, it is also critical to understand how these shape changes are affected by other factors, such as sex, disease diagnosis, IQ, etc. Current approaches to longitudinal shape analysis have focused on modeling age-related shape changes, but have not included the ability to handle covariates. In this paper, we present a novel Bayesian mixed-effects shape model that incorporates simultaneous relationships between longitudinal shape data and multiple predictors or covariates to the model. Moreover, we place an Automatic Relevance Determination (ARD) prior on the parameters, that lets us automatically select which covariates are most relevant to the model based on observed data. We evaluate our proposed model and inference procedure on a longitudinal study of Huntington's disease from PREDICT-HD. We first show the utility of the ARD prior for model selection in a univariate modeling of striatal volume, and next we apply the full high-dimensional longitudinal shape model to putamen shapes. PMID:28090246
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Soler, María José; Riera, Marta; Batlle, Daniel
2012-01-01
Diabetic nephropathy (DN) is the leading cause of end-stage renal disease. The use of experimental models of DN has provided valuable information regarding many aspects of DN, including pathophysiology, progression, implicated genes, and new therapeutic strategies. A large number of mouse models of diabetes have been identified and their kidney disease was characterized to various degrees. Most experimental models of type 2 DN are helpful in studying early stages of DN, but these models have not been able to reproduce the characteristic features of more advanced DN in humans such as nodules in the glomerular tuft or glomerulosclerosis. The generation of new experimental models of DN created by crossing, knockdown, or knockin of genes continues to provide improved tools for studying DN. These models provide an opportunity to search for new mechanisms involving the development of DN, but their shortcomings should be recognized as well. Moreover, it is important to recognize that the genetic background has a substantial effect on the susceptibility to diabetes and kidney disease development in the various models of diabetes. PMID:22461787
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Modelling human disease with pluripotent stem cells.
Siller, Richard; Greenhough, Sebastian; Park, In-Hyun; Sullivan, Gareth J
2013-04-01
Recent progress in the field of cellular reprogramming has opened up the doors to a new era of disease modelling, as pluripotent stem cells representing a myriad of genetic diseases can now be produced from patient tissue. These cells can be expanded and differentiated to produce a potentially limitless supply of the affected cell type, which can then be used as a tool to improve understanding of disease mechanisms and test therapeutic interventions. This process requires high levels of scrutiny and validation at every stage, but international standards for the characterisation of pluripotent cells and their progeny have yet to be established. Here we discuss the current state of the art with regard to modelling diseases affecting the ectodermal, mesodermal and endodermal lineages, focussing on studies which have demonstrated a disease phenotype in the tissue of interest. We also discuss the utility of pluripotent cell technology for the modelling of cancer and infectious disease. Finally, we spell out the technical and scientific challenges which must be addressed if the field is to deliver on its potential and produce improved patient outcomes in the clinic.
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Improving the physiological realism of experimental models.
Vinnakota, Kalyan C; Cha, Chae Y; Rorsman, Patrik; Balaban, Robert S; La Gerche, Andre; Wade-Martins, Richard; Beard, Daniel A; Jeneson, Jeroen A L
2016-04-06
The Virtual Physiological Human (VPH) project aims to develop integrative, explanatory and predictive computational models (C-Models) as numerical investigational tools to study disease, identify and design effective therapies and provide an in silico platform for drug screening. Ultimately, these models rely on the analysis and integration of experimental data. As such, the success of VPH depends on the availability of physiologically realistic experimental models (E-Models) of human organ function that can be parametrized to test the numerical models. Here, the current state of suitable E-models, ranging from in vitro non-human cell organelles to in vivo human organ systems, is discussed. Specifically, challenges and recent progress in improving the physiological realism of E-models that may benefit the VPH project are highlighted and discussed using examples from the field of research on cardiovascular disease, musculoskeletal disorders, diabetes and Parkinson's disease.
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Chang, Xuling; Salim, Agus; Dorajoo, Rajkumar; Han, Yi; Khor, Chiea-Chuen; van Dam, Rob M; Yuan, Jian-Min; Koh, Woon-Puay; Liu, Jianjun; Goh, Daniel Yt; Wang, Xu; Teo, Yik-Ying; Friedlander, Yechiel; Heng, Chew-Kiat
2017-01-01
Background Although numerous phenotype based equations for predicting risk of 'hard' coronary heart disease are available, data on the utility of genetic information for such risk prediction is lacking in Chinese populations. Design Case-control study nested within the Singapore Chinese Health Study. Methods A total of 1306 subjects comprising 836 men (267 incident cases and 569 controls) and 470 women (128 incident cases and 342 controls) were included. A Genetic Risk Score comprising 156 single nucleotide polymorphisms that have been robustly associated with coronary heart disease or its risk factors ( p < 5 × 10 -8 ) in at least two independent cohorts of genome-wide association studies was built. For each gender, three base models were used: recalibrated Adult Treatment Panel III (ATPIII) Model (M 1 ); ATP III model fitted using Singapore Chinese Health Study data (M 2 ) and M 3 : M 2 + C-reactive protein + creatinine. Results The Genetic Risk Score was significantly associated with incident 'hard' coronary heart disease ( p for men: 1.70 × 10 -10 -1.73 × 10 -9 ; p for women: 0.001). The inclusion of the Genetic Risk Score in the prediction models improved discrimination in both genders (c-statistics: 0.706-0.722 vs. 0.663-0.695 from base models for men; 0.788-0.790 vs. 0.765-0.773 for women). In addition, the inclusion of the Genetic Risk Score also improved risk classification with a net gain of cases being reclassified to higher risk categories (men: 12.4%-16.5%; women: 10.2% (M 3 )), while not significantly reducing the classification accuracy in controls. Conclusions The Genetic Risk Score is an independent predictor for incident 'hard' coronary heart disease in our ethnic Chinese population. Inclusion of genetic factors into coronary heart disease prediction models could significantly improve risk prediction performance.
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Wessler, Benjamin S; Lai Yh, Lana; Kramer, Whitney; Cangelosi, Michael; Raman, Gowri; Lutz, Jennifer S; Kent, David M
2015-07-01
Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease, there are numerous CPMs available although the extent of this literature is not well described. We conducted a systematic review for articles containing CPMs for cardiovascular disease published between January 1990 and May 2012. Cardiovascular disease includes coronary heart disease, heart failure, arrhythmias, stroke, venous thromboembolism, and peripheral vascular disease. We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. Seven hundred seventeen (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions, including 215 CPMs for patients with coronary artery disease, 168 CPMs for population samples, and 79 models for patients with heart failure. There are 77 distinct index/outcome pairings. Of the de novo models in this database, 450 (63%) report a c-statistic and 259 (36%) report some information on calibration. There is an abundance of CPMs available for a wide assortment of cardiovascular disease conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood. © 2015 American Heart Association, Inc.
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An image-based model of brain volume biomarker changes in Huntington's disease.
Wijeratne, Peter A; Young, Alexandra L; Oxtoby, Neil P; Marinescu, Razvan V; Firth, Nicholas C; Johnson, Eileanoir B; Mohan, Amrita; Sampaio, Cristina; Scahill, Rachael I; Tabrizi, Sarah J; Alexander, Daniel C
2018-05-01
Determining the sequence in which Huntington's disease biomarkers become abnormal can provide important insights into the disease progression and a quantitative tool for patient stratification. Here, we construct and present a uniquely fine-grained model of temporal progression of Huntington's disease from premanifest through to manifest stages. We employ a probabilistic event-based model to determine the sequence of appearance of atrophy in brain volumes, learned from structural MRI in the Track-HD study, as well as to estimate the uncertainty in the ordering. We use longitudinal and phenotypic data to demonstrate the utility of the patient staging system that the resulting model provides. The model recovers the following order of detectable changes in brain region volumes: putamen, caudate, pallidum, insula white matter, nonventricular cerebrospinal fluid, amygdala, optic chiasm, third ventricle, posterior insula, and basal forebrain. This ordering is mostly preserved even under cross-validation of the uncertainty in the event sequence. Longitudinal analysis performed using 6 years of follow-up data from baseline confirms efficacy of the model, as subjects consistently move to later stages with time, and significant correlations are observed between the estimated stages and nonimaging phenotypic markers. We used a data-driven method to provide new insight into Huntington's disease progression as well as new power to stage and predict conversion. Our results highlight the potential of disease progression models, such as the event-based model, to provide new insight into Huntington's disease progression and to support fine-grained patient stratification for future precision medicine in Huntington's disease.
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Genome-wide association mapping identifies multiple loci for a canine SLE-related disease complex.
Wilbe, Maria; Jokinen, Päivi; Truvé, Katarina; Seppala, Eija H; Karlsson, Elinor K; Biagi, Tara; Hughes, Angela; Bannasch, Danika; Andersson, Göran; Hansson-Hamlin, Helene; Lohi, Hannes; Lindblad-Toh, Kerstin
2010-03-01
The unique canine breed structure makes dogs an excellent model for studying genetic diseases. Within a dog breed, linkage disequilibrium is extensive, enabling genome-wide association (GWA) with only around 15,000 SNPs and fewer individuals than in human studies. Incidences of specific diseases are elevated in different breeds, indicating that a few genetic risk factors might have accumulated through drift or selective breeding. In this study, a GWA study with 81 affected dogs (cases) and 57 controls from the Nova Scotia duck tolling retriever breed identified five loci associated with a canine systemic lupus erythematosus (SLE)-related disease complex that includes both antinuclear antibody (ANA)-positive immune-mediated rheumatic disease (IMRD) and steroid-responsive meningitis-arteritis (SRMA). Fine mapping with twice as many dogs validated these loci. Our results indicate that the homogeneity of strong genetic risk factors within dog breeds allows multigenic disorders to be mapped with fewer than 100 cases and 100 controls, making dogs an excellent model in which to identify pathways involved in human complex diseases.
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An online spatio-temporal prediction model for dengue fever epidemic in Kaohsiung,Taiwan
NASA Astrophysics Data System (ADS)
Cheng, Ming-Hung; Yu, Hwa-Lung; Angulo, Jose; Christakos, George
2013-04-01
Dengue Fever (DF) is one of the most serious vector-borne infectious diseases in tropical and subtropical areas. DF epidemics occur in Taiwan annually especially during summer and fall seasons. Kaohsiung city has been one of the major DF hotspots in decades. The emergence and re-emergence of the DF epidemic is complex and can be influenced by various factors including space-time dynamics of human and vector populations and virus serotypes as well as the associated uncertainties. This study integrates a stochastic space-time "Susceptible-Infected-Recovered" model under Bayesian maximum entropy framework (BME-SIR) to perform real-time prediction of disease diffusion across space-time. The proposed model is applied for spatiotemporal prediction of the DF epidemic at Kaohsiung city during 2002 when the historical series of high DF cases was recorded. The online prediction by BME-SIR model updates the parameters of SIR model and infected cases across districts over time. Results show that the proposed model is rigorous to initial guess of unknown model parameters, i.e. transmission and recovery rates, which can depend upon the virus serotypes and various human interventions. This study shows that spatial diffusion can be well characterized by BME-SIR model, especially at the district surrounding the disease outbreak locations. The prediction performance at DF hotspots, i.e. Cianjhen and Sanmin, can be degraded due to the implementation of various disease control strategies during the epidemics. The proposed online disease prediction BME-SIR model can provide the governmental agency with a valuable reference to timely identify, control, and efficiently prevent DF spread across space-time.
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Norouzi, Jamshid; Yadollahpour, Ali; Mirbagheri, Seyed Ahmad; Mazdeh, Mitra Mahdavi; Hosseini, Seyed Ahmad
2016-01-01
Chronic kidney disease (CKD) is a covert disease. Accurate prediction of CKD progression over time is necessary for reducing its costs and mortality rates. The present study proposes an adaptive neurofuzzy inference system (ANFIS) for predicting the renal failure timeframe of CKD based on real clinical data. This study used 10-year clinical records of newly diagnosed CKD patients. The threshold value of 15 cc/kg/min/1.73 m(2) of glomerular filtration rate (GFR) was used as the marker of renal failure. A Takagi-Sugeno type ANFIS model was used to predict GFR values. Variables of age, sex, weight, underlying diseases, diastolic blood pressure, creatinine, calcium, phosphorus, uric acid, and GFR were initially selected for the predicting model. Weight, diastolic blood pressure, diabetes mellitus as underlying disease, and current GFR(t) showed significant correlation with GFRs and were selected as the inputs of model. The comparisons of the predicted values with the real data showed that the ANFIS model could accurately estimate GFR variations in all sequential periods (Normalized Mean Absolute Error lower than 5%). Despite the high uncertainties of human body and dynamic nature of CKD progression, our model can accurately predict the GFR variations at long future periods.
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Impact of the gut microbiota on rodent models of human disease.
Hansen, Axel Kornerup; Hansen, Camilla Hartmann Friis; Krych, Lukasz; Nielsen, Dennis Sandris
2014-12-21
Traditionally bacteria have been considered as either pathogens, commensals or symbionts. The mammal gut harbors 10(14) organisms dispersed on approximately 1000 different species. Today, diagnostics, in contrast to previous cultivation techniques, allow the identification of close to 100% of bacterial species. This has revealed that a range of animal models within different research areas, such as diabetes, obesity, cancer, allergy, behavior and colitis, are affected by their gut microbiota. Correlation studies may for some diseases show correlation between gut microbiota composition and disease parameters higher than 70%. Some disease phenotypes may be transferred when recolonizing germ free mice. The mechanistic aspects are not clear, but some examples on how gut bacteria stimulate receptors, metabolism, and immune responses are discussed. A more deeper understanding of the impact of microbiota has its origin in the overall composition of the microbiota and in some newly recognized species, such as Akkermansia muciniphila, Segmented filamentous bacteria and Faecalibacterium prausnitzii, which seem to have an impact on more or less severe disease in specific models. Thus, the impact of the microbiota on animal models is of a magnitude that cannot be ignored in future research. Therefore, either models with specific microbiota must be developed, or the microbiota must be characterized in individual studies and incorporated into data evaluation.
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Parodi, Jorge; Ormeño, David; Ochoa-de la Paz, Lenin D
2015-01-01
Alzheimer's disease severely compromises cognitive function. One of the mechanisms to explain the pathology of Alzheimer's disease has been the hypotheses of amyloid-pore/channel formation by complex Aβ-aggregates. Clinical studies suggested the moderate alcohol consumption can reduces probability developing neurodegenerative pathologies. A recent report explored the ability of ethanol to disrupt the generation of complex Aβ in vitro and reduce the toxicity in two cell lines. Molecular dynamics simulations were applied to understand how ethanol blocks the aggregation of amyloid. On the other hand, the in silico modeling showed ethanol effect over the dynamics assembling for complex Aβ-aggregates mediated by break the hydrosaline bridges between Asp 23 and Lys 28, was are key element for amyloid dimerization. The amyloid pore/channel hypothesis has been explored only in neuronal models, however recently experiments suggested the frog oocytes such an excellent model to explore the mechanism of the amyloid pore/channel hypothesis. So, the used of frog oocytes to explored the mechanism of amyloid aggregates is new, mainly for amyloid/pore hypothesis. Therefore, this experimental model is a powerful tool to explore the mechanism implicates in the Alzheimer's disease pathology and also suggests a model to prevent the Alzheimer's disease pathology.
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Evaluation of the impacts of climate change on disease vectors through ecological niche modelling.
Carvalho, B M; Rangel, E F; Vale, M M
2017-08-01
Vector-borne diseases are exceptionally sensitive to climate change. Predicting vector occurrence in specific regions is a challenge that disease control programs must meet in order to plan and execute control interventions and climate change adaptation measures. Recently, an increasing number of scientific articles have applied ecological niche modelling (ENM) to study medically important insects and ticks. With a myriad of available methods, it is challenging to interpret their results. Here we review the future projections of disease vectors produced by ENM, and assess their trends and limitations. Tropical regions are currently occupied by many vector species; but future projections indicate poleward expansions of suitable climates for their occurrence and, therefore, entomological surveillance must be continuously done in areas projected to become suitable. The most commonly applied methods were the maximum entropy algorithm, generalized linear models, the genetic algorithm for rule set prediction, and discriminant analysis. Lack of consideration of the full-known current distribution of the target species on models with future projections has led to questionable predictions. We conclude that there is no ideal 'gold standard' method to model vector distributions; researchers are encouraged to test different methods for the same data. Such practice is becoming common in the field of ENM, but still lags behind in studies of disease vectors.
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Niendorf, Thoralf; Pohlmann, Andreas; Reimann, Henning M.; Waiczies, Helmar; Peper, Eva; Huelnhagen, Till; Seeliger, Erdmann; Schreiber, Adrian; Kettritz, Ralph; Strobel, Klaus; Ku, Min-Chi; Waiczies, Sonia
2015-01-01
Research in pathologies of the brain, heart and kidney have gained immensely from the plethora of studies that have helped shape new methods in magnetic resonance (MR) for characterizing preclinical disease models. Methodical probing into preclinical animal models by MR is invaluable since it allows a careful interpretation and extrapolation of data derived from these models to human disease. In this review we will focus on the applications of cryogenic radiofrequency (RF) coils in small animal MR as a means of boosting image quality (e.g., by supporting MR microscopy) and making data acquisition more efficient (e.g., by reducing measuring time); both being important constituents for thorough investigational studies on animal models of disease. This review attempts to make the (bio)medical imaging, molecular medicine, and pharmaceutical communities aware of this productive ferment and its outstanding significance for anatomical and functional MR in small rodents. The goal is to inspire a more intense interdisciplinary collaboration across the fields to further advance and progress non-invasive MR methods that ultimately support thorough (patho)physiological characterization of animal disease models. In this review, current and potential future applications for the RF coil technology in cardiovascular, neurovascular, and renal disease will be discussed. PMID:26617515
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Alterations of urinary metabolite profile in model diabetic nephropathy
DOE Office of Scientific and Technical Information (OSTI.GOV)
Stec, Donald F.; Wang, Suwan; Stothers, Cody
2015-01-09
Highlights: • {sup 1}H NMR spectroscopy was employed to study urinary metabolite profile in diabetic mouse models. • Mouse urinary metabolome showed major changes that are also found in human diabetic nephropathy. • These models can be new tools to study urinary biomarkers that are relevant to human disease. - Abstract: Countering the diabetes pandemic and consequent complications, such as nephropathy, will require better understanding of disease mechanisms and development of new diagnostic methods. Animal models can be versatile tools in studies of diabetic renal disease when model pathology is relevant to human diabetic nephropathy (DN). Diabetic models using endothelialmore » nitric oxide synthase (eNOS) knock-out mice develop major renal lesions characteristic of human disease. However, it is unknown whether they can also reproduce changes in urinary metabolites found in human DN. We employed Type 1 and Type 2 diabetic mouse models of DN, i.e. STZ-eNOS{sup −/−} C57BLKS and eNOS{sup −/−} C57BLKS db/db, with the goal of determining changes in urinary metabolite profile using proton nuclear magnetic resonance (NMR). Six urinary metabolites with significantly lower levels in diabetic compared to control mice have been identified. Specifically, major changes were found in metabolites from tricarboxylic acid (TCA) cycle and aromatic amino acid catabolism including 3-indoxyl sulfate, cis-aconitate, 2-oxoisocaproate, N-phenyl-acetylglycine, 4-hydroxyphenyl acetate, and hippurate. Levels of 4-hydroxyphenyl acetic acid and hippuric acid showed the strongest reverse correlation to albumin-to-creatinine ratio (ACR), which is an indicator of renal damage. Importantly, similar changes in urinary hydroxyphenyl acetate and hippurate were previously reported in human renal disease. We demonstrated that STZ-eNOS{sup −/−} C57BLKS and eNOS{sup −/−} C57BLKS db/db mouse models can recapitulate changes in urinary metabolome found in human DN and therefore can be useful new tools in metabolomic studies relevant to human pathology.« less
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Drosophila as an In Vivo Model for Human Neurodegenerative Disease
McGurk, Leeanne; Berson, Amit; Bonini, Nancy M.
2015-01-01
With the increase in the ageing population, neurodegenerative disease is devastating to families and poses a huge burden on society. The brain and spinal cord are extraordinarily complex: they consist of a highly organized network of neuronal and support cells that communicate in a highly specialized manner. One approach to tackling problems of such complexity is to address the scientific questions in simpler, yet analogous, systems. The fruit fly, Drosophila melanogaster, has been proven tremendously valuable as a model organism, enabling many major discoveries in neuroscientific disease research. The plethora of genetic tools available in Drosophila allows for exquisite targeted manipulation of the genome. Due to its relatively short lifespan, complex questions of brain function can be addressed more rapidly than in other model organisms, such as the mouse. Here we discuss features of the fly as a model for human neurodegenerative disease. There are many distinct fly models for a range of neurodegenerative diseases; we focus on select studies from models of polyglutamine disease and amyotrophic lateral sclerosis that illustrate the type and range of insights that can be gleaned. In discussion of these models, we underscore strengths of the fly in providing understanding into mechanisms and pathways, as a foundation for translational and therapeutic research. PMID:26447127
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Drosophila as an In Vivo Model for Human Neurodegenerative Disease.
McGurk, Leeanne; Berson, Amit; Bonini, Nancy M
2015-10-01
With the increase in the ageing population, neurodegenerative disease is devastating to families and poses a huge burden on society. The brain and spinal cord are extraordinarily complex: they consist of a highly organized network of neuronal and support cells that communicate in a highly specialized manner. One approach to tackling problems of such complexity is to address the scientific questions in simpler, yet analogous, systems. The fruit fly, Drosophila melanogaster, has been proven tremendously valuable as a model organism, enabling many major discoveries in neuroscientific disease research. The plethora of genetic tools available in Drosophila allows for exquisite targeted manipulation of the genome. Due to its relatively short lifespan, complex questions of brain function can be addressed more rapidly than in other model organisms, such as the mouse. Here we discuss features of the fly as a model for human neurodegenerative disease. There are many distinct fly models for a range of neurodegenerative diseases; we focus on select studies from models of polyglutamine disease and amyotrophic lateral sclerosis that illustrate the type and range of insights that can be gleaned. In discussion of these models, we underscore strengths of the fly in providing understanding into mechanisms and pathways, as a foundation for translational and therapeutic research. Copyright © 2015 by the Genetics Society of America.
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A preclinical mouse model of invasive lobular breast cancer metastasis.
Doornebal, Chris W; Klarenbeek, Sjoerd; Braumuller, Tanya M; Klijn, Christiaan N; Ciampricotti, Metamia; Hau, Cheei-Sing; Hollmann, Markus W; Jonkers, Jos; de Visser, Karin E
2013-01-01
Metastatic disease accounts for more than 90% of cancer-related deaths, but the development of effective antimetastatic agents has been hampered by the paucity of clinically relevant preclinical models of human metastatic disease. Here, we report the development of a mouse model of spontaneous breast cancer metastasis, which recapitulates key events in its formation and clinical course. Specifically, using the conditional K14cre;Cdh1(F/F);Trp53(F/F) model of de novo mammary tumor formation, we orthotopically transplanted invasive lobular carcinoma (mILC) fragments into mammary glands of wild-type syngeneic hosts. Once primary tumors were established in recipient mice, we mimicked the clinical course of treatment by conducting a mastectomy. After surgery, recipient mice succumbed to widespread overt metastatic disease in lymph nodes, lungs, and gastrointestinal tract. Genomic profiling of paired mammary tumors and distant metastases showed that our model provides a unique tool to further explore the biology of metastatic disease. Neoadjuvant and adjuvant intervention studies using standard-of-care chemotherapeutics showed the value of this model in determining therapeutic agents that can target early- and late-stage metastatic disease. In obtaining a more accurate preclinical model of metastatic lobular breast cancer, our work offers advances supporting the development of more effective treatment strategies for metastatic disease.
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Genome Editing for the Study of Cardiovascular Diseases.
Chadwick, Alexandra C; Musunuru, Kiran
2017-03-01
The opportunities afforded through the recent advent of genome-editing technologies have allowed investigators to more easily study a number of diseases. The advantages and limitations of the most prominent genome-editing technologies are described in this review, along with potential applications specifically focused on cardiovascular diseases. The recent genome-editing tools using programmable nucleases, such as zinc-finger nucleases, transcription activator-like effector nucleases, and clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated 9 (Cas9), have rapidly been adapted to manipulate genes in a variety of cellular and animal models. A number of recent cardiovascular disease-related publications report cases in which specific mutations are introduced into disease models for functional characterization and for testing of therapeutic strategies. Recent advances in genome-editing technologies offer new approaches to understand and treat diseases. Here, we discuss genome editing strategies to easily characterize naturally occurring mutations and offer strategies with potential clinical relevance.
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CRISPR-Cas Genome Surgery in Ophthalmology
DiCarlo, James E.; Sengillo, Jesse D.; Justus, Sally; Cabral, Thiago; Tsang, Stephen H.; Mahajan, Vinit B.
2017-01-01
Genetic disease affecting vision can significantly impact patient quality of life. Gene therapy seeks to slow the progression of these diseases by treating the underlying etiology at the level of the genome. Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated systems (Cas) represent powerful tools for studying diseases through the creation of model organisms generated by targeted modification and by the correction of disease mutations for therapeutic purposes. CRISPR-Cas systems have been applied successfully to the visual sciences and study of ophthalmic disease – from the modification of zebrafish and mammalian models of eye development and disease, to the correction of pathogenic mutations in patient-derived stem cells. Recent advances in CRISPR-Cas delivery and optimization boast improved functionality that continues to enhance genome-engineering applications in the eye. This review provides a synopsis of the recent implementations of CRISPR-Cas tools in the field of ophthalmology. PMID:28573077
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Cowpox virus infection of cynomolgus macaques as a model of hemorrhagic smallpox.
Johnson, Reed F; Yellayi, Srikanth; Cann, Jennifer A; Johnson, Anthony; Smith, Alvin L; Paragas, Jason; Jahrling, Peter B; Blaney, Joseph E
2011-09-30
Hemorrhagic smallpox was a rare but severe manifestation of variola virus infection that resulted in nearly 100% mortality. Here we describe intravenous (IV) inoculation of cowpox virus Brighton Red strain in cynomolgus macaques (Macaca fascicularis) which resulted in disease similar in presentation to hemorrhagic smallpox in humans. IV inoculation of macaques resulted in a uniformly lethal disease within 12 days post-inoculation in two independent experiments. Clinical observations and hematological and histopathological findings support hemorrhagic disease. Cowpox virus replicated to high levels in blood (8.0-9.0 log(10) gene copies/mL) and tissues including lymph nodes, thymus, spleen, bone marrow, and lungs. This unique model of hemorrhagic orthopoxvirus infection provides an accessible means to further study orthopoxvirus pathogenesis and to identify virus-specific and nonspecific therapies. Such studies will serve to complement the existing nonhuman primate models of more classical poxviral disease. Published by Elsevier Inc.
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Animal models of Helicobacter-induced disease: methods to successfully infect the mouse.
Taylor, Nancy S; Fox, James G
2012-01-01
Animal models of microbial diseases in humans are an essential component for determining fulfillment of Koch's postulates and determining how the organism causes disease, host response(s), disease prevention, and treatment. In the case of Helicobacter pylori, establishing an animal model to fulfill Koch's postulates initially proved so challenging that out of frustration a human volunteer undertook an experiment to become infected with H. pylori and to monitor disease progression in order to determine if it did cause gastritis. For the discovery of the organism and his fulfillment of Koch's postulates he and a colleague were awarded the Nobel Prize in Medicine. After H. pylori was established as a gastric pathogen, it took several years before a model was developed in mice, opening the study of the organism and its pathogenicity to the general scientific community. However, while the model is widely utilized, there are a number of difficulties that can arise and need to be overcome. The purpose of this chapter is to raise awareness regarding the problems, and to offer reliable protocols for successfully establishing the H. pylori mouse model.
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NASA Astrophysics Data System (ADS)
Amano, Ayako; Sakuma, Taisuke; Kazama, So
This study evaluated waterborne infectious diseases risk and incidence rate around Phonm Penh in Cambodia. We use the hydraulic flood simulation, coliform bacterium diffusion model, dose-response model and outpatient data for quantitative analysis. The results obtained are as follows; 1. The incidence (incidence rate) of diarrhea as water borne diseases risk is 0.14 million people (9%) in the inundation area. 2. The residents in the inundation area are exposed up to 4 times as high risk as daily mean calculated by the integrated model combined in the regional scale. 3.The infectious disease risk due to floods and inundation indicated is effective as an element to explain the risk. The scenario explains 34% number of patient estimated by the outpatient data.
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Toward developmental models of psychiatric disorders in zebrafish
Norton, William H. J.
2013-01-01
Psychiatric disorders are a diverse set of diseases that affect all aspects of mental function including social interaction, thinking, feeling, and mood. Although psychiatric disorders place a large economic burden on society, the drugs available to treat them are often palliative with variable efficacy and intolerable side-effects. The development of novel drugs has been hindered by a lack of knowledge about the etiology of these diseases. It is thus necessary to further investigate psychiatric disorders using a combination of human molecular genetics, gene-by-environment studies, in vitro pharmacological and biochemistry experiments, animal models, and investigation of the non-biological basis of these diseases, such as environmental effects. Many psychiatric disorders, including autism spectrum disorder, attention-deficit/hyperactivity disorder, mental retardation, and schizophrenia can be triggered by alterations to neural development. The zebrafish is a popular model for developmental biology that is increasingly used to study human disease. Recent work has extended this approach to examine psychiatric disorders as well. However, since psychiatric disorders affect complex mental functions that might be human specific, it is not possible to fully model them in fish. In this review, I will propose that the suitability of zebrafish for developmental studies, and the genetic tools available to manipulate them, provide a powerful model to study the roles of genes that are linked to psychiatric disorders during neural development. The relative speed and ease of conducting experiments in zebrafish can be used to address two areas of future research: the contribution of environmental factors to disease onset, and screening for novel therapeutic compounds. PMID:23637652
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Xenopus: An Emerging Model for Studying Congenital Heart Disease
Kaltenbrun, Erin; Tandon, Panna; Amin, Nirav M.; Waldron, Lauren; Showell, Chris; Conlon, Frank L.
2011-01-01
Congenital heart defects affect nearly 1% of all newborns and are a significant cause of infant death. Clinical studies have identified a number of congenital heart syndromes associated with mutations in genes that are involved in the complex process of cardiogenesis. The African clawed frog, Xenopus, has been instrumental in studies of vertebrate heart development and provides a valuable tool to investigate the molecular mechanisms underlying human congenital heart diseases. In this review, we discuss the methodologies that make Xenopus an ideal model system to investigate heart development and disease. We also outline congenital heart conditions linked to cardiac genes that have been well-studied in Xenopus and describe some emerging technologies that will further aid in the study of these complex syndromes. PMID:21538812
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Identification of Alfalfa Leaf Diseases Using Image Recognition Technology
Qin, Feng; Liu, Dongxia; Sun, Bingda; Ruan, Liu; Ma, Zhanhong; Wang, Haiguang
2016-01-01
Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease. PMID:27977767
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Identification of Alfalfa Leaf Diseases Using Image Recognition Technology.
Qin, Feng; Liu, Dongxia; Sun, Bingda; Ruan, Liu; Ma, Zhanhong; Wang, Haiguang
2016-01-01
Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease.
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Iddamalgoda, Lahiru; Das, Partha S; Aponso, Achala; Sundararajan, Vijayaraghava S; Suravajhala, Prashanth; Valadi, Jayaraman K
2016-01-01
Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how the genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately prioritizing the single nucleotide polymorphisms (SNP) associated with the disease. In this commentary, we review the state-of-art data mining and pattern recognition models for identifying inherited diseases and deliberate the need of binary classification- and scoring-based prioritization methods in determining causal variants. While we discuss the pros and cons associated with these methods known, we argue that the gene prioritization methods and the protein interaction (PPI) methods in conjunction with the K nearest neighbors' could be used in accurately categorizing the genetic factors in disease causation.
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Li, Xue-Ming; Chen, Ying-Dan; Xu, Long-Qi; Zhou, Chang-Hai; Ou-Yang, Yi; Lin, Rui; Yang, Fang-Fang; Zhang, Xiao-Juan; Wang, Ge; Liu, Teng; Wang, Jing
2011-12-01
To explore a new prevention and control model on soil-borne parasitic diseases in rural areas of China. Eight provinces and autonomous regions were selected in China as demonstration areas implementing integrated control on soil-borne parasitic diseases. The integrated control measures included authority organization and harmonization, health education, deworming, and environment modification. After three years, the infection rates of soil-borne parasitic diseases were significantly decreased in these areas. There were three safe guard and organization modes, three health education modes, four mass worming medication modes, and two modes of water, toilet and environment changes. The work in the various demonstration areas was summarized which pointed out a new model with efficiency and local characteristics on soil-borne parasitic disease prevention and control.
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The development of a simulation model of the treatment of coronary heart disease.
Cooper, Keith; Davies, Ruth; Roderick, Paul; Chase, Debbie; Raftery, James
2002-11-01
A discrete event simulation models the progress of patients who have had a coronary event, through their treatment pathways and subsequent coronary events. The main risk factors in the model are age, sex, history of previous events and the extent of the coronary vessel disease. The model parameters are based on data collected from epidemiological studies of incidence and prognosis, efficacy studies. national surveys and treatment audits. The simulation results were validated against different sources of data. The initial results show that increasing revascularisation has considerable implications for resource use but has little impact on patient mortality.
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Common polygenic variation enhances risk prediction for Alzheimer’s disease
Sims, Rebecca; Bannister, Christian; Harold, Denise; Vronskaya, Maria; Majounie, Elisa; Badarinarayan, Nandini; Morgan, Kevin; Passmore, Peter; Holmes, Clive; Powell, John; Brayne, Carol; Gill, Michael; Mead, Simon; Goate, Alison; Cruchaga, Carlos; Lambert, Jean-Charles; van Duijn, Cornelia; Maier, Wolfgang; Ramirez, Alfredo; Holmans, Peter; Jones, Lesley; Hardy, John; Seshadri, Sudha; Schellenberg, Gerard D.; Amouyel, Philippe
2015-01-01
The identification of subjects at high risk for Alzheimer’s disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer’s disease and the accuracy of Alzheimer’s disease prediction models, including and excluding the polygenic component in the model. This study used genotype data from the powerful dataset comprising 17 008 cases and 37 154 controls obtained from the International Genomics of Alzheimer’s Project (IGAP). Polygenic score analysis tested whether the alleles identified to associate with disease in one sample set were significantly enriched in the cases relative to the controls in an independent sample. The disease prediction accuracy was investigated in a subset of the IGAP data, a sample of 3049 cases and 1554 controls (for whom APOE genotype data were available) by means of sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and positive and negative predictive values. We observed significant evidence for a polygenic component enriched in Alzheimer’s disease (P = 4.9 × 10−26). This enrichment remained significant after APOE and other genome-wide associated regions were excluded (P = 3.4 × 10−19). The best prediction accuracy AUC = 78.2% (95% confidence interval 77–80%) was achieved by a logistic regression model with APOE, the polygenic score, sex and age as predictors. In conclusion, Alzheimer’s disease has a significant polygenic component, which has predictive utility for Alzheimer’s disease risk and could be a valuable research tool complementing experimental designs, including preventative clinical trials, stem cell selection and high/low risk clinical studies. In modelling a range of sample disease prevalences, we found that polygenic scores almost doubles case prediction from chance with increased prediction at polygenic extremes. PMID:26490334
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Environmental and socio-economic risk modelling for Chagas disease in Bolivia.
Mischler, Paula; Kearney, Michael; McCarroll, Jennifer C; Scholte, Ronaldo G C; Vounatsou, Penelope; Malone, John B
2012-09-01
Accurately defining disease distributions and calculating disease risk is an important step in the control and prevention of diseases. Geographical information systems (GIS) and remote sensing technologies, with maximum entropy (Maxent) ecological niche modelling computer software, were used to create predictive risk maps for Chagas disease in Bolivia. Prevalence rates were calculated from 2007 to 2009 household infection survey data for Bolivia, while environmental data were compiled from the Worldclim database and MODIS satellite imagery. Socio-economic data were obtained from the Bolivian National Institute of Statistics. Disease models identified altitudes at 500-3,500 m above the mean sea level (MSL), low annual precipitation (45-250 mm), and higher diurnal range of temperature (10-19 °C; peak 16 °C) as compatible with the biological requirements of the insect vectors. Socio-economic analyses demonstrated the importance of improved housing materials and water source. Home adobe wall materials and having to fetch drinking water from rivers or wells without pump were found to be highly related to distribution of the disease by the receiver operator characteristic (ROC) area under the curve (AUC) (0.69 AUC, 0.67 AUC and 0.62 AUC, respectively), while areas with hardwood floors demonstrated a direct negative relationship (-0.71 AUC). This study demonstrates that Maxent modelling can be used in disease prevalence and incidence studies to provide governmental agencies with an easily learned, understandable method to define areas as either high, moderate or low risk for the disease. This information may be used in resource planning, targeting and implementation. However, access to high-resolution, sub-municipality socio-economic data (e.g. census tracts) would facilitate elucidation of the relative influence of poverty-related factors on regional disease dynamics.
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Animal models of ulcerative colitis and their application in drug research
Low, Daren; Nguyen, Deanna D; Mizoguchi, Emiko
2013-01-01
The specific pathogenesis underlying inflammatory bowel disease is complex, and it is even more difficult to decipher the pathophysiology to explain for the similarities and differences between two of its major subtypes, Crohn’s disease and ulcerative colitis (UC). Animal models are indispensable to pry into mechanistic details that will facilitate better preclinical drug/therapy design to target specific components involved in the disease pathogenesis. This review focuses on common animal models that are particularly useful for the study of UC and its therapeutic strategy. Recent reports of the latest compounds, therapeutic strategies, and approaches tested on UC animal models are also discussed. PMID:24250223
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The application of epidemiology in aquatic animal health -opportunities and challenges
2011-01-01
Over recent years the growth in aquaculture, accompanied by the emergence of new and transboundary diseases, has stimulated epidemiological studies of aquatic animal diseases. Great potential exists for both observational and theoretical approaches to investigate the processes driving emergence but, to date, compared to terrestrial systems, relatively few studies exist in aquatic animals. Research using risk methods has assessed routes of introduction of aquatic animal pathogens to facilitate safe trade (e.g. import risk analyses) and support biosecurity. Epidemiological studies of risk factors for disease in aquaculture (most notably Atlantic salmon farming) have effectively supported control measures. Methods developed for terrestrial livestock diseases (e.g. risk-based surveillance) could improve the capacity of aquatic animal surveillance systems to detect disease incursions and emergence. The study of disease in wild populations presents many challenges and the judicious use of theoretical models offers some solutions. Models, parameterised from observational studies of host pathogen interactions, have been used to extrapolate estimates of impacts on the individual to the population level. These have proved effective in estimating the likely impact of parasite infections on wild salmonid populations in Switzerland and Canada (where the importance of farmed salmon as a reservoir of infection was investigated). A lack of data is often the key constraint in the application of new approaches to surveillance and modelling. The need for epidemiological approaches to protect aquatic animal health will inevitably increase in the face of the combined challenges of climate change, increasing anthropogenic pressures, limited water sources and the growth in aquaculture. Table of contents 1 Introduction 4 2 The development of aquatic epidemiology 7 3 Transboundary and emerging diseases 9 3.1 Import risk analysis (IRA) 10 3.2 Aquaculture and disease emergence 11 3.3 Climate change and disease emergence 13 3.4 Outbreak investigations 13 4 Surveillance and surveys 15 4.1 Investigation of disease prevalence 15 4.2 Developments in surveillance methodology 16 4.2.1 Risk-based surveillance and scenario tree modelling 16 4.2.2 Spatial and temporal analysis 16 4.3 Test validation 17 5 Spread, establishment and impact of pathogens 18 5.1 Identifying routes of spread 18 5.1.1 Ex-ante studies of disease spread 19 5.1.2 Ex-post observational studies 21 5.2 Identifying risk factors for disease establishment 23 5.3 Assessing impact at the population level 24 5.3.1 Recording mortality 24 5.3.2 Farm health and production records 26 5.3.3 Assessing the impact of disease in wild populations 27 6 Conclusions 31 7 Competing interests 32 8 Authors' contributions 32 9 Acknowledgements 33 10 References 33 PMID:21834990
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Epidemic modeling with discrete-space scheduled walkers: extensions and research opportunities
2009-01-01
Background This exploratory paper outlines an epidemic simulator built on an agent-based, data-driven model of the spread of a disease within an urban environment. An intent of the model is to provide insight into how a disease may reach a tipping point, spreading to an epidemic of uncontrollable proportions. Methods As a complement to analytical methods, simulation is arguably an effective means of gaining a better understanding of system-level disease dynamics within a population and offers greater utility in its modeling capabilities. Our investigation is based on this conjecture, supported by data-driven models that are reasonable, realistic and practical, in an attempt to demonstrate their efficacy in studying system-wide epidemic phenomena. An agent-based model (ABM) offers considerable flexibility in extending the study of the phenomena before, during and after an outbreak or catastrophe. Results An agent-based model was developed based on a paradigm of a 'discrete-space scheduled walker' (DSSW), modeling a medium-sized North American City of 650,000 discrete agents, built upon a conceptual framework of statistical reasoning (law of large numbers, statistical mechanics) as well as a correct-by-construction bias. The model addresses where, who, when and what elements, corresponding to network topography and agent characteristics, behaviours, and interactions upon that topography. The DSSW-ABM has an interface and associated scripts that allow for a variety of what-if scenarios modeling disease spread throughout the population, and for data to be collected and displayed via a web browser. Conclusion This exploratory paper also presents several research opportunities for exploiting data sources of a non-obvious and disparate nature for the purposes of epidemic modeling. There is an increasing amount and variety of data that will continue to contribute to the accuracy of agent-based models and improve their utility in modeling disease spread. The model developed here is well suited to diseases where there is not a predisposition for contraction within the population. One of the advantages of agent-based modeling is the ability to set up a rare event and develop policy as to how one may mitigate damages arising from it. PMID:19922684
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Epidemic modeling with discrete-space scheduled walkers: extensions and research opportunities.
Borkowski, Maciej; Podaima, Blake W; McLeod, Robert D
2009-11-18
This exploratory paper outlines an epidemic simulator built on an agent-based, data-driven model of the spread of a disease within an urban environment. An intent of the model is to provide insight into how a disease may reach a tipping point, spreading to an epidemic of uncontrollable proportions. As a complement to analytical methods, simulation is arguably an effective means of gaining a better understanding of system-level disease dynamics within a population and offers greater utility in its modeling capabilities. Our investigation is based on this conjecture, supported by data-driven models that are reasonable, realistic and practical, in an attempt to demonstrate their efficacy in studying system-wide epidemic phenomena. An agent-based model (ABM) offers considerable flexibility in extending the study of the phenomena before, during and after an outbreak or catastrophe. An agent-based model was developed based on a paradigm of a 'discrete-space scheduled walker' (DSSW), modeling a medium-sized North American City of 650,000 discrete agents, built upon a conceptual framework of statistical reasoning (law of large numbers, statistical mechanics) as well as a correct-by-construction bias. The model addresses where, who, when and what elements, corresponding to network topography and agent characteristics, behaviours, and interactions upon that topography. The DSSW-ABM has an interface and associated scripts that allow for a variety of what-if scenarios modeling disease spread throughout the population, and for data to be collected and displayed via a web browser. This exploratory paper also presents several research opportunities for exploiting data sources of a non-obvious and disparate nature for the purposes of epidemic modeling. There is an increasing amount and variety of data that will continue to contribute to the accuracy of agent-based models and improve their utility in modeling disease spread. The model developed here is well suited to diseases where there is not a predisposition for contraction within the population. One of the advantages of agent-based modeling is the ability to set up a rare event and develop policy as to how one may mitigate damages arising from it.
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Models for the effects of host movement in vector-borne disease systems.
Cosner, Chris
2015-12-01
Host and/or vector movement patterns have been shown to have significant effects in both empirical studies and mathematical models of vector-borne diseases. The processes of economic development and globalization seem likely to make host movement even more important in the future. This article is a brief survey of some of the approaches that have been used to study the effects of host movement in analytic mathematical models for vector-borne diseases. It describes the formulation and interpretation of various types of spatial models and describes a few of the conclusions that can be drawn from them. It is not intended to be comprehensive but rather to provide sufficient background material and references to the literature to serve as an entry point into this area of research for interested readers. Copyright © 2015 Elsevier Inc. All rights reserved.
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Urine-derived induced pluripotent stem cells as a modeling tool to study rare human diseases
Shi, Liang; Cui, Yazhou; Luan, Jing; Zhou, Xiaoyan; Han, Jinxiang
2016-01-01
Summary Rare diseases with a low prevalence are a key public health issue because the causes of those diseases are difficult to determine and those diseases lack a clearly established or curative treatment. Thus, investigating the molecular mechanisms that underlie the pathology of rare diseases and facilitating the development of novel therapies using disease models is crucial. Human induced pluripotent stem cells (iPSCs) are well suited to modeling rare diseases since they have the capacity for self-renewal and pluripotency. In addition, iPSC technology provides a valuable tool to generate patient-specific iPSCs. These cells can be differentiated into cell types that have been affected by a disease. These cells would circumvent ethical concerns and avoid immunological rejection, so they could be used in cell replacement therapy or regenerative medicine. To date, human iPSCs could have been generated from multiple donor sources, such as skin, adipose tissue, and peripheral blood. However, these cells are obtained via invasive procedures. In contrast, several groups of researchers have found that urine may be a better source for producing iPSCs from normal individuals or patients. This review discusses urinary iPSC (UiPSC) as a candidate for modeling rare diseases. Cells obtained from urine have overwhelming advantages compared to other donor sources since they are safely, affordably, and frequently obtained and they are readily obtained from patients. The use of iPSC-based models is also discussed. UiPSCs may prove to be a key means of modeling rare diseases and they may facilitate the treatment of those diseases in the future. PMID:27672542
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Matrix factorization-based data fusion for the prediction of lncRNA-disease associations.
Fu, Guangyuan; Wang, Jun; Domeniconi, Carlotta; Yu, Guoxian
2018-05-01
Long non-coding RNAs (lncRNAs) play crucial roles in complex disease diagnosis, prognosis, prevention and treatment, but only a small portion of lncRNA-disease associations have been experimentally verified. Various computational models have been proposed to identify lncRNA-disease associations by integrating heterogeneous data sources. However, existing models generally ignore the intrinsic structure of data sources or treat them as equally relevant, while they may not be. To accurately identify lncRNA-disease associations, we propose a Matrix Factorization based LncRNA-Disease Association prediction model (MFLDA in short). MFLDA decomposes data matrices of heterogeneous data sources into low-rank matrices via matrix tri-factorization to explore and exploit their intrinsic and shared structure. MFLDA can select and integrate the data sources by assigning different weights to them. An iterative solution is further introduced to simultaneously optimize the weights and low-rank matrices. Next, MFLDA uses the optimized low-rank matrices to reconstruct the lncRNA-disease association matrix and thus to identify potential associations. In 5-fold cross validation experiments to identify verified lncRNA-disease associations, MFLDA achieves an area under the receiver operating characteristic curve (AUC) of 0.7408, at least 3% higher than those given by state-of-the-art data fusion based computational models. An empirical study on identifying masked lncRNA-disease associations again shows that MFLDA can identify potential associations more accurately than competing models. A case study on identifying lncRNAs associated with breast, lung and stomach cancers show that 38 out of 45 (84%) associations predicted by MFLDA are supported by recent biomedical literature and further proves the capability of MFLDA in identifying novel lncRNA-disease associations. MFLDA is a general data fusion framework, and as such it can be adopted to predict associations between other biological entities. The source code for MFLDA is available at: http://mlda.swu.edu.cn/codes.php? name = MFLDA. gxyu@swu.edu.cn. Supplementary data are available at Bioinformatics online.
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Van Dyke, Miriam E; Komro, Kelli A; Shah, Monica P; Livingston, Melvin D; Kramer, Michael R
2018-07-01
Despite substantial declines since the 1960's, heart disease remains the leading cause of death in the United States (US) and geographic disparities in heart disease mortality have grown. State-level socioeconomic factors might be important contributors to geographic differences in heart disease mortality. This study examined the association between state-level minimum wage increases above the federal minimum wage and heart disease death rates from 1980 to 2015 among 'working age' individuals aged 35-64 years in the US. Annual, inflation-adjusted state and federal minimum wage data were extracted from legal databases and annual state-level heart disease death rates were obtained from CDC Wonder. Although most minimum wage and health studies to date use conventional regression models, we employed marginal structural models to account for possible time-varying confounding. Quasi-experimental, marginal structural models accounting for state, year, and state × year fixed effects estimated the association between increases in the state-level minimum wage above the federal minimum wage and heart disease death rates. In models of 'working age' adults (35-64 years old), a $1 increase in the state-level minimum wage above the federal minimum wage was on average associated with ~6 fewer heart disease deaths per 100,000 (95% CI: -10.4, -1.99), or a state-level heart disease death rate that was 3.5% lower per year. In contrast, for older adults (65+ years old) a $1 increase was on average associated with a 1.1% lower state-level heart disease death rate per year (b = -28.9 per 100,000, 95% CI: -71.1, 13.3). State-level economic policies are important targets for population health research. Copyright © 2018 Elsevier Inc. All rights reserved.
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Synuclein impairs trafficking and signaling of BDNF in a mouse model of Parkinson's disease.
Fang, Fang; Yang, Wanlin; Florio, Jazmin B; Rockenstein, Edward; Spencer, Brian; Orain, Xavier M; Dong, Stephanie X; Li, Huayan; Chen, Xuqiao; Sung, Kijung; Rissman, Robert A; Masliah, Eliezer; Ding, Jianqing; Wu, Chengbiao
2017-06-20
Recent studies have demonstrated that hyperphosphorylation of tau protein plays a role in neuronal toxicities of α-synuclein (ASYN) in neurodegenerative disease such as familial Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and Parkinson's disease. Using a transgenic mouse model of Parkinson's disease (PD) that expresses GFP-ASYN driven by the PDGF-β promoter, we investigated how accumulation of ASYN impacted axonal function. We found that retrograde axonal trafficking of brain-derived neurotrophic factor (BDNF) in DIV7 cultures of E18 cortical neurons was markedly impaired at the embryonic stage, even though hyperphosphorylation of tau was not detectable in these neurons at this stage. Interestingly, we found that overexpressed ASYN interacted with dynein and induced a significant increase in the activated levels of small Rab GTPases such as Rab5 and Rab7, both key regulators of endocytic processes. Furthermore, expression of ASYN resulted in neuronal atrophy in DIV7 cortical cultures of either from E18 transgenic mouse model or from rat E18 embryos that were transiently transfected with ASYN-GFP for 72 hrs. Our studies suggest that excessive ASYN likely alters endocytic pathways leading to axonal dysfunction in embryonic cortical neurons in PD mouse models.
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A structural model of health behavior modification among patients with cardiovascular disease.
Goong, Hwasoo; Ryu, Seungmi; Xu, Lijuan
2016-02-01
The purpose of the study was to test a structural equation model in which social support, health beliefs, and stage of change predict the health behaviors of patients with cardiovascular disease. A cross-sectional correlational design was used. Using convenience sampling, a survey about social support, health belief, stage of change, and health behavior was completed by 314 adults with cardiovascular disease from outpatient clinics in 2 university hospitals in Korea. Data were analyzed using a structural equation model with the Analysis of Moment program. The participants were aged 53.44±13.19 years (mean±SD), and about 64% of them were male. The proposed model fit the data from the study well, explaining 19% and 60% of the variances in the stage of change and health behavior, respectively. The findings indicate that the performance of health behavior modification among the patients with cardiovascular disease can be explained by social support, health belief, and stage of change based on a health-belief and stage-of-change model. Further studies are warranted to confirm the efficacy of health-promoting strategies in initiating and maintaining the performance of health behaviors by providing social support from family and medical staff and enhancing health belief. Copyright © 2015 Elsevier Inc. All rights reserved.
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Updates on Dietary Models of Nonalcoholic Fatty Liver Disease: Current Studies and Insights
Stephenson, Kristen; Kennedy, Lindsey; Hargrove, Laura; Demieville, Jennifer; Thomson, Joanne; Alpini, Gianfranco; Francis, Heather
2018-01-01
Nonalcoholic fatty liver disease (NAFLD) is a disease of increasing interest, as its prevalence is on the rise. NAFLD has been linked to metabolic syndrome, which is becoming more common due to the Western diet. Because NAFLD can lead to cirrhosis and related complications including hepatocellular carcinoma, the increasing prevalence is concerning, and medical therapy aimed at treating NAFLD is of great interest. Researchers studying the effects of medical therapy on NAFLD use dietary mouse models. The two main types of mouse model diets are the methionine- and choline-deficient (MCD) diet and the Western-like diet (WD). Although both induce NAFLD, the mechanisms are very different. We reviewed several studies conducted within the last 5 years that used MCD diet or WD mouse models in order to mimic this disease in a way most similar to humans. The MCD diet inconsistently induces NAFLD and fibrosis and does not completely induce metabolic syndrome. Thus, the clinical significance of the MCD diet is questionable. In contrast, WD mouse models consisting of high fat, cholesterol, and a combination of high-fructose corn syrup, sucrose, fructose, or glucose not only lead to metabolic syndrome but also induce NAFLD with fibrosis, making these choices most suitable for research. PMID:29096730
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Routine Discovery of Complex Genetic Models using Genetic Algorithms
Moore, Jason H.; Hahn, Lance W.; Ritchie, Marylyn D.; Thornton, Tricia A.; White, Bill C.
2010-01-01
Simulation studies are useful in various disciplines for a number of reasons including the development and evaluation of new computational and statistical methods. This is particularly true in human genetics and genetic epidemiology where new analytical methods are needed for the detection and characterization of disease susceptibility genes whose effects are complex, nonlinear, and partially or solely dependent on the effects of other genes (i.e. epistasis or gene-gene interaction). Despite this need, the development of complex genetic models that can be used to simulate data is not always intuitive. In fact, only a few such models have been published. We have previously developed a genetic algorithm approach to discovering complex genetic models in which two single nucleotide polymorphisms (SNPs) influence disease risk solely through nonlinear interactions. In this paper, we extend this approach for the discovery of high-order epistasis models involving three to five SNPs. We demonstrate that the genetic algorithm is capable of routinely discovering interesting high-order epistasis models in which each SNP influences risk of disease only through interactions with the other SNPs in the model. This study opens the door for routine simulation of complex gene-gene interactions among SNPs for the development and evaluation of new statistical and computational approaches for identifying common, complex multifactorial disease susceptibility genes. PMID:20948983
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Nishiguchi, S; Ito, H; Yamada, M; Yoshitomi, H; Furu, M; Ito, T; Shinohara, A; Ura, T; Okamoto, K; Aoyama, T; Tsuboyama, Tadao
2016-01-01
This article is part of the Focus Theme of Methods of Information in Medicine on "Methodologies, Models and Algorithms for Patients Rehabilitation". Rheumatoid arthritis (RA) is a progressive inflammatory disease that causes damage to multiple joints, decline in functional status, and premature mortality. Thus, effective and frequent objective assessments are necessary. Then, we developed a self-assessment system for RA patients based on a smartphone application. The purpose of this study was to investigate the feasibility of a self-assessment system for RA patients using a smartphone application. We measured daily disease activity in nine RA patients who used the smartphone application for a period of three months. A disease activity score (DAS28) predictive model was used and feedback comments relating to disease activity were shown to patients via the smartphone application each day. To assess participants' RA disease activity, the DAS28 based on the C-reactive protein level was measured by a rheumatologist during monthly clinical visits. The disease activity measured by the application correlated well with the patients' actual disease activity during the 3-month period, as assessed by clinical examination. Furthermore, most participants gave favourable responses to a questionnaire administered at the end of the 3-month period containing questions relating to the ease of use and usefulness of the system. The results of this feasibility study indicated that the DAS28 predictive model can longitudinally predict DAS28 and may be an acceptable and useful tool for assessment of RA disease activity for both patients and healthcare providers.
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Kane, Jennifer; Landes, Megan; Carroll, Christopher; Nolen, Amy; Sodhi, Sumeet
2017-03-23
Chronic diseases, primarily cardiovascular disease, respiratory disease, diabetes and cancer, are the leading cause of death and disability worldwide. In sub-Saharan Africa (SSA), where communicable disease prevalence still outweighs that of non-communicable disease (NCDs), rates of NCDs are rapidly rising and evidence for primary healthcare approaches for these emerging NCDs is needed. A systematic review and evidence synthesis of primary care approaches for chronic disease in SSA. Quantitative and qualitative primary research studies were included that focused on priority NCDs interventions. The method used was best-fit framework synthesis. Three conceptual models of care for NCDs in low- and middle-income countries were identified and used to develop an a priori framework for the synthesis. The literature search for relevant primary research studies generated 3759 unique citations of which 12 satisfied the inclusion criteria. Eleven studies were quantitative and one used mixed methods. Three higher-level themes of screening, prevention and management of disease were derived. This synthesis permitted the development of a new evidence-based conceptual model of care for priority NCDs in SSA. For this review there was a near-consensus that passive rather than active case-finding approaches are suitable in resource-poor settings. Modifying risk factors among existing patients through advice on diet and lifestyle was a common element of healthcare approaches. The priorities for disease management in primary care were identified as: availability of essential diagnostic tools and medications at local primary healthcare clinics and the use of standardized protocols for diagnosis, treatment, monitoring and referral to specialist care.
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Petri net modeling of high-order genetic systems using grammatical evolution.
Moore, Jason H; Hahn, Lance W
2003-11-01
Understanding how DNA sequence variations impact human health through a hierarchy of biochemical and physiological systems is expected to improve the diagnosis, prevention, and treatment of common, complex human diseases. We have previously developed a hierarchical dynamic systems approach based on Petri nets for generating biochemical network models that are consistent with genetic models of disease susceptibility. This modeling approach uses an evolutionary computation approach called grammatical evolution as a search strategy for optimal Petri net models. We have previously demonstrated that this approach routinely identifies biochemical network models that are consistent with a variety of genetic models in which disease susceptibility is determined by nonlinear interactions between two DNA sequence variations. In the present study, we evaluate whether the Petri net approach is capable of identifying biochemical networks that are consistent with disease susceptibility due to higher order nonlinear interactions between three DNA sequence variations. The results indicate that our model-building approach is capable of routinely identifying good, but not perfect, Petri net models. Ideas for improving the algorithm for this high-dimensional problem are presented.
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Jain, Ruchi; Dey, Bappaditya; Tyagi, Anil K
2012-10-02
The Guinea pig (Cavia porcellus) is one of the most extensively used animal models to study infectious diseases. However, despite its tremendous contribution towards understanding the establishment, progression and control of a number of diseases in general and tuberculosis in particular, the lack of fully annotated guinea pig genome sequence as well as appropriate molecular reagents has severely hampered detailed genetic and immunological analysis in this animal model. By employing the cross-species hybridization technique, we have developed an oligonucleotide microarray with 44,000 features assembled from different mammalian species, which to the best of our knowledge is the first attempt to employ microarray to study the global gene expression profile in guinea pigs. To validate and demonstrate the merit of this microarray, we have studied, as an example, the expression profile of guinea pig lungs during the advanced phase of M. tuberculosis infection. A significant upregulation of 1344 genes and a marked down regulation of 1856 genes in the lungs identified a disease signature of pulmonary tuberculosis infection. We report the development of first comprehensive microarray for studying the global gene expression profile in guinea pigs and validation of its usefulness with tuberculosis as a case study. An important gap in the area of infectious diseases has been addressed and a valuable molecular tool is provided to optimally harness the potential of guinea pig model to develop better vaccines and therapies against human diseases.
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Validating Lung Models Using the ASL 5000 Breathing Simulator.
Dexter, Amanda; McNinch, Neil; Kaznoch, Destiny; Volsko, Teresa A
2018-04-01
This study sought to validate pediatric models with normal and altered pulmonary mechanics. PubMed and CINAHL databases were searched for studies directly measuring pulmonary mechanics of healthy infants and children, infants with severe bronchopulmonary dysplasia and neuromuscular disease. The ASL 5000 was used to construct models using tidal volume (VT), inspiratory time (TI), respiratory rate, resistance, compliance, and esophageal pressure gleaned from literature. Data were collected for a 1-minute period and repeated three times for each model. t tests compared modeled data with data abstracted from the literature. Repeated measures analyses evaluated model performance over multiple iterations. Statistical significance was established at a P value of less than 0.05. Maximum differences of means (experimental iteration mean - clinical standard mean) for TI and VT are the following: term infant without lung disease (TI = 0.09 s, VT = 0.29 mL), severe bronchopulmonary dysplasia (TI = 0.08 s, VT = 0.17 mL), child without lung disease (TI = 0.10 s, VT = 0.17 mL), and child with neuromuscular disease (TI = 0.09 s, VT = 0.57 mL). One-sample testing demonstrated statistically significant differences between clinical controls and VT and TI values produced by the ASL 5000 for each iteration and model (P < 0.01). The greatest magnitude of differences was negligible (VT < 1.6%, TI = 18%) and not clinically relevant. Inconsistencies occurred with the models constructed on the ASL 5000. It was deemed accurate for the study purposes. It is therefore essential to test models and evaluate magnitude of differences before use.
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Cao, Qi; Buskens, Erik; Feenstra, Talitha; Jaarsma, Tiny; Hillege, Hans; Postmus, Douwe
2016-01-01
Continuous-time state transition models may end up having large unwieldy structures when trying to represent all relevant stages of clinical disease processes by means of a standard Markov model. In such situations, a more parsimonious, and therefore easier-to-grasp, model of a patient's disease progression can often be obtained by assuming that the future state transitions do not depend only on the present state (Markov assumption) but also on the past through time since entry in the present state. Despite that these so-called semi-Markov models are still relatively straightforward to specify and implement, they are not yet routinely applied in health economic evaluation to assess the cost-effectiveness of alternative interventions. To facilitate a better understanding of this type of model among applied health economic analysts, the first part of this article provides a detailed discussion of what the semi-Markov model entails and how such models can be specified in an intuitive way by adopting an approach called vertical modeling. In the second part of the article, we use this approach to construct a semi-Markov model for assessing the long-term cost-effectiveness of 3 disease management programs for heart failure. Compared with a standard Markov model with the same disease states, our proposed semi-Markov model fitted the observed data much better. When subsequently extrapolating beyond the clinical trial period, these relatively large differences in goodness-of-fit translated into almost a doubling in mean total cost and a 60-d decrease in mean survival time when using the Markov model instead of the semi-Markov model. For the disease process considered in our case study, the semi-Markov model thus provided a sensible balance between model parsimoniousness and computational complexity. © The Author(s) 2015.
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Ma, Xiaoye; Chen, Yong; Cole, Stephen R; Chu, Haitao
2016-12-01
To account for between-study heterogeneity in meta-analysis of diagnostic accuracy studies, bivariate random effects models have been recommended to jointly model the sensitivities and specificities. As study design and population vary, the definition of disease status or severity could differ across studies. Consequently, sensitivity and specificity may be correlated with disease prevalence. To account for this dependence, a trivariate random effects model had been proposed. However, the proposed approach can only include cohort studies with information estimating study-specific disease prevalence. In addition, some diagnostic accuracy studies only select a subset of samples to be verified by the reference test. It is known that ignoring unverified subjects may lead to partial verification bias in the estimation of prevalence, sensitivities, and specificities in a single study. However, the impact of this bias on a meta-analysis has not been investigated. In this paper, we propose a novel hybrid Bayesian hierarchical model combining cohort and case-control studies and correcting partial verification bias at the same time. We investigate the performance of the proposed methods through a set of simulation studies. Two case studies on assessing the diagnostic accuracy of gadolinium-enhanced magnetic resonance imaging in detecting lymph node metastases and of adrenal fluorine-18 fluorodeoxyglucose positron emission tomography in characterizing adrenal masses are presented. © The Author(s) 2014.
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Ma, Xiaoye; Chen, Yong; Cole, Stephen R.; Chu, Haitao
2014-01-01
To account for between-study heterogeneity in meta-analysis of diagnostic accuracy studies, bivariate random effects models have been recommended to jointly model the sensitivities and specificities. As study design and population vary, the definition of disease status or severity could differ across studies. Consequently, sensitivity and specificity may be correlated with disease prevalence. To account for this dependence, a trivariate random effects model had been proposed. However, the proposed approach can only include cohort studies with information estimating study-specific disease prevalence. In addition, some diagnostic accuracy studies only select a subset of samples to be verified by the reference test. It is known that ignoring unverified subjects may lead to partial verification bias in the estimation of prevalence, sensitivities and specificities in a single study. However, the impact of this bias on a meta-analysis has not been investigated. In this paper, we propose a novel hybrid Bayesian hierarchical model combining cohort and case-control studies and correcting partial verification bias at the same time. We investigate the performance of the proposed methods through a set of simulation studies. Two case studies on assessing the diagnostic accuracy of gadolinium-enhanced magnetic resonance imaging in detecting lymph node metastases and of adrenal fluorine-18 fluorodeoxyglucose positron emission tomography in characterizing adrenal masses are presented. PMID:24862512
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Animal models of aging research: implications for human aging and age-related diseases.
Mitchell, Sarah J; Scheibye-Knudsen, Morten; Longo, Dan L; de Cabo, Rafael
2015-01-01
Aging is characterized by an increasing morbidity and functional decline that eventually results in the death of an organism. Aging is the largest risk factor for numerous human diseases, and understanding the aging process may thereby facilitate the development of new treatments for age-associated diseases. The use of humans in aging research is complicated by many factors, including ethical issues; environmental and social factors; and perhaps most importantly, their long natural life span. Although cellular models of human disease provide valuable mechanistic information, they are limited in that they may not replicate the in vivo biology. Almost all organisms age, and thus animal models can be useful for studying aging. Herein, we review some of the major models currently used in aging research and discuss their benefits and pitfalls, including interventions known to extend life span and health span. Finally, we conclude by discussing the future of animal models in aging research.
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Fan, Lijuan; Fu, Guoning; Ding, Yuanyuan; Lv, Peng; Li, Hongyun
2017-03-01
Bactericidal/permeability increasing protein (BPI) gene polymorphisms have been extensively investigated in terms of their associations with inflammatory bowel disease (IBD), with contradictory results. The aim of this meta-analysis was to evaluate associations between BPI gene polymorphisms and the risk of IBD, Crohn's disease (CD), and ulcerative colitis (UC). Eligible studies from PubMed, Embase, and Cochrane library databases were identified. Ten studies (five CD and five UC) published in five papers were included in this meta-analysis. G645A polymorphism was associated with a decreased risk of UC in allele model, dominant model, and homozygous model. Our data suggested that BPI G645A polymorphism was associated with a decreased risk of UC; the BPI G645A polymorphism was not associated with the risk of CD.
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Extension of the lod score: the mod score.
Clerget-Darpoux, F
2001-01-01
In 1955 Morton proposed the lod score method both for testing linkage between loci and for estimating the recombination fraction between them. If a disease is controlled by a gene at one of these loci, the lod score computation requires the prior specification of an underlying model that assigns the probabilities of genotypes from the observed phenotypes. To address the case of linkage studies for diseases with unknown mode of inheritance, we suggested (Clerget-Darpoux et al., 1986) extending the lod score function to a so-called mod score function. In this function, the variables are both the recombination fraction and the disease model parameters. Maximizing the mod score function over all these parameters amounts to maximizing the probability of marker data conditional on the disease status. Under the absence of linkage, the mod score conforms to a chi-square distribution, with extra degrees of freedom in comparison to the lod score function (MacLean et al., 1993). The mod score is asymptotically maximum for the true disease model (Clerget-Darpoux and Bonaïti-Pellié, 1992; Hodge and Elston, 1994). Consequently, the power to detect linkage through mod score will be highest when the space of models where the maximization is performed includes the true model. On the other hand, one must avoid overparametrization of the model space. For example, when the approach is applied to affected sibpairs, only two constrained disease model parameters should be used (Knapp et al., 1994) for the mod score maximization. It is also important to emphasize the existence of a strong correlation between the disease gene location and the disease model. Consequently, there is poor resolution of the location of the susceptibility locus when the disease model at this locus is unknown. Of course, this is true regardless of the statistics used. The mod score may also be applied in a candidate gene strategy to model the potential effect of this gene in the disease. Since, however, it ignores the information provided both by disease segregation and by linkage disequilibrium between the marker alleles and the functional disease alleles, its power of discrimination between genetic models is weak. The MASC method (Clerget-Darpoux et al., 1988) has been designed to address more efficiently the objectives of a candidate gene approach.
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NASA Astrophysics Data System (ADS)
Hapugoda, J. C.; Sooriyarachchi, M. R.
2017-09-01
Survival time of patients with a disease and the incidence of that particular disease (count) is frequently observed in medical studies with the data of a clustered nature. In many cases, though, the survival times and the count can be correlated in a way that, diseases that occur rarely could have shorter survival times or vice versa. Due to this fact, joint modelling of these two variables will provide interesting and certainly improved results than modelling these separately. Authors have previously proposed a methodology using Generalized Linear Mixed Models (GLMM) by joining the Discrete Time Hazard model with the Poisson Regression model to jointly model survival and count model. As Aritificial Neural Network (ANN) has become a most powerful computational tool to model complex non-linear systems, it was proposed to develop a new joint model of survival and count of Dengue patients of Sri Lanka by using that approach. Thus, the objective of this study is to develop a model using ANN approach and compare the results with the previously developed GLMM model. As the response variables are continuous in nature, Generalized Regression Neural Network (GRNN) approach was adopted to model the data. To compare the model fit, measures such as root mean square error (RMSE), absolute mean error (AME) and correlation coefficient (R) were used. The measures indicate the GRNN model fits the data better than the GLMM model.
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A general stochastic model for studying time evolution of transition networks
NASA Astrophysics Data System (ADS)
Zhan, Choujun; Tse, Chi K.; Small, Michael
2016-12-01
We consider a class of complex networks whose nodes assume one of several possible states at any time and may change their states from time to time. Such networks represent practical networks of rumor spreading, disease spreading, language evolution, and so on. Here, we derive a model describing the dynamics of this kind of network and a simulation algorithm for studying the network evolutionary behavior. This model, derived at a microscopic level, can reveal the transition dynamics of every node. A numerical simulation is taken as an ;experiment; or ;realization; of the model. We use this model to study the disease propagation dynamics in four different prototypical networks, namely, the regular nearest-neighbor (RN) network, the classical Erdös-Renyí (ER) random graph, the Watts-Strogátz small-world (SW) network, and the Barabási-Albert (BA) scalefree network. We find that the disease propagation dynamics in these four networks generally have different properties but they do share some common features. Furthermore, we utilize the transition network model to predict user growth in the Facebook network. Simulation shows that our model agrees with the historical data. The study can provide a useful tool for a more thorough understanding of the dynamics networks.
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Advances in Swine Biomedical Model Genomics
Lunney, Joan K.
2007-01-01
This review is a short update on the diversity of swine biomedical models and the importance of genomics in their continued development. The swine has been used as a major mammalian model for human studies because of the similarity in size and physiology, and in organ development and disease progression. The pig model allows for deliberately timed studies, imaging of internal vessels and organs using standard human technologies, and collection of repeated peripheral samples and, at kill, detailed mucosal tissues. The ability to use pigs from the same litter, or cloned or transgenic pigs, facilitates comparative analyses and genetic mapping. The availability of numerous well defined cell lines, representing a broad range of tissues, further facilitates testing of gene expression, drug susceptibility, etc. Thus the pig is an excellent biomedical model for humans. For genomic applications it is an asset that the pig genome has high sequence and chromosome structure homology with humans. With the swine genome sequence now well advanced there are improving genetic and proteomic tools for these comparative analyses. The review will discuss some of the genomic approaches used to probe these models. The review will highlight genomic studies of melanoma and of infectious disease resistance, discussing issues to consider in designing such studies. It will end with a short discussion of the potential for genomic approaches to develop new alternatives for control of the most economically important disease of pigs, porcine reproductive and respiratory syndrome (PRRS), and the potential for applying knowledge gained with this virus for human viral infectious disease studies. PMID:17384736
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Kirsch, Florian
2016-12-01
Disease management programs (DMPs) for chronic diseases are being increasingly implemented worldwide. To present a systematic overview of the economic effects of DMPs with Markov models. The quality of the models is assessed, the method by which the DMP intervention is incorporated into the model is examined, and the differences in the structure and data used in the models are considered. A literature search was conducted; the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement was followed to ensure systematic selection of the articles. Study characteristics e.g. results, the intensity of the DMP and usual care, model design, time horizon, discount rates, utility measures, and cost-of-illness were extracted from the reviewed studies. Model quality was assessed by two researchers with two different appraisals: one proposed by Philips et al. (Good practice guidelines for decision-analytic modelling in health technology assessment: a review and consolidation of quality asessment. Pharmacoeconomics 2006;24:355-71) and the other proposed by Caro et al. (Questionnaire to assess relevance and credibility of modeling studies for informing health care decision making: an ISPOR-AMCP-NPC Good Practice Task Force report. Value Health 2014;17:174-82). A total of 16 studies (9 on chronic heart disease, 2 on asthma, and 5 on diabetes) met the inclusion criteria. Five studies reported cost savings and 11 studies reported additional costs. In the quality, the overall score of the models ranged from 39% to 65%, it ranged from 34% to 52%. Eleven models integrated effectiveness derived from a clinical trial or a meta-analysis of complete DMPs and only five models combined intervention effects from different sources into a DMP. The main limitations of the models are bad reporting practice and the variation in the selection of input parameters. Eleven of the 14 studies reported cost-effectiveness results of less than $30,000 per quality-adjusted life-year and the remaining two studies less than $30,000 per life-year gained. Nevertheless, if the reporting and selection of data problems are addressed, then Markov models should provide more reliable information for decision makers, because understanding under what circumstances a DMP is cost-effective is an important determinant of efficient resource allocation. Copyright © 2016 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.
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Twin methodology in epigenetic studies.
Tan, Qihua; Christiansen, Lene; von Bornemann Hjelmborg, Jacob; Christensen, Kaare
2015-01-01
Since the final decades of the last century, twin studies have made a remarkable contribution to the genetics of human complex traits and diseases. With the recent rapid development in modern biotechnology of high-throughput genetic and genomic analyses, twin modelling is expanding from analysis of diseases to molecular phenotypes in functional genomics especially in epigenetics, a thriving field of research that concerns the environmental regulation of gene expression through DNA methylation, histone modification, microRNA and long non-coding RNA expression, etc. The application of the twin method to molecular phenotypes offers new opportunities to study the genetic (nature) and environmental (nurture) contributions to epigenetic regulation of gene activity during developmental, ageing and disease processes. Besides the classical twin model, the case co-twin design using identical twins discordant for a trait or disease is becoming a popular and powerful design for epigenome-wide association study in linking environmental exposure to differential epigenetic regulation and to disease status while controlling for individual genetic make-up. It can be expected that novel uses of twin methods in epigenetic studies are going to help with efficiently unravelling the genetic and environmental basis of epigenomics in human complex diseases. © 2015. Published by The Company of Biologists Ltd.
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The Alzheimer’s Disease Neuroimaging Initiative 2 Biomarker Core: A review of progress and plans
Kang, Ju-Hee; Korecka, Magdalena; Figurski, Michal J.; Toledo, Jon B.; Blennow, Kaj; Zetterberg, Henrik; Waligorska, Teresa; Brylska, Magdalena; Fields, Leona; Shah, Nirali; Soares, Holly; Dean, Robert A.; Vanderstichele, Hugo; Petersen, Ronald C.; Aisen, Paul S.; Saykin, Andrew J.; Weiner, Michael W.; Trojanowski, John Q.; Shaw, Leslie M.
2016-01-01
Introduction We describe Alzheimer’s Disease Neuroimaging Initiative (ADNI) Biomarker Core progress including: the Biobank; cerebrospinal fluid (CSF) amyloid beta (Aβ1–42), t-tau, and p-tau181 analytical performance, definition of Alzheimer’s disease (AD) profile for plaque, and tangle burden detection and increased risk for progression to AD; AD disease heterogeneity; progress in standardization; and new studies using ADNI biofluids. Methods Review publications authored or coauthored by ADNI Biomarker core faculty and selected non-ADNI studies to deepen the understanding and interpretation of CSF Aβ1–42, t-tau, and p-tau181 data. Results CSFAD biomarker measurements with the qualified AlzBio3 immunoassay detects neuropathologic AD hallmarks in preclinical and prodromal disease stages, based on CSF studies in non-ADNI living subjects followed by the autopsy confirmation of AD. Collaboration across ADNI cores generated the temporal ordering model of AD biomarkers varying across individuals because of genetic/environmental factors that increase/decrease resilience to AD pathologies. Discussion Further studies will refine this model and enable the use of biomarkers studied in ADNI clinically and in disease-modifying therapeutic trials. PMID:26194312
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Swindell, William R; Johnston, Andrew; Carbajal, Steve; Han, Gangwen; Wohn, Christian; Lu, Jun; Xing, Xianying; Nair, Rajan P; Voorhees, John J; Elder, James T; Wang, Xiao-Jing; Sano, Shigetoshi; Prens, Errol P; DiGiovanni, John; Pittelkow, Mark R; Ward, Nicole L; Gudjonsson, Johann E
2011-04-04
Development of a suitable mouse model would facilitate the investigation of pathomechanisms underlying human psoriasis and would also assist in development of therapeutic treatments. However, while many psoriasis mouse models have been proposed, no single model recapitulates all features of the human disease, and standardized validation criteria for psoriasis mouse models have not been widely applied. In this study, whole-genome transcriptional profiling is used to compare gene expression patterns manifested by human psoriatic skin lesions with those that occur in five psoriasis mouse models (K5-Tie2, imiquimod, K14-AREG, K5-Stat3C and K5-TGFbeta1). While the cutaneous gene expression profiles associated with each mouse phenotype exhibited statistically significant similarity to the expression profile of psoriasis in humans, each model displayed distinctive sets of similarities and differences in comparison to human psoriasis. For all five models, correspondence to the human disease was strong with respect to genes involved in epidermal development and keratinization. Immune and inflammation-associated gene expression, in contrast, was more variable between models as compared to the human disease. These findings support the value of all five models as research tools, each with identifiable areas of convergence to and divergence from the human disease. Additionally, the approach used in this paper provides an objective and quantitative method for evaluation of proposed mouse models of psoriasis, which can be strategically applied in future studies to score strengths of mouse phenotypes relative to specific aspects of human psoriasis.
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Niche harmony search algorithm for detecting complex disease associated high-order SNP combinations.
Tuo, Shouheng; Zhang, Junying; Yuan, Xiguo; He, Zongzhen; Liu, Yajun; Liu, Zhaowen
2017-09-14
Genome-wide association study is especially challenging in detecting high-order disease-causing models due to model diversity, possible low or even no marginal effect of the model, and extraordinary search and computations. In this paper, we propose a niche harmony search algorithm where joint entropy is utilized as a heuristic factor to guide the search for low or no marginal effect model, and two computationally lightweight scores are selected to evaluate and adapt to diverse of disease models. In order to obtain all possible suspected pathogenic models, niche technique merges with HS, which serves as a taboo region to avoid HS trapping into local search. From the resultant set of candidate SNP-combinations, we use G-test statistic for testing true positives. Experiments were performed on twenty typical simulation datasets in which 12 models are with marginal effect and eight ones are with no marginal effect. Our results indicate that the proposed algorithm has very high detection power for searching suspected disease models in the first stage and it is superior to some typical existing approaches in both detection power and CPU runtime for all these datasets. Application to age-related macular degeneration (AMD) demonstrates our method is promising in detecting high-order disease-causing models.
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Biofilm models of polymicrobial infection.
Gabrilska, Rebecca A; Rumbaugh, Kendra P
2015-01-01
Interactions between microbes are complex and play an important role in the pathogenesis of infections. These interactions can range from fierce competition for nutrients and niches to highly evolved cooperative mechanisms between different species that support their mutual growth. An increasing appreciation for these interactions, and desire to uncover the mechanisms that govern them, has resulted in a shift from monomicrobial to polymicrobial biofilm studies in different disease models. Here we provide an overview of biofilm models used to study select polymicrobial infections and highlight the impact that the interactions between microbes within these biofilms have on disease progression. Notable recent advances in the development of polymicrobial biofilm-associated infection models and challenges facing the study of polymicrobial biofilms are addressed.
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Naish, Suchithra; Mengersen, Kerrie; Hu, Wenbiao; Tong, Shilu
2013-01-01
Mosquito-borne diseases are climate sensitive and there has been increasing concern over the impact of climate change on future disease risk. This paper projected the potential future risk of Barmah Forest virus (BFV) disease under climate change scenarios in Queensland, Australia. We obtained data on notified BFV cases, climate (maximum and minimum temperature and rainfall), socio-economic and tidal conditions for current period 2000-2008 for coastal regions in Queensland. Grid-data on future climate projections for 2025, 2050 and 2100 were also obtained. Logistic regression models were built to forecast the otential risk of BFV disease distribution under existing climatic, socio-economic and tidal conditions. The model was applied to estimate the potential geographic distribution of BFV outbreaks under climate change scenarios. The predictive model had good model accuracy, sensitivity and specificity. Maps on potential risk of future BFV disease indicated that disease would vary significantly across coastal regions in Queensland by 2100 due to marked differences in future rainfall and temperature projections. We conclude that the results of this study demonstrate that the future risk of BFV disease would vary across coastal regions in Queensland. These results may be helpful for public health decision making towards developing effective risk management strategies for BFV disease control and prevention programs in Queensland.
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Fatty liver promotes fibrosis in monkeys consuming high fructose.
Cydylo, Michael A; Davis, Ashley T; Kavanagh, Kylie
2017-02-01
Nonalcoholic fatty liver diseases (NAFLD) are related to development of liver fibrosis which currently has few therapeutic options. Rodent models of NAFLD inadequately model the fibrotic aspects of the disease and fail to demonstrate the spectrum of cardiometabolic diseases without genetic manipulation. This study aimed to document a monkey model of fatty liver and fibrosis, which naturally develop cardiometabolic disease pathophysiologies. Twenty-seven cynomolgus monkeys (Macaca fascicularis) fed diets either low or high in simple carbohydrates, supplied as fructose [control and high-fructose diet (HRr)], on low-fat, cholesterol-free background were studied. The HFr was consumed for up to 7 years, and liver tissue was histologically evaluated for fat and fibrosis extent. The HFr diet increased steatosis, and its extent was related to duration of fructose exposure. Lipid droplet size also increased with HFr duration; however, compared with control, the lipid droplets were smaller on average. Fibrosis extent was significantly greater with fructose feeding and was predicted by fructose exposure, extent of fatty liver, and age. These data are the first to demonstrate that high-carbohydrate diets alone can generate both liver fat and fibrosis and thus allow further study of mechanisms and therapeutic options in the translational animal model. © 2017 The Obesity Society.
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A method for using real world data in breast cancer modeling.
Pobiruchin, Monika; Bochum, Sylvia; Martens, Uwe M; Kieser, Meinhard; Schramm, Wendelin
2016-04-01
Today, hospitals and other health care-related institutions are accumulating a growing bulk of real world clinical data. Such data offer new possibilities for the generation of disease models for the health economic evaluation. In this article, we propose a new approach to leverage cancer registry data for the development of Markov models. Records of breast cancer patients from a clinical cancer registry were used to construct a real world data driven disease model. We describe a model generation process which maps database structures to disease state definitions based on medical expert knowledge. Software was programmed in Java to automatically derive a model structure and transition probabilities. We illustrate our method with the reconstruction of a published breast cancer reference model derived primarily from clinical study data. In doing so, we exported longitudinal patient data from a clinical cancer registry covering eight years. The patient cohort (n=892) comprised HER2-positive and HER2-negative women treated with or without Trastuzumab. The models generated with this method for the respective patient cohorts were comparable to the reference model in their structure and treatment effects. However, our computed disease models reflect a more detailed picture of the transition probabilities, especially for disease free survival and recurrence. Our work presents an approach to extract Markov models semi-automatically using real world data from a clinical cancer registry. Health care decision makers may benefit from more realistic disease models to improve health care-related planning and actions based on their own data. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.
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Innate immunity in Alzheimer's disease: the relevance of animal models?
Franco Bocanegra, Diana K; Nicoll, James A R; Boche, Delphine
2018-05-01
The mouse is one of the organisms most widely used as an animal model in biomedical research, due to the particular ease with which it can be handled and reproduced in laboratory. As a member of the mammalian class, mice share with humans many features regarding metabolic pathways, cell morphology and anatomy. However, important biological differences between mice and humans exist and must be taken into consideration when interpreting research results, to properly translate evidence from experimental studies into information that can be useful for human disease prevention and/or treatment. With respect to Alzheimer's disease (AD), much of the experimental information currently known about this disease has been gathered from studies using mainly mice as models. Therefore, it is notably important to fully characterise the differences between mice and humans regarding important aspects of the disease. It is now widely known that inflammation plays an important role in the development of AD, a role that is not only a response to the surrounding pathological environment, but rather seems to be strongly implicated in the aetiology of the disease as indicated by the genetic studies. This review highlights relevant differences in inflammation and in microglia, the innate immune cell of the brain, between mice and humans regarding genetics and morphology in normal ageing, and the relationship of microglia with AD-like pathology, the inflammatory profile, and cognition. We conclude that some noteworthy differences exist between mice and humans regarding microglial characteristics, in distribution, gene expression, and states of activation. This may have repercussions in the way that transgenic mice respond to, and influence, the AD-like pathology. However, despite these differences, human and mouse microglia also show similarities in morphology and behaviour, such that the mouse is a suitable model for studying the role of microglia, as long as these differences are taken into consideration when delineating new strategies to approach the study of neurodegenerative diseases.
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Anantha, Malathi; Warburton, Corinna; Waterhouse, Dawn; Yan, Hong; Yang, Young-joo; Strut, Dita; Osooly, Maryam; Masin, Dana; Bally, Marcel B
2011-01-01
A significant issue in drug efficacy studies is animal study design. Here we hypothesize that when evaluating new or existing therapeutics for the treatment of cancer, the location of disease burden will influence drug efficacy. To study this, female NCr nude mice were inoculated with luciferase-positive human breast cancer cells (LCC6WT-luc) orthotopically (o.t.), intraperitoneally (i.p.) or intracardiacly (i.c.) to create localized, ascites or disseminated disease, respectively. Tumor development was monitored using bioluminescence imaging. Docetaxel (Dt) pharmacokinetics and distribution to sites of tumor growth were determined. Disease progression was followed in animals treated with Dt alone and in combination with QLT0267, an integrin linked kinase inhibitor. Tumor related morbidity was most rapid when cells were inoculated i.c., where disease progression was observed in brain, ovaries, adrenal glands and lungs. Dt pharmacokinetics were comparable regardless of the model used (mean plasma AUC0–24 hrs 482.6 ng/ml*hr), however, Dt levels were lowest in those tissues developing disease following i.c. cell injection. Treatment with low dose Dt (5 mg/kg) increased overall survival and reduced tumor cell growth in all three models but the activity was greatest in mice with orthotopic tumors. Higher doses of Dt (15 mg/kg) was able to prolong survival in animals bearing i.p. tumors but not i.c. tumors. Addition of QLT0267 provided no added benefit above Dt alone in the disseminated model. These studies highlight a need for more comprehensive in vivo efficacy studies designed to assess multiple disease models and multiple endpoints, focusing analysis of drug parameters on the most chemoresistant disease. PMID:21358264
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Modeling Human Bone Marrow Failure Syndromes Using Pluripotent Stem Cells and Genome Engineering.
Jung, Moonjung; Dunbar, Cynthia E; Winkler, Thomas
2015-12-01
The combination of epigenetic reprogramming with advanced genome editing technologies opened a new avenue to study disease mechanisms, particularly of disorders with depleted target tissue. Bone marrow failure syndromes (BMFS) typically present with a marked reduction of peripheral blood cells due to a destroyed or dysfunctional bone marrow compartment. Somatic and germline mutations have been etiologically linked to many cases of BMFS. However, without the ability to study primary patient material, the exact pathogenesis for many entities remained fragmentary. Capturing the pathological genotype in induced pluripotent stem cells (iPSCs) allows studying potential developmental defects leading to a particular phenotype. The lack of hematopoietic stem and progenitor cells in these patients can also be overcome by differentiating patient-derived iPSCs into hematopoietic lineages. With fast growing genome editing techniques, such as CRISPR/Cas9, correction of disease-causing mutations in iPSCs or introduction of mutations in cells from healthy individuals enable comparative studies that may identify other genetic or epigenetic events contributing to a specific disease phenotype. In this review, we present recent progresses in disease modeling of inherited and acquired BMFS using reprogramming and genome editing techniques. We also discuss the challenges and potential shortcomings of iPSC-based models for hematological diseases.
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Marital History and the Burden of Cardiovascular Disease in Midlife
ERIC Educational Resources Information Center
Zhang, Zhenmei
2006-01-01
This study examines the effects of marital history on the burden of cardiovascular disease in midlife. With use of data from the 1992 Health and Retirement Study, a series of nested logistic regression models was used to estimate the association between marital history and the likelihood of cardiovascular disease. Results suggest that, in midlife,…
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ERIC Educational Resources Information Center
Kim, Yunjung; Choi, Yaelin
2017-01-01
Purpose: The present study aimed to compare acoustic models of speech intelligibility in individuals with the same disease (Parkinson's disease [PD]) and presumably similar underlying neuropathologies but with different native languages (American English [AE] and Korean). Method: A total of 48 speakers from the 4 speaker groups (AE speakers with…
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Huang, Chien-Hao; Li, Shih-Ming; Shu, Bih-Ching
2016-09-01
Stigma affects patients with schizophrenia and may influence perceptions of the illness, which may affect how family members interact with and care for these patients. The aims of this study were to (a) explore the relationship between perceptions of schizophrenia and the negative emotions of family members within the context of an affiliate stigma model, and (b) validate the proposed affiliate stigma model. A cross-sectional design was used. Eligibility for participation was limited to the relatives of patients with schizophrenia. The participants were recruited from two regional psychiatric hospitals in central Taiwan. The study was approved by an Institutional Review Board, and all potential participants signed informed consent before enrollment. Sixty-two participants completed the set of self-administered questionnaires, including (a) a demographic questionnaire, (b) Affiliate Stigma Scale, and (c) the Illness Perception Questionnaire for Schizophrenia-Relatives version. Canonical correlations and structural equation modeling in STATISTICA 6.0 were used to validate the model of illness perceptions and negative emotions. (a) There were three domains of perception regarding schizophrenia for the relatives of patients: disease chronicity, disease in control, and disease treatability. The correlation between these dimensions and negative emotion was r = .42. (b) The adjusted goodness of fit for the proposed affiliate stigma model was .79. The results of this study suggest that the affiliate stigma model is an appropriate resource for developing practical disease management strategies for the relatives of patients with schizophrenia.
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Animal models of hospital-acquired pneumonia: current practices and future perspectives
Bielen, Kenny; ’S Jongers, Bart; Malhotra-Kumar, Surbhi; Jorens, Philippe G.; Goossens, Herman
2017-01-01
Lower respiratory tract infections are amongst the leading causes of mortality and morbidity worldwide. Especially in hospital settings and more particularly in critically ill ventilated patients, nosocomial pneumonia is one of the most serious infectious complications frequently caused by opportunistic pathogens. Pseudomonas aeruginosa is one of the most important causes of ventilator-associated pneumonia as well as the major cause of chronic pneumonia in cystic fibrosis patients. Animal models of pneumonia allow us to investigate distinct types of pneumonia at various disease stages, studies that are not possible in patients. Different animal models of pneumonia such as one-hit acute pneumonia models, ventilator-associated pneumonia models and biofilm pneumonia models associated with cystic fibrosis have been extensively studied and have considerably aided our understanding of disease pathogenesis and testing and developing new treatment strategies. The present review aims to guide investigators in choosing appropriate animal pneumonia models by describing and comparing the relevant characteristics of each model using P. aeruginosa as a model etiology for hospital-acquired pneumonia. Key to establishing and studying these animal models of infection are well-defined end-points that allow precise monitoring and characterization of disease development that could ultimately aid in translating these findings to patient populations in order to guide therapy. In this respect, and discussed here, is the development of humanized animal models of bacterial pneumonia that could offer unique advantages to study bacterial virulence factor expression and host cytokine production for translational purposes. PMID:28462212
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Haji Ali Afzali, Hossein; Gray, Jodi; Karnon, Jonathan
2013-04-01
Decision analytic models play an increasingly important role in the economic evaluation of health technologies. Given uncertainties around the assumptions used to develop such models, several guidelines have been published to identify and assess 'best practice' in the model development process, including general modelling approach (e.g., time horizon), model structure, input data and model performance evaluation. This paper focuses on model performance evaluation. In the absence of a sufficient level of detail around model performance evaluation, concerns regarding the accuracy of model outputs, and hence the credibility of such models, are frequently raised. Following presentation of its components, a review of the application and reporting of model performance evaluation is presented. Taking cardiovascular disease as an illustrative example, the review investigates the use of face validity, internal validity, external validity, and cross model validity. As a part of the performance evaluation process, model calibration is also discussed and its use in applied studies investigated. The review found that the application and reporting of model performance evaluation across 81 studies of treatment for cardiovascular disease was variable. Cross-model validation was reported in 55 % of the reviewed studies, though the level of detail provided varied considerably. We found that very few studies documented other types of validity, and only 6 % of the reviewed articles reported a calibration process. Considering the above findings, we propose a comprehensive model performance evaluation framework (checklist), informed by a review of best-practice guidelines. This framework provides a basis for more accurate and consistent documentation of model performance evaluation. This will improve the peer review process and the comparability of modelling studies. Recognising the fundamental role of decision analytic models in informing public funding decisions, the proposed framework should usefully inform guidelines for preparing submissions to reimbursement bodies.
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Improving the physiological realism of experimental models
Vinnakota, Kalyan C.; Cha, Chae Y.; Rorsman, Patrik; Balaban, Robert S.; La Gerche, Andre; Wade-Martins, Richard; Beard, Daniel A.
2016-01-01
The Virtual Physiological Human (VPH) project aims to develop integrative, explanatory and predictive computational models (C-Models) as numerical investigational tools to study disease, identify and design effective therapies and provide an in silico platform for drug screening. Ultimately, these models rely on the analysis and integration of experimental data. As such, the success of VPH depends on the availability of physiologically realistic experimental models (E-Models) of human organ function that can be parametrized to test the numerical models. Here, the current state of suitable E-models, ranging from in vitro non-human cell organelles to in vivo human organ systems, is discussed. Specifically, challenges and recent progress in improving the physiological realism of E-models that may benefit the VPH project are highlighted and discussed using examples from the field of research on cardiovascular disease, musculoskeletal disorders, diabetes and Parkinson's disease. PMID:27051507
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A mathematical study of a model for childhood diseases with non-permanent immunity
NASA Astrophysics Data System (ADS)
Moghadas, S. M.; Gumel, A. B.
2003-08-01
Protecting children from diseases that can be prevented by vaccination is a primary goal of health administrators. Since vaccination is considered to be the most effective strategy against childhood diseases, the development of a framework that would predict the optimal vaccine coverage level needed to prevent the spread of these diseases is crucial. This paper provides this framework via qualitative and quantitative analysis of a deterministic mathematical model for the transmission dynamics of a childhood disease in the presence of a preventive vaccine that may wane over time. Using global stability analysis of the model, based on constructing a Lyapunov function, it is shown that the disease can be eradicated from the population if the vaccination coverage level exceeds a certain threshold value. It is also shown that the disease will persist within the population if the coverage level is below this threshold. These results are verified numerically by constructing, and then simulating, a robust semi-explicit second-order finite-difference method.
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Dou, Kaili; Yu, Ping; Deng, Ning; Liu, Fang; Guan, YingPing; Li, Zhenye; Ji, Yumeng; Du, Ningkai; Lu, Xudong; Duan, Huilong
2017-12-06
Chronic disease patients often face multiple challenges from difficult comorbidities. Smartphone health technology can be used to help them manage their conditions only if they accept and use the technology. The aim of this study was to develop and test a theoretical model to predict and explain the factors influencing patients' acceptance of smartphone health technology for chronic disease management. Multiple theories and factors that may influence patients' acceptance of smartphone health technology have been reviewed. A hybrid theoretical model was built based on the technology acceptance model, dual-factor model, health belief model, and the factors identified from interviews that might influence patients' acceptance of smartphone health technology for chronic disease management. Data were collected from patient questionnaire surveys and computer log records about 157 hypertensive patients' actual use of a smartphone health app. The partial least square method was used to test the theoretical model. The model accounted for .412 of the variance in patients' intention to adopt the smartphone health technology. Intention to use accounted for .111 of the variance in actual use and had a significant weak relationship with the latter. Perceived ease of use was affected by patients' smartphone usage experience, relationship with doctor, and self-efficacy. Although without a significant effect on intention to use, perceived ease of use had a significant positive influence on perceived usefulness. Relationship with doctor and perceived health threat had significant positive effects on perceived usefulness, countering the negative influence of resistance to change. Perceived usefulness, perceived health threat, and resistance to change significantly predicted patients' intentions to use the technology. Age and gender had no significant influence on patients' acceptance of smartphone technology. The study also confirmed the positive relationship between intention to use and actual use of smartphone health apps for chronic disease management. This study developed a theoretical model to predict patients' acceptance of smartphone health technology for chronic disease management. Although resistance to change is a significant barrier to technology acceptance, careful management of doctor-patient relationship, and raising patients' awareness of the negative effect of chronic disease can negate the effect of resistance and encourage acceptance and use of smartphone health technology to support chronic disease management for patients in the community. ©Kaili Dou, Ping Yu, Ning Deng, Fang Liu, YingPing Guan, Zhenye Li, Yumeng Ji, Ningkai Du, Xudong Lu, Huilong Duan. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 06.12.2017.
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Carbonell, Felix; Iturria-Medina, Yasser; Evans, Alan C
2018-01-01
Protein misfolding refers to a process where proteins become structurally abnormal and lose their specific 3-dimensional spatial configuration. The histopathological presence of misfolded protein (MP) aggregates has been associated as the primary evidence of multiple neurological diseases, including Prion diseases, Alzheimer's disease, Parkinson's disease, and Creutzfeldt-Jacob disease. However, the exact mechanisms of MP aggregation and propagation, as well as their impact in the long-term patient's clinical condition are still not well understood. With this aim, a variety of mathematical models has been proposed for a better insight into the kinetic rate laws that govern the microscopic processes of protein aggregation. Complementary, another class of large-scale models rely on modern molecular imaging techniques for describing the phenomenological effects of MP propagation over the whole brain. Unfortunately, those neuroimaging-based studies do not take full advantage of the tremendous capabilities offered by the chemical kinetics modeling approach. Actually, it has been barely acknowledged that the vast majority of large-scale models have foundations on previous mathematical approaches that describe the chemical kinetics of protein replication and propagation. The purpose of the current manuscript is to present a historical review about the development of mathematical models for describing both microscopic processes that occur during the MP aggregation and large-scale events that characterize the progression of neurodegenerative MP-mediated diseases.
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NASA Astrophysics Data System (ADS)
Brown, Heidi E.
Spatially explicit information is increasingly available for infectious disease modeling. However, such information is reluctantly or inappropriately incorporated. My dissertation research uses spatially explicit data to assess relationships between landscape and mosquito species distribution and discusses challenges regarding accurate predictive risk modeling. The goal of my research is to use remotely sensed environmental information and spatial statistical methods to better understand mosquito-borne disease epidemiology for improvement of public health responses. In addition to reviewing the progress of spatial infectious disease modeling, I present four research projects. I begin by evaluating the biases in surveillance data and build up to predictive modeling of mosquito species presence. In the first study I explore how mosquito surveillance trap types influence estimations of mosquito populations. Then. I use county-based human surveillance data and landscape variables to identify risk factors for West Nile virus disease. The third study uses satellite-based vegetation indices to identify spatial variation among West Nile virus vectors in an urban area and relates the variability to virus transmission dynamics. Finally, I explore how information from three satellite sensors of differing spatial and spectral resolution can be used to identify and distinguish mosquito habitat across central Connecticut wetlands. Analyses presented here constitute improvements to the prediction of mosquito distribution and therefore identification of disease risk factors. Current methods for mosquito surveillance data collection are labor intensive and provide an extremely limited, incomplete picture of the species composition and abundance. Human surveillance data offers additional challenges with respect to reporting bias and resolution, but is nonetheless informative in identifying environmental risk factors and disease transmission dynamics. Remotely sensed imagery supports mosquito and human disease surveillance data by providing spatially explicit, line resolution information about environmental factors relevant to vector-borne disease processes. Together, surveillance and remotely sensed environmental data facilitate improved description and modeling of disease transmission. Remote sensing can be used to develop predictive maps of mosquito distribution in relation to disease risk. This has implications for increased accuracy of mosquito control efforts. The projects presented in this dissertation enhance current public health capacities by examining the applications of spatial modeling with respect to mosquito-borne disease.
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Wang, Hui; Qin, Feng; Ruan, Liu; Wang, Rui; Liu, Qi; Ma, Zhanhong; Li, Xiaolong; Cheng, Pei; Wang, Haiguang
2016-01-01
It is important to implement detection and assessment of plant diseases based on remotely sensed data for disease monitoring and control. Hyperspectral data of healthy leaves, leaves in incubation period and leaves in diseased period of wheat stripe rust and wheat leaf rust were collected under in-field conditions using a black-paper-based measuring method developed in this study. After data preprocessing, the models to identify the diseases were built using distinguished partial least squares (DPLS) and support vector machine (SVM), and the disease severity inversion models of stripe rust and the disease severity inversion models of leaf rust were built using quantitative partial least squares (QPLS) and support vector regression (SVR). All the models were validated by using leave-one-out cross validation and external validation. The diseases could be discriminated using both distinguished partial least squares and support vector machine with the accuracies of more than 99%. For each wheat rust, disease severity levels were accurately retrieved using both the optimal QPLS models and the optimal SVR models with the coefficients of determination (R2) of more than 0.90 and the root mean square errors (RMSE) of less than 0.15. The results demonstrated that identification and severity evaluation of stripe rust and leaf rust at the leaf level could be implemented based on the hyperspectral data acquired using the developed method. A scientific basis was provided for implementing disease monitoring by using aerial and space remote sensing technologies.
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Ruan, Liu; Wang, Rui; Liu, Qi; Ma, Zhanhong; Li, Xiaolong; Cheng, Pei; Wang, Haiguang
2016-01-01
It is important to implement detection and assessment of plant diseases based on remotely sensed data for disease monitoring and control. Hyperspectral data of healthy leaves, leaves in incubation period and leaves in diseased period of wheat stripe rust and wheat leaf rust were collected under in-field conditions using a black-paper-based measuring method developed in this study. After data preprocessing, the models to identify the diseases were built using distinguished partial least squares (DPLS) and support vector machine (SVM), and the disease severity inversion models of stripe rust and the disease severity inversion models of leaf rust were built using quantitative partial least squares (QPLS) and support vector regression (SVR). All the models were validated by using leave-one-out cross validation and external validation. The diseases could be discriminated using both distinguished partial least squares and support vector machine with the accuracies of more than 99%. For each wheat rust, disease severity levels were accurately retrieved using both the optimal QPLS models and the optimal SVR models with the coefficients of determination (R2) of more than 0.90 and the root mean square errors (RMSE) of less than 0.15. The results demonstrated that identification and severity evaluation of stripe rust and leaf rust at the leaf level could be implemented based on the hyperspectral data acquired using the developed method. A scientific basis was provided for implementing disease monitoring by using aerial and space remote sensing technologies. PMID:27128464
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Wasserberg, Gideon; Osnas, Erik E; Rolley, Robert E; Samuel, Michael D
2009-04-01
Emerging wildlife diseases pose a significant threat to natural and human systems. Because of real or perceived risks of delayed actions, disease management strategies such as culling are often implemented before thorough scientific knowledge of disease dynamics is available. Adaptive management is a valuable approach in addressing the uncertainty and complexity associated with wildlife disease problems and can be facilitated by using a formal model.We developed a multi-state computer simulation model using age, sex, infection-stage, and seasonality as a tool for scientific learning and managing chronic wasting disease (CWD) in white-tailed deer Odocoileus virginianus. Our matrix model used disease transmission parameters based on data collected through disease management activities. We used this model to evaluate management issues on density- (DD) and frequency-dependent (FD) transmission, time since disease introduction, and deer culling on the demographics, epizootiology, and management of CWD.Both DD and FD models fit the Wisconsin data for a harvested white-tailed deer population, but FD was slightly better. Time since disease introduction was estimated as 36 (95% CI, 24-50) and 188 (41->200) years for DD and FD transmission, respectively. Deer harvest using intermediate to high non-selective rates can be used to reduce uncertainty between DD and FD transmission and improve our prediction of long-term epidemic patterns and host population impacts. A higher harvest rate allows earlier detection of these differences, but substantially reduces deer abundance.Results showed that CWD has spread slowly within Wisconsin deer populations, and therefore, epidemics and disease management are expected to last for decades. Non-hunted deer populations can develop and sustain a high level of infection, generating a substantial risk of disease spread. In contrast, CWD prevalence remains lower in hunted deer populations, but at a higher prevalence the disease competes with recreational hunting to reduce deer abundance.Synthesis and applications. Uncertainty about density- or frequency-dependent transmission hinders predictions about the long-term impacts of chronic wasting disease on cervid populations and the development of appropriate management strategies. An adaptive management strategy using computer modelling coupled with experimental management and monitoring can be used to test model predictions, identify the likely mode of disease transmission, and evaluate the risks of alternative management responses.
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Kim, Su Ran; Lee, Hye Won; Jun, Ji Hee; Ko, Byoung-Seob
2017-03-01
Gan Mai Da Zao (GMDZ) decoction is widely used for the treatment of various diseases of the internal organ and of the central nervous system. The aim of this study is to investigate the effects of GMDZ decoction on neuropsychiatric disorders in an animal model. We searched seven databases for randomized animal studies published until April 2015: Pubmed, four Korean databases (DBpia, Oriental Medicine Advanced Searching Integrated System, Korean Studies Information Service System, and Research Information Sharing Service), and one Chinese database (China National Knowledge Infrastructure). The randomized animal studies were included if the effects of GMDZ decoction were tested on neuropsychiatric disorders. All articles were read in full and extracted predefined criteria by two independent reviewers. From a total of 258 hits, six randomized controlled animal studies were included. Five studies used a Sprague Dawley rat model for acute psychological stress, post-traumatic stress disorders, and unpredictable mild stress depression whereas one study used a Kunming mouse model for prenatal depression. The results of the studies showed that GMDZ decoction improved the related outcomes. Regardless of the dose and concentration used, GMDZ decoction significantly improved neuropsychiatric disease-related outcomes in animal models. However, additional systematic and extensive studies should be conducted to establish a strong conclusion.
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Hernández, Jaime; Núñez, Ignacia; Bacigalupo, Antonella; Cattan, Pedro E
2013-05-31
Chagas disease is caused by the protozoan Trypanosoma cruzi, which is transmitted to mammal hosts by triatomine insect vectors. The goal of this study was to model the spatial distribution of triatomine species in an endemic area. Vector's locations were obtained with a rural householders' survey. This information was combined with environmental data obtained from remote sensors, land use maps and topographic SRTM data, using the machine learning algorithm Random Forests to model species distribution. We analysed the combination of variables on three scales: 10 km, 5 km and 2.5 km cell size grids. The best estimation, explaining 46.2% of the triatomines spatial distribution, was obtained for 5 km of spatial resolution. Presence probability distribution increases from central Chile towards the north, tending to cover the central-coastal region and avoiding areas of the Andes range. The methodology presented here was useful to model the distribution of triatomines in an endemic area; it is best explained using 5 km of spatial resolution, and their presence increases in the northern part of the study area. This study's methodology can be replicated in other countries with Chagas disease or other vectorial transmitted diseases, and be used to locate high risk areas and to optimize resource allocation, for prevention and control of vectorial diseases.
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Genetic and environmental melanoma models in fish
Patton, E Elizabeth; Mitchell, David L; Nairn, Rodney S
2010-01-01
Experimental animal models are extremely valuable for the study of human diseases, especially those with underlying genetic components. The exploitation of various animal models, from fruitflies to mice, has led to major advances in our understanding of the etiologies of many diseases, including cancer. Cutaneous malignant melanoma is a form of cancer for which both environmental insult (i.e., UV) and hereditary predisposition are major causative factors. Fish melanoma models have been used in studies of both spontaneous and induced melanoma formation. Genetic hybrids between platyfish and swordtails, different species of the genus Xiphophorus, have been studied since the 1920s to identify genetic determinants of pigmentation and melanoma formation. Recently, transgenesis has been used to develop zebrafish and medaka models for melanoma research. This review will provide a historical perspective on the use of fish models in melanoma research, and an updated summary of current and prospective studies using these unique experimental systems. PMID:20230482
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The role of ryanodine receptor type 3 in a mouse model of Alzheimer disease
Liu, Jie; Supnet, Charlene; Sun, Suya; Zhang, Hua; Good, Levi; Popugaeva, Elena; Bezprozvanny, Ilya
2014-01-01
Dysregulated endoplasmic reticulum (ER) calcium (Ca2+) signaling is reported to play an important role in Alzheimer disease (AD) pathogenesis. The role of ER Ca2+ release channels, the ryanodine receptors (RyanRs), has been extensively studied in AD models and RyanR expression and activity are upregulated in the brains of various familial AD (FAD) models. The objective of this study was to utilize a genetic approach to evaluate the importance of RyanR type 3 (RyanR3) in the context of AD pathology. PMID:24476841
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New animal models of cystic fibrosis: what are they teaching us?
Keiser, Nicholas W.; Engelhardt, John F.
2013-01-01
Purpose of review Cystic fibrosis is the first human genetic disease to benefit from the directed engineering of three different species of animal models (mice, pigs, and ferrets). Recent studies on the cystic fibrosis pig and ferret models are providing new information about the pathophysiology of cystic fibrosis in various organ systems. Additionally, new conditional cystic fibrosis transmembrane conductance regulator (CFTR) knockout mice are teaching unexpected lessons about CFTR function in surprising cellular locations. Comparisons between these animal models and the human condition are key to dissecting the complexities of disease pathophysiology in cystic fibrosis. Recent findings Cystic fibrosis pigs and ferrets have provided new models to study the spontaneous development of disease in the lung and pancreas, two organs that are largely spared overt spontaneous disease in cystic fibrosis mice. New cystic fibrosis mouse models are now interrogating CFTR functions involved in growth and inflammation at an organ-based level using conditional knockout technology. Together, these models are providing new insights on the human condition. Summary Basic and clinical cystic fibrosis research will benefit greatly from the comparative pathophysiology of cystic fibrosis mice, pigs, and ferrets. Both similarities and differences between these three cystic fibrosis models will inform pathophysiologically important mechanisms of CFTR function in humans and aid in the development of both organ-specific and general therapies for cystic fibrosis. PMID:21857224
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Beck, J D; Weintraub, J A; Disney, J A; Graves, R C; Stamm, J W; Kaste, L M; Bohannan, H M
1992-12-01
The purpose of this analysis is to compare three different statistical models for predicting children likely to be at risk of developing dental caries over a 3-yr period. Data are based on 4117 children who participated in the University of North Carolina Caries Risk Assessment Study, a longitudinal study conducted in the Aiken, South Carolina, and Portland, Maine areas. The three models differed with respect to either the types of variables included or the definition of disease outcome. The two "Prediction" models included both risk factor variables thought to cause dental caries and indicator variables that are associated with dental caries, but are not thought to be causal for the disease. The "Etiologic" model included only etiologic factors as variables. A dichotomous outcome measure--none or any 3-yr increment, was used in the "Any Risk Etiologic model" and the "Any Risk Prediction Model". Another outcome, based on a gradient measure of disease, was used in the "High Risk Prediction Model". The variables that are significant in these models vary across grades and sites, but are more consistent among the Etiologic model than the Predictor models. However, among the three sets of models, the Any Risk Prediction Models have the highest sensitivity and positive predictive values, whereas the High Risk Prediction Models have the highest specificity and negative predictive values. Considerations in determining model preference are discussed.
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Swimming Associated Disease Outbreaks.
ERIC Educational Resources Information Center
Cabelli, V. J.
1978-01-01
Presents a literature review of recreational waterborne outbreaks and cases of disease, covering publications of 1976-77. This review includes: (1) retrospective and prospective epidemiological studies; (2) predictive models of the risk of recreational waterborn disease. A list of 35 references is also presented. (HM)
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Zebrafish models flex their muscles to shed light on muscular dystrophies.
Berger, Joachim; Currie, Peter D
2012-11-01
Muscular dystrophies are a group of genetic disorders that specifically affect skeletal muscle and are characterized by progressive muscle degeneration and weakening. To develop therapies and treatments for these diseases, a better understanding of the molecular basis of muscular dystrophies is required. Thus, identification of causative genes mutated in specific disorders and the study of relevant animal models are imperative. Zebrafish genetic models of human muscle disorders often closely resemble disease pathogenesis, and the optical clarity of zebrafish embryos and larvae enables visualization of dynamic molecular processes in vivo. As an adjunct tool, morpholino studies provide insight into the molecular function of genes and allow rapid assessment of candidate genes for human muscular dystrophies. This unique set of attributes makes the zebrafish model system particularly valuable for the study of muscle diseases. This review discusses how recent research using zebrafish has shed light on the pathological basis of muscular dystrophies, with particular focus on the muscle cell membrane and the linkage between the myofibre cytoskeleton and the extracellular matrix.
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Spread of a disease and its effect on population dynamics in an eco-epidemiological system
NASA Astrophysics Data System (ADS)
Upadhyay, Ranjit Kumar; Roy, Parimita
2014-12-01
In this paper, an eco-epidemiological model with simple law of mass action and modified Holling type II functional response has been proposed and analyzed to understand how a disease may spread among natural populations. The proposed model is a modification of the model presented by Upadhyay et al. (2008) [1]. Existence of the equilibria and their stability analysis (linear and nonlinear) has been studied. The dynamical transitions in the model have been studied by identifying the existence of backward Hopf-bifurcations and demonstrated the period-doubling route to chaos when the death rate of predator (μ1) and the growth rate of susceptible prey population (r) are treated as bifurcation parameters. Our studies show that the system exhibits deterministic chaos when some control parameters attain their critical values. Chaotic dynamics is depicted using the 2D parameter scans and bifurcation analysis. Possible implications of the results for disease eradication or its control are discussed.
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Conserved gene regulation during acute inflammation between zebrafish and mammals
Forn-Cuní, G.; Varela, M.; Pereiro, P.; Novoa, B.; Figueras, A.
2017-01-01
Zebrafish (Danio rerio), largely used as a model for studying developmental processes, has also emerged as a valuable system for modelling human inflammatory diseases. However, in a context where even mice have been questioned as a valid model for these analysis, a systematic study evaluating the reproducibility of human and mammalian inflammatory diseases in zebrafish is still lacking. In this report, we characterize the transcriptomic regulation to lipopolysaccharide in adult zebrafish kidney, liver, and muscle tissues using microarrays and demonstrate how the zebrafish genomic responses can effectively reproduce the mammalian inflammatory process induced by acute endotoxin stress. We provide evidence that immune signaling pathways and single gene expression is well conserved throughout evolution and that the zebrafish and mammal acute genomic responses after lipopolysaccharide stimulation are highly correlated despite the differential susceptibility between species to that compound. Therefore, we formally confirm that zebrafish inflammatory models are suited to study the basic mechanisms of inflammation in human inflammatory diseases, with great translational impact potential. PMID:28157230
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Miller, Silke; Hill Della Puppa, Geraldine; Reidling, Jack; Marcora, Edoardo; Thompson, Leslie M; Treanor, James
2014-01-01
Phosphodiesterase 10A (PDE10A) is expressed at high levels in the striatum and has been proposed both as a biomarker for Huntington's disease pathology and as a target for intervention. PDE10A radiotracers have been successfully used to measure changes in binding density in Huntington's disease patients, but little is known about PDE10A binding in mouse models that are used extensively to model pathology and test therapeutic interventions. Our study investigated changes in PDE10A binding using the selective tracer 3H-7980 at specific ages of two Huntington's disease transgenic mouse models: R6/2, a short-lived model carrying exon-1 of mutant HTT and BACHD, a longer-lived model carrying full-length mutant HTT. PDE10A binding was compared to binding of known markers of striatal atrophy in Huntington's disease, e.g. dopamine transporter (DAT) and dopamine receptors D1 and D2. We found that in the R6/2 model at 6 weeks of age, mice showed high variability of binding, however binding of all ligands was significantly decreased at 8 and 12 weeks of age. In contrast, no changes were detectable in the BACHD model at 8, 10 or 12 month of age. These findings suggest that radiotracer binding of PDE10A, DAT, D1 and D2 receptor in the R6/2 model may be a good indicator of striatal pathological changes that are observed in Huntington's disease patients, and that the first 12 months in the BACHD model may be more reflective of early stages of the disease.
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Animal models on HTLV-1 and related viruses: what did we learn?
Hajj, Hiba El; Nasr, Rihab; Kfoury, Youmna; Dassouki, Zeina; Nasser, Roudaina; Kchour, Ghada; Hermine, Olivier; de Thé, Hugues; Bazarbachi, Ali
2012-01-01
Retroviruses are associated with a wide variety of diseases, including immunological, neurological disorders, and different forms of cancer. Among retroviruses, Oncovirinae regroup according to their genetic structure and sequence, several related viruses such as human T-cell lymphotropic viruses types 1 and 2 (HTLV-1 and HTLV-2), simian T cell lymphotropic viruses types 1 and 2 (STLV-1 and STLV-2), and bovine leukemia virus (BLV). As in many diseases, animal models provide a useful tool for the studies of pathogenesis, treatment, and prevention. In the current review, an overview on different animal models used in the study of these viruses will be provided. A specific attention will be given to the HTLV-1 virus which is the causative agent of adult T-cell leukemia/lymphoma (ATL) but also of a number of inflammatory diseases regrouping the HTLV-associated myelopathy/tropical spastic paraparesis (HAM/TSP), infective dermatitis and some lung inflammatory diseases. Among these models, rabbits, monkeys but also rats provide an excellent in vivo tool for early HTLV-1 viral infection and transmission as well as the induced host immune response against the virus. But ideally, mice remain the most efficient method of studying human afflictions. Genetically altered mice including both transgenic and knockout mice, offer important models to test the role of specific viral and host genes in the development of HTLV-1-associated leukemia. The development of different strains of immunodeficient mice strains (SCID, NOD, and NOG SCID mice) provide a useful and rapid tool of humanized and xenografted mice models, to test new drugs and targeted therapy against HTLV-1-associated leukemia, to identify leukemia stem cells candidates but also to study the innate immunity mediated by the virus. All together, these animal models have revolutionized the biology of retroviruses, their manipulation of host genes and more importantly the potential ways to either prevent their infection or to treat their associated diseases. PMID:23049525
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Caroli, A; Frisoni, G B
2010-08-01
The aim of this study was to investigate the dynamics of four of the most validated biomarkers for Alzheimer's disease (AD), cerebro-spinal fluid (CSF) Abeta 1-42, tau, hippocampal volume, and FDG-PET, in patients at different stage of AD. Two hundred twenty-nine cognitively healthy subjects, 154 mild cognitive impairment (MCI) patients converted to AD, and 193 (95 early and 98 late) AD patients were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. For each biomarker, individual values were Z-transformed and plotted against ADAS-cog scores, and sigmoid and linear fits were compared. For most biomarkers the sigmoid model fitted data significantly better than the linear model. Abeta 1-42 time course followed a steep curve, stabilizing early in the disease course. CSF tau and hippocampal volume changed later showing similar monotonous trends, reflecting disease progression. Hippocampal loss trend was steeper and occurred earlier in time in APOE epsilon4 carriers than in non-carriers. FDG-PET started changing early in time and likely followed a linear decline. In conclusion, this study provides the first evidence in favor of the dynamic biomarker model which has recently been proposed. 2010 Elsevier Inc. All rights reserved.
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Sexual transmission of Lyme disease: challenging the tickborne disease paradigm.
Stricker, Raphael B; Middelveen, Marianne J
2015-01-01
Lyme disease caused by the spirochete Borrelia burgdorferi has become a major worldwide epidemic. In this article, we explore the clinical, epidemiological and experimental evidence for sexual transmission of Lyme disease in animal models and humans. Although the likelihood of sexual transmission of the Lyme spirochete remains speculative, the possibility of Lyme disease transmission via intimate human contact merits further study.
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Animal Models for Periodontal Disease
Oz, Helieh S.; Puleo, David A.
2011-01-01
Animal models and cell cultures have contributed new knowledge in biological sciences, including periodontology. Although cultured cells can be used to study physiological processes that occur during the pathogenesis of periodontitis, the complex host response fundamentally responsible for this disease cannot be reproduced in vitro. Among the animal kingdom, rodents, rabbits, pigs, dogs, and nonhuman primates have been used to model human periodontitis, each with advantages and disadvantages. Periodontitis commonly has been induced by placing a bacterial plaque retentive ligature in the gingival sulcus around the molar teeth. In addition, alveolar bone loss has been induced by inoculation or injection of human oral bacteria (e.g., Porphyromonas gingivalis) in different animal models. While animal models have provided a wide range of important data, it is sometimes difficult to determine whether the findings are applicable to humans. In addition, variability in host responses to bacterial infection among individuals contributes significantly to the expression of periodontal diseases. A practical and highly reproducible model that truly mimics the natural pathogenesis of human periodontal disease has yet to be developed. PMID:21331345
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Brasil, Pedro Emmanuel Alvarenga Americano do; Xavier, Sergio Salles; Holanda, Marcelo Teixeira; Hasslocher-Moreno, Alejandro Marcel; Braga, José Ueleres
2016-01-01
With the globalization of Chagas disease, unexperienced health care providers may have difficulties in identifying which patients should be examined for this condition. This study aimed to develop and validate a diagnostic clinical prediction model for chronic Chagas disease. This diagnostic cohort study included consecutive volunteers suspected to have chronic Chagas disease. The clinical information was blindly compared to serological tests results, and a logistic regression model was fit and validated. The development cohort included 602 patients, and the validation cohort included 138 patients. The Chagas disease prevalence was 19.9%. Sex, age, referral from blood bank, history of living in a rural area, recognizing the kissing bug, systemic hypertension, number of siblings with Chagas disease, number of relatives with a history of stroke, ECG with low voltage, anterosuperior divisional block, pathologic Q wave, right bundle branch block, and any kind of extrasystole were included in the final model. Calibration and discrimination in the development and validation cohorts (ROC AUC 0.904 and 0.912, respectively) were good. Sensitivity and specificity analyses showed that specificity reaches at least 95% above the predicted 43% risk, while sensitivity is at least 95% below the predicted 7% risk. Net benefit decision curves favor the model across all thresholds. A nomogram and an online calculator (available at http://shiny.ipec.fiocruz.br:3838/pedrobrasil/chronic_chagas_disease_prediction/) were developed to aid in individual risk estimation.
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Dengue fever spreading based on probabilistic cellular automata with two lattices
NASA Astrophysics Data System (ADS)
Pereira, F. M. M.; Schimit, P. H. T.
2018-06-01
Modeling and simulation of mosquito-borne diseases have gained attention due to a growing incidence in tropical countries in the past few years. Here, we study the dengue spreading in a population modeled by cellular automata, where there are two lattices to model the human-mosquitointeraction: one lattice for human individuals, and one lattice for mosquitoes in order to enable different dynamics in populations. The disease considered is the dengue fever with one, two or three different serotypes coexisting in population. Although many regions exhibit the incidence of only one serotype, here we set a complete framework to also study the occurrence of two and three serotypes at the same time in a population. Furthermore, the flexibility of the model allows its use to other mosquito-borne diseases, like chikungunya, yellow fever and malaria. An approximation of the cellular automata is proposed in terms of ordinary differential equations; the spreading of mosquitoes is studied and the influence of some model parameters are analyzed with numerical simulations. Finally, a method to combat dengue spreading is simulated based on a reduction of mosquito birth and mosquito bites in population.
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Prevalence of Dupuytren disease in The Netherlands.
Lanting, Rosanne; van den Heuvel, Edwin R; Westerink, Bram; Werker, Paul M N
2013-08-01
Dupuytren disease is a fibroproliferative disease of palmar fascias of the hand. The prevalence of Dupuytren disease and the association with potential risk factors have been the subject of several studies, although there is a paucity of such data from The Netherlands. To study the prevalence of Dupuytren disease, the authors drew a random sample of 1360 individuals, stratified by age, from the northern part of The Netherlands. Of this sample, 763 individuals aged 50 to 89 years participated in this cross-sectional study. The authors examined both hands for signs of Dupuytren disease, and a questionnaire was conducted to identify potential risk factors. The effects of these risk factors were investigated using logistic regression analysis. Additional analyses were performed to develop a logistic prediction model for the prevalence of Dupuytren disease. The prevalence of Dupuytren disease was 22.1 percent. Nodules and cords were seen in 17.9 percent, and flexion contractures were present in 4.2 percent of the study population. Prevalence increased with age, from 4.9 percent in participants aged 50 to 55 years to 52.6 percent among those aged 76 to 80 years. Men were more often affected than women; 26.4 percent versus 18.6 percent, respectively (p=0.007). Other significant risk factors were previous hand injury, excessive alcohol consumption, familial occurrence of Dupuytren disease, and presence of Ledderhose disease. The results show a high prevalence of Dupuytren disease in The Netherlands, particularly the nodular form. Using the developed logistic prediction model, the prevalence of Dupuytren disease can be estimated, based on the presence of significant risk factors. Risk, III.
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Schell, Greggory J; Lavieri, Mariel S; Stein, Joshua D; Musch, David C
2013-12-21
Open-angle glaucoma (OAG) is a prevalent, degenerate ocular disease which can lead to blindness without proper clinical management. The tests used to assess disease progression are susceptible to process and measurement noise. The aim of this study was to develop a methodology which accounts for the inherent noise in the data and improve significant disease progression identification. Longitudinal observations from the Collaborative Initial Glaucoma Treatment Study (CIGTS) were used to parameterize and validate a Kalman filter model and logistic regression function. The Kalman filter estimates the true value of biomarkers associated with OAG and forecasts future values of these variables. We develop two logistic regression models via generalized estimating equations (GEE) for calculating the probability of experiencing significant OAG progression: one model based on the raw measurements from CIGTS and another model based on the Kalman filter estimates of the CIGTS data. Receiver operating characteristic (ROC) curves and associated area under the ROC curve (AUC) estimates are calculated using cross-fold validation. The logistic regression model developed using Kalman filter estimates as data input achieves higher sensitivity and specificity than the model developed using raw measurements. The mean AUC for the Kalman filter-based model is 0.961 while the mean AUC for the raw measurements model is 0.889. Hence, using the probability function generated via Kalman filter estimates and GEE for logistic regression, we are able to more accurately classify patients and instances as experiencing significant OAG progression. A Kalman filter approach for estimating the true value of OAG biomarkers resulted in data input which improved the accuracy of a logistic regression classification model compared to a model using raw measurements as input. This methodology accounts for process and measurement noise to enable improved discrimination between progression and nonprogression in chronic diseases.
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Decision-making for foot-and-mouth disease control: Objectives matter
Probert, William J. M.; Shea, Katriona; Fonnesbeck, Christopher J.; Runge, Michael C.; Carpenter, Tim E.; Durr, Salome; Garner, M. Graeme; Harvey, Neil; Stevenson, Mark A.; Webb, Colleen T.; Werkman, Marleen; Tildesley, Michael J.; Ferrari, Matthew J.
2016-01-01
Formal decision-analytic methods can be used to frame disease control problems, the first step of which is to define a clear and specific objective. We demonstrate the imperative of framing clearly-defined management objectives in finding optimal control actions for control of disease outbreaks. We illustrate an analysis that can be applied rapidly at the start of an outbreak when there are multiple stakeholders involved with potentially multiple objectives, and when there are also multiple disease models upon which to compare control actions. The output of our analysis frames subsequent discourse between policy-makers, modellers and other stakeholders, by highlighting areas of discord among different management objectives and also among different models used in the analysis. We illustrate this approach in the context of a hypothetical foot-and-mouth disease (FMD) outbreak in Cumbria, UK using outputs from five rigorously-studied simulation models of FMD spread. We present both relative rankings and relative performance of controls within each model and across a range of objectives. Results illustrate how control actions change across both the base metric used to measure management success and across the statistic used to rank control actions according to said metric. This work represents a first step towards reconciling the extensive modelling work on disease control problems with frameworks for structured decision making.
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Experimental models of demyelination and remyelination.
Torre-Fuentes, L; Moreno-Jiménez, L; Pytel, V; Matías-Guiu, J A; Gómez-Pinedo, U; Matías-Guiu, J
2017-08-29
Experimental animal models constitute a useful tool to deepen our knowledge of central nervous system disorders. In the case of multiple sclerosis, however, there is no such specific model able to provide an overview of the disease; multiple models covering the different pathophysiological features of the disease are therefore necessary. We reviewed the different in vitro and in vivo experimental models used in multiple sclerosis research. Concerning in vitro models, we analysed cell cultures and slice models. As for in vivo models, we examined such models of autoimmunity and inflammation as experimental allergic encephalitis in different animals and virus-induced demyelinating diseases. Furthermore, we analysed models of demyelination and remyelination, including chemical lesions caused by cuprizone, lysolecithin, and ethidium bromide; zebrafish; and transgenic models. Experimental models provide a deeper understanding of the different pathogenic mechanisms involved in multiple sclerosis. Choosing one model or another depends on the specific aims of the study. Copyright © 2017 Sociedad Española de Neurología. Publicado por Elsevier España, S.L.U. All rights reserved.
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Boyer, Justin G.; Ferrier, Andrew; Kothary, Rashmi
2013-01-01
Spinal muscular atrophy (SMA), amyotrophic lateral sclerosis (ALS), and spinal-bulbar muscular atrophy (SBMA) are devastating diseases characterized by the degeneration of motor neurons. Although the molecular causes underlying these diseases differ, recent findings have highlighted the contribution of intrinsic skeletal muscle defects in motor neuron diseases. The use of cell culture and animal models has led to the important finding that muscle defects occur prior to and independently of motor neuron degeneration in motor neuron diseases. In SMA for instance, the muscle specific requirements of the SMA disease-causing gene have been demonstrated by a series of genetic rescue experiments in SMA models. Conditional ALS mouse models expressing a muscle specific mutant SOD1 gene develop atrophy and muscle degeneration in the absence of motor neuron pathology. Treating SBMA mice by over-expressing IGF-1 in a skeletal muscle-specific manner attenuates disease severity and improves motor neuron pathology. In the present review, we provide an in depth description of muscle intrinsic defects, and discuss how they impact muscle function in these diseases. Furthermore, we discuss muscle-specific therapeutic strategies used to treat animal models of SMA, ALS, and SBMA. The study of intrinsic skeletal muscle defects is crucial for the understanding of the pathophysiology of these diseases and will open new therapeutic options for the treatment of motor neuron diseases. PMID:24391590
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Dog Models of Naturally Occurring Cancer
Rowell, Jennie L.; McCarthy, Donna O.; Alvarez, Carlos E.
2011-01-01
Studies using dogs provide an ideal solution to the gap in animal models of natural disease and translational medicine. This is evidenced by approximately 400 inherited disorders being characterized in domesticated dogs, most of which are relevant to humans. There are several hundred isolated populations of dogs (breeds) and each has vastly reduced genetic variation compared to humans; this simplifies disease mapping and pharmacogenomics. Dogs age five to eight-fold faster than humans, share environments with their owners, are usually kept until old age, and receive a high level of health care. Farseeing investigators recognized this potential and, over the last decade, developed the necessary tools and infrastructure to utilize this powerful model of human disease, including the sequencing of the dog genome in 2005. Here we review the nascent convergence of genetic and translational canine models of spontaneous disease, focusing on cancer. PMID:21439907
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Cuadrado-Tejedor, Mar; García-Osta, Ana
2016-01-01
A comprehensive chronic mild stress (CMS) procedure is presented, which consists in the application of unpredictable mild stressors to animal models in a random order for several weeks. This assay can be applied to Alzheimer's disease (AD) mouse models, leading to accelerated onset and increased severity of AD phenotypes and signs, including memory deficits and the accumulation of amyloid-β and phospho-tau. These assays open the way towards advanced studies on the influence of sustained mild stress, stress responses and pathways on the onset and propagation of Alzheimer's disease.
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Jiang, Rui ; Yang, Hua ; Zhou, Linqi ; Kuo, C.-C. Jay ; Sun, Fengzhu ; Chen, Ting
2007-01-01
The increasing demand for the identification of genetic variation responsible for common diseases has translated into a need for sophisticated methods for effectively prioritizing mutations occurring in disease-associated genetic regions. In this article, we prioritize candidate nonsynonymous single-nucleotide polymorphisms (nsSNPs) through a bioinformatics approach that takes advantages of a set of improved numeric features derived from protein-sequence information and a new statistical learning model called “multiple selection rule voting” (MSRV). The sequence-based features can maximize the scope of applications of our approach, and the MSRV model can capture subtle characteristics of individual mutations. Systematic validation of the approach demonstrates that this approach is capable of prioritizing causal mutations for both simple monogenic diseases and complex polygenic diseases. Further studies of familial Alzheimer diseases and diabetes show that the approach can enrich mutations underlying these polygenic diseases among the top of candidate mutations. Application of this approach to unclassified mutations suggests that there are 10 suspicious mutations likely to cause diseases, and there is strong support for this in the literature. PMID:17668383
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Zhang, Bo; Chen, Zhen; Albert, Paul S
2012-01-01
High-dimensional biomarker data are often collected in epidemiological studies when assessing the association between biomarkers and human disease is of interest. We develop a latent class modeling approach for joint analysis of high-dimensional semicontinuous biomarker data and a binary disease outcome. To model the relationship between complex biomarker expression patterns and disease risk, we use latent risk classes to link the 2 modeling components. We characterize complex biomarker-specific differences through biomarker-specific random effects, so that different biomarkers can have different baseline (low-risk) values as well as different between-class differences. The proposed approach also accommodates data features that are common in environmental toxicology and other biomarker exposure data, including a large number of biomarkers, numerous zero values, and complex mean-variance relationship in the biomarkers levels. A Monte Carlo EM (MCEM) algorithm is proposed for parameter estimation. Both the MCEM algorithm and model selection procedures are shown to work well in simulations and applications. In applying the proposed approach to an epidemiological study that examined the relationship between environmental polychlorinated biphenyl (PCB) exposure and the risk of endometriosis, we identified a highly significant overall effect of PCB concentrations on the risk of endometriosis.
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How informative is the mouse for human gut microbiota research?
Nguyen, Thi Loan Anh; Vieira-Silva, Sara; Liston, Adrian; Raes, Jeroen
2015-01-01
The microbiota of the human gut is gaining broad attention owing to its association with a wide range of diseases, ranging from metabolic disorders (e.g. obesity and type 2 diabetes) to autoimmune diseases (such as inflammatory bowel disease and type 1 diabetes), cancer and even neurodevelopmental disorders (e.g. autism). Having been increasingly used in biomedical research, mice have become the model of choice for most studies in this emerging field. Mouse models allow perturbations in gut microbiota to be studied in a controlled experimental setup, and thus help in assessing causality of the complex host-microbiota interactions and in developing mechanistic hypotheses. However, pitfalls should be considered when translating gut microbiome research results from mouse models to humans. In this Special Article, we discuss the intrinsic similarities and differences that exist between the two systems, and compare the human and murine core gut microbiota based on a meta-analysis of currently available datasets. Finally, we discuss the external factors that influence the capability of mouse models to recapitulate the gut microbiota shifts associated with human diseases, and investigate which alternative model systems exist for gut microbiota research. PMID:25561744
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How informative is the mouse for human gut microbiota research?
Nguyen, Thi Loan Anh; Vieira-Silva, Sara; Liston, Adrian; Raes, Jeroen
2015-01-01
The microbiota of the human gut is gaining broad attention owing to its association with a wide range of diseases, ranging from metabolic disorders (e.g. obesity and type 2 diabetes) to autoimmune diseases (such as inflammatory bowel disease and type 1 diabetes), cancer and even neurodevelopmental disorders (e.g. autism). Having been increasingly used in biomedical research, mice have become the model of choice for most studies in this emerging field. Mouse models allow perturbations in gut microbiota to be studied in a controlled experimental setup, and thus help in assessing causality of the complex host-microbiota interactions and in developing mechanistic hypotheses. However, pitfalls should be considered when translating gut microbiome research results from mouse models to humans. In this Special Article, we discuss the intrinsic similarities and differences that exist between the two systems, and compare the human and murine core gut microbiota based on a meta-analysis of currently available datasets. Finally, we discuss the external factors that influence the capability of mouse models to recapitulate the gut microbiota shifts associated with human diseases, and investigate which alternative model systems exist for gut microbiota research. © 2015. Published by The Company of Biologists Ltd.
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Gene-Environment Interactions in Cardiovascular Disease
Flowers, Elena; Froelicher, Erika Sivarajan; Aouizerat, Bradley E.
2011-01-01
Background Historically, models to describe disease were exclusively nature-based or nurture-based. Current theoretical models for complex conditions such as cardiovascular disease acknowledge the importance of both biologic and non-biologic contributors to disease. A critical feature is the occurrence of interactions between numerous risk factors for disease. The interaction between genetic (i.e. biologic, nature) and environmental (i.e. non-biologic, nurture) causes of disease is an important mechanism for understanding both the etiology and public health impact of cardiovascular disease. Objectives The purpose of this paper is to describe theoretical underpinnings of gene-environment interactions, models of interaction, methods for studying gene-environment interactions, and the related concept of interactions between epigenetic mechanisms and the environment. Discussion Advances in methods for measurement of genetic predictors of disease have enabled an increasingly comprehensive understanding of the causes of disease. In order to fully describe the effects of genetic predictors of disease, it is necessary to place genetic predictors within the context of known environmental risk factors. The additive or multiplicative effect of the interaction between genetic and environmental risk factors is often greater than the contribution of either risk factor alone. PMID:21684212
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Agent-based modeling of noncommunicable diseases: a systematic review.
Nianogo, Roch A; Arah, Onyebuchi A
2015-03-01
We reviewed the use of agent-based modeling (ABM), a systems science method, in understanding noncommunicable diseases (NCDs) and their public health risk factors. We systematically reviewed studies in PubMed, ScienceDirect, and Web of Sciences published from January 2003 to July 2014. We retrieved 22 relevant articles; each had an observational or interventional design. Physical activity and diet were the most-studied outcomes. Often, single agent types were modeled, and the environment was usually irrelevant to the studied outcome. Predictive validation and sensitivity analyses were most used to validate models. Although increasingly used to study NCDs, ABM remains underutilized and, where used, is suboptimally reported in public health studies. Its use in studying NCDs will benefit from clarified best practices and improved rigor to establish its usefulness and facilitate replication, interpretation, and application.
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Agent-Based Modeling of Noncommunicable Diseases: A Systematic Review
Arah, Onyebuchi A.
2015-01-01
We reviewed the use of agent-based modeling (ABM), a systems science method, in understanding noncommunicable diseases (NCDs) and their public health risk factors. We systematically reviewed studies in PubMed, ScienceDirect, and Web of Sciences published from January 2003 to July 2014. We retrieved 22 relevant articles; each had an observational or interventional design. Physical activity and diet were the most-studied outcomes. Often, single agent types were modeled, and the environment was usually irrelevant to the studied outcome. Predictive validation and sensitivity analyses were most used to validate models. Although increasingly used to study NCDs, ABM remains underutilized and, where used, is suboptimally reported in public health studies. Its use in studying NCDs will benefit from clarified best practices and improved rigor to establish its usefulness and facilitate replication, interpretation, and application. PMID:25602871
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Maeda, Mayuko; Murakami, Tomoaki; Muhammad, Naeem; Inoshima, Yasuo; Ishiguro, Naotaka
2016-11-01
AA amyloidosis is a protein misfolding disease characterized by extracellular deposition of amyloid A (AA) fibrils. AA amyloidosis has been identified in food animals, and it has been postulated that AA amyloidosis may be transmissible to different animal species. Since the precursor protein of AA fibrils is serum amyloid A (SAA), which is an inflammatory acute phase protein, AA amyloidosis is considered to be associated with inflammatory diseases such as rheumatoid arthritis. Chronic diseases such as autoimmune disease and type 2 diabetes mellitus could be potential factors for AA amyloidosis. In this study, to examine the relationship between the induction of AA amyloidosis and chromic abnormalities such as autoimmune disease or type 2 diabetes mellitus, amyloid fibrils from mice, cattle, or chickens were experimentally injected into disease model mice. Wild-type mice were used as controls. The concentrations of SAA, IL-6, and IL-10 in autoimmune disease model mice were higher than those of control mice. However, induction of AA amyloidosis in autoimmune disease and type 2 diabetes mellitus model mice was lower than that in control mice, and the amount of amyloid deposits in the spleens of both mouse models was lower than that of control mice according to Congo red staining and immunohistochemistry. These results suggest that factors other than SAA levels, such as an inflammatory or anti-inflammatory environment in the immune response, may be involved in amyloid deposition.
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Dorjee, S; Revie, C W; Poljak, Z; McNab, W B; Sanchez, J
2013-10-01
Understanding contact networks are important for modelling and managing the spread and control of communicable diseases in populations. This study characterizes the swine shipment network of a multi-site production system in southwestern Ontario, Canada. Data were extracted from a company's database listing swine shipments among 251 swine farms, including 20 sow, 69 nursery and 162 finishing farms, for the 2-year period of 2006 to 2007. Several network metrics were generated. The number of shipments per week between pairs of farms ranged from 1 to 6. The medians (and ranges) of out-degree were: sow 6 (1-21), nursery 8 (0-25), and finishing 0 (0-4), over the entire 2-year study period. Corresponding estimates for in-degree of nursery and finishing farms were 3 (0-9) and 3 (0-12) respectively. Outgoing and incoming infection chains (OIC and IIC), were also measured. The medians (ranges) of the monthly OIC and IIC were 0 (0-8) and 0 (0-6), respectively, with very similar measures observed for 2-week intervals. Nursery farms exhibited high measures of centrality. This indicates that they pose greater risks of disease spread in the network. Therefore, they should be given a high priority for disease prevention and control measures affecting all age groups alike. The network demonstrated scale-free and small-world topologies as observed in other livestock shipment studies. This heterogeneity in contacts among farm types and network topologies should be incorporated in simulation models to improve their validity. In conclusion, this study provided useful epidemiological information and parameters for the control and modelling of disease spread among swine farms, for the first time from Ontario, Canada. Copyright © 2013 Elsevier B.V. All rights reserved.
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Huang, Hui-Ru; Chen, Chi-Wen; Chen, Chin-Mi; Yang, Hsiao-Ling; Su, Wen-Jen; Wang, Jou-Kou; Tsai, Pei-Kwei
2018-03-01
Health-promoting behaviors could serve as a major strategy to optimize long-term outcomes for adolescents with congenital heart disease. The associations assessed from a positive perspective of knowledge, attitudes, and practice model would potentially cultivate health-promoting behaviors during adolescence. The purpose of this study was to examine the relationships between disease knowledge, resilience, family functioning, and health-promoting behaviors in adolescents with congenital heart disease. A total of 320 adolescents with congenital heart disease who were aged 12-18 years were recruited from pediatric cardiology outpatient departments, and participated in a cross-sectional survey. The participants completed the Leuven Knowledge Questionnaire for Congenital Heart Disease; Haase Adolescent Resilience in Illness Scale; Family Adaptability, Partnership, Growth, Affection, and Resolve; and Adolescent Health Promotion scales. The collected data were analyzed using descriptive statistics and three multiple regression models. Greater knowledge of prevention of complications and higher resilience had a more powerful effect in enhancing health-promoting behaviors. Having symptoms and moderate or severe family dysfunction were significantly more negatively predictive of health-promoting behaviors than not having symptoms and positive family function. The third model explained 40% of the variance in engaging in health-promoting behaviors among adolescents with congenital heart disease. The findings of this study provide new insights into the role of disease knowledge, resilience, and family functioning in the health-promoting behavior of adolescents with congenital heart disease. Continued efforts are required to plan family care programs that promote the acquisition of sufficient disease knowledge and the development of resilience for adolescents with congenital heart disease.
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Disease introduction is associated with a phase transition in bighorn sheep demographics
Manlove, Kezia; Cassirer, E. Frances; Cross, Paul Chafee; Plowright, Raina K.; Hudson, Peter J.
2016-01-01
Ecological theory suggests that pathogens are capable of regulating or limiting host population dynamics, and this relationship has been empirically established in several settings. However, although studies of childhood diseases were integral to the development of disease ecology, few studies show population limitation by a disease affecting juveniles. Here, we present empirical evidence that disease in lambs constrains population growth in bighorn sheep (Ovis canadensis) based on 45 years of population-level and 18 years of individual-level monitoring across 12 populations. While populations generally increased (lambda =1.11) prior to disease introduction, most of these same populations experienced an abrupt change in trajectory at the time of disease invasion, usually followed by stagnant-to-declining growth rates (lambda = 0.98) over the next twenty years. Disease-induced juvenile mortality imposed strong constraints on population growth that were not observed prior to disease introduction, even as adult survival returned to pre-invasion levels. Simulations suggested that models including persistent disease-induced mortality in juveniles qualitatively matched observed population trajectories, whereas models that only incorporated all-age disease events did not. We use these results to argue that pathogen persistence may pose a lasting, but under-recognized, threat to host populations, particularly in cases where clinical disease manifests primarily in juveniles. PMID:27859120
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Disease introduction is associated with a phase transition in bighorn sheep demographics
Manlove, Kezia; Cassirer, E. Frances; Cross, Paul C.; Plowright, Raina K.; Hudson, Peter J.
2016-01-01
Ecological theory suggests that pathogens are capable of regulating or limiting host population dynamics, and this relationship has been empirically established in several settings. However, although studies of childhood diseases were integral to the development of disease ecology, few studies show population limitation by a disease affecting juveniles. Here, we present empirical evidence that disease in lambs constrains population growth in bighorn sheep (Ovis canadensis) based on 45 years of population-level and 18 years of individual-level monitoring across 12 populations. While populations generally increased (λ = 1.11) prior to disease introduction, most of these same populations experienced an abrupt change in trajectory at the time of disease invasion, usually followed by stagnant-to-declining growth rates (λ = 0.98) over the next 20 years. Disease-induced juvenile mortality imposed strong constraints on population growth that were not observed prior to disease introduction, even as adult survival returned to pre-invasion levels. Simulations suggested that models including persistent disease-induced mortality in juveniles qualitatively matched observed population trajectories, whereas models that only incorporated all-age disease events did not. We use these results to argue that pathogen persistence may pose a lasting, but under-recognized, threat to host populations, particularly in cases where clinical disease manifests primarily in juveniles.
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From the baker to the bedside: yeast models of Parkinson's disease
Menezes, Regina; Tenreiro, Sandra; Macedo, Diana; Santos, Cláudia N.; Outeiro, Tiago F.
2015-01-01
The baker’s yeast Saccharomyces cerevisiae has been extensively explored for our understanding of fundamental cell biology processes highly conserved in the eukaryotic kingdom. In this context, they have proven invaluable in the study of complex mechanisms such as those involved in a variety of human disorders. Here, we first provide a brief historical perspective on the emergence of yeast as an experimental model and on how the field evolved to exploit the potential of the model for tackling the intricacies of various human diseases. In particular, we focus on existing yeast models of the molecular underpinnings of Parkinson’s disease (PD), focusing primarily on the central role of protein quality control systems. Finally, we compile and discuss the major discoveries derived from these studies, highlighting their far-reaching impact on the elucidation of PD-associated mechanisms as well as in the identification of candidate therapeutic targets and compounds with therapeutic potential. PMID:28357302
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Surveillance of dengue fever virus: a review of epidemiological models and early warning systems.
Racloz, Vanessa; Ramsey, Rebecca; Tong, Shilu; Hu, Wenbiao
2012-01-01
Dengue fever affects over a 100 million people annually hence is one of the world's most important vector-borne diseases. The transmission area of this disease continues to expand due to many direct and indirect factors linked to urban sprawl, increased travel and global warming. Current preventative measures include mosquito control programs, yet due to the complex nature of the disease and the increased importation risk along with the lack of efficient prophylactic measures, successful disease control and elimination is not realistic in the foreseeable future. Epidemiological models attempt to predict future outbreaks using information on the risk factors of the disease. Through a systematic literature review, this paper aims at analyzing the different modeling methods and their outputs in terms of acting as an early warning system. We found that many previous studies have not sufficiently accounted for the spatio-temporal features of the disease in the modeling process. Yet with advances in technology, the ability to incorporate such information as well as the socio-environmental aspect allowed for its use as an early warning system, albeit limited geographically to a local scale.
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ERIC Educational Resources Information Center
de Heer, Hendrik Dirk; Balcazar, Hector G.; Castro, Felipe; Schulz, Leslie
2012-01-01
This study assessed effectiveness of an educational community intervention taught by "promotoras de salud" in reducing cardiovascular disease (CVD) risk among Hispanics using a structural equation modeling (SEM) approach. Model development was guided by a social ecological framework proposing CVD risk reduction through improvement of…
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Primary Care Physicians and Coronary Heart Disease Prevention: A Practice Model.
ERIC Educational Resources Information Center
Makrides, Lydia; Veinot, Paula L.; Richard, Josie; Allen, Michael J.
1997-01-01
The role of primary care physicians in coronary heart disease prevention is explored, and a model for patient education by physicians is offered. A qualitative study in Nova Scotia examines physicians' expectations about their role in prevention, obstacles to providing preventive care, and mechanisms by which preventive care occurs. (Author/EMK)
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A Mental Models Approach to Assessing Public Understanding of Zika Virus, Guatemala.
Southwell, Brian G; Ray, Sarah E; Vazquez, Natasha N; Ligorria, Tere; Kelly, Bridget J
2018-05-01
Mental models are cognitive representations of phenomena that can constrain efforts to reduce infectious disease. In a study of Zika virus awareness in Guatemala, many participants referred to experiences with other mosquitoborne diseases during discussions of Zika virus. These results highlight the importance of past experiences for Zika virus understanding.
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A Drug-Centric View of Drug Development: How Drugs Spread from Disease to Disease.
Rodriguez-Esteban, Raul
2016-04-01
Drugs are often seen as ancillary to the purpose of fighting diseases. Here an alternative view is proposed in which they occupy a spearheading role. In this view, drugs are technologies with an inherent therapeutic potential. Once created, they can spread from disease to disease independently of the drug creator's original intentions. Through the analysis of extensive literature and clinical trial records, it can be observed that successful drugs follow a life cycle in which they are studied at an increasing rate, and for the treatment of an increasing number of diseases, leading to clinical advancement. Such initial growth, following a power law on average, has a degree of momentum, but eventually decelerates, leading to stagnation and decay. A network model can describe the propagation of drugs from disease to disease in which diseases communicate with each other by receiving and sending drugs. Within this model, some diseases appear more prone to influence other diseases than be influenced, and vice versa. Diseases can also be organized into a drug-centric disease taxonomy based on the drugs that each adopts. This taxonomy reflects not only biological similarities across diseases, but also the level of differentiation of existing therapies. In sum, this study shows that drugs can become contagious technologies playing a driving role in the fight against disease. By better understanding such dynamics, pharmaceutical developers may be able to manage drug projects more effectively.
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The senescence accelerated mouse prone 8 (SAMP8): A novel murine model for cardiac aging.
Karuppagounder, Vengadeshprabhu; Arumugam, Somasundaram; Babu, Sahana Suresh; Palaniyandi, Suresh S; Watanabe, Kenichi; Cooke, John P; Thandavarayan, Rajarajan A
2017-05-01
Because cardiovascular disease remains the major cause of mortality and morbidity world-wide, there remains a compelling need for new insights and novel therapeutic avenues. In this regard, the senescence-accelerated mouse prone 8 (SAMP8) line is a particularly good model for studying the effects of aging on cardiovascular health. Accumulating evidence suggests that this model may shed light on age-associated cardiac and vascular dysfunction and disease. These animals manifest evidence of inflammation, oxidative stress and adverse cardiac remodeling that may recapitulate processes involved in human disease. Early alterations in oxidative damage promote endoplasmic reticulum stress to trigger apoptosis and cytokine production in this genetically susceptible mouse strain. Conversely, pharmacological treatments that reduce inflammation and oxidative stress improve cardiac function in these animals. Therefore, the SAMP8 mouse model provides an exciting opportunity to expand our knowledge of aging in cardiovascular disease and the potential identification of novel targets of treatment. Herein, we review the previous studies performed in SAMP8 mice that provide insight into age-related cardiovascular alterations. Copyright © 2016 Elsevier B.V. All rights reserved.
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Dog as a model in studies on human hereditary diseases and their gene therapy.
Switonski, Marek
2014-03-01
During the last 15 years spectacular progress has been achieved in knowledge on the dog genome organization and the molecular background of hereditary diseases in this species. A majority of canine genetic diseases have their counterparts in humans and thus dogs are considered as a very important large animal model in human biomedicine. Among canine monogenic diseases with known causative gene mutations there are two large groups classified as retinal dystrophies and lysosomal storage diseases. Specific types of these diseases are usually diagnosed in a single or several breeds. A well known disorder, restricted to a single breed, is congenital stationary night blindness described in Briards. This disease is a counterpart of Leber amaurosis in children. On the other hand, one of the most common monogenic human diseases (Duchenne muscular dystrophy), has its canine counterparts in several breeds (e.g., the Golden retriever, Beagle and German short-haired pointer). For some of the canine diseases gene therapy strategy was successfully applied, e.g., for congenital stationary night blindness, rod-cone dystrophy and muccopolysaccharydoses type I, IIIB and VII. Since phenotypic variability between the breeds is exceptionally high, the dog is an interesting model to study the molecular background of congenital malformations (e.g., dwarfism and osteoporosis imperfecta). Also disorders of sexual development (DSD), especially testicular or ovotesticular DSD (78,XX; SRY-negative), which is widely distributed across dozens of breeds, are of particular interest. Studies on the genetic background of canine cancers, a major health problem in this species, are also quite advanced. On the other hand, genetic studies on canine counterparts of major human complex diseases (e.g., obesity, the metabolic syndrome and diabetes mellitus) are still in their infancy. Copyright © 2014 Society for Biology of Reproduction & the Institute of Animal Reproduction and Food Research of Polish Academy of Sciences in Olsztyn. Published by Elsevier Urban & Partner Sp. z o.o. All rights reserved.
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Emotions as infectious diseases in a large social network: the SISa model
Hill, Alison L.; Rand, David G.; Nowak, Martin A.; Christakis, Nicholas A.
2010-01-01
Human populations are arranged in social networks that determine interactions and influence the spread of diseases, behaviours and ideas. We evaluate the spread of long-term emotional states across a social network. We introduce a novel form of the classical susceptible–infected–susceptible disease model which includes the possibility for ‘spontaneous’ (or ‘automatic’) infection, in addition to disease transmission (the SISa model). Using this framework and data from the Framingham Heart Study, we provide formal evidence that positive and negative emotional states behave like infectious diseases spreading across social networks over long periods of time. The probability of becoming content is increased by 0.02 per year for each content contact, and the probability of becoming discontent is increased by 0.04 per year per discontent contact. Our mathematical formalism allows us to derive various quantities from the data, such as the average lifetime of a contentment ‘infection’ (10 years) or discontentment ‘infection’ (5 years). Our results give insight into the transmissive nature of positive and negative emotional states. Determining to what extent particular emotions or behaviours are infectious is a promising direction for further research with important implications for social science, epidemiology and health policy. Our model provides a theoretical framework for studying the interpersonal spread of any state that may also arise spontaneously, such as emotions, behaviours, health states, ideas or diseases with reservoirs. PMID:20610424
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Emotions as infectious diseases in a large social network: the SISa model.
Hill, Alison L; Rand, David G; Nowak, Martin A; Christakis, Nicholas A
2010-12-22
Human populations are arranged in social networks that determine interactions and influence the spread of diseases, behaviours and ideas. We evaluate the spread of long-term emotional states across a social network. We introduce a novel form of the classical susceptible-infected-susceptible disease model which includes the possibility for 'spontaneous' (or 'automatic') infection, in addition to disease transmission (the SISa model). Using this framework and data from the Framingham Heart Study, we provide formal evidence that positive and negative emotional states behave like infectious diseases spreading across social networks over long periods of time. The probability of becoming content is increased by 0.02 per year for each content contact, and the probability of becoming discontent is increased by 0.04 per year per discontent contact. Our mathematical formalism allows us to derive various quantities from the data, such as the average lifetime of a contentment 'infection' (10 years) or discontentment 'infection' (5 years). Our results give insight into the transmissive nature of positive and negative emotional states. Determining to what extent particular emotions or behaviours are infectious is a promising direction for further research with important implications for social science, epidemiology and health policy. Our model provides a theoretical framework for studying the interpersonal spread of any state that may also arise spontaneously, such as emotions, behaviours, health states, ideas or diseases with reservoirs.
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Systems biology in hepatology: approaches and applications.
Mardinoglu, Adil; Boren, Jan; Smith, Ulf; Uhlen, Mathias; Nielsen, Jens
2018-06-01
Detailed insights into the biological functions of the liver and an understanding of its crosstalk with other human tissues and the gut microbiota can be used to develop novel strategies for the prevention and treatment of liver-associated diseases, including fatty liver disease, cirrhosis, hepatocellular carcinoma and type 2 diabetes mellitus. Biological network models, including metabolic, transcriptional regulatory, protein-protein interaction, signalling and co-expression networks, can provide a scaffold for studying the biological pathways operating in the liver in connection with disease development in a systematic manner. Here, we review studies in which biological network models were used to integrate multiomics data to advance our understanding of the pathophysiological responses of complex liver diseases. We also discuss how this mechanistic approach can contribute to the discovery of potential biomarkers and novel drug targets, which might lead to the design of targeted and improved treatment strategies. Finally, we present a roadmap for the successful integration of models of the liver and other human tissues with the gut microbiota to simulate whole-body metabolic functions in health and disease.
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What do we know about managing Dupuytren's disease cost-effectively?
Dritsaki, Melina; Rivero-Arias, Oliver; Gray, Alastair; Ball, Catherine; Nanchahal, Jagdeep
2018-01-25
Dupuytren's disease (DD) is a common and progressive, fibroproliferative disorder of the palmar and digital fascia of the hand. Various treatments have been recommended for advanced disease or to retard progression of early disease and to prevent deterioration of the finger contracture and quality of life. Recent studies have tried to evaluate the clinical and cost-effectiveness of therapies for DD, but there is currently no systematic assessment and appraisal of the economic evaluations. A systematic literature review was conducted, following PRISMA guidelines, to identify studies reporting economic evaluations of interventions for managing DD. Databases searched included the Ovid MEDLINE/Embase (without time restriction), National Health Service (NHS) Economic Evaluation Database (all years) and the National Institute for Health Research (NIHR) Journals Library) Health Technology Assessment (HTA). Cost-effectiveness analyses of treating DD were identified and their quality was assessed using the CHEERS assessment tool for quality of reporting and Phillips checklist for model evaluation. A total of 103 studies were screened, of which 4 met the study inclusion criteria. Two studies were from the US, one from the UK and one from Canada. They all assessed the same interventions for advanced DD, namely collagenase Clostridium histolyticum injection, percutaneous needle fasciotomy and partial fasciectomy. All studies conducting a cost-utility analysis, two implemented a decision analytic model and two a Markov model approach. None of them were based on a single randomised controlled trial, but rather synthesised evidence from various sources. Studies varied in their time horizon, sources of utility estimates and perspective of analysis. The overall quality of study reporting was good based on the CHEERS checklist. The quality of the model reporting in terms of model structure, data synthesis and model consistency varied across the included studies. Cost-effectiveness analyses for patients with advanced DD are limited and have applied different approaches with respect to modelling. Future studies should improve the way they are conducted and report their findings according to established guidance for conducting economic modelling of health care technologies. The protocol was registered ( CRD42016032989 ; date 08/01/2016) with the PROSPERO international prospective register of systematic reviews.
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Ho, Wen-Hsien; Lee, King-Teh; Chen, Hong-Yaw; Ho, Te-Wei; Chiu, Herng-Chia
2012-01-01
Background A database for hepatocellular carcinoma (HCC) patients who had received hepatic resection was used to develop prediction models for 1-, 3- and 5-year disease-free survival based on a set of clinical parameters for this patient group. Methods The three prediction models included an artificial neural network (ANN) model, a logistic regression (LR) model, and a decision tree (DT) model. Data for 427, 354 and 297 HCC patients with histories of 1-, 3- and 5-year disease-free survival after hepatic resection, respectively, were extracted from the HCC patient database. From each of the three groups, 80% of the cases (342, 283 and 238 cases of 1-, 3- and 5-year disease-free survival, respectively) were selected to provide training data for the prediction models. The remaining 20% of cases in each group (85, 71 and 59 cases in the three respective groups) were assigned to validation groups for performance comparisons of the three models. Area under receiver operating characteristics curve (AUROC) was used as the performance index for evaluating the three models. Conclusions The ANN model outperformed the LR and DT models in terms of prediction accuracy. This study demonstrated the feasibility of using ANNs in medical decision support systems for predicting disease-free survival based on clinical databases in HCC patients who have received hepatic resection. PMID:22235270
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van Baarlen, Peter; van Belkum, Alex; Thomma, Bart P H J
2007-02-01
Relatively simple eukaryotic model organisms such as the genetic model weed plant Arabidopsis thaliana possess an innate immune system that shares important similarities with its mammalian counterpart. In fact, some human pathogens infect Arabidopsis and cause overt disease with human symptomology. In such cases, decisive elements of the plant's immune system are likely to be targeted by the same microbial factors that are necessary for causing disease in humans. These similarities can be exploited to identify elementary microbial pathogenicity factors and their corresponding targets in a green host. This circumvents important cost aspects that often frustrate studies in humans or animal models and, in addition, results in facile ethical clearance.
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Perspectives on Systems Modeling of Human Peripheral Blood Mononuclear Cells
Sen, Partho; Kemppainen, Esko; Orešič, Matej
2018-01-01
Human peripheral blood mononuclear cells (PBMCs) are the key drivers of the immune responses. These cells undergo activation, proliferation and differentiation into various subsets. During these processes they initiate metabolic reprogramming, which is coordinated by specific gene and protein activities. PBMCs as a model system have been widely used to study metabolic and autoimmune diseases. Herein we review various omics and systems-based approaches such as transcriptomics, epigenomics, proteomics, and metabolomics as applied to PBMCs, particularly T helper subsets, that unveiled disease markers and the underlying mechanisms. We also discuss and emphasize several aspects of T cell metabolic modeling in healthy and disease states using genome-scale metabolic models. PMID:29376056
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Incorporating heterogeneity into the transmission dynamics of a waterborne disease model.
Collins, O C; Govinder, K S
2014-09-07
We formulate a mathematical model that captures the essential dynamics of waterborne disease transmission to study the effects of heterogeneity on the spread of the disease. The effects of heterogeneity on some important mathematical features of the model such as the basic reproduction number, type reproduction number and final outbreak size are analysed accordingly. We conduct a real-world application of this model by using it to investigate the heterogeneity in transmission in the recent cholera outbreak in Haiti. By evaluating the measure of heterogeneity between the administrative departments in Haiti, we discover a significant difference in the dynamics of the cholera outbreak between the departments. Copyright © 2014 Elsevier Ltd. All rights reserved.
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Kong, Ping; Zhang, Ben-Shu; Lei, Ping; Kong, Xiao-Dong; Zhang, Shi-Shuang; Li, Dai; Zhang, Yun
2015-08-01
Parkinson's disease is a degenerative disorder of the central nervous system. In spite of extensive research, neither the cause nor the mechanisms have been firmly established thus far. One assumption is that certain toxic substances may exist in the cerebro-spinal fluid (CSF) of Parkinson's disease patients. To confirm the neurotoxicity of CSF and study the potential correlation between neurotoxicity and the severity of Parkinson's disease, CSF was added to cultured cells. By observation of cell morphology, changes in the levels of lactate dehydrogenase, the ratio of tyrosine hydroxylase-positive cells, and the expression of tyrosine hydroxylase mRNA and protein, the differences between the two groups were shown. The created in vitro model of dopaminergic neurons using primary culture of mouse embryonic mesencephalic tissue is suitable for the study of neurotoxicity. The observations of the present study indicated that CSF from Parkinson's disease patients contains factors that can cause specific injury to cultured dopaminergic neurons. However, no obvious correlation was found between the neurotoxicity of CSF and the severity of Parkinson's disease.
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Dynamic population flow based risk analysis of infectious disease propagation in a metropolis.
Zhang, Nan; Huang, Hong; Duarte, Marlyn; Zhang, Junfeng Jim
2016-09-01
Knowledge on the characteristics of infectious disease propagation in metropolises plays a critical role in guiding public health intervention strategies to reduce death tolls, disease incidence, and possible economic losses. Based on the SIR model, we established a comprehensive spatiotemporal risk assessment model to compute infectious disease propagation within an urban setting using Beijing, China as a case study. The model was developed for a dynamic population distribution using actual data on location, density of residences and offices, and means of public transportation (e.g., subways, buses and taxis). We evaluated four influencing factors including biological, behavioral, environmental parameters and infectious sources. The model output resulted in a set of maps showing how the four influencing factors affected the trend and characteristics of airborne infectious disease propagation in Beijing. We compared the scenarios for the long-term dynamic propagation of infectious disease without governmental interventions versus scenarios with government intervention and hospital coordinated emergency responses. Lastly, the sensitivity of the average number of people at different location in spreading infections is analyzed. Based on our results, we provide valuable recommendations to governmental agencies and the public in order to minimize the disease propagation. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Computational Modeling of Human Metabolism and Its Application to Systems Biomedicine.
Aurich, Maike K; Thiele, Ines
2016-01-01
Modern high-throughput techniques offer immense opportunities to investigate whole-systems behavior, such as those underlying human diseases. However, the complexity of the data presents challenges in interpretation, and new avenues are needed to address the complexity of both diseases and data. Constraint-based modeling is one formalism applied in systems biology. It relies on a genome-scale reconstruction that captures extensive biochemical knowledge regarding an organism. The human genome-scale metabolic reconstruction is increasingly used to understand normal cellular and disease states because metabolism is an important factor in many human diseases. The application of human genome-scale reconstruction ranges from mere querying of the model as a knowledge base to studies that take advantage of the model's topology and, most notably, to functional predictions based on cell- and condition-specific metabolic models built based on omics data.An increasing number and diversity of biomedical questions are being addressed using constraint-based modeling and metabolic models. One of the most successful biomedical applications to date is cancer metabolism, but constraint-based modeling also holds great potential for inborn errors of metabolism or obesity. In addition, it offers great prospects for individualized approaches to diagnostics and the design of disease prevention and intervention strategies. Metabolic models support this endeavor by providing easy access to complex high-throughput datasets. Personalized metabolic models have been introduced. Finally, constraint-based modeling can be used to model whole-body metabolism, which will enable the elucidation of metabolic interactions between organs and disturbances of these interactions as either causes or consequence of metabolic diseases. This chapter introduces constraint-based modeling and describes some of its contributions to systems biomedicine.
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Stability analysis and application of a mathematical cholera model.
Liao, Shu; Wang, Jin
2011-07-01
In this paper, we conduct a dynamical analysis of the deterministic cholera model proposed in [9]. We study the stability of both the disease-free and endemic equilibria so as to explore the complex epidemic and endemic dynamics of the disease. We demonstrate a real-world application of this model by investigating the recent cholera outbreak in Zimbabwe. Meanwhile, we present numerical simulation results to verify the analytical predictions.
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Cardiovascular disease risk scores in the current practice: which to use in rheumatoid arthritis?
Purcarea, A; Sovaila, S; Gheorghe, A; Udrea, G; Stoica, V
2014-01-01
Cardiovascular disease (CVD) is the highest prevalence disease in the general population (GP) and it accounts for 20 million deaths worldwide each year. Its prevalence is even higher in rheumatoid arthritis. Early detection of subclinical disease is critical and the use of cardiovascular risk prediction models and calculators is widely spread. The impact of such techniques in the GP was previously studied. Despite their common background and similarities, some disagreement exists between most scores and their importance in special high-risk populations like rheumatoid arthritis (RA), having a low level of evidence. The current article aims to single out those predictive models (models) that could be most useful in the care of rheumatoid arthritis patients.
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Henaux, Viviane; Jane Parmley,; Catherine Soos,; Samuel, Michael D.
2013-01-01
Synthesis and applications. Our study highlights the potential of integrating incomplete surveillance data with epizootic models to quantify disease transmission and immunity. This modelling approach provides an important tool to understand spatial and temporal epizootic dynamics and inform disease surveillance. Our findings suggest focusing highly pathogenic avian influenza virus (HPAIv) surveillance on postbreeding areas where mortality of immunologically naïve hatch-year birds is most likely to occur, and collecting serology to enhance HPAIv detection. Our modelling approach can integrate various types of disease data facilitating its use with data from other surveillance programs (as illustrated by the estimation of infection rate during an HPAIv outbreak in mute swansCygnus olor in Europe).