Lafora disease fibroblasts exemplify the molecular interdependence between thioredoxin 1 and the proteasome in mammalian cells.

PubMed

García-Giménez, José Luis; Seco-Cervera, Marta; Aguado, Carmen; Romá-Mateo, Carlos; Dasí, Francisco; Priego, Sonia; Markovic, Jelena; Knecht, Erwin; Sanz, Pascual; Pallardó, Federico V

2013-12-01

Thioredoxin 1 (Trx1) is a key regulator of cellular redox balance and participates in cellular signaling events. Recent evidence from yeast indicates that members of the Trx family interact with the 20S proteasome, indicating redox regulation of proteasome activity. However, there is little information about the interrelationship of Trx proteins with the proteasome system in mammalian cells, especially in the nucleus. Here, we have investigated this relationship under various cellular conditions in mammalian cells. We show that Trx1 levels and its subcellular localization (cytosol, endoplasmic reticulum, and nucleus) depend on proteasome activity during the cell cycle in NIH3T3 fibroblasts and under stress conditions, when proteasomes are inhibited. In addition, we also studied in these cells how the main cellular antioxidant systems are stimulated when proteasome activity is inhibited. Finally, we describe a reduction in Trx1 levels in Lafora disease fibroblasts and demonstrate that the nuclear colocalization of Trx1 with 20S proteasomes in laforin-deficient cells is altered compared with control cells. Our results indicate a close relationship between Trx1 and the 20S nuclear proteasome and give a new perspective to the study of diseases or physiopathological conditions in which defects in the proteasome system are associated with oxidative stress. © 2013 Elsevier Inc. All rights reserved.

Polyglucosan inclusions (Lafora bodies) in a gray-headed flying fox (Pteropus poliocephalus).

PubMed

Gabor, Les J; Srivastava, Mukesh

2010-03-01

Polyglucosan bodies (Lafora bodies) were identified in a juvenile gray-headed flying fox (Pteropus poliocephalus) with neurological signs. The structures were only noted in the brain stem, and no associated degenerative changes were present. These structures have not been previously identified in any species in the order Chiroptera.

Exploring the structural insights on human laforin mutation K87A in Lafora disease--a molecular dynamics study.

PubMed

Srikumar, P S; Rohini, K

2013-10-01

Lafora disease (LD) is an autosomal recessive, progressive form of myoclonus epilepsy which affects worldwide. LD occurs mainly in countries like southern Europe, northern Africa, South India, and in the Middle East. LD occurs with its onset mainly in teenagers and leads to decline and death within 2 to 10 years. The genes EPM2A and EPM2B are commonly involved in 90 % of LD cases. EPM2A codes for protein laforin which contains an amino terminal carbohydrate binding module (CBM) belonging to the CBM20 family and a carboxy terminal dual specificity phosphatase domain. Mutations in laforin are found to abolish glycogen binding and have been reported in wet lab methods. In order to investigate on structural insights on laforin mutation K81A, we performed molecular dynamics (MD) simulation studies for native and mutant protein. MD simulation results showed loss of stability due to mutation K87A which confirmed the structural reason for conformational changes observed in laforin. The conformational change of mutant laforin was confirmed by analysis using root mean square deviation, root mean square fluctuation, solvent accessibility surface area, radius of gyration, hydrogen bond, and principle component analysis. Our results identified that the flexibility of K87A mutated laforin structure, with replacement of acidic amino acid to aliphatic amino acid in functional CBM domain, have more impact in abolishing glycogen binding that favors LD.

Brain glycogen in health and disease.

PubMed

Duran, Jordi; Guinovart, Joan J

2015-12-01

Glycogen is present in the brain at much lower concentrations than in muscle or liver. However, by characterizing an animal depleted of brain glycogen, we have shown that the polysaccharide plays a key role in learning capacity and in activity-dependent changes in hippocampal synapse strength. Since glycogen is essentially found in astrocytes, the diverse roles proposed for this polysaccharide in the brain have been attributed exclusively to these cells. However, we have demonstrated that neurons have an active glycogen metabolism that contributes to tolerance to hypoxia. However, these cells can store only minute amounts of glycogen, since the progressive accumulation of this molecule leads to neuronal loss. Loss-of-function mutations in laforin and malin cause Lafora disease. This condition is characterized by the presence of high numbers of insoluble polyglucosan bodies, known as Lafora bodies, in neuronal cells. Our findings reveal that the accumulation of this aberrant glycogen accounts for the neurodegeneration and functional consequences, as well as the impaired autophagy, observed in models of this disease. Similarly glycogen synthase is responsible for the accumulation of corpora amylacea, which are polysaccharide-based aggregates present in the neurons of aged human brains. Our findings change the current view of the role of glycogen in the brain and reveal that endogenous neuronal glycogen metabolism is important under stress conditions and that neuronal glycogen accumulation contributes to neurodegenerative diseases and to aging-related corpora amylacea formation. Copyright © 2015 Elsevier Ltd. All rights reserved.

SGK1 (glucose transport), dishevelled2 (wnt signaling), LC3/p62 (autophagy) and p53 (apoptosis) proteins are unaltered in Lafora disease.

PubMed

Wang, Peixiang; Israelian, Lori; Xue, Yunlin; Song, Siyuan; Attisano, Liliana; Minassian, Berge A

2016-01-01

Glycogen forms through the concerted actions of glycogen synthase (GS) which elongates glycogen strands, and glycogen branching enzyme (GBE). Lafora disease (LD) is a fatal neurodegenerative epilepsy that results from neuronal accumulation of hyperphosphorylated glycogen with excessively long strands (called polyglucosans). There is no GBE deficiency in LD. Instead, the disease is caused by loss-of-function mutations in the EPM2A or EPM2B genes, encoding, respectively, a phosphatase, laforin, and an E3 ubiquiting ligase, malin. A number of experimentally derived hypotheses have been published to explain LD, including: The SGK1 hypothesis - Phosphorylated SGK1 (pSGK1) raises cellular glucose uptake and levels, which would activate GS. Based on observing increased pSGK1 in LD mice it was proposed that raised pSGK1 leads to polyglucosan generation through GS hyperactivation. The Dishevelled2 hypothesis - Downregulating malin in cell culture was reported to increase levels of dishevelled2, which through the wnt/glycogen synthase kinase-3 pathway would likewise overactivate GS. The Autophagic defect hypothesis - Polyglucosans may be natural byproducts of normal glycogen metabolism. LD mice were reported to be autophagy-defective. LD would arise from failed autophagy leading to failed polyglucosan clearance. Finally, the p53 hypothesis - laforin and malin were reported to downregulate p53, their absence leading to increased p53, which would activate apoptosis, leading to the neurodegeneration of LD. In the present work we repeat key experiments that underlie these four hypotheses. We are unable to confirm increased pSGK1, dishevelled2, or p53 in LD mice, nor the reported autophagic defects. Our work does not support the above hypotheses in understanding this unique and severe form of epilepsy.

Mechanism suppressing glycogen synthesis in neurons and its demise in progressive myoclonus epilepsy.

PubMed

Vilchez, David; Ros, Susana; Cifuentes, Daniel; Pujadas, Lluís; Vallès, Jordi; García-Fojeda, Belén; Criado-García, Olga; Fernández-Sánchez, Elena; Medraño-Fernández, Iria; Domínguez, Jorge; García-Rocha, Mar; Soriano, Eduardo; Rodríguez de Córdoba, Santiago; Guinovart, Joan J

2007-11-01

Glycogen synthesis is normally absent in neurons. However, inclusion bodies resembling abnormal glycogen accumulate in several neurological diseases, particularly in progressive myoclonus epilepsy or Lafora disease. We show here that mouse neurons have the enzymatic machinery for synthesizing glycogen, but that it is suppressed by retention of muscle glycogen synthase (MGS) in the phosphorylated, inactive state. This suppression was further ensured by a complex of laforin and malin, which are the two proteins whose mutations cause Lafora disease. The laforin-malin complex caused proteasome-dependent degradation both of the adaptor protein targeting to glycogen, PTG, which brings protein phosphatase 1 to MGS for activation, and of MGS itself. Enforced expression of PTG led to glycogen deposition in neurons and caused apoptosis. Therefore, the malin-laforin complex ensures a blockade of neuronal glycogen synthesis even under intense glycogenic conditions. Here we explain the formation of polyglucosan inclusions in Lafora disease by demonstrating a crucial role for laforin and malin in glycogen synthesis.

Expression, purification and characterization of soluble red rooster laforin as a fusion protein in Escherichia coli.

PubMed

Brewer, M Kathryn; Husodo, Satrio; Dukhande, Vikas V; Johnson, Mary Beth; Gentry, Matthew S

2014-04-02

The gene that encodes laforin, a dual-specificity phosphatase with a carbohydrate-binding module, is mutated in Lafora disease (LD). LD is an autosomal recessive, fatal progressive myoclonus epilepsy characterized by the intracellular buildup of insoluble, hyperphosphorylated glycogen-like particles, called Lafora bodies. Laforin dephosphorylates glycogen and other glucans in vitro, but the structural basis of its activity remains unknown. Recombinant human laforin when expressed in and purified from E. coli is largely insoluble and prone to aggregation and precipitation. Identification of a laforin ortholog that is more soluble and stable in vitro would circumvent this issue. In this study, we cloned multiple laforin orthologs, established a purification scheme for each, and tested their solubility and stability. Gallus gallus (Gg) laforin is more stable in vitro than human laforin, Gg-laforin is largely monomeric, and it possesses carbohydrate binding and phosphatase activity similar to human laforin. Gg-laforin is more soluble and stable than human laforin in vitro, and possesses similar activity as a glucan phosphatase. Therefore, it can be used to model human laforin in structure-function studies. We have established a protocol for purifying recombinant Gg-laforin in sufficient quantity for crystallographic and other biophysical analyses, in order to better understand the function of laforin and define the molecular mechanisms of Lafora disease.

[A family with progressive myoclonus epilepsy (author's transl)].

PubMed

Ammann, F; Schweingruber, R; Paro, M

1978-01-01

To begin, a survey of the literature concerning the group of progressive myoclonic epilepsies is presented, from the initial descriptions of Unverricht (1891) and Lundborg (1903) to the present. Recently several subforms of this nosologic entity have been delineated according to the mode of inheritance, time of manifestation, severity of course, and biochemical characteristics (i.e, eventual demonstration of mucopolysaccharide storage in Lafora bodies or diffuse in the central nervous system and other organs). The most useful classification stems from Diebold (1972): early (I) and late (II) forms of the Lafora type having autosomal recessive inheritance; an autosomal recessive early form (III) and an autosomal dominant late form (IV) with degenerative changes in the central nervous system without biochemical disturbances. The authors describe 3 young siblings from Southern Tyrol, who clinically manifested the cardinal symptoms of the disease in addition to extrapyramidal cerebellar disturbances. In spite of extensive bioptic and biochemical examinations, neither Lafora bodies nor diffuse deposits or excretion of mucopolysaccharides could be demonstrated. The distant blood relationship between the parents of these patients supports the assumption of an autosomal recessive mode of transmission. The relatively early manifestation of the disease and the demonstration of degenerative changes within the central nervous system suggest assignment of these patients to Diebold's subgroup III of the progressive myoclonic epilepsy.

The laforin-malin complex negatively regulates glycogen synthesis by modulating cellular glucose uptake via glucose transporters.

PubMed

Singh, Pankaj Kumar; Singh, Sweta; Ganesh, Subramaniam

2012-02-01

Lafora disease (LD), an inherited and fatal neurodegenerative disorder, is characterized by increased cellular glycogen content and the formation of abnormally branched glycogen inclusions, called Lafora bodies, in the affected tissues, including neurons. Therefore, laforin phosphatase and malin ubiquitin E3 ligase, the two proteins that are defective in LD, are thought to regulate glycogen synthesis through an unknown mechanism, the defects in which are likely to underlie some of the symptoms of LD. We show here that laforin's subcellular localization is dependent on the cellular glycogen content and that the stability of laforin is determined by the cellular ATP level, the activity of 5'-AMP-activated protein kinase, and the affinity of malin toward laforin. By using cell and animal models, we further show that the laforin-malin complex regulates cellular glucose uptake by modulating the subcellular localization of glucose transporters; loss of malin or laforin resulted in an increased abundance of glucose transporters in the plasma membrane and therefore excessive glucose uptake. Loss of laforin or malin, however, did not affect glycogen catabolism. Thus, the excessive cellular glucose level appears to be the primary trigger for the abnormally higher levels of cellular glycogen seen in LD.

Muscle Glycogen Remodeling and Glycogen Phosphate Metabolism following Exhaustive Exercise of Wild Type and Laforin Knockout Mice*

PubMed Central

Irimia, Jose M.; Tagliabracci, Vincent S.; Meyer, Catalina M.; Segvich, Dyann M.; DePaoli-Roach, Anna A.; Roach, Peter J.

2015-01-01

Glycogen, the repository of glucose in many cell types, contains small amounts of covalent phosphate, of uncertain function and poorly understood metabolism. Loss-of-function mutations in the laforin gene cause the fatal neurodegenerative disorder, Lafora disease, characterized by increased glycogen phosphorylation and the formation of abnormal deposits of glycogen-like material called Lafora bodies. It is generally accepted that the phosphate is removed by the laforin phosphatase. To study the dynamics of skeletal muscle glycogen phosphorylation in vivo under physiological conditions, mice were subjected to glycogen-depleting exercise and then monitored while they resynthesized glycogen. Depletion of glycogen by exercise was associated with a substantial reduction in total glycogen phosphate and the newly resynthesized glycogen was less branched and less phosphorylated. Branching returned to normal on a time frame of days, whereas phosphorylation remained suppressed over a longer period of time. We observed no change in markers of autophagy. Exercise of 3-month-old laforin knock-out mice caused a similar depletion of glycogen but no loss of glycogen phosphate. Furthermore, remodeling of glycogen to restore the basal branching pattern was delayed in the knock-out animals. From these results, we infer that 1) laforin is responsible for glycogen dephosphorylation during exercise and acts during the cytosolic degradation of glycogen, 2) excess glycogen phosphorylation in the absence of laforin delays the normal remodeling of the branching structure, and 3) the accumulation of glycogen phosphate is a relatively slow process involving multiple cycles of glycogen synthesis-degradation, consistent with the slow onset of the symptoms of Lafora disease. PMID:26216881

Deleterious effects of neuronal accumulation of glycogen in flies and mice

PubMed Central

Duran, Jordi; Tevy, María Florencia; Garcia-Rocha, Mar; Calbó, Joaquim; Milán, Marco; Guinovart, Joan J

2012-01-01

Under physiological conditions, most neurons keep glycogen synthase (GS) in an inactive form and do not show detectable levels of glycogen. Nevertheless, aberrant glycogen accumulation in neurons is a hallmark of patients suffering from Lafora disease or other polyglucosan disorders. Although these diseases are associated with mutations in genes involved in glycogen metabolism, the role of glycogen accumulation remains elusive. Here, we generated mouse and fly models expressing an active form of GS to force neuronal accumulation of glycogen. We present evidence that the progressive accumulation of glycogen in mouse and Drosophila neurons leads to neuronal loss, locomotion defects and reduced lifespan. Our results highlight glycogen accumulation in neurons as a direct cause of neurodegeneration. PMID:22549942

Deleterious effects of neuronal accumulation of glycogen in flies and mice.

PubMed

Duran, Jordi; Tevy, María Florencia; Garcia-Rocha, Mar; Calbó, Joaquim; Milán, Marco; Guinovart, Joan J

2012-08-01

Under physiological conditions, most neurons keep glycogen synthase (GS) in an inactive form and do not show detectable levels of glycogen. Nevertheless, aberrant glycogen accumulation in neurons is a hallmark of patients suffering from Lafora disease or other polyglucosan disorders. Although these diseases are associated with mutations in genes involved in glycogen metabolism, the role of glycogen accumulation remains elusive. Here, we generated mouse and fly models expressing an active form of GS to force neuronal accumulation of glycogen. We present evidence that the progressive accumulation of glycogen in mouse and Drosophila neurons leads to neuronal loss, locomotion defects and reduced lifespan. Our results highlight glycogen accumulation in neurons as a direct cause of neurodegeneration. Copyright © 2012 EMBO Molecular Medicine.

Laforin, a dual specificity phosphatase that dephosphorylates complex carbohydrates.

PubMed

Worby, Carolyn A; Gentry, Matthew S; Dixon, Jack E

2006-10-13

Laforin is the only phosphatase in the animal kingdom that contains a carbohydrate-binding module. Mutations in the gene encoding laforin result in Lafora disease, a fatal autosomal recessive neurodegenerative disorder, which is diagnosed by the presence of intracellular deposits of insoluble complex carbohydrates known as Lafora bodies. We demonstrate that laforin interacts with proteins known to be involved in glycogen metabolism and rule out several of these proteins as potential substrates. Surprisingly, we find that laforin displays robust phosphatase activity against a phosphorylated complex carbohydrate. Furthermore, this activity is unique to laforin, since several other phosphatases are unable to dephosphorylate polysaccharides. Finally, fusing the carbohydrate-binding module of laforin to the dual specific phosphatase VHR does not result in the ability of this phosphatase to dephosphorylate polysaccharides. Therefore, we hypothesize that laforin is unique in its ability to utilize a phosphorylated complex carbohydrate as a substrate and that this function may be necessary for the maintenance of normal cellular glycogen.

The Madrid School of Neurology (1885-1939).

PubMed

Giménez-Roldán, S

2015-01-01

The emergence of neurology in Madrid between 1885 and 1939 had well-defined characteristics. On foundations laid by Cajal and Río-Hortega, pioneers combined clinical practice with cutting-edge neurohistology and neuropathology research. Luis Simarro, trained in Paris, taught many talented students including Gayarre, Achúcarro and Lafora. The untimely death of Nicolás Achúcarro curtailed his promising career, but he still completed the clinicopathological study of the first American case of Alzheimer's disease. On returning to Spain, he studied glial cells, including rod cells. Rodríguez Lafora described progressive myoclonus epilepsy and completed experimental studies of corpus callosum lesions and clinical and neuropathology studies of senile dementia. He fled to Mexico at the end of the Spanish Civil War (1936-1939). Sanchís Banús, a sterling clinical neurologist, described the first cluster of Huntington's disease in Spain, and he and Río-Hortega joined efforts to determine that pallidal degeneration underlies rigidity in advanced stages of the disease. Just after the war, Alberca Llorente eruditely described inflammatory diseases of the neuraxis. Manuel Peraita studied "the neurology of hunger" with data collected during the siege of Madrid. Dionisio Nieto, like many exiled intellectuals, settled in Mexico DF, where he taught neurohistological methods and neuropsychiatry in the tradition of the Madrid School of Neurology. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

A conceptual disease model for adult Pompe disease.

PubMed

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.

Best practice assessment of disease modelling for infectious disease outbreaks.

PubMed

Dembek, Z F; Chekol, T; Wu, A

2018-05-08

During emerging disease outbreaks, public health, emergency management officials and decision-makers increasingly rely on epidemiological models to forecast outbreak progression and determine the best response to health crisis needs. Outbreak response strategies derived from such modelling may include pharmaceutical distribution, immunisation campaigns, social distancing, prophylactic pharmaceuticals, medical care, bed surge, security and other requirements. Infectious disease modelling estimates are unavoidably subject to multiple interpretations, and full understanding of a model's limitations may be lost when provided from the disease modeller to public health practitioner to government policymaker. We review epidemiological models created for diseases which are of greatest concern for public health protection. Such diseases, whether transmitted from person-to-person (Ebola, influenza, smallpox), via direct exposure (anthrax), or food and waterborne exposure (cholera, typhoid) may cause severe illness and death in a large population. We examine disease-specific models to determine best practices characterising infectious disease outbreaks and facilitating emergency response and implementation of public health policy and disease control measures.

Universal etiology, multifactorial diseases and the constitutive model of disease classification.

PubMed

Fuller, Jonathan

2018-02-01

Infectious diseases are often said to have a universal etiology, while chronic and noncommunicable diseases are said to be multifactorial in their etiology. It has been argued that the universal etiology of an infectious disease results from its classification using a monocausal disease model. In this article, I will reconstruct the monocausal model and argue that modern 'multifactorial diseases' are not monocausal by definition. 'Multifactorial diseases' are instead defined according to a constitutive disease model. On closer analysis, infectious diseases are also defined using the constitutive model rather than the monocausal model. As a result, our classification models alone cannot explain why infectious diseases have a universal etiology while chronic and noncommunicable diseases lack one. The explanation is instead provided by the Nineteenth Century germ theorists. Copyright © 2017 Elsevier Ltd. All rights reserved.

Engineered in vitro disease models.

PubMed

Benam, Kambez H; Dauth, Stephanie; Hassell, Bryan; Herland, Anna; Jain, Abhishek; Jang, Kyung-Jin; Karalis, Katia; Kim, Hyun Jung; MacQueen, Luke; Mahmoodian, Roza; Musah, Samira; Torisawa, Yu-suke; van der Meer, Andries D; Villenave, Remi; Yadid, Moran; Parker, Kevin K; Ingber, Donald E

2015-01-01

The ultimate goal of most biomedical research is to gain greater insight into mechanisms of human disease or to develop new and improved therapies or diagnostics. Although great advances have been made in terms of developing disease models in animals, such as transgenic mice, many of these models fail to faithfully recapitulate the human condition. In addition, it is difficult to identify critical cellular and molecular contributors to disease or to vary them independently in whole-animal models. This challenge has attracted the interest of engineers, who have begun to collaborate with biologists to leverage recent advances in tissue engineering and microfabrication to develop novel in vitro models of disease. As these models are synthetic systems, specific molecular factors and individual cell types, including parenchymal cells, vascular cells, and immune cells, can be varied independently while simultaneously measuring system-level responses in real time. In this article, we provide some examples of these efforts, including engineered models of diseases of the heart, lung, intestine, liver, kidney, cartilage, skin and vascular, endocrine, musculoskeletal, and nervous systems, as well as models of infectious diseases and cancer. We also describe how engineered in vitro models can be combined with human inducible pluripotent stem cells to enable new insights into a broad variety of disease mechanisms, as well as provide a test bed for screening new therapies.

Modeling human disease using organotypic cultures.

PubMed

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.

Transgenic mouse models of Parkinson's disease and Huntington's disease.

PubMed

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.

Molecular dynamics simulations and principal component analysis on human laforin mutation W32G and W32G/K87A.

PubMed

Srikumar, P S; Rohini, K; Rajesh, Perumbilavil Kaithamanakallam

2014-06-01

Mutations in human laforin lead to an autosomal neurodegenerative disorder Lafora disease. In N-terminal carbohydrate binding domain of laforin, two mutations W32G and K87A are reported as highly disease causing laforin mutants. Experimental studies reported that mutations are responsible for the abolishment of glycogen binding which is a critical function of laforin. Our current computational study focused on the role of conformational changes in human laforin structure due to existing single mutation W32G and prepared double mutation W32G/K87A related to loss of glycogen binding. We performed 10 ns molecular dynamics (MD) simulation studies in the Gromacs package for both mutations and analyzed the trajectories. From the results, the global properties like root mean square deviation, root mean square fluctuation, radius of gyration, solvent accessible surface area and hydrogen bonds showed structural changes in atomic level observed in W32G and W32G/K87A laforin mutants. The conformational change induced by mutants influenced the loss of the overall stability of the native laforin. Moreover, the change in overall motion of protein was analyzed by principal component analysis and results showed protein clusters expanded more than native and also change in direction in case of double mutant in conformational space. Overall, our report provides theoretical information on loss of structure-function relationship due to flexible nature of laforin mutants. In conclusion, comparative MD simulation studies support the experimental data on W32G and W32G/K87A related to the lafora disease mechanism on glycogen binding.

Changing shapes of glycogen-autophagy nexus in neurons: perspective from a rare epilepsy.

PubMed

Singh, Pankaj Kumar; Singh, Sweta

2015-01-01

In brain, glycogen metabolism is predominantly restricted to astrocytes but it also indirectly supports neuronal functions. Increased accumulation of glycogen in neurons is mysteriously pathogenic triggering neurodegeneration as seen in "Lafora disease" (LD) and in other transgenic animal models of neuronal glycogen accumulation. LD is a fatal neurodegenerative disorder with excessive glycogen inclusions in neurons. Autophagy, a pathway for bulk degradation of obsolete cellular constituents also degrades metabolites like lipid and glycogen. Recently, defects in this pathway emerged as a plausible reason for glycogen accumulation in neurons in LD, although some contradictions prevail. Albeit surprising, a reciprocal regulation of autophagy by glycogen in neurons has also just been proposed. Notably, increasing evidences of interaction between proteins of autophagy and glycogen metabolism from diverse model systems indicate a conserved, dynamic, and regulatory cross-talk between these two pathways. Concerning these findings, we herein provide certain models for the molecular basis of this cross-talk and discuss its potential implication in the pathophysiology of LD.

Translational models of lung disease.

PubMed

Mercer, Paul F; Abbott-Banner, Katharine; Adcock, Ian M; Knowles, Richard G

2015-02-01

The 2nd Cross Company Respiratory Symposium (CCRS), held in Horsham, U.K. in 2012, brought together representatives from across the pharmaceutical industry with expert academics, in the common interest of improving the design and translational predictiveness of in vivo models of respiratory disease. Organized by the respiratory representatives of the European Federation of Pharmaceutical Industries and Federations (EFPIA) group of companies involved in the EU-funded project (U-BIOPRED), the aim of the symposium was to identify state-of-the-art improvements in the utility and design of models of respiratory disease, with a view to improving their translational potential and reducing wasteful animal usage. The respiratory research and development community is responding to the challenge of improving translation in several ways: greater collaboration and open sharing of data, careful selection of the species, complexity and chronicity of the models, improved practices in preclinical research, continued refinement in models of respiratory diseases and their sub-types, greater understanding of the biology underlying human respiratory diseases and their sub-types, and finally greater use of human (and especially disease-relevant) cells, tissues and explants. The present review highlights these initiatives, combining lessons from the symposium and papers published in Clinical Science arising from the symposium, with critiques of the models currently used in the settings of asthma, idiopathic pulmonary fibrosis and COPD. The ultimate hope is that this will contribute to a more rational, efficient and sustainable development of a range of new treatments for respiratory diseases that continue to cause substantial morbidity and mortality across the world.

Animal models of chronic obstructive pulmonary disease.

PubMed

Pérez-Rial, Sandra; Girón-Martínez, Álvaro; Peces-Barba, Germán

2015-03-01

Animal models of disease have always been welcomed by the scientific community because they provide an approach to the investigation of certain aspects of the disease in question. Animal models of COPD cannot reproduce the heterogeneity of the disease and usually only manage to represent the disease in its milder stages. Moreover, airflow obstruction, the variable that determines patient diagnosis, not always taken into account in the models. For this reason, models have focused on the development of emphysema, easily detectable by lung morphometry, and have disregarded other components of the disease, such as airway injury or associated vascular changes. Continuous, long-term exposure to cigarette smoke is considered the main risk factor for this disease, justifying the fact that the cigarette smoke exposure model is the most widely used. Some variations on this basic model, related to exposure time, the association of other inducers or inhibitors, exacerbations or the use of transgenic animals to facilitate the identification of pathogenic pathways have been developed. Some variations or heterogeneity of this disease, then, can be reproduced and models can be designed for resolving researchers' questions on disease identification or treatment responses. Copyright © 2014 SEPAR. Published by Elsevier Espana. All rights reserved.

Imaging plus X: multimodal models of neurodegenerative disease.

PubMed

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

Genetically Engineered Pig Models for Human Diseases

PubMed Central

Prather, Randall S.; Lorson, Monique; Ross, Jason W.; Whyte, Jeffrey J.; Walters, Eric

2015-01-01

Although pigs are used widely as models of human disease, their utility as models has been enhanced by genetic engineering. Initially, transgenes were added randomly to the genome, but with the application of homologous recombination, zinc finger nucleases, and transcription activator-like effector nuclease (TALEN) technologies, now most any genetic change that can be envisioned can be completed. To date these genetic modifications have resulted in animals that have the potential to provide new insights into human diseases for which a good animal model did not exist previously. These new animal models should provide the preclinical data for treatments that are developed for diseases such as Alzheimer's disease, cystic fibrosis, retinitis pigmentosa, spinal muscular atrophy, diabetes, and organ failure. These new models will help to uncover aspects and treatments of these diseases that were otherwise unattainable. The focus of this review is to describe genetically engineered pigs that have resulted in models of human diseases. PMID:25387017

Avian models with spontaneous autoimmune diseases

PubMed Central

Wick, Georg; Andersson, Leif; Hala, Karel; Gershwin, M. Eric; Selmi, Carlo F.; Erf, Gisela F.; Lamont, Susan J.; Sgonc, Roswitha

2012-01-01

Autoimmune diseases in human patients only become clinically manifest when the disease process has developed to a stage where functional compensation by the afflicted organ or system is not possible any more. In order to understand the initial etiologic and pathogenic events that are generally not yet accessible in humans, appropriate animal models are required. In this respect, spontaneously developing models - albeit rare – reflect the situation in humans much more closely than experimentally induced models, including knockout and transgenic mice. The present review describes three spontaneous chicken models for human autoimmune diseases, the Obese strain (OS) with a Hashimoto-like autoimmune thyroiditis, the University of California at Davis lines 200 and 206 (UCD-200 and 206) with a scleroderma-like disease and the amelanotic Smyth line with a vitiligo-like syndrome (SLV). Special emphasis is given to the new opportunities to unravel the genetic basis of these diseases in view of the recently completed sequencing of the chicken genome. PMID:17145302

Poisson Mixture Regression Models for Heart Disease Prediction.

PubMed

Mufudza, Chipo; Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

Poisson Mixture Regression Models for Heart Disease Prediction

PubMed Central

Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

Development of a Conceptual Model of Disease Progression for Use in Economic Modeling of Chronic Obstructive Pulmonary Disease.

PubMed

Tabberer, Maggie; Gonzalez-McQuire, Sebastian; Muellerova, Hana; Briggs, Andrew H; Rutten-van Mölken, Maureen P M H; Chambers, Mike; Lomas, David A

2017-05-01

To develop and validate a new conceptual model (CM) of chronic obstructive pulmonary disease (COPD) for use in disease progression and economic modeling. The CM identifies and describes qualitative associations between disease attributes, progression and outcomes. A literature review was performed to identify any published CMs or literature reporting the impact and association of COPD disease attributes with outcomes. After critical analysis of the literature, a Steering Group of experts from the disciplines of health economics, epidemiology and clinical medicine was convened to develop a draft CM, which was refined using a Delphi process. The refined CM was validated by testing for associations between attributes using data from the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE). Disease progression attributes included in the final CM were history and occurrence of exacerbations, lung function, exercise capacity, signs and symptoms (cough, sputum, dyspnea), cardiovascular disease comorbidities, 'other' comorbidities (including depression), body composition (body mass index), fibrinogen as a biomarker, smoking and demographic characteristics (age, gender). Mortality and health-related quality of life were determined to be the most relevant final outcome measures for this model, intended to be the foundation of an economic model of COPD. The CM is being used as the foundation for developing a new COPD model of disease progression and to provide a framework for the analysis of patient-level data. The CM is available as a reference for the implementation of further disease progression and economic models.

Model-based economic evaluation in Alzheimer's disease: a review of the methods available to model Alzheimer's disease progression.

PubMed

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.

Animal Models for Periodontal Disease

PubMed Central

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

Induced Pluripotent Stem Cells for Disease Modeling and Drug Discovery in Neurodegenerative Diseases.

PubMed

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.

Disease Prediction Models and Operational Readiness

PubMed Central

Corley, Courtney D.; Pullum, Laura L.; Hartley, David M.; Benedum, Corey; Noonan, Christine; Rabinowitz, Peter M.; Lancaster, Mary J.

2014-01-01

The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4), spatial (26), ecological niche (28), diagnostic or clinical (6), spread or response (9), and reviews (3). The model parameters (e.g., etiology, climatic, spatial, cultural) and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological) were recorded and reviewed. A component of this review is the identification of verification and validation (V&V) methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology Readiness

Disease Prediction Models and Operational Readiness

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

Corley, Courtney D.; Pullum, Laura L.; Hartley, David M.

2014-03-19

INTRODUCTION: The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. One of the primary goals of this research was to characterize the viability of biosurveillance models to provide operationally relevant information for decision makers to identify areas for future research. Two critical characteristics differentiate this work from other infectious disease modeling reviews. First, we reviewed models that attempted to predict the disease event, not merely its transmission dynamics. Second, we considered models involving pathogens of concern as determined by the USmore » National Select Agent Registry (as of June 2011). Methods: We searched dozens of commercial and government databases and harvested Google search results for eligible models utilizing terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche-modeling, The publication date of search results returned are bound by the dates of coverage of each database and the date in which the search was performed, however all searching was completed by December 31, 2010. This returned 13,767 webpages and 12,152 citations. After de-duplication and removal of extraneous material, a core collection of 6,503 items was established and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. Next, PNNL’s IN-SPIRE visual analytics software was used to cross-correlate these publications with the definition for a biosurveillance model resulting in the selection of 54 documents that matched the criteria resulting Ten of these documents, However, dealt purely with disease spread models, inactivation of bacteria, or the modeling of human immune system responses to pathogens rather than predicting disease events. As a result, we systematically reviewed 44 papers

Economic Modeling Considerations for Rare Diseases.

PubMed

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.

[The experimental models of Parkinson's disease in animals].

PubMed

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.

Drosophila melanogaster as a Model Organism of Brain Diseases

PubMed Central

Jeibmann, Astrid; Paulus, Werner

2009-01-01

Drosophila melanogaster has been utilized to model human brain diseases. In most of these invertebrate transgenic models, some aspects of human disease are reproduced. Although investigation of rodent models has been of significant impact, invertebrate models offer a wide variety of experimental tools that can potentially address some of the outstanding questions underlying neurological disease. This review considers what has been gleaned from invertebrate models of neurodegenerative diseases, including Alzheimer’s disease, Parkinson’s disease, metabolic diseases such as Leigh disease, Niemann-Pick disease and ceroid lipofuscinoses, tumor syndromes such as neurofibromatosis and tuberous sclerosis, epilepsy as well as CNS injury. It is to be expected that genetic tools in Drosophila will reveal new pathways and interactions, which hopefully will result in molecular based therapy approaches. PMID:19333415

Primary immunodeficiency disease: a model for case management of chronic diseases.

PubMed

Burton, Janet; Murphy, Elyse; Riley, Patty

2010-01-01

Patient-centered chronic care management is a new model for the management of rare chronic diseases such as primary immunodeficiency disease (PIDD). This approach emphasizes helping patients become experts on the management of their disease as informed, involved, and interactive partners in healthcare decisions with providers. Because only a few patients are affected by rare illnesses, these patients are forced to become knowledgeable about their disease and therapies and to seek treatment from a healthcare team, which includes physicians and nurse specialists who are equipped to manage the complexity of the disease and its comorbidities. Importantly, therapy for PIDD can be self-administered at home, which has encouraged the transition toward a proactive stance that is at the heart of patient-centered chronic care management. We discuss the evolution of therapy, the issues with the disease, and challenges with its management within the framework of other chronic disease management programs. Suggestions and rationale to move case management of PIDD forward are presented with the intent that sharing our experiences will improve process and better manage outcomes in this patient population. The patient-centered model for the management of PIDD is applicable to the primary care settings, where nurse case managers assist patients through education, support them and their families, and facilitate access to community resources in an approach, which has been described as "guided care." The model also applies specifically to immunology centers where patients receive treatment or instruction on its self-administration at home. Patient-centered management of PIDD, with its emphasis on full involvement of patients in their treatment, has the potential to improve compliance with treatment, and thus patient outcomes, as well as patients' quality of life. The patient-centered model expands the traditional model of chronic disease management, which relies on evidence

Modeling Addictive Consumption as an Infectious Disease*

PubMed Central

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

Modeling seasonal behavior changes and disease transmission with application to chronic wasting disease.

PubMed

Oraby, Tamer; Vasilyeva, Olga; Krewski, Daniel; Lutscher, Frithjof

2014-01-07

Behavior and habitat of wildlife animals change seasonally according to environmental conditions. Mathematical models need to represent this seasonality to be able to make realistic predictions about the future of a population and the effectiveness of human interventions. Managing and modeling disease in wild animal populations requires particular care in that disease transmission dynamics is a critical consideration in the etiology of both human and animal diseases, with different transmission paradigms requiring different disease risk management strategies. Since transmission of infectious diseases among wildlife depends strongly on social behavior, mechanisms of disease transmission could also change seasonally. A specific consideration in this regard confronted by modellers is whether the contact rate between individuals is density-dependent or frequency-dependent. We argue that seasonal behavior changes could lead to a seasonal shift between density and frequency dependence. This hypothesis is explored in the case of chronic wasting disease (CWD), a fatal disease that affects deer, elk and moose in many areas of North America. Specifically, we introduce a strategic CWD risk model based on direct disease transmission that accounts for the seasonal change in the transmission dynamics and habitats occupied, guided by information derived from cervid ecology. The model is composed of summer and winter susceptible-infected (SI) equations, with frequency-dependent and density-dependent transmission dynamics, respectively. The model includes impulsive birth events with density-dependent birth rate. We determine the basic reproduction number as a weighted average of two seasonal reproduction numbers. We parameterize the model from data derived from the scientific literature on CWD and deer ecology, and conduct global and local sensitivity analyses of the basic reproduction number. We explore the effectiveness of different culling strategies for the management of CWD

Bioprinting technologies for disease modeling.

PubMed

Memic, Adnan; Navaei, Ali; Mirani, Bahram; Cordova, Julio Alvin Vacacela; Aldhahri, Musab; Dolatshahi-Pirouz, Alireza; Akbari, Mohsen; Nikkhah, Mehdi

2017-09-01

There is a great need for the development of biomimetic human tissue models that allow elucidation of the pathophysiological conditions involved in disease initiation and progression. Conventional two-dimensional (2D) in vitro assays and animal models have been unable to fully recapitulate the critical characteristics of human physiology. Alternatively, three-dimensional (3D) tissue models are often developed in a low-throughput manner and lack crucial native-like architecture. The recent emergence of bioprinting technologies has enabled creating 3D tissue models that address the critical challenges of conventional in vitro assays through the development of custom bioinks and patient derived cells coupled with well-defined arrangements of biomaterials. Here, we provide an overview on the technological aspects of 3D bioprinting technique and discuss how the development of bioprinted tissue models have propelled our understanding of diseases' characteristics (i.e. initiation and progression). The future perspectives on the use of bioprinted 3D tissue models for drug discovery application are also highlighted.

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.

Fluctuations in epidemic modeling - disease extinction and control

NASA Astrophysics Data System (ADS)

Schwartz, Ira

2009-03-01

The analysis of infectious disease fluctuations has recently seen an increasing rise in the use of new tools and models from stochastic dynamics and statistical physics. Examples arise in modeling fluctuations of multi-strain diseases, in modeling adaptive social behavior and its impact on disease fluctuations, and in the analysis of disease extinction in finite population models. Proper stochastic model reduction [1] allows one to predict unobserved fluctuations from observed data in multi-strain models [2]. Degree alteration and power law behavior is predicted in adaptive network epidemic models [3,4]. And extinction rates derived from large fluctuation theory exhibit scaling with respect to distance to the bifurcation point of disease onset with an unusual exponent [5]. In addition to outbreak prediction, another main goal of epidemic modeling is one of eliminating the disease to extinction through various control mechanisms, such as vaccine implementation or quarantine. In this talk, a description will be presented of the fluctuational behavior of several epidemic models and their extinction rates. A general framework and analysis of the effect of non-Gaussian control actuations which enhance the rate to disease extinction will be described. In particular, in it is shown that even in the presence of a small Poisson distributed vaccination program, there is an exponentially enhanced rate to disease extinction. These ideas may lead to improved methods of controlling disease where random vaccinations are prevalent. [4pt] Recent papers:[0pt] [1] E. Forgoston and I. B. Schwartz, ``Escape Rates in a Stochastic Environment with Multiple Scales,'' arXiv:0809.1345 2008.[0pt] [2] L. B. Shaw, L. Billings, I. B. Schwartz, ``Using dimension reduction to improve outbreak predictability of multi-strain diseases,'' J. Math. Bio. 55, 1 2007.[0pt] [3] L. B. Shaw and I. B. Schwartz, ``Fluctuating epidemics on adaptive networks,'' Physical Review E 77, 066101 2008.[0pt] [4] L. B

Disease modeling in genetic kidney diseases: zebrafish.

PubMed

Schenk, Heiko; Müller-Deile, Janina; Kinast, Mark; Schiffer, Mario

2017-07-01

Growing numbers of translational genomics studies are based on the highly efficient and versatile zebrafish (Danio rerio) vertebrate model. The increasing types of zebrafish models have improved our understanding of inherited kidney diseases, since they not only display pathophysiological changes but also give us the opportunity to develop and test novel treatment options in a high-throughput manner. New paradigms in inherited kidney diseases have been developed on the basis of the distinct genome conservation of approximately 70 % between zebrafish and humans in terms of existing gene orthologs. Several options are available to determine the functional role of a specific gene or gene sets. Permanent genome editing can be induced via complete gene knockout by using the CRISPR/Cas-system, among others, or via transient modification by using various morpholino techniques. Cross-species rescues succeeding knockdown techniques are employed to determine the functional significance of a target gene or a specific mutation. This article summarizes the current techniques and discusses their perspectives.

Disease models for the development of therapies for lysosomal storage diseases.

PubMed

Xu, Miao; Motabar, Omid; Ferrer, Marc; Marugan, Juan J; Zheng, Wei; Ottinger, Elizabeth A

2016-05-01

Lysosomal storage diseases (LSDs) are a group of rare diseases in which the function of the lysosome is disrupted by the accumulation of macromolecules. The complexity underlying the pathogenesis of LSDs and the small, often pediatric, population of patients make the development of therapies for these diseases challenging. Current treatments are only available for a small subset of LSDs and have not been effective at treating neurological symptoms. Disease-relevant cellular and animal models with high clinical predictability are critical for the discovery and development of new treatments for LSDs. In this paper, we review how LSD patient primary cells and induced pluripotent stem cell-derived cellular models are providing novel assay systems in which phenotypes are more similar to those of the human LSD physiology. Furthermore, larger animal disease models are providing additional tools for evaluation of the efficacy of drug candidates. Early predictors of efficacy and better understanding of disease biology can significantly affect the translational process by focusing efforts on those therapies with the higher probability of success, thus decreasing overall time and cost spent in clinical development and increasing the overall positive outcomes in clinical trials. © 2016 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals Inc. on behalf of The New York Academy of Sciences.

Computational modeling of neurostimulation in brain diseases.

PubMed

Wang, Yujiang; Hutchings, Frances; Kaiser, Marcus

2015-01-01

Neurostimulation as a therapeutic tool has been developed and used for a range of different diseases such as Parkinson's disease, epilepsy, and migraine. However, it is not known why the efficacy of the stimulation varies dramatically across patients or why some patients suffer from severe side effects. This is largely due to the lack of mechanistic understanding of neurostimulation. Hence, theoretical computational approaches to address this issue are in demand. This chapter provides a review of mechanistic computational modeling of brain stimulation. In particular, we will focus on brain diseases, where mechanistic models (e.g., neural population models or detailed neuronal models) have been used to bridge the gap between cellular-level processes of affected neural circuits and the symptomatic expression of disease dynamics. We show how such models have been, and can be, used to investigate the effects of neurostimulation in the diseased brain. We argue that these models are crucial for the mechanistic understanding of the effect of stimulation, allowing for a rational design of stimulation protocols. Based on mechanistic models, we argue that the development of closed-loop stimulation is essential in order to avoid inference with healthy ongoing brain activity. Furthermore, patient-specific data, such as neuroanatomic information and connectivity profiles obtainable from neuroimaging, can be readily incorporated to address the clinical issue of variability in efficacy between subjects. We conclude that mechanistic computational models can and should play a key role in the rational design of effective, fully integrated, patient-specific therapeutic brain stimulation. © 2015 Elsevier B.V. All rights reserved.

Alzheimer's disease: insights from Drosophila melanogaster models

PubMed Central

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

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.

FlyBase portals to human disease research using Drosophila models

PubMed Central

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

FlyBase portals to human disease research using Drosophila models.

PubMed

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.

Rodent Models of Nonalcoholic Fatty Liver Disease/Nonalcoholic Steatohepatitis

PubMed Central

Imajo, Kento; Yoneda, Masato; Kessoku, Takaomi; Ogawa, Yuji; Maeda, Shin; Sumida, Yoshio; Hyogo, Hideyuki; Eguchi, Yuichiro; Wada, Koichiro; Nakajima, Atsushi

2013-01-01

Research in nonalcoholic fatty liver disease (NAFLD), including nonalcoholic steatohepatitis (NASH), has been limited by the availability of suitable models for this disease. A number of rodent models have been described in which the relevant liver pathology develops in an appropriate metabolic context. These models are promising tools for researchers investigating one of the key issues of NASH: not so much why steatosis occurs, but what causes the transition from simple steatosis to the inflammatory, progressive fibrosing condition of steatohepatitis. The different rodent models can be classified into two large groups. The first includes models in which the disease is acquired after dietary or pharmacological manipulation, and the second, genetically modified models in which liver disease develops spontaneously. To date, no single rodent model has encompassed the full spectrum of human disease progression, but individual models can imitate particular characteristics of human disease. Therefore, it is important that researchers choose the appropriate rodent models. The purpose of the present review is to discuss the metabolic abnormalities present in the currently available rodent models of NAFLD, summarizing the strengths and weaknesses of the established models and the key findings that have furthered our understanding of the disease’s pathogenesis. PMID:24192824

Climate-Agriculture-Modeling and Decision Tool for Disease (CAMDT-Disease) for seasonal climate forecast-based crop disease risk management in agriculture

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.

Modelling human disease with pluripotent stem cells.

PubMed

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.

Large Mammalian Animal Models of Heart Disease

PubMed Central

Camacho, Paula; Fan, Huimin; Liu, Zhongmin; He, Jia-Qiang

2016-01-01

Due to the biological complexity of the cardiovascular system, the animal model is an urgent pre-clinical need to advance our knowledge of cardiovascular disease and to explore new drugs to repair the damaged heart. Ideally, a model system should be inexpensive, easily manipulated, reproducible, a biological representative of human disease, and ethically sound. Although a larger animal model is more expensive and difficult to manipulate, its genetic, structural, functional, and even disease similarities to humans make it an ideal model to first consider. This review presents the commonly-used large animals—dog, sheep, pig, and non-human primates—while the less-used other large animals—cows, horses—are excluded. The review attempts to introduce unique points for each species regarding its biological property, degrees of susceptibility to develop certain types of heart diseases, and methodology of induced conditions. For example, dogs barely develop myocardial infarction, while dilated cardiomyopathy is developed quite often. Based on the similarities of each species to the human, the model selection may first consider non-human primates—pig, sheep, then dog—but it also depends on other factors, for example, purposes, funding, ethics, and policy. We hope this review can serve as a basic outline of large animal models for cardiovascular researchers and clinicians. PMID:29367573

Deterministic and stochastic CTMC models from Zika disease transmission

NASA Astrophysics Data System (ADS)

Zevika, Mona; Soewono, Edy

2018-03-01

Zika infection is one of the most important mosquito-borne diseases in the world. Zika virus (ZIKV) is transmitted by many Aedes-type mosquitoes including Aedes aegypti. Pregnant women with the Zika virus are at risk of having a fetus or infant with a congenital defect and suffering from microcephaly. Here, we formulate a Zika disease transmission model using two approaches, a deterministic model and a continuous-time Markov chain stochastic model. The basic reproduction ratio is constructed from a deterministic model. Meanwhile, the CTMC stochastic model yields an estimate of the probability of extinction and outbreaks of Zika disease. Dynamical simulations and analysis of the disease transmission are shown for the deterministic and stochastic models.

A complete categorization of multiscale models of infectious disease systems.

PubMed

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.

A mathematical model of insulin resistance in Parkinson's disease.

PubMed

Braatz, Elise M; Coleman, Randolph A

2015-06-01

This paper introduces a mathematical model representing the biochemical interactions between insulin signaling and Parkinson's disease. The model can be used to examine the changes that occur over the course of the disease as well as identify which processes would be the most effective targets for treatment. The model is mathematized using biochemical systems theory (BST). It incorporates a treatment strategy that includes several experimental drugs along with current treatments. In the past, BST models of neurodegeneration have used power law analysis and simulation (PLAS) to model the system. This paper recommends the use of MATLAB instead. MATLAB allows for more flexibility in both the model itself and in data analysis. Previous BST analyses of neurodegeneration began treatment at disease onset. As shown in this model, the outcomes of delayed, realistic treatment and full treatment at disease onset are significantly different. The delayed treatment strategy is an important development in BST modeling of neurodegeneration. It emphasizes the importance of early diagnosis, and allows for a more accurate representation of disease and treatment interactions. Copyright © 2015 Elsevier Ltd. All rights reserved.

Classic and new animal models of Parkinson's disease.

PubMed

Blesa, Javier; Phani, Sudarshan; Jackson-Lewis, Vernice; Przedborski, Serge

2012-01-01

Neurological disorders can be modeled in animals so as to recreate specific pathogenic events and behavioral outcomes. Parkinson's Disease (PD) is the second most common neurodegenerative disease of an aging population, and although there have been several significant findings about the PD disease process, much of this process still remains a mystery. Breakthroughs in the last two decades using animal models have offered insights into the understanding of the PD disease process, its etiology, pathology, and molecular mechanisms. Furthermore, while cellular models have helped to identify specific events, animal models, both toxic and genetic, have replicated almost all of the hallmarks of PD and are useful for testing new neuroprotective or neurorestorative strategies. Moreover, significant advances in the modeling of additional PD features have come to light in both classic and newer models. In this review, we try to provide an updated summary of the main characteristics of these models as well as the strengths and weaknesses of what we believe to be the most popular PD animal models. These models include those produced by 6-hydroxydopamine (6-OHDA), 1-methyl-1,2,3,6-tetrahydropiridine (MPTP), rotenone, and paraquat, as well as several genetic models like those related to alpha-synuclein, PINK1, Parkin and LRRK2 alterations.

Preclinical models of Graves' disease and associated secondary complications.

PubMed

Moshkelgosha, Sajad; So, Po-Wah; Diaz-Cano, Salvador; Banga, J Paul

2015-01-01

Autoimmune thyroid disease is the most common organ-specific autoimmune disorder which consists of two opposing clinical syndromes, Hashimoto's thyroiditis and Graves' (hyperthyroidism) disease. Graves' disease is characterized by goiter, hyperthyroidism, and the orbital complication known as Graves' orbitopathy (GO), or thyroid eye disease. The hyperthyroidism in Graves' disease is caused by stimulation of function of thyrotropin hormone receptor (TSHR), resulting from the production of agonist antibodies to the receptor. A variety of induced mouse models of Graves' disease have been developed over the past two decades, with some reproducible models leading to high disease incidence of autoimmune hyperthyroidism. However, none of the models show any signs of the orbital manifestation of GO. We have recently developed an experimental mouse model of GO induced by immunization of the plasmid encoded ligand binding domain of human TSHR cDNA by close field electroporation that recapitulates the orbital pathology in GO. As in human GO patients, immune mice with hyperthyroid or hypothyroid disease induced by anti-TSHR antibodies exhibited orbital pathology and chemosis, characterized by inflammation of orbital muscles and extensive adipogenesis leading to expansion of the orbital retrobulbar space. Magnetic resonance imaging of the head region in immune mice showed a significant expansion of the orbital space, concurrent with proptosis. This review discusses the different strategies for developing mouse models in Graves' disease, with a particular focus on GO. Furthermore, it outlines how this new model will facilitate molecular investigations into pathophysiology of the orbital disease and evaluation of new therapeutic interventions.

[Stochastic model of infectious diseases transmission].

PubMed

Ruiz-Ramírez, Juan; Hernández-Rodríguez, Gabriela Eréndira

2009-01-01

Propose a mathematic model that shows how population structure affects the size of infectious disease epidemics. This study was conducted during 2004 at the University of Colima. It used generalized small-world network topology to represent contacts that occurred within and between families. To that end, two programs in MATLAB were conducted to calculate the efficiency of the network. The development of a program in the C programming language was also required, that represents the stochastic susceptible-infectious-removed model, and simultaneous results were obtained for the number of infected people. An increased number of families connected by meeting sites impacted the size of the infectious diseases by roughly 400%. Population structure influences the rapid spread of infectious diseases, reaching epidemic effects.

Engineering Large Animal Species to Model Human Diseases.

PubMed

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.

Stochastic modelling of infectious diseases for heterogeneous populations.

PubMed

Ming, Rui-Xing; Liu, Ji-Ming; W Cheung, William K; Wan, Xiang

2016-12-22

Infectious diseases such as SARS and H1N1 can significantly impact people's lives and cause severe social and economic damages. Recent outbreaks have stressed the urgency of effective research on the dynamics of infectious disease spread. However, it is difficult to predict when and where outbreaks may emerge and how infectious diseases spread because many factors affect their transmission, and some of them may be unknown. One feasible means to promptly detect an outbreak and track the progress of disease spread is to implement surveillance systems in regional or national health and medical centres. The accumulated surveillance data, including temporal, spatial, clinical, and demographic information can provide valuable information that can be exploited to better understand and model the dynamics of infectious disease spread. The aim of this work is to develop and empirically evaluate a stochastic model that allows the investigation of transmission patterns of infectious diseases in heterogeneous populations. We test the proposed model on simulation data and apply it to the surveillance data from the 2009 H1N1 pandemic in Hong Kong. In the simulation experiment, our model achieves high accuracy in parameter estimation (less than 10.0 % mean absolute percentage error). In terms of the forward prediction of case incidence, the mean absolute percentage errors are 17.3 % for the simulation experiment and 20.0 % for the experiment on the real surveillance data. We propose a stochastic model to study the dynamics of infectious disease spread in heterogeneous populations from temporal-spatial surveillance data. The proposed model is evaluated using both simulated data and the real data from the 2009 H1N1 epidemic in Hong Kong and achieves acceptable prediction accuracy. We believe that our model can provide valuable insights for public health authorities to predict the effect of disease spread and analyse its underlying factors and to guide new control efforts.

Toward Standardizing a Lexicon of Infectious Disease Modeling Terms.

PubMed

Milwid, Rachael; Steriu, Andreea; Arino, Julien; Heffernan, Jane; Hyder, Ayaz; Schanzer, Dena; Gardner, Emma; Haworth-Brockman, Margaret; Isfeld-Kiely, Harpa; Langley, Joanne M; Moghadas, Seyed M

2016-01-01

Disease modeling is increasingly being used to evaluate the effect of health intervention strategies, particularly for infectious diseases. However, the utility and application of such models are hampered by the inconsistent use of infectious disease modeling terms between and within disciplines. We sought to standardize the lexicon of infectious disease modeling terms and develop a glossary of terms commonly used in describing models' assumptions, parameters, variables, and outcomes. We combined a comprehensive literature review of relevant terms with an online forum discussion in a virtual community of practice, mod4PH (Modeling for Public Health). Using a convergent discussion process and consensus amongst the members of mod4PH, a glossary of terms was developed as an online resource. We anticipate that the glossary will improve inter- and intradisciplinary communication and will result in a greater uptake and understanding of disease modeling outcomes in heath policy decision-making. We highlight the role of the mod4PH community of practice and the methodologies used in this endeavor to link theory, policy, and practice in the public health domain.

Airway disease phenotypes in animal models of cystic fibrosis.

PubMed

McCarron, Alexandra; Donnelley, Martin; Parsons, David

2018-04-02

In humans, cystic fibrosis (CF) lung disease is characterised by chronic infection, inflammation, airway remodelling, and mucus obstruction. A lack of pulmonary manifestations in CF mouse models has hindered investigations of airway disease pathogenesis, as well as the development and testing of potential therapeutics. However, recently generated CF animal models including rat, ferret and pig models demonstrate a range of well characterised lung disease phenotypes with varying degrees of severity. This review discusses the airway phenotypes of currently available CF animal models and presents potential applications of each model in airway-related CF research.

[Animal models of neurodegenerative diseases].

PubMed

Langui, Dominique; Lachapelle, François; Duyckaerts, Charles

2007-02-01

. Human diseases have to be studied in parallel with their animal models to ensure that the model mimic at least a few original mechanisms, on which new therapeutics may be tested.

Disease Extinction Versus Persistence in Discrete-Time Epidemic Models.

PubMed

van den Driessche, P; Yakubu, Abdul-Aziz

2018-04-12

We focus on discrete-time infectious disease models in populations that are governed by constant, geometric, Beverton-Holt or Ricker demographic equations, and give a method for computing the basic reproduction number, [Formula: see text]. When [Formula: see text] and the demographic population dynamics are asymptotically constant or under geometric growth (non-oscillatory), we prove global asymptotic stability of the disease-free equilibrium of the disease models. Under the same demographic assumption, when [Formula: see text], we prove uniform persistence of the disease. We apply our theoretical results to specific discrete-time epidemic models that are formulated for SEIR infections, cholera in humans and anthrax in animals. Our simulations show that a unique endemic equilibrium of each of the three specific disease models is asymptotically stable whenever [Formula: see text].

Fly Models of Human Diseases: Drosophila as a Model for Understanding Human Mitochondrial Mutations and Disease.

PubMed

Sen, A; Cox, R T

2017-01-01

Mitochondrial diseases are a prevalent, heterogeneous class of diseases caused by defects in oxidative phosphorylation, whose severity depends upon particular genetic mutations. These diseases can be difficult to diagnose, and current therapeutics have limited efficacy, primarily treating only symptoms. Because mitochondria play a pivotal role in numerous cellular functions, especially ATP production, their diminished activity has dramatic physiological consequences. While this in and of itself makes treating mitochondrial disease complex, these organelles contain their own DNA, mtDNA, whose products are required for ATP production, in addition to the hundreds of nucleus-encoded proteins. Drosophila offers a tractable whole-animal model to understand the mechanisms underlying loss of mitochondrial function, the subsequent cellular and tissue damage that results, and how these organelles are inherited. Human and Drosophila mtDNAs encode the same set of products, and the homologous nucleus-encoded genes required for mitochondrial function are conserved. In addition, Drosophila contain sufficiently complex organ systems to effectively recapitulate many basic symptoms of mitochondrial diseases, yet are relatively easy and fast to genetically manipulate. There are several Drosophila models for specific mitochondrial diseases, which have been recently reviewed (Foriel, Willems, Smeitink, Schenck, & Beyrath, 2015). In this review, we highlight the conservation between human and Drosophila mtDNA, the present and future techniques for creating mtDNA mutations for further study, and how Drosophila has contributed to our current understanding of mitochondrial inheritance. © 2017 Elsevier Inc. All rights reserved.

Toward Standardizing a Lexicon of Infectious Disease Modeling Terms

PubMed Central

Milwid, Rachael; Steriu, Andreea; Arino, Julien; Heffernan, Jane; Hyder, Ayaz; Schanzer, Dena; Gardner, Emma; Haworth-Brockman, Margaret; Isfeld-Kiely, Harpa; Langley, Joanne M.; Moghadas, Seyed M.

2016-01-01

Disease modeling is increasingly being used to evaluate the effect of health intervention strategies, particularly for infectious diseases. However, the utility and application of such models are hampered by the inconsistent use of infectious disease modeling terms between and within disciplines. We sought to standardize the lexicon of infectious disease modeling terms and develop a glossary of terms commonly used in describing models’ assumptions, parameters, variables, and outcomes. We combined a comprehensive literature review of relevant terms with an online forum discussion in a virtual community of practice, mod4PH (Modeling for Public Health). Using a convergent discussion process and consensus amongst the members of mod4PH, a glossary of terms was developed as an online resource. We anticipate that the glossary will improve inter- and intradisciplinary communication and will result in a greater uptake and understanding of disease modeling outcomes in heath policy decision-making. We highlight the role of the mod4PH community of practice and the methodologies used in this endeavor to link theory, policy, and practice in the public health domain. PMID:27734014

A vector space model approach to identify genetically related diseases.

PubMed

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.

PDON: Parkinson's disease ontology for representation and modeling of the Parkinson's disease knowledge domain.

PubMed

Younesi, Erfan; Malhotra, Ashutosh; Gündel, Michaela; Scordis, Phil; Kodamullil, Alpha Tom; Page, Matt; Müller, Bernd; Springstubbe, Stephan; Wüllner, Ullrich; Scheller, Dieter; Hofmann-Apitius, Martin

2015-09-22

Despite the unprecedented and increasing amount of data, relatively little progress has been made in molecular characterization of mechanisms underlying Parkinson's disease. In the area of Parkinson's research, there is a pressing need to integrate various pieces of information into a meaningful context of presumed disease mechanism(s). Disease ontologies provide a novel means for organizing, integrating, and standardizing the knowledge domains specific to disease in a compact, formalized and computer-readable form and serve as a reference for knowledge exchange or systems modeling of disease mechanism. The Parkinson's disease ontology was built according to the life cycle of ontology building. Structural, functional, and expert evaluation of the ontology was performed to ensure the quality and usability of the ontology. A novelty metric has been introduced to measure the gain of new knowledge using the ontology. Finally, a cause-and-effect model was built around PINK1 and two gene expression studies from the Gene Expression Omnibus database were re-annotated to demonstrate the usability of the ontology. The Parkinson's disease ontology with a subclass-based taxonomic hierarchy covers the broad spectrum of major biomedical concepts from molecular to clinical features of the disease, and also reflects different views on disease features held by molecular biologists, clinicians and drug developers. The current version of the ontology contains 632 concepts, which are organized under nine views. The structural evaluation showed the balanced dispersion of concept classes throughout the ontology. The functional evaluation demonstrated that the ontology-driven literature search could gain novel knowledge not present in the reference Parkinson's knowledge map. The ontology was able to answer specific questions related to Parkinson's when evaluated by experts. Finally, the added value of the Parkinson's disease ontology is demonstrated by ontology-driven modeling of PINK1

Modelling the impacts of pests and diseases on agricultural systems.

PubMed

Donatelli, M; Magarey, R D; Bregaglio, S; Willocquet, L; Whish, J P M; Savary, S

2017-07-01

The improvement and application of pest and disease models to analyse and predict yield losses including those due to climate change is still a challenge for the scientific community. Applied modelling of crop diseases and pests has mostly targeted the development of support capabilities to schedule scouting or pesticide applications. There is a need for research to both broaden the scope and evaluate the capabilities of pest and disease models. Key research questions not only involve the assessment of the potential effects of climate change on known pathosystems, but also on new pathogens which could alter the (still incompletely documented) impacts of pests and diseases on agricultural systems. Yield loss data collected in various current environments may no longer represent a adequate reference to develop tactical, decision-oriented, models for plant diseases and pests and their impacts, because of the ongoing changes in climate patterns. Process-based agricultural simulation modelling, on the other hand, appears to represent a viable methodology to estimate the impacts of these potential effects. A new generation of tools based on state-of-the-art knowledge and technologies is needed to allow systems analysis including key processes and their dynamics over appropriate suitable range of environmental variables. This paper offers a brief overview of the current state of development in coupling pest and disease models to crop models, and discusses technical and scientific challenges. We propose a five-stage roadmap to improve the simulation of the impacts caused by plant diseases and pests; i) improve the quality and availability of data for model inputs; ii) improve the quality and availability of data for model evaluation; iii) improve the integration with crop models; iv) improve the processes for model evaluation; and v) develop a community of plant pest and disease modelers.

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

Model Organisms Facilitate Rare Disease Diagnosis and Therapeutic Research

PubMed Central

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

Use of model organism and disease databases to support matchmaking for human disease gene discovery.

PubMed

Mungall, Christopher J; Washington, Nicole L; Nguyen-Xuan, Jeremy; Condit, Christopher; Smedley, Damian; Köhler, Sebastian; Groza, Tudor; Shefchek, Kent; Hochheiser, Harry; Robinson, Peter N; Lewis, Suzanna E; Haendel, Melissa A

2015-10-01

The Matchmaker Exchange application programming interface (API) allows searching a patient's genotypic or phenotypic profiles across clinical sites, for the purposes of cohort discovery and variant disease causal validation. This API can be used not only to search for matching patients, but also to match against public disease and model organism data. This public disease data enable matching known diseases and variant-phenotype associations using phenotype semantic similarity algorithms developed by the Monarch Initiative. The model data can provide additional evidence to aid diagnosis, suggest relevant models for disease mechanism and treatment exploration, and identify collaborators across the translational divide. The Monarch Initiative provides an implementation of this API for searching multiple integrated sources of data that contextualize the knowledge about any given patient or patient family into the greater biomedical knowledge landscape. While this corpus of data can aid diagnosis, it is also the beginning of research to improve understanding of rare human diseases. © 2015 WILEY PERIODICALS, INC.

Concise Review: Cardiac Disease Modeling Using Induced Pluripotent Stem Cells.

PubMed

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.

Modeling Alzheimer’s disease: from past to future

PubMed Central

Saraceno, Claudia; Musardo, Stefano; Marcello, Elena; Pelucchi, Silvia; Di Luca, Monica

2013-01-01

Alzheimer’s disease (AD) is emerging as the most prevalent and socially disruptive illness of aging populations, as more people live long enough to become affected. Although AD is placing a considerable and increasing burden on society, it represents the largest unmet medical need in neurology, because current drugs improve symptoms, but do not have profound disease-modifying effects. Although AD pathogenesis is multifaceted and difficult to pinpoint, genetic and cell biological studies led to the amyloid hypothesis, which posits that amyloid β (Aβ) plays a pivotal role in AD pathogenesis. Amyloid precursor protein (APP), as well as β- and γ-secretases are the principal players involved in Aβ production, while α-secretase cleavage on APP prevents Aβ deposition. The association of early onset familial AD with mutations in the APP and γ-secretase components provided a potential tool of generating animal models of the disease. However, a model that recapitulates all the aspects of AD has not yet been produced. Here, we face the problem of modeling AD pathology describing several models, which have played a major role in defining critical disease-related mechanisms and in exploring novel potential therapeutic approaches. In particular, we will provide an extensive overview on the distinct features and pros and contras of different AD models, ranging from invertebrate to rodent models and finally dealing with computational models and induced pluripotent stem cells. PMID:23801962

Modeling Huntington׳s disease with patient-derived neurons.

PubMed

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.

Optimizing agent-based transmission models for infectious diseases.

PubMed

Willem, Lander; Stijven, Sean; Tijskens, Engelbert; Beutels, Philippe; Hens, Niel; Broeckhove, Jan

2015-06-02

Infectious disease modeling and computational power have evolved such that large-scale agent-based models (ABMs) have become feasible. However, the increasing hardware complexity requires adapted software designs to achieve the full potential of current high-performance workstations. We have found large performance differences with a discrete-time ABM for close-contact disease transmission due to data locality. Sorting the population according to the social contact clusters reduced simulation time by a factor of two. Data locality and model performance can also be improved by storing person attributes separately instead of using person objects. Next, decreasing the number of operations by sorting people by health status before processing disease transmission has also a large impact on model performance. Depending of the clinical attack rate, target population and computer hardware, the introduction of the sort phase decreased the run time from 26% up to more than 70%. We have investigated the application of parallel programming techniques and found that the speedup is significant but it drops quickly with the number of cores. We observed that the effect of scheduling and workload chunk size is model specific and can make a large difference. Investment in performance optimization of ABM simulator code can lead to significant run time reductions. The key steps are straightforward: the data structure for the population and sorting people on health status before effecting disease propagation. We believe these conclusions to be valid for a wide range of infectious disease ABMs. We recommend that future studies evaluate the impact of data management, algorithmic procedures and parallelization on model performance.

Spatial modelling of disease using data- and knowledge-driven approaches.

PubMed

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.

An object simulation model for modeling hypothetical disease epidemics – EpiFlex

PubMed Central

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

Disease elimination and re-emergence in differential-equation models.

PubMed

Greenhalgh, Scott; Galvani, Alison P; Medlock, Jan

2015-12-21

Traditional differential equation models of disease transmission are often used to predict disease trajectories and evaluate the effectiveness of alternative intervention strategies. However, such models cannot account explicitly for probabilistic events, such as those that dominate dynamics when disease prevalence is low during the elimination and re-emergence phases of an outbreak. To account for the dynamics at low prevalence, i.e. the elimination and risk of disease re-emergence, without the added analytical and computational complexity of a stochastic model, we develop a novel application of control theory. We apply our approach to analyze historical data of measles elimination and re-emergence in Iceland from 1923 to 1938, predicting the temporal trajectory of local measles elimination and re-emerge as a result of disease migration from Copenhagen, Denmark. Copyright © 2015 Elsevier Ltd. All rights reserved.

Zero-inflated spatio-temporal models for disease mapping.

PubMed

Torabi, Mahmoud

2017-05-01

In this paper, our aim is to analyze geographical and temporal variability of disease incidence when spatio-temporal count data have excess zeros. To that end, we consider random effects in zero-inflated Poisson models to investigate geographical and temporal patterns of disease incidence. Spatio-temporal models that employ conditionally autoregressive smoothing across the spatial dimension and B-spline smoothing over the temporal dimension are proposed. The analysis of these complex models is computationally difficult from the frequentist perspective. On the other hand, the advent of the Markov chain Monte Carlo algorithm has made the Bayesian analysis of complex models computationally convenient. Recently developed data cloning method provides a frequentist approach to mixed models that is also computationally convenient. We propose to use data cloning, which yields to maximum likelihood estimation, to conduct frequentist analysis of zero-inflated spatio-temporal modeling of disease incidence. One of the advantages of the data cloning approach is that the prediction and corresponding standard errors (or prediction intervals) of smoothing disease incidence over space and time is easily obtained. We illustrate our approach using a real dataset of monthly children asthma visits to hospital in the province of Manitoba, Canada, during the period April 2006 to March 2010. Performance of our approach is also evaluated through a simulation study. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Mouse models rarely mimic the transcriptome of human neurodegenerative diseases: A systematic bioinformatics-based critique of preclinical models.

PubMed

Burns, Terry C; Li, Matthew D; Mehta, Swapnil; Awad, Ahmed J; Morgan, Alexander A

2015-07-15

Translational research for neurodegenerative disease depends intimately upon animal models. Unfortunately, promising therapies developed using mouse models mostly fail in clinical trials, highlighting uncertainty about how well mouse models mimic human neurodegenerative disease at the molecular level. We compared the transcriptional signature of neurodegeneration in mouse models of Alzheimer׳s disease (AD), Parkinson׳s disease (PD), Huntington׳s disease (HD) and amyotrophic lateral sclerosis (ALS) to human disease. In contrast to aging, which demonstrated a conserved transcriptome between humans and mice, only 3 of 19 animal models showed significant enrichment for gene sets comprising the most dysregulated up- and down-regulated human genes. Spearman׳s correlation analysis revealed even healthy human aging to be more closely related to human neurodegeneration than any mouse model of AD, PD, ALS or HD. Remarkably, mouse models frequently upregulated stress response genes that were consistently downregulated in human diseases. Among potential alternate models of neurodegeneration, mouse prion disease outperformed all other disease-specific models. Even among the best available animal models, conserved differences between mouse and human transcriptomes were found across multiple animal model versus human disease comparisons, surprisingly, even including aging. Relative to mouse models, mouse disease signatures demonstrated consistent trends toward preserved mitochondrial function protein catabolism, DNA repair responses, and chromatin maintenance. These findings suggest a more complex and multifactorial pathophysiology in human neurodegeneration than is captured through standard animal models, and suggest that even among conserved physiological processes such as aging, mice are less prone to exhibit neurodegeneration-like changes. This work may help explain the poor track record of mouse-based translational therapies for neurodegeneration and provides a path

A simulation model to estimate cost-offsets for a disease-management program for chronic kidney disease.

PubMed

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.

Advances and Limitations of Disease Biogeography Using Ecological Niche Modeling

PubMed Central

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

Advances and Limitations of Disease Biogeography Using Ecological Niche Modeling.

PubMed

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.

Predicting survival across chronic interstitial lung disease: the ILD-GAP model.

PubMed

Ryerson, Christopher J; Vittinghoff, Eric; Ley, Brett; Lee, Joyce S; Mooney, Joshua J; Jones, Kirk D; Elicker, Brett M; Wolters, Paul J; Koth, Laura L; King, Talmadge E; Collard, Harold R

2014-04-01

Risk prediction is challenging in chronic interstitial lung disease (ILD) because of heterogeneity in disease-specific and patient-specific variables. Our objective was to determine whether mortality is accurately predicted in patients with chronic ILD using the GAP model, a clinical prediction model based on sex, age, and lung physiology, that was previously validated in patients with idiopathic pulmonary fibrosis. Patients with idiopathic pulmonary fibrosis (n=307), chronic hypersensitivity pneumonitis (n=206), connective tissue disease-associated ILD (n=281), idiopathic nonspecific interstitial pneumonia (n=45), or unclassifiable ILD (n=173) were selected from an ongoing database (N=1,012). Performance of the previously validated GAP model was compared with novel prediction models in each ILD subtype and the combined cohort. Patients with follow-up pulmonary function data were used for longitudinal model validation. The GAP model had good performance in all ILD subtypes (c-index, 74.6 in the combined cohort), which was maintained at all stages of disease severity and during follow-up evaluation. The GAP model had similar performance compared with alternative prediction models. A modified ILD-GAP Index was developed for application across all ILD subtypes to provide disease-specific survival estimates using a single risk prediction model. This was done by adding a disease subtype variable that accounted for better adjusted survival in connective tissue disease-associated ILD, chronic hypersensitivity pneumonitis, and idiopathic nonspecific interstitial pneumonia. The GAP model accurately predicts risk of death in chronic ILD. The ILD-GAP model accurately predicts mortality in major chronic ILD subtypes and at all stages of disease.

Genetic variants associated with neurodegenerative Alzheimer disease in natural models.

PubMed

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.

Mathematical Model of Cytomegalovirus (CMV) Disease

NASA Astrophysics Data System (ADS)

Sriningsih, R.; Subhan, M.; Nasution, M. L.

2018-04-01

The article formed the mathematical model of cytomegalovirus (CMV) disease. Cytomegalovirus (CMV) is a type of herpes virus. This virus is actually not dangerous, but if the body's immune weakens the virus can cause serious problems for health and even can cause death. This virus is also susceptible to infect pregnant women. In addition, the baby may also be infected through the placenta. If this is experienced early in pregnancy, it will increase the risk of miscarriage. If the baby is born, it can cause disability in the baby. The model is formed by determining its variables and parameters based on assumptions. The goal is to analyze the dynamics of cytomegalovirus (CMV) disease spread.

A model to evaluate quality and effectiveness of disease management.

PubMed

Lemmens, K M M; Nieboer, A P; van Schayck, C P; Asin, J D; Huijsman, R

2008-12-01

Disease management has emerged as a new strategy to enhance quality of care for patients suffering from chronic conditions, and to control healthcare costs. So far, however, the effects of this strategy remain unclear. Although current models define the concept of disease management, they do not provide a systematic development or an explanatory theory of how disease management affects the outcomes of care. The objective of this paper is to present a framework for valid evaluation of disease-management initiatives. The evaluation model is built on two pillars of disease management: patient-related and professional-directed interventions. The effectiveness of these interventions is thought to be affected by the organisational design of the healthcare system. Disease management requires a multifaceted approach; hence disease-management programme evaluations should focus on the effects of multiple interventions, namely patient-related, professional-directed and organisational interventions. The framework has been built upon the conceptualisation of these disease-management interventions. Analysis of the underlying mechanisms of these interventions revealed that learning and behavioural theories support the core assumptions of disease management. The evaluation model can be used to identify the components of disease-management programmes and the mechanisms behind them, making valid comparison feasible. In addition, this model links the programme interventions to indicators that can be used to evaluate the disease-management programme. Consistent use of this framework will enable comparisons among disease-management programmes and outcomes in evaluation research.

A review of presented mathematical models in Parkinson's disease: black- and gray-box models.

PubMed

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.

Analysis of a waterborne disease model with socioeconomic classes.

PubMed

Collins, O C; Robertson, Suzanne L; Govinder, K S

2015-11-01

Waterborne diseases such as cholera continue to pose serious public health problems in the world today. Transmission parameters can vary greatly with socioeconomic class (SEC) and the availability of clean water. We formulate a multi-patch waterborne disease model such that each patch represents a particular SEC with its own water source, allowing individuals to move between SECs. For a 2-SEC model, we investigate the conditions under which each SEC is responsible for driving a cholera outbreak. We determine the effect of SECs on disease transmission dynamics by comparing the basic reproduction number of the 2-SEC model to that of a homogeneous model that does not take SECs into account. We conclude by extending several results of the 2-SEC model to an n-SEC model. Copyright © 2015 Elsevier Inc. All rights reserved.

Animal Models of Fibrotic Lung Disease

PubMed Central

Lawson, William E.; Oury, Tim D.; Sisson, Thomas H.; Raghavendran, Krishnan; Hogaboam, Cory M.

2013-01-01

Interstitial lung fibrosis can develop as a consequence of occupational or medical exposure, as a result of genetic defects, and after trauma or acute lung injury leading to fibroproliferative acute respiratory distress syndrome, or it can develop in an idiopathic manner. The pathogenesis of each form of lung fibrosis remains poorly understood. They each result in a progressive loss of lung function with increasing dyspnea, and most forms ultimately result in mortality. To better understand the pathogenesis of lung fibrotic disorders, multiple animal models have been developed. This review summarizes the common and emerging models of lung fibrosis to highlight their usefulness in understanding the cell–cell and soluble mediator interactions that drive fibrotic responses. Recent advances have allowed for the development of models to study targeted injuries of Type II alveolar epithelial cells, fibroblastic autonomous effects, and targeted genetic defects. Repetitive dosing in some models has more closely mimicked the pathology of human fibrotic lung disease. We also have a much better understanding of the fact that the aged lung has increased susceptibility to fibrosis. Each of the models reviewed in this report offers a powerful tool for studying some aspect of fibrotic lung disease. PMID:23526222

Scenario tree model for animal disease freedom framed in the OIE context using the example of a generic swine model for Aujeszky's disease in commercial swine in Canada.

PubMed

Christensen, Jette; Vallières, André

2016-01-01

"Freedom from animal disease" is an ambiguous concept that may have a different meaning in trade and science. For trade alone, there are different levels of freedom from OIE listed diseases. A country can: be recognized by OIE to be "officially free"; self-declare freedom, with no official recognition by the OIE; or report animal disease as absent (no occurrence) in six-monthly reports. In science, we apply scenario tree models to calculate the probability of a population being free from disease at a given prevalence to provide evidence of freedom from animal disease. Here, we link science with application by describing how a scenario tree model may contribute to a country's claim of freedom from animal disease. We combine the idea of a standardized presentation of scenario tree models for disease freedom and having a similar model for two different animal diseases to suggest that a simple generic model may help veterinary authorities to build and evaluate scenario tree models for disease freedom. Here, we aim to develop a generic scenario tree model for disease freedom that is: animal species specific, population specific, and has a simple structure. The specific objectives were: to explore the levels of freedom described in the OIE Terrestrial Animal Health Code; to describe how scenario tree models may contribute to a country's claim of freedom from animal disease; and to present a generic swine scenario tree model for disease freedom in Canada's domestic (commercial) swine applied to Aujeszky's disease (AD). In particular, to explore how historical survey data, and data mining may affect the probability of freedom and to explore different sampling strategies. Finally, to frame the generic scenario tree model in the context of Canada's claim of freedom from AD. We found that scenario tree models are useful to support a country's claim of freedom either as "recognized officially free" or as part of a self-declaration but the models should not stand alone in a

Large Animal Models for Batten Disease: A Review

PubMed Central

Weber, Krystal; Pearce, David A.

2014-01-01

The neuronal ceroid lipofuscinoses, collectively referred to as Batten disease, make up a group of inherited childhood disorders that result in blindness, motor and cognitive regression, brain atrophy, and seizures, ultimately leading to premature death. So far more than 10 genes have been implicated in different forms of the neuronal ceroid lipofuscinoses. Most related research has involved mouse models, but several naturally occurring large animal models have recently been discovered. In this review, we discuss the different large animal models and their significance in Batten disease research. PMID:24014507

Zebrafish models of human eye and inner ear diseases.

PubMed

Blanco-Sánchez, B; Clément, A; Phillips, J B; Westerfield, M

2017-01-01

Eye and inner ear diseases are the most common sensory impairments that greatly impact quality of life. Zebrafish have been intensively employed to understand the fundamental mechanisms underlying eye and inner ear development. The zebrafish visual and vestibulo-acoustic systems are very similar to these in humans, and although not yet mature, they are functional by 5days post-fertilization (dpf). In this chapter, we show how the zebrafish has significantly contributed to the field of biomedical research and how researchers, by establishing disease models and meticulously characterizing their phenotypes, have taken the first steps toward therapies. We review here models for (1) eye diseases, (2) ear diseases, and (3) syndromes affecting eye and/or ear. The use of new genome editing technologies and high-throughput screening systems should increase considerably the speed at which knowledge from zebrafish disease models is acquired, opening avenues for better diagnostics, treatments, and therapies. Copyright © 2017 Elsevier Inc. All rights reserved.

A Stochastic Tick-Borne Disease Model: Exploring the Probability of Pathogen Persistence.

PubMed

Maliyoni, Milliward; Chirove, Faraimunashe; Gaff, Holly D; Govinder, Keshlan S

2017-09-01

We formulate and analyse a stochastic epidemic model for the transmission dynamics of a tick-borne disease in a single population using a continuous-time Markov chain approach. The stochastic model is based on an existing deterministic metapopulation tick-borne disease model. We compare the disease dynamics of the deterministic and stochastic models in order to determine the effect of randomness in tick-borne disease dynamics. The probability of disease extinction and that of a major outbreak are computed and approximated using the multitype Galton-Watson branching process and numerical simulations, respectively. Analytical and numerical results show some significant differences in model predictions between the stochastic and deterministic models. In particular, we find that a disease outbreak is more likely if the disease is introduced by infected deer as opposed to infected ticks. These insights demonstrate the importance of host movement in the expansion of tick-borne diseases into new geographic areas.

The cost of simplifying air travel when modeling disease spread.

PubMed

Lessler, Justin; Kaufman, James H; Ford, Daniel A; Douglas, Judith V

2009-01-01

Air travel plays a key role in the spread of many pathogens. Modeling the long distance spread of infectious disease in these cases requires an air travel model. Highly detailed air transportation models can be over determined and computationally problematic. We compared the predictions of a simplified air transport model with those of a model of all routes and assessed the impact of differences on models of infectious disease. Using U.S. ticket data from 2007, we compared a simplified "pipe" model, in which individuals flow in and out of the air transport system based on the number of arrivals and departures from a given airport, to a fully saturated model where all routes are modeled individually. We also compared the pipe model to a "gravity" model where the probability of travel is scaled by physical distance; the gravity model did not differ significantly from the pipe model. The pipe model roughly approximated actual air travel, but tended to overestimate the number of trips between small airports and underestimate travel between major east and west coast airports. For most routes, the maximum number of false (or missed) introductions of disease is small (<1 per day) but for a few routes this rate is greatly underestimated by the pipe model. If our interest is in large scale regional and national effects of disease, the simplified pipe model may be adequate. If we are interested in specific effects of interventions on particular air routes or the time for the disease to reach a particular location, a more complex point-to-point model will be more accurate. For many problems a hybrid model that independently models some frequently traveled routes may be the best choice. Regardless of the model used, the effect of simplifications and sensitivity to errors in parameter estimation should be analyzed.

Neuropathic pain in a Fabry disease rat model

PubMed Central

Miller, James J.; Aoki, Kazuhiro; Murphy, Carly A.; O’Hara, Crystal L.; Tiemeyer, Michael; Stucky, Cheryl L.; Dahms, Nancy M.

2018-01-01

Fabry disease, the most common lysosomal storage disease, affects multiple organs and results in a shortened life span. This disease is caused by a deficiency of the lysosomal enzyme α-galactosidase A, which leads to glycosphingolipid accumulation in many cell types. Neuropathic pain is an early and severely debilitating symptom in patients with Fabry disease, but the cellular and molecular mechanisms that cause the pain are unknown. We generated a rat model of Fabry disease, the first nonmouse model to our knowledge. Fabry rats had substantial serum and tissue accumulation of α-galactosyl glycosphingolipids and had pronounced mechanical pain behavior. Additionally, Fabry rat dorsal root ganglia displayed global N-glycan alterations, sensory neurons were laden with inclusions, and sensory neuron somata exhibited prominent sensitization to mechanical force. We found that the cation channel transient receptor potential ankyrin 1 (TRPA1) is sensitized in Fabry rat sensory neurons and that TRPA1 antagonism reversed the behavioral mechanical sensitization. This study points toward TRPA1 as a potentially novel target to treat the pain experienced by patients with Fabry disease. PMID:29563343

A Japanese model of disease management.

PubMed

Nakashima, Naoki; Kobayashi, Kunihisa; Inoguchi, Toyoshi; Nishida, Daisuke; Tanaka, Naomi; Nakazono, Hiromi; Hoshino, Akihiko; Soejima, Hidehisa; Takayanagi, Ryoichi; Nawata, Hajime

2007-01-01

We started a disease management model, Carna, that includes two programs: one for primary prevention of lifestyle diseases and one for secondary/tertiary prevention of diabetes mellitus. These programs support the family doctor system and education for participants to allow the concept of disease management to take root in Japan. We developed a critical pathway system that can optimize health care of individual participants by matching individual status. This is the core technology of the project. Under the primary prevention program, we can perform the health check-up/ instruction tasks in the 'Tokutei Kenshin', which will start for all Japanese citizens aged 40-74 years in April 2008. In the diabetic program, Carna matches doctors and new patients, prevents patient dropout, supports detection of early-stage complications by distributing questionnaires periodically, and facilitates medical specialists' cooperation with family doctors. Carna promotes periodic medical examinations and quickly provides the result of blood tests to patients. We are conducting a study to assess the medical outcomes and business model. The study will continue until the end of 2007.

Disease modeling and drug screening for neurological diseases using human induced pluripotent stem cells.

PubMed

Xu, Xiao-hong; Zhong, Zhong

2013-06-01

With the general decline of pharmaceutical research productivity, there are concerns that many components of the drug discovery process need to be redesigned and optimized. For example, the human immortalized cell lines or animal primary cells commonly used in traditional drug screening may not faithfully recapitulate the pathological mechanisms of human diseases, leading to biases in assays, targets, or compounds that do not effectively address disease mechanisms. Recent advances in stem cell research, especially in the development of induced pluripotent stem cell (iPSC) technology, provide a new paradigm for drug screening by permitting the use of human cells with the same genetic makeup as the patients without the typical quantity constraints associated with patient primary cells. In this article, we will review the progress made to date on cellular disease models using human stem cells, with a focus on patient-specific iPSCs for neurological diseases. We will discuss the key challenges and the factors that associated with the success of using stem cell models for drug discovery through examples from monogenic diseases, diseases with various known genetic components, and complex diseases caused by a combination of genetic, environmental and other factors.

Mathematical modeling of infectious disease dynamics

PubMed Central

Siettos, Constantinos I.; Russo, Lucia

2013-01-01

Over the last years, an intensive worldwide effort is speeding up the developments in the establishment of a global surveillance network for combating pandemics of emergent and re-emergent infectious diseases. Scientists from different fields extending from medicine and molecular biology to computer science and applied mathematics have teamed up for rapid assessment of potentially urgent situations. Toward this aim mathematical modeling plays an important role in efforts that focus on predicting, assessing, and controlling potential outbreaks. To better understand and model the contagious dynamics the impact of numerous variables ranging from the micro host–pathogen level to host-to-host interactions, as well as prevailing ecological, social, economic, and demographic factors across the globe have to be analyzed and thoroughly studied. Here, we present and discuss the main approaches that are used for the surveillance and modeling of infectious disease dynamics. We present the basic concepts underpinning their implementation and practice and for each category we give an annotated list of representative works. PMID:23552814

Disease Modeling and Gene Therapy of Copper Storage Disease in Canine Hepatic Organoids.

PubMed

Nantasanti, Sathidpak; Spee, Bart; Kruitwagen, Hedwig S; Chen, Chen; Geijsen, Niels; Oosterhoff, Loes A; van Wolferen, Monique E; Pelaez, Nicolas; Fieten, Hille; Wubbolts, Richard W; Grinwis, Guy C; Chan, Jefferson; Huch, Meritxell; Vries, Robert R G; Clevers, Hans; de Bruin, Alain; Rothuizen, Jan; Penning, Louis C; Schotanus, Baukje A

2015-11-10

The recent development of 3D-liver stem cell cultures (hepatic organoids) opens up new avenues for gene and/or stem cell therapy to treat liver disease. To test safety and efficacy, a relevant large animal model is essential but not yet established. Because of its shared pathologies and disease pathways, the dog is considered the best model for human liver disease. Here we report the establishment of a long-term canine hepatic organoid culture allowing undifferentiated expansion of progenitor cells that can be differentiated toward functional hepatocytes. We show that cultures can be initiated from fresh and frozen liver tissues using Tru-Cut or fine-needle biopsies. The use of Wnt agonists proved important for canine organoid proliferation and inhibition of differentiation. Finally, we demonstrate that successful gene supplementation in hepatic organoids of COMMD1-deficient dogs restores function and can be an effective means to cure copper storage disease. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Seven challenges for modelling indirect transmission: vector-borne diseases, macroparasites and neglected tropical diseases.

PubMed

Hollingsworth, T Déirdre; Pulliam, Juliet R C; Funk, Sebastian; Truscott, James E; Isham, Valerie; Lloyd, Alun L

2015-03-01

Many of the challenges which face modellers of directly transmitted pathogens also arise when modelling the epidemiology of pathogens with indirect transmission--whether through environmental stages, vectors, intermediate hosts or multiple hosts. In particular, understanding the roles of different hosts, how to measure contact and infection patterns, heterogeneities in contact rates, and the dynamics close to elimination are all relevant challenges, regardless of the mode of transmission. However, there remain a number of challenges that are specific and unique to modelling vector-borne diseases and macroparasites. Moreover, many of the neglected tropical diseases which are currently targeted for control and elimination are vector-borne, macroparasitic, or both, and so this article includes challenges which will assist in accelerating the control of these high-burden diseases. Here, we discuss the challenges of indirect measures of infection in humans, whether through vectors or transmission life stages and in estimating the contribution of different host groups to transmission. We also discuss the issues of "evolution-proof" interventions against vector-borne disease. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Seven challenges for modelling indirect transmission: Vector-borne diseases, macroparasites and neglected tropical diseases

PubMed Central

Hollingsworth, T. Déirdre; Pulliam, Juliet R.C.; Funk, Sebastian; Truscott, James E.; Isham, Valerie; Lloyd, Alun L.

2015-01-01

Many of the challenges which face modellers of directly transmitted pathogens also arise when modelling the epidemiology of pathogens with indirect transmission – whether through environmental stages, vectors, intermediate hosts or multiple hosts. In particular, understanding the roles of different hosts, how to measure contact and infection patterns, heterogeneities in contact rates, and the dynamics close to elimination are all relevant challenges, regardless of the mode of transmission. However, there remain a number of challenges that are specific and unique to modelling vector-borne diseases and macroparasites. Moreover, many of the neglected tropical diseases which are currently targeted for control and elimination are vector-borne, macroparasitic, or both, and so this article includes challenges which will assist in accelerating the control of these high-burden diseases. Here, we discuss the challenges of indirect measures of infection in humans, whether through vectors or transmission life stages and in estimating the contribution of different host groups to transmission. We also discuss the issues of “evolution-proof” interventions against vector-borne disease. PMID:25843376

Interprofessional Collaborative Practice Models in Chronic Disease Management.

PubMed

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.

Non-alcoholic fatty liver disease (NAFLD) models in drug discovery.

PubMed

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.

Mouse models of neurodegenerative diseases: criteria and general methodology.

PubMed

Janus, Christopher; Welzl, Hans

2010-01-01

The major symptom of Alzheimer's disease is rapidly progressing dementia, coinciding with the formation of amyloid and tau deposits in the central nervous system, and neuronal death. At present familial cases of dementias provide the most promising foundation for modelling neurodegeneration. We describe the mnemonic and other major behavioral symptoms of tauopathies, briefly outline the genetics underlying familiar cases and discuss the arising implications for modelling the disease in mostly transgenic mouse lines. We then depict to what degree the most recent mouse models replicate pathological and cognitive characteristics observed in patients.There is no universally valid behavioral test battery to evaluate mouse models. The selection of individual tests depends on the behavioral and/or memory system in focus, the type of a model and how well it replicates the pathology of a disease and the amount of control over the genetic background of the mouse model. However it is possible to provide guidelines and criteria for modelling the neurodegeneration, setting up the experiments and choosing relevant tests. One should not adopt a "one (trans)gene, one disease" interpretation, but should try to understand how the mouse genome copes with the protein expression of the transgene in question. Further, it is not possible to recommend some mouse models over others since each model is valuable within its own constraints, and the way experiments are performed often reflects the idiosyncratic reality of specific laboratories. Our purpose is to improve bridging molecular and behavioural approaches in translational research.

Neurophysiology of Drosophila Models of Parkinson's Disease

PubMed Central

West, Ryan J. H.; Furmston, Rebecca; Williams, Charles A. C.; Elliott, Christopher J. H.

2015-01-01

We provide an insight into the role Drosophila has played in elucidating neurophysiological perturbations associated with Parkinson's disease- (PD-) related genes. Synaptic signalling deficits are observed in motor, central, and sensory systems. Given the neurological impact of disease causing mutations within these same genes in humans the phenotypes observed in fly are of significant interest. As such we observe four unique opportunities provided by fly nervous system models of Parkinson's disease. Firstly, Drosophila models are instrumental in exploring the mechanisms of neurodegeneration, with several PD-related mutations eliciting related phenotypes including sensitivity to energy supply and vesicular deformities. These are leading to the identification of plausible cellular mechanisms, which may be specific to (dopaminergic) neurons and synapses rather than general cellular phenotypes. Secondly, models show noncell autonomous signalling within the nervous system, offering the opportunity to develop our understanding of the way pathogenic signalling propagates, resembling Braak's scheme of spreading pathology in PD. Thirdly, the models link physiological deficits to changes in synaptic structure. While the structure-function relationship is complex, the genetic tractability of Drosophila offers the chance to separate fundamental changes from downstream consequences. Finally, the strong neuronal phenotypes permit relevant first in vivo drug testing. PMID:25960916

A policy model of cardiovascular disease in moderate-to-advanced chronic kidney disease.

PubMed

Schlackow, Iryna; Kent, Seamus; Herrington, William; Emberson, Jonathan; Haynes, Richard; Reith, Christina; Wanner, Christoph; Fellström, Bengt; Gray, Alastair; Landray, Martin J; Baigent, Colin; Mihaylova, Borislava

2017-12-01

To present a long-term policy model of cardiovascular disease (CVD) in moderate-to-advanced chronic kidney disease (CKD). A Markov model with transitions between CKD stages (3B, 4, 5, on dialysis, with kidney transplant) and cardiovascular events (major atherosclerotic events, haemorrhagic stroke, vascular death) was developed with individualised CKD and CVD risks estimated using the 5 years' follow-up data of the 9270 patients with moderate-to-severe CKD in the Study of Heart and Renal Protection (SHARP) and multivariate parametric survival analysis. The model was assessed in three further CKD cohorts and compared with currently used risk scores. Higher age, previous cardiovascular events and advanced CKD were the main contributors to increased individual disease risks. CKD and CVD risks predicted by the state-transition model corresponded well to risks observed in SHARP and external cohorts. The model's predictions of vascular risk and progression to end-stage renal disease were better than, or comparable to, those produced by other risk scores. As an illustration, at age 60-69 years, projected survival for SHARP participants in CKD stage 3B was 13.5 years (10.6 quality-adjusted life years (QALYs)) in men and 14.8 years (10.7 QALYs) in women. Corresponding projections for participants on dialysis were 7.5 (5.6 QALYs) and 7.8 years (5.4 QALYs). A non-fatal major atherosclerotic event reduced life expectancy by about 2 years in stage 3B and by 1 year in dialysis. The SHARP CKD-CVD model is a novel resource for evaluating health outcomes and cost-effectiveness of interventions in CKD. NCT00125593 and ISRCTN54137607; Post-results. © 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.

Airborne spread of foot-and-mouth disease - model intercomparison

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

Gloster, J; Jones, A; Redington, A

2008-09-04

Foot-and-mouth disease is a highly infectious vesicular disease of cloven-hoofed animals caused by foot-and-mouth disease virus. It spreads by direct contact between animals, by animal products (milk, meat and semen), by mechanical transfer on people or fomites and by the airborne route - with the relative importance of each mechanism depending on the particular outbreak characteristics. Over the years a number of workers have developed or adapted atmospheric dispersion models to assess the risk of foot-and-mouth disease virus spread through the air. Six of these models were compared at a workshop hosted by the Institute for Animal Health/Met Office duringmore » 2008. A number of key issues emerged from the workshop and subsequent modelling work: (1) in general all of the models predicted similar directions for 'at risk' livestock with much of the remaining differences strongly related to differences in the meteorological data used; (2) determination of an accurate sequence of events is highly important, especially if the meteorological conditions vary substantially during the virus emission period; and (3) differences in assumptions made about virus release, environmental fate, and subsequent infection can substantially modify the size and location of the downwind risk area. Close relationships have now been established between participants, which in the event of an outbreak of disease could be readily activated to supply advice or modelling support.« less

Omics analysis of mouse brain models of human diseases.

PubMed

Paban, Véronique; Loriod, Béatrice; Villard, Claude; Buee, Luc; Blum, David; Pietropaolo, Susanna; Cho, Yoon H; Gory-Faure, Sylvie; Mansour, Elodie; Gharbi, Ali; Alescio-Lautier, Béatrice

2017-02-05

The identification of common gene/protein profiles related to brain alterations, if they exist, may indicate the convergence of the pathogenic mechanisms driving brain disorders. Six genetically engineered mouse lines modelling neurodegenerative diseases and neuropsychiatric disorders were considered. Omics approaches, including transcriptomic and proteomic methods, were used. The gene/protein lists were used for inter-disease comparisons and further functional and network investigations. When the inter-disease comparison was performed using the gene symbol identifiers, the number of genes/proteins involved in multiple diseases decreased rapidly. Thus, no genes/proteins were shared by all 6 mouse models. Only one gene/protein (Gfap) was shared among 4 disorders, providing strong evidence that a common molecular signature does not exist among brain diseases. The inter-disease comparison of functional processes showed the involvement of a few major biological processes indicating that brain diseases of diverse aetiologies might utilize common biological pathways in the nervous system, without necessarily involving similar molecules. Copyright © 2016 Elsevier B.V. All rights reserved.

Using induced pluripotent stem cells derived neurons to model brain diseases.

PubMed

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.

Computational modeling of the obstructive lung diseases asthma and COPD

PubMed Central

2014-01-01

Asthma and chronic obstructive pulmonary disease (COPD) are characterized by airway obstruction and airflow limitation and pose a huge burden to society. These obstructive lung diseases impact the lung physiology across multiple biological scales. Environmental stimuli are introduced via inhalation at the organ scale, and consequently impact upon the tissue, cellular and sub-cellular scale by triggering signaling pathways. These changes are propagated upwards to the organ level again and vice versa. In order to understand the pathophysiology behind these diseases we need to integrate and understand changes occurring across these scales and this is the driving force for multiscale computational modeling. There is an urgent need for improved diagnosis and assessment of obstructive lung diseases. Standard clinical measures are based on global function tests which ignore the highly heterogeneous regional changes that are characteristic of obstructive lung disease pathophysiology. Advances in scanning technology such as hyperpolarized gas MRI has led to new regional measurements of ventilation, perfusion and gas diffusion in the lungs, while new image processing techniques allow these measures to be combined with information from structural imaging such as Computed Tomography (CT). However, it is not yet known how to derive clinical measures for obstructive diseases from this wealth of new data. Computational modeling offers a powerful approach for investigating this relationship between imaging measurements and disease severity, and understanding the effects of different disease subtypes, which is key to developing improved diagnostic methods. Gaining an understanding of a system as complex as the respiratory system is difficult if not impossible via experimental methods alone. Computational models offer a complementary method to unravel the structure-function relationships occurring within a multiscale, multiphysics system such as this. Here we review the current

Using Human Induced Pluripotent Stem Cells to Model Skeletal Diseases.

PubMed

Barruet, Emilie; Hsiao, Edward C

2016-01-01

Musculoskeletal disorders affecting the bones and joints are major health problems among children and adults. Major challenges such as the genetic origins or poor diagnostics of severe skeletal disease hinder our understanding of human skeletal diseases. The recent advent of human induced pluripotent stem cells (human iPS cells) provides an unparalleled opportunity to create human-specific models of human skeletal diseases. iPS cells have the ability to self-renew, allowing us to obtain large amounts of starting material, and have the potential to differentiate into any cell types in the body. In addition, they can carry one or more mutations responsible for the disease of interest or be genetically corrected to create isogenic controls. Our work has focused on modeling rare musculoskeletal disorders including fibrodysplasia ossificans progressive (FOP), a congenital disease of increased heterotopic ossification. In this review, we will discuss our experiences and protocols differentiating human iPS cells toward the osteogenic lineage and their application to model skeletal diseases. A number of critical challenges and exciting new approaches are also discussed, which will allow the skeletal biology field to harness the potential of human iPS cells as a critical model system for understanding diseases of abnormal skeletal formation and bone regeneration.

New Insights from Rodent Models of Fatty Liver Disease

PubMed Central

2011-01-01

Abstract Rodent models of fatty liver disease are essential research tools that provide a window into disease pathogenesis and a testing ground for prevention and treatment. Models come in many varieties involving dietary and genetic manipulations, and sometimes both. High-energy diets that induce obesity do not uniformly cause fatty liver disease; this has prompted close scrutiny of specific macronutrients and nutrient combinations to determine which have the greatest potential for hepatotoxicity. At the same time, diets that do not cause obesity or the metabolic syndrome but do cause severe steatohepatitis have been exploited to study factors important to progressive liver injury, including cell death, oxidative stress, and immune activation. Rodents with a genetic predisposition to overeating offer yet another model in which to explore the evolution of fatty liver disease. In some animals that overeat, steatohepatitis can develop even without resorting to a high-energy diet. Importantly, these models and others have been used to document that aerobic exercise can prevent or reduce fatty liver disease. This review focuses primarily on lessons learned about steatohepatitis from manipulations of diet and eating behavior. Numerous additional insights about hepatic lipid metabolism, which have been gained from genetically engineered mice, are also mentioned. Antioxid. Redox Signal. 15, 535–550. PMID:21126212

Drosophila as an In Vivo Model for Human Neurodegenerative Disease

PubMed Central

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

Induced Pluripotent Stem Cells for Disease Modeling and Evaluation of Therapeutics for Niemann-Pick Disease Type A.

PubMed

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

Simulation of Impacts of Annosus Root Disease with the Western Root Disease Model

Treesearch

Charles G. Shaw III; Donald J. Goheen; Bov B. Eav

1989-01-01

The Western Root Disease Model as it currently exists is described, and the assumptions that were made to adapt the model to simulate attack by Heterobasidion annosum in coniferous forests of south-central Oregon are defined. Some simulations produced by this adapted model are presented to stimulate provocative discussion, thought, and action. These...

Cardiac image modelling: Breadth and depth in heart disease.

PubMed

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.

A model of self-regulation for control of chronic disease.

PubMed

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.

Humanized mouse models: Application to human diseases.

PubMed

Ito, Ryoji; Takahashi, Takeshi; Ito, Mamoru

2018-05-01

Humanized mice are superior to rodents for preclinical evaluation of the efficacy and safety of drug candidates using human cells or tissues. During the past decade, humanized mouse technology has been greatly advanced by the establishment of novel platforms of genetically modified immunodeficient mice. Several human diseases can be recapitulated using humanized mice due to the improved engraftment and differentiation capacity of human cells or tissues. In this review, we discuss current advanced humanized mouse models that recapitulate human diseases including cancer, allergy, and graft-versus-host disease. © 2017 Wiley Periodicals, Inc.

Concise Review: Stem Cell Trials Using Companion Animal Disease Models.

PubMed

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.

MicroRNAs and complex diseases: from experimental results to computational models.

PubMed

Chen, Xing; Xie, Di; Zhao, Qi; You, Zhu-Hong

2017-10-17

Plenty of microRNAs (miRNAs) were discovered at a rapid pace in plants, green algae, viruses and animals. As one of the most important components in the cell, miRNAs play a growing important role in various essential and important biological processes. For the recent few decades, amounts of experimental methods and computational models have been designed and implemented to identify novel miRNA-disease associations. In this review, the functions of miRNAs, miRNA-target interactions, miRNA-disease associations and some important publicly available miRNA-related databases were discussed in detail. Specially, considering the important fact that an increasing number of miRNA-disease associations have been experimentally confirmed, we selected five important miRNA-related human diseases and five crucial disease-related miRNAs and provided corresponding introductions. Identifying disease-related miRNAs has become an important goal of biomedical research, which will accelerate the understanding of disease pathogenesis at the molecular level and molecular tools design for disease diagnosis, treatment and prevention. Computational models have become an important means for novel miRNA-disease association identification, which could select the most promising miRNA-disease pairs for experimental validation and significantly reduce the time and cost of the biological experiments. Here, we reviewed 20 state-of-the-art computational models of predicting miRNA-disease associations from different perspectives. Finally, we summarized four important factors for the difficulties of predicting potential disease-related miRNAs, the framework of constructing powerful computational models to predict potential miRNA-disease associations including five feasible and important research schemas, and future directions for further development of computational models. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Beyond the zebrafish: diverse fish species for modeling human disease

PubMed Central

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

Basal exon skipping and genetic pleiotropy: A predictive model of disease pathogenesis.

PubMed

Drivas, Theodore G; Wojno, Adam P; Tucker, Budd A; Stone, Edwin M; Bennett, Jean

2015-06-10

Genetic pleiotropy, the phenomenon by which mutations in the same gene result in markedly different disease phenotypes, has proven difficult to explain with traditional models of disease pathogenesis. We have developed a model of pleiotropic disease that explains, through the process of basal exon skipping, how different mutations in the same gene can differentially affect protein production, with the total amount of protein produced correlating with disease severity. Mutations in the centrosomal protein of 290 kDa (CEP290) gene are associated with a spectrum of phenotypically distinct human diseases (the ciliopathies). Molecular biologic examination of CEP290 transcript and protein expression in cells from patients carrying CEP290 mutations, measured by quantitative polymerase chain reaction and Western blotting, correlated with disease severity and corroborated our model. We show that basal exon skipping may be the mechanism underlying the disease pleiotropy caused by CEP290 mutations. Applying our model to a different disease gene, CC2D2A (coiled-coil and C2 domains-containing protein 2A), we found that the same correlations held true. Our model explains the phenotypic diversity of two different inherited ciliopathies and may establish a new model for the pathogenesis of other pleiotropic human diseases. Copyright © 2015, American Association for the Advancement of Science.

A surface hydrology model for regional vector borne disease models

NASA Astrophysics Data System (ADS)

Tompkins, Adrian; Asare, Ernest; Bomblies, Arne; Amekudzi, Leonard

2016-04-01

Small, sun-lit temporary pools that form during the rainy season are important breeding sites for many key mosquito vectors responsible for the transmission of malaria and other diseases. The representation of this surface hydrology in mathematical disease models is challenging, due to their small-scale, dependence on the terrain and the difficulty of setting soil parameters. Here we introduce a model that represents the temporal evolution of the aggregate statistics of breeding sites in a single pond fractional coverage parameter. The model is based on a simple, geometrical assumption concerning the terrain, and accounts for the processes of surface runoff, pond overflow, infiltration and evaporation. Soil moisture, soil properties and large-scale terrain slope are accounted for using a calibration parameter that sets the equivalent catchment fraction. The model is calibrated and then evaluated using in situ pond measurements in Ghana and ultra-high (10m) resolution explicit simulations for a village in Niger. Despite the model's simplicity, it is shown to reproduce the variability and mean of the pond aggregate water coverage well for both locations and validation techniques. Example malaria simulations for Uganda will be shown using this new scheme with a generic calibration setting, evaluated using district malaria case data. Possible methods for implementing regional calibration will be briefly discussed.

An agent-based approach for modeling dynamics of contagious disease spread

PubMed Central

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

Salt, hypertension and renal disease: comparative medicine, models and real diseases.

PubMed Central

Michell, A. R.

1994-01-01

Dogs are well established as experimental animals for the study of both renal disease and hypertension, but most work is based on surgical or pharmacological models and relatively little on spontaneous diseases. This review argues for the latter as an underexploited aspect of comparative medicine. The most important feature of canine hypertension may not be the ease with which models can be produced but the fact that dogs are actually rather resistant to hypertension, and perhaps to its effects, even when they have chronic renal failure. The importance of natural models of chronic renal failure is strengthened by the evidence that self-sustaining progression is a consequence of extreme nephron loss, that is, a late event, rather than the dominant feature of the course of the disease. The role of salt in hypertension is discussed and emphasis given to the importance of understanding the physiological basis of nutritional requirement and recognizing that it is unlikely to exceed 0.6 mmol/kg/day for most healthy adult mammals except during pregnancy or lactation. Such a perspective is essential to the evaluation of experiments, whether in animals or humans, in order to avoid arbitrary definitions of 'high' or 'low' sodium intake, and the serious misinterpretations of data which result. An age-related rise in arterial pressure may well be a warning of excess salt intake, rather than a normal occurrence. Problems of defining hypertension in the face of variability of arterial pressure are also discussed. PMID:7831161

Modeling neurodegenerative diseases with patient-derived induced pluripotent cells: Possibilities and challenges.

PubMed

Poon, Anna; Zhang, Yu; Chandrasekaran, Abinaya; Phanthong, Phetcharat; Schmid, Benjamin; Nielsen, Troels T; Freude, Kristine K

2017-10-25

The rising prevalence of progressive neurodegenerative diseases coupled with increasing longevity poses an economic burden at individual and societal levels. There is currently no effective cure for the majority of neurodegenerative diseases and disease-affected tissues from patients have been difficult to obtain for research and drug discovery in pre-clinical settings. While the use of animal models has contributed invaluable mechanistic insights and potential therapeutic targets, the translational value of animal models could be further enhanced when combined with in vitro models derived from patient-specific induced pluripotent stem cells (iPSCs) and isogenic controls generated using CRISPR-Cas9 mediated genome editing. The iPSCs are self-renewable and capable of being differentiated into the cell types affected by the diseases. These in vitro models based on patient-derived iPSCs provide the opportunity to model disease development, uncover novel mechanisms and test potential therapeutics. Here we review findings from iPSC-based modeling of selected neurodegenerative diseases, including Alzheimer's disease, frontotemporal dementia and spinocerebellar ataxia. Furthermore, we discuss the possibilities of generating three-dimensional (3D) models using the iPSCs-derived cells and compare their advantages and disadvantages to conventional two-dimensional (2D) models. Copyright © 2017 Elsevier B.V. All rights reserved.

Nucleotide excision repair deficient mouse models and neurological disease

PubMed Central

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

A model to predict disease progression in patients with autosomal dominant polycystic kidney disease (ADPKD): the ADPKD Outcomes Model.

PubMed

McEwan, Phil; Bennett Wilton, Hayley; Ong, Albert C M; Ørskov, Bjarne; Sandford, Richard; Scolari, Francesco; Cabrera, Maria-Cristina V; Walz, Gerd; O'Reilly, Karl; Robinson, Paul

2018-02-13

Autosomal dominant polycystic kidney disease (ADPKD) is the leading inheritable cause of end-stage renal disease (ESRD); however, the natural course of disease progression is heterogeneous between patients. This study aimed to develop a natural history model of ADPKD that predicted progression rates and long-term outcomes in patients with differing baseline characteristics. The ADPKD Outcomes Model (ADPKD-OM) was developed using available patient-level data from the placebo arm of the Tolvaptan Efficacy and Safety in Management of ADPKD and its Outcomes Study (TEMPO 3:4; ClinicalTrials.gov identifier NCT00428948). Multivariable regression equations estimating annual rates of ADPKD progression, in terms of total kidney volume (TKV) and estimated glomerular filtration rate, formed the basis of the lifetime patient-level simulation model. Outputs of the ADPKD-OM were compared against external data sources to validate model accuracy and generalisability to other ADPKD patient populations, then used to predict long-term outcomes in a cohort matched to the overall TEMPO 3:4 study population. A cohort with baseline patient characteristics consistent with TEMPO 3:4 was predicted to reach ESRD at a mean age of 52 years. Most patients (85%) were predicted to reach ESRD by the age of 65 years, with many progressing to ESRD earlier in life (18, 36 and 56% by the age of 45, 50 and 55 years, respectively). Consistent with previous research and clinical opinion, analyses supported the selection of baseline TKV as a prognostic factor for ADPKD progression, and demonstrated its value as a strong predictor of future ESRD risk. Validation exercises and illustrative analyses confirmed the ability of the ADPKD-OM to accurately predict disease progression towards ESRD across a range of clinically-relevant patient profiles. The ADPKD-OM represents a robust tool to predict natural disease progression and long-term outcomes in ADPKD patients, based on readily available and/or measurable

Time series regression model for infectious disease and weather.

PubMed

Imai, Chisato; Armstrong, Ben; Chalabi, Zaid; Mangtani, Punam; Hashizume, Masahiro

2015-10-01

Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

A nonhuman primate model of chikungunya disease

PubMed Central

Higgs, Stephen; Ziegler, Sarah A.

2010-01-01

Chikungunya disease is a severely debilitating, mosquito-borne, viral illness that has reached epidemic proportions in Africa, Asia, and the islands of the Indian Ocean. A mutation enhancing the ability of the chikungunya virus (CHIKV) to infect and be transmitted by Aedes albopictus has increased the geographical range at risk for infection due to the continuing global spread of this mosquito. Research into disease pathogenesis, vaccine development, and therapeutic design has been hindered by the lack of appropriate animal models of this disease. The meticulous study reported in this issue of the JCI by Labadie et al. is one of the first reports describing CHIKV infection of adult immunocompetent nonhuman primates. Using traditional and modern molecular and immunological approaches, the authors demonstrate that macaques infected with CHIKV are a good model of human CHIKV infection and also show that persistent arthralgia in humans may be caused by persistent CHIKV infection of macrophages. PMID:20179348

Drosophila as an In Vivo Model for Human Neurodegenerative Disease.

PubMed

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.

Disease management with ARIMA model in time series.

PubMed

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.

Modeling human gastrointestinal inflammatory diseases using microphysiological culture systems.

PubMed

Hartman, Kira G; Bortner, James D; Falk, Gary W; Ginsberg, Gregory G; Jhala, Nirag; Yu, Jian; Martín, Martín G; Rustgi, Anil K; Lynch, John P

2014-09-01

Gastrointestinal illnesses are a significant health burden for the US population, with 40 million office visits each year for gastrointestinal complaints and nearly 250,000 deaths. Acute and chronic inflammations are a common element of many gastrointestinal diseases. Inflammatory processes may be initiated by a chemical injury (acid reflux in the esophagus), an infectious agent (Helicobacter pylori infection in the stomach), autoimmune processes (graft versus host disease after bone marrow transplantation), or idiopathic (as in the case of inflammatory bowel diseases). Inflammation in these settings can contribute to acute complaints (pain, bleeding, obstruction, and diarrhea) as well as chronic sequelae including strictures and cancer. Research into the pathophysiology of these conditions has been limited by the availability of primary human tissues or appropriate animal models that attempt to physiologically model the human disease. With the many recent advances in tissue engineering and primary human cell culture systems, it is conceivable that these approaches can be adapted to develop novel human ex vivo systems that incorporate many human cell types to recapitulate in vivo growth and differentiation in inflammatory microphysiological environments. Such an advance in technology would improve our understanding of human disease progression and enhance our ability to test for disease prevention strategies and novel therapeutics. We will review current models for the inflammatory and immunological aspects of Barrett's esophagus, acute graft versus host disease, and inflammatory bowel disease and explore recent advances in culture methodologies that make these novel microphysiological research systems possible. © 2014 by the Society for Experimental Biology and Medicine.

Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database.

PubMed

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.

Rodent Models of Experimental Endometriosis: Identifying Mechanisms of Disease and Therapeutic Targets

PubMed Central

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.

Modeling two strains of disease via aggregate-level infectivity curves.

PubMed

Romanescu, Razvan; Deardon, Rob

2016-04-01

Well formulated models of disease spread, and efficient methods to fit them to observed data, are powerful tools for aiding the surveillance and control of infectious diseases. Our project considers the problem of the simultaneous spread of two related strains of disease in a context where spatial location is the key driver of disease spread. We start our modeling work with the individual level models (ILMs) of disease transmission, and extend these models to accommodate the competing spread of the pathogens in a two-tier hierarchical population (whose levels we refer to as 'farm' and 'animal'). The postulated interference mechanism between the two strains is a period of cross-immunity following infection. We also present a framework for speeding up the computationally intensive process of fitting the ILM to data, typically done using Markov chain Monte Carlo (MCMC) in a Bayesian framework, by turning the inference into a two-stage process. First, we approximate the number of animals infected on a farm over time by infectivity curves. These curves are fit to data sampled from farms, using maximum likelihood estimation, then, conditional on the fitted curves, Bayesian MCMC inference proceeds for the remaining parameters. Finally, we use posterior predictive distributions of salient epidemic summary statistics, in order to assess the model fitted.

Toxin Models of Mitochondrial Dysfunction in Parkinson's Disease

PubMed Central

Martinez, Terina N.

2012-01-01

Abstract Significance: Parkinson's disease (PD) is a neurodegenerative disorder characterized, in part, by the progressive and selective loss of dopaminergic neuron cell bodies within the substantia nigra pars compacta (SNpc) and the associated deficiency of the neurotransmitter dopamine (DA) in the striatum, which gives rise to the typical motor symptoms of PD. The mechanisms that contribute to the induction and progressive cell death of dopaminergic neurons in PD are multi-faceted and remain incompletely understood. Data from epidemiological studies in humans and molecular studies in genetic, as well as toxin-induced animal models of parkinsonism, indicate that mitochondrial dysfunction occurs early in the pathogenesis of both familial and idiopathic PD. In this review, we provide an overview of toxin models of mitochondrial dysfunction in experimental Parkinson's disease and discuss mitochondrial mechanisms of neurotoxicity. Recent Advances: A new toxin model using the mitochondrial toxin trichloroethylene was recently described and novel methods, such as intranasal exposure to toxins, have been explored. Additionally, recent research conducted in toxin models of parkinsonism provides an emerging emphasis on extranigral aspects of PD pathology. Critical Issues: Unfortunately, none of the existing animal models of experimental PD completely mimics the etiology, progression, and pathology of human PD. Future Directions: Continued efforts to optimize established animal models of parkinsonism, as well as the development and characterization of new animal models are essential, as there still remains a disconnect in terms of translating mechanistic observations in animal models of experimental PD into bona fide disease-modifying therapeutics for human PD patients. Antioxid. Redox Signal. 16, 920–934. PMID:21554057

Preclinical murine models of Chronic Obstructive Pulmonary Disease.

PubMed

Vlahos, Ross; Bozinovski, Steven

2015-07-15

Chronic Obstructive Pulmonary Disease (COPD) is a major incurable global health burden and is the 4th leading cause of death worldwide. It is believed that an exaggerated inflammatory response to cigarette smoke causes progressive airflow limitation. This inflammation, where macrophages, neutrophils and T lymphocytes are prominent, leads to oxidative stress, emphysema, small airway fibrosis and mucus hypersecretion. Much of the disease burden and health care utilisation in COPD is associated with the management of its comorbidities and infectious (viral and bacterial) exacerbations (AECOPD). Comorbidities, defined as other chronic medical conditions, in particular skeletal muscle wasting and cardiovascular disease markedly impact on disease morbidity, progression and mortality. The mechanisms and mediators underlying COPD and its comorbidities are poorly understood and current COPD therapy is relatively ineffective. Thus, there is an obvious need for new therapies that can prevent the induction and progression of COPD and effectively treat AECOPD and comorbidities of COPD. Given that access to COPD patients can be difficult and that clinical samples often represent a "snapshot" at a particular time in the disease process, many researchers have used animal modelling systems to explore the mechanisms underlying COPD, AECOPD and comorbidities of COPD with the goal of identifying novel therapeutic targets. This review highlights the mouse models used to define the cellular, molecular and pathological consequences of cigarette smoke exposure and the recent advances in modelling infectious exacerbations and comorbidities of COPD. Copyright © 2015 Elsevier B.V. All rights reserved.

A network-patch methodology for adapting agent-based models for directly transmitted disease to mosquito-borne disease.

PubMed

Manore, Carrie A; Hickmann, Kyle S; Hyman, James M; Foppa, Ivo M; Davis, Justin K; Wesson, Dawn M; Mores, Christopher N

2015-01-01

Mosquito-borne diseases cause significant public health burden and are widely re-emerging or emerging. Understanding, predicting, and mitigating the spread of mosquito-borne disease in diverse populations and geographies are ongoing modelling challenges. We propose a hybrid network-patch model for the spread of mosquito-borne pathogens that accounts for individual movement through mosquito habitats, extending the capabilities of existing agent-based models (ABMs) to include vector-borne diseases. The ABM are coupled with differential equations representing 'clouds' of mosquitoes in patches accounting for mosquito ecology. We adapted an ABM for humans using this method and investigated the importance of heterogeneity in pathogen spread, motivating the utility of models of individual behaviour. We observed that the final epidemic size is greater in patch models with a high risk patch frequently visited than in a homogeneous model. Our hybrid model quantifies the importance of the heterogeneity in the spread of mosquito-borne pathogens, guiding mitigation strategies.

Gene Therapy Models of Alzheimer’s Disease and Other Dementias

PubMed Central

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

Disease-induced mortality in density-dependent discrete-time S-I-S epidemic models.

PubMed

Franke, John E; Yakubu, Abdul-Aziz

2008-12-01

The dynamics of simple discrete-time epidemic models without disease-induced mortality are typically characterized by global transcritical bifurcation. We prove that in corresponding models with disease-induced mortality a tiny number of infectious individuals can drive an otherwise persistent population to extinction. Our model with disease-induced mortality supports multiple attractors. In addition, we use a Ricker recruitment function in an SIS model and obtained a three component discrete Hopf (Neimark-Sacker) cycle attractor coexisting with a fixed point attractor. The basin boundaries of the coexisting attractors are fractal in nature, and the example exhibits sensitive dependence of the long-term disease dynamics on initial conditions. Furthermore, we show that in contrast to corresponding models without disease-induced mortality, the disease-free state dynamics do not drive the disease dynamics.

Humanized Mouse Model of Ebola Virus Disease Mimics the Immune Responses in Human Disease.

PubMed

Bird, Brian H; Spengler, Jessica R; Chakrabarti, Ayan K; Khristova, Marina L; Sealy, Tara K; Coleman-McCray, JoAnn D; Martin, Brock E; Dodd, Kimberly A; Goldsmith, Cynthia S; Sanders, Jeanine; Zaki, Sherif R; Nichol, Stuart T; Spiropoulou, Christina F

2016-03-01

Animal models recapitulating human Ebola virus disease (EVD) are critical for insights into virus pathogenesis. Ebola virus (EBOV) isolates derived directly from human specimens do not, without adaptation, cause disease in immunocompetent adult rodents. Here, we describe EVD in mice engrafted with human immune cells (hu-BLT). hu-BLT mice developed EVD following wild-type EBOV infection. Infection with high-dose EBOV resulted in rapid, lethal EVD with high viral loads, alterations in key human antiviral immune cytokines and chemokines, and severe histopathologic findings similar to those shown in the limited human postmortem data available. A dose- and donor-dependent clinical course was observed in hu-BLT mice infected with lower doses of either Mayinga (1976) or Makona (2014) isolates derived from human EBOV cases. Engraftment of the human cellular immune system appeared to be essential for the observed virulence, as nonengrafted mice did not support productive EBOV replication or develop lethal disease. hu-BLT mice offer a unique model for investigating the human immune response in EVD and an alternative animal model for EVD pathogenesis studies and therapeutic screening. Published by Oxford University Press for the Infectious Diseases Society of America 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.

An architecture model for multiple disease management information systems.

PubMed

Chen, Lichin; Yu, Hui-Chu; Li, Hao-Chun; Wang, Yi-Van; Chen, Huang-Jen; Wang, I-Ching; Wang, Chiou-Shiang; Peng, Hui-Yu; Hsu, Yu-Ling; Chen, Chi-Huang; Chuang, Lee-Ming; Lee, Hung-Chang; Chung, Yufang; Lai, Feipei

2013-04-01

Disease management is a program which attempts to overcome the fragmentation of healthcare system and improve the quality of care. Many studies have proven the effectiveness of disease management. However, the case managers were spending the majority of time in documentation, coordinating the members of the care team. They need a tool to support them with daily practice and optimizing the inefficient workflow. Several discussions have indicated that information technology plays an important role in the era of disease management. Whereas applications have been developed, it is inefficient to develop information system for each disease management program individually. The aim of this research is to support the work of disease management, reform the inefficient workflow, and propose an architecture model that enhance on the reusability and time saving of information system development. The proposed architecture model had been successfully implemented into two disease management information system, and the result was evaluated through reusability analysis, time consumed analysis, pre- and post-implement workflow analysis, and user questionnaire survey. The reusability of the proposed model was high, less than half of the time was consumed, and the workflow had been improved. The overall user aspect is positive. The supportiveness during daily workflow is high. The system empowers the case managers with better information and leads to better decision making.

A survey of basic reproductive ratios in vector-borne disease transmission modeling

NASA Astrophysics Data System (ADS)

Soewono, E.; Aldila, D.

2015-03-01

Vector-borne diseases are commonly known in tropical and subtropical countries. These diseases have contributed to more than 10% of world infectious disease cases. Among the vectors responsible for transmitting the diseases are mosquitoes, ticks, fleas, flies, bugs and worms. Several of the diseases are known to contribute to the increasing threat to human health such as malaria, dengue, filariasis, chikungunya, west nile fever, yellow fever, encephalistis, and anthrax. It is necessary to understand the real process of infection, factors which contribute to the complication of the transmission in order to come up with a good and sound mathematical model. Although it is not easy to simulate the real transmission process of the infection, we could say that almost all models have been developed from the already long known Host-Vector model. It constitutes the main transmission processes i.e. birth, death, infection and recovery. From this simple model, the basic concepts of Disease Free and Endemic Equilibria and Basic Reproductive Ratio can be well explained and understood. Theoretical, modeling, control and treatment aspects of disease transmission problems have then been developed for various related diseases. General construction as well as specific forms of basic reproductive ratios for vector-borne diseases are discusses here.

“Wrong, but Useful”: Negotiating Uncertainty in Infectious Disease Modelling

PubMed Central

Christley, Robert M.; Mort, Maggie; Wynne, Brian; Wastling, Jonathan M.; Heathwaite, A. Louise; Pickup, Roger; Austin, Zoë; Latham, Sophia M.

2013-01-01

For infectious disease dynamical models to inform policy for containment of infectious diseases the models must be able to predict; however, it is well recognised that such prediction will never be perfect. Nevertheless, the consensus is that although models are uncertain, some may yet inform effective action. This assumes that the quality of a model can be ascertained in order to evaluate sufficiently model uncertainties, and to decide whether or not, or in what ways or under what conditions, the model should be ‘used’. We examined uncertainty in modelling, utilising a range of data: interviews with scientists, policy-makers and advisors, and analysis of policy documents, scientific publications and reports of major inquiries into key livestock epidemics. We show that the discourse of uncertainty in infectious disease models is multi-layered, flexible, contingent, embedded in context and plays a critical role in negotiating model credibility. We argue that usability and stability of a model is an outcome of the negotiation that occurs within the networks and discourses surrounding it. This negotiation employs a range of discursive devices that renders uncertainty in infectious disease modelling a plastic quality that is amenable to ‘interpretive flexibility’. The utility of models in the face of uncertainty is a function of this flexibility, the negotiation this allows, and the contexts in which model outputs are framed and interpreted in the decision making process. We contend that rather than being based predominantly on beliefs about quality, the usefulness and authority of a model may at times be primarily based on its functional status within the broad social and political environment in which it acts. PMID:24146851

Critical Behavior in Cellular Automata Animal Disease Transmission Model

NASA Astrophysics Data System (ADS)

Morley, P. D.; Chang, Julius

Using cellular automata model, we simulate the British Government Policy (BGP) in the 2001 foot and mouth epidemic in Great Britain. When clinical symptoms of the disease appeared in a farm, there is mandatory slaughter (culling) of all livestock in an infected premise (IP). Those farms in the neighboring of an IP (contiguous premise, CP), are also culled, aka nearest neighbor interaction. Farms where the disease may be prevalent from animal, human, vehicle or airborne transmission (dangerous contact, DC), are additionally culled, aka next-to-nearest neighbor interactions and lightning factor. The resulting mathematical model possesses a phase transition, whereupon if the physical disease transmission kernel exceeds a critical value, catastrophic loss of animals ensues. The nonlocal disease transport probability can be as low as 0.01% per day and the disease can still be in the high mortality phase. We show that the fundamental equation for sustainable disease transport is the criticality equation for neutron fission cascade. Finally, we calculate that the percentage of culled animals that are actually healthy is ≈30%.

Animal models of Parkinson's disease: limits and relevance to neuroprotection studies.

PubMed

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.

Probability-based collaborative filtering model for predicting gene-disease associations.

PubMed

Zeng, Xiangxiang; Ding, Ningxiang; Rodríguez-Patón, Alfonso; Zou, Quan

2017-12-28

Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene-disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses. We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our model. Firstly, on the basis of a typical latent factorization model, we propose model I with an average heterogeneous regularization. Secondly, we develop modified model II with personal heterogeneous regularization to enhance the accuracy of aforementioned models. In this model, vector space similarity or Pearson correlation coefficient metrics and data on related species are also used. We compared the results of PCFM with the results of four state-of-arts approaches. The results show that PCFM performs better than other advanced approaches. PCFM model can be leveraged for predictions of disease genes, especially for new human genes or diseases with no known relationships.

Modeling a Mobile Health Management Business Model for Chronic Kidney Disease.

PubMed

Lee, Ying-Li; Chang, Polun

2016-01-01

In these decades, chronic kidney disease (CKD) has become a global public health problem. Information technology (IT) tools have been used widely to empower the patients with chronic disease (e.g., diabetes and hypertension). It is also a potential application to advance the CKD care. In this project, we analyzed the requirements of a mobile health management system for healthcare workers, patients and their families to design a health management business model for CKD patients.

Research Techniques Made Simple: Mouse Models of Autoimmune Blistering Diseases.

PubMed

Pollmann, Robert; Eming, Rüdiger

2017-01-01

Autoimmune blistering diseases are examples of autoantibody-mediated, organ-specific autoimmune disorders. Based on a genetic susceptibility, such as a strong HLA-class II association, as yet unknown triggering factors induce the formation of circulating and tissue-bound autoantibodies that are mainly directed against adhesion structures of the skin and mucous membranes. Compared with other autoimmune diseases, especially systemic disorders, the pathogenicity of autoimmune blistering diseases is relatively well described. Several animal models of autoimmune blistering diseases have been established that helped to uncover the immunological and molecular mechanisms underlying the blistering phenotypes. Each in vivo model focuses on specific aspects of the autoimmune cascade, from loss of immunological tolerance on the level of T and B cells to the pathogenic effects of autoantibodies upon binding to their target autoantigen. We discuss current mouse models of autoimmune blistering diseases, including models of pemphigus vulgaris, bullous pemphigoid, epidermolysis bullosa acquisita, and dermatitis herpetiformis. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Statistical Modeling of Disease Progression for Chronic Obstructive Pulmonary Disease Using Data from the ECLIPSE Study.

PubMed

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.

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.

Thermoneutral housing exacerbates non-alcoholic fatty liver disease in mice and allows for sex-independent disease modeling

PubMed Central

Giles, Daniel A; Moreno-Fernandez, Maria E; Stankiewicz, Traci E; Graspeuntner, Simon; Cappelletti, Monica; Wu, David; Mukherjee, Rajib; Chan, Calvin C; Lawson, Matthew J; Klarquist, Jared; Sünderhauf, Annika; Softic, Samir; Kahn, C Ronald; Stemmer, Kerstin; Iwakura, Yoichiro; Aronow, Bruce J; Karns, Rebekah; Steinbrecher, Kris A; Karp, Christopher L; Sheridan, Rachel; Shanmukhappa, Shiva K; Reynaud, Damien; Haslam, David B; Sina, Christian; Rupp, Jan; Hogan, Simon P; Divanovic, Senad

2017-01-01

Non-alcoholic fatty liver disease (NAFLD), a common prelude to cirrhosis and hepatocellular carcinoma, is the most common chronic liver disease worldwide. Defining the molecular mechanisms underlying the pathogenesis of NAFLD has been hampered by a lack of animal models that closely recapitulate the severe end of the human disease spectrum, including bridging hepatic fibrosis. Here, we demonstrate that a novel experimental model employing thermoneutral housing, as opposed to standard housing, resulted in lower stress-driven production of corticosterone, augmented mouse proinflammatory immune responses and markedly exacerbated high fat diet (HFD)-induced NAFLD pathogenesis. Disease exacerbation at thermoneutrality was conserved across multiple mouse strains and was associated with augmented intestinal permeability, an altered microbiome and activation of inflammatory pathways associated with human disease. Depletion of Gram-negative microbiota, hematopoietic cell deletion of Toll-like receptor 4 (TLR4) and inactivation of the interleukin-17 (IL-17) axis resulted in altered immune responsiveness and protection from thermoneutral housing-driven NAFLD amplification. Finally, female mice, typically resistant to HFD-induced obesity and NAFLD, develop full-blown disease at thermoneutrality. Thus, thermoneutral housing provides a sex-independent model of exacerbated NAFLD in mice and represents a novel approach for interrogation of the cellular and molecular mechanisms underlying disease pathogenesis. PMID:28604704

Three-Dimension Visualization for Primary Wheat Diseases Based on Simulation Model

NASA Astrophysics Data System (ADS)

Shijuan, Li; Yeping, Zhu

Crop simulation model has been becoming the core of agricultural production management and resource optimization management. Displaying crop growth process makes user observe the crop growth and development intuitionisticly. On the basis of understanding and grasping the occurrence condition, popularity season, key impact factors for main wheat diseases of stripe rust, leaf rust, stem rust, head blight and powdery mildew from research material and literature, we designed 3D visualization model for wheat growth and diseases occurrence. The model system will help farmer, technician and decision-maker to use crop growth simulation model better and provide decision-making support. Now 3D visualization model for wheat growth on the basis of simulation model has been developed, and the visualization model for primary wheat diseases is in the process of development.

Stargardt disease: towards developing a model to predict phenotype.

PubMed

Heathfield, Laura; Lacerda, Miguel; Nossek, Christel; Roberts, Lisa; Ramesar, Rajkumar S

2013-10-01

Stargardt disease is an ABCA4-associated retinopathy, which generally follows an autosomal recessive inheritance pattern and is a frequent cause of macular degeneration in childhood. ABCA4 displays significant allelic heterogeneity whereby different mutations can cause retinal diseases with varying severity and age of onset. A genotype-phenotype model has been proposed linking ABCA4 mutations, purported ABCA4 functional protein activity and severity of disease, as measured by degree of visual loss and the age of onset. It has, however, been difficult to verify this model statistically in observational studies, as the number of individuals sharing any particular mutation combination is typically low. Seven founder mutations have been identified in a large number of Caucasian Afrikaner patients in South Africa, making it possible to test the genotype-phenotype model. A generalised linear model was developed to predict and assess the relative pathogenic contribution of the seven mutations to the age of onset of Stargardt disease. It is shown that the pathogenicity of an individual mutation can differ significantly depending on the genetic context in which it occurs. The results reported here may be used to identify suitable candidates for inclusion in clinical trials, as well as guide the genetic counselling of affected individuals and families.

Stargardt Disease: towards developing a model to predict phenotype

PubMed Central

Heathfield, Laura; Lacerda, Miguel; Nossek, Christel; Roberts, Lisa; Ramesar, Rajkumar S

2013-01-01

Stargardt disease is an ABCA4-associated retinopathy, which generally follows an autosomal recessive inheritance pattern and is a frequent cause of macular degeneration in childhood. ABCA4 displays significant allelic heterogeneity whereby different mutations can cause retinal diseases with varying severity and age of onset. A genotype–phenotype model has been proposed linking ABCA4 mutations, purported ABCA4 functional protein activity and severity of disease, as measured by degree of visual loss and the age of onset. It has, however, been difficult to verify this model statistically in observational studies, as the number of individuals sharing any particular mutation combination is typically low. Seven founder mutations have been identified in a large number of Caucasian Afrikaner patients in South Africa, making it possible to test the genotype–phenotype model. A generalised linear model was developed to predict and assess the relative pathogenic contribution of the seven mutations to the age of onset of Stargardt disease. It is shown that the pathogenicity of an individual mutation can differ significantly depending on the genetic context in which it occurs. The results reported here may be used to identify suitable candidates for inclusion in clinical trials, as well as guide the genetic counselling of affected individuals and families. PMID:23695285

Porcine models of digestive disease: the future of large animal translational research

PubMed Central

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

Adaptive modeling of viral diseases in bats with a focus on rabies.

PubMed

Dimitrov, Dobromir T; Hallam, Thomas G; Rupprecht, Charles E; McCracken, Gary F

2008-11-07

Many emerging and reemerging viruses, such as rabies, SARS, Marburg, and Ebola have bat populations as disease reservoirs. Understanding the spillover from bats to humans and other animals, and the associated health risks requires an analysis of the disease dynamics in bat populations. Traditional compartmental epizootic models, which are relatively easy to implement and analyze, usually impose unrealistic aggregation assumptions about disease-related structure and depend on parameters that frequently are not measurable in field conditions. We propose a novel combination of computational and adaptive modeling approaches that address the maintenance of emerging diseases in bat colonies through individual (intra-host) models of the response of the host to a viral challenge. The dynamics of the individual models are used to define survival, susceptibility and transmission conditions relevant to epizootics as well as to develop and parametrize models of the disease evolution into uniform and diverse populations. Applications of the proposed approach to modeling the effects of immunological heterogeneity on the dynamics of bat rabies are presented.

Dry eye disease and uveitis: A closer look at immune mechanisms in animal models of two ocular autoimmune diseases.

PubMed

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.

Disease-specific induced pluripotent stem cells: a platform for human disease modeling and drug discovery.

PubMed

Jang, Jiho; Yoo, Jeong-Eun; Lee, Jeong-Ah; Lee, Dongjin R; Kim, Ji Young; Huh, Yong Jun; Kim, Dae-Sung; Park, Chul-Yong; Hwang, Dong-Youn; Kim, Han-Soo; Kang, Hoon-Chul; Kim, Dong-Wook

2012-03-31

The generation of disease-specific induced pluripotent stem cell (iPSC) lines from patients with incurable diseases is a promising approach for studying disease mechanisms and drug screening. Such innovation enables to obtain autologous cell sources in regenerative medicine. Herein, we report the generation and characterization of iPSCs from fibroblasts of patients with sporadic or familial diseases, including Parkinson's disease (PD), Alzheimer's disease (AD), juvenile-onset, type I diabetes mellitus (JDM), and Duchenne type muscular dystrophy (DMD), as well as from normal human fibroblasts (WT). As an example to modeling disease using disease-specific iPSCs, we also discuss the previously established childhood cerebral adrenoleukodystrophy (CCALD)- and adrenomyeloneuropathy (AMN)-iPSCs by our group. Through DNA fingerprinting analysis, the origins of generated disease-specific iPSC lines were identified. Each iPSC line exhibited an intense alkaline phosphatase activity, expression of pluripotent markers, and the potential to differentiate into all three embryonic germ layers: the ectoderm, endoderm, and mesoderm. Expression of endogenous pluripotent markers and downregulation of retrovirus-delivered transgenes [OCT4 (POU5F1), SOX2, KLF4, and c-MYC] were observed in the generated iPSCs. Collectively, our results demonstrated that disease-specific iPSC lines characteristically resembled hESC lines. Furthermore, we were able to differentiate PD-iPSCs, one of the disease-specific-iPSC lines we generated, into dopaminergic (DA) neurons, the cell type mostly affected by PD. These PD-specific DA neurons along with other examples of cell models derived from disease-specific iPSCs would provide a powerful platform for examining the pathophysiology of relevant diseases at the cellular and molecular levels and for developing new drugs and therapeutic regimens.

Development and Operation of Space-Based Disease Early Warning Models

NASA Astrophysics Data System (ADS)

John, M. M.

2010-12-01

Millions of people die every year from preventable diseases such as malaria and cholera. Pandemics put the entire world population at risk and have the potential to kill thousands and cripple the global economy. In light of these dangers, it is fortunate that the data and imagery gathered by remote sensing satellites can be used to develop models that predict areas at risk for outbreaks. These warnings can help decision makers to distribute preventative medicine and other forms of aid to save lives. There are already many Earth observing satellites in orbit with the ability to provide data and imagery. Researchers have created a number of models based on this information, and some are being used in real-life situations. These capabilities should be further developed and supported by governments and international organizations to benefit as many people as possible. To understand the benefits and challenges of disease early warning models, it is useful to understand how they are developed. A number of steps must occur for satellite data and imagery to be used to prevent disease outbreaks; each requires a variety of inputs and may include a range of experts and stakeholders. This paper discusses the inputs, outputs, and basic processes involved in each of six main steps to developing models, including: identifying and validating links between a disease and environmental factors, creating and validating a software model to predict outbreaks, transitioning a model to operational use, using a model operationally, and taking action on the data provided by the model. The paper briefly overviews past research regarding the link between remote sensing data and disease, and identifies ongoing research in academic centers around the world. The activities of three currently operational models are discussed, including the U.S. Department of Defense Global Emerging Infections Surveillance and Response System (DoD-GEIS), NASA carries out its Malaria Modeling and Surveillance

Induced pluripotent stem cells in Alzheimer's disease: applications for disease modeling and cell-replacement therapy.

PubMed

Yang, Juan; Li, Song; He, Xi-Biao; Cheng, Cheng; Le, Weidong

2016-05-17

Alzheimer's disease (AD) is the most common cause of dementia in those over the age of 65. While a numerous of disease-causing genes and risk factors have been identified, the exact etiological mechanisms of AD are not yet completely understood, due to the inability to test theoretical hypotheses on non-postmortem and patient-specific research systems. The use of recently developed and optimized induced pluripotent stem cells (iPSCs) technology may provide a promising platform to create reliable models, not only for better understanding the etiopathological process of AD, but also for efficient anti-AD drugs screening. More importantly, human-sourced iPSCs may also provide a beneficial tool for cell-replacement therapy against AD. Although considerable progress has been achieved, a number of key challenges still require to be addressed in iPSCs research, including the identification of robust disease phenotypes in AD modeling and the clinical availabilities of iPSCs-based cell-replacement therapy in human. In this review, we highlight recent progresses of iPSCs research and discuss the translational challenges of AD patients-derived iPSCs in disease modeling and cell-replacement therapy.

Seven challenges in modeling vaccine preventable diseases.

PubMed

Metcalf, C J E; Andreasen, V; Bjørnstad, O N; Eames, K; Edmunds, W J; Funk, S; Hollingsworth, T D; Lessler, J; Viboud, C; Grenfell, B T

2015-03-01

Vaccination has been one of the most successful public health measures since the introduction of basic sanitation. Substantial mortality and morbidity reductions have been achieved via vaccination against many infections, and the list of diseases that are potentially controllable by vaccines is growing steadily. We introduce key challenges for modeling in shaping our understanding and guiding policy decisions related to vaccine preventable diseases. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Age- and bite-structured models for vector-borne diseases.

PubMed

Rock, K S; Wood, D A; Keeling, M J

2015-09-01

The biology and behaviour of biting insects is a vitally important aspect in the spread of vector-borne diseases. This paper aims to determine, through the use of mathematical models, what effect incorporating vector senescence and realistic feeding patterns has on disease. A novel model is developed to enable the effects of age- and bite-structure to be examined in detail. This original PDE framework extends previous age-structured models into a further dimension to give a new insight into the role of vector biting and its interaction with vector mortality and spread of disease. Through the PDE model, the roles of the vector death and bite rates are examined in a way which is impossible under the traditional ODE formulation. It is demonstrated that incorporating more realistic functions for vector biting and mortality in a model may give rise to different dynamics than those seen under a more simple ODE formulation. The numerical results indicate that the efficacy of control methods that increase vector mortality may not be as great as predicted under a standard host-vector model, whereas other controls including treatment of humans may be more effective than previously thought. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Modeling Chagas Disease at Population Level to Explain Venezuela's Real Data

PubMed Central

González-Parra, Gilberto; Chen-Charpentier, Benito M.; Bermúdez, Moises

2015-01-01

Objectives In this paper we present an age-structured epidemiological model for Chagas disease. This model includes the interactions between human and vector populations that transmit Chagas disease. Methods The human population is divided into age groups since the proportion of infected individuals in this population changes with age as shown by real prevalence data. Moreover, the age-structured model allows more accurate information regarding the prevalence, which can help to design more specific control programs. We apply this proposed model to data from the country of Venezuela for two periods, 1961–1971, and 1961–1991 taking into account real demographic data for these periods. Results Numerical computer simulations are presented to show the suitability of the age-structured model to explain the real data regarding prevalence of Chagas disease in each of the age groups. In addition, a numerical simulation varying the death rate of the vector is done to illustrate prevention and control strategies against Chagas disease. Conclusion The proposed model can be used to determine the effect of control strategies in different age groups. PMID:26929912

Global economic burden of Chagas disease: a computational simulation model.

PubMed

Lee, Bruce Y; Bacon, Kristina M; Bottazzi, Maria Elena; Hotez, Peter J

2013-04-01

As Chagas disease continues to expand beyond tropical and subtropical zones, a growing need exists to better understand its resulting economic burden to help guide stakeholders such as policy makers, funders, and product developers. We developed a Markov simulation model to estimate the global and regional health and economic burden of Chagas disease from the societal perspective. Our Markov model structure had a 1 year cycle length and consisted of five states: acute disease, indeterminate disease, cardiomyopathy with or without congestive heart failure, megaviscera, and death. Major model parameter inputs, including the annual probabilities of transitioning from one state to another, and present case estimates for Chagas disease came from various sources, including WHO and other epidemiological and disease-surveillance-based reports. We calculated annual and lifetime health-care costs and disability-adjusted life-years (DALYs) for individuals, countries, and regions. We used a discount rate of 3% to adjust all costs and DALYs to present-day values. On average, an infected individual incurs US$474 in health-care costs and 0·51 DALYs annually. Over his or her lifetime, an infected individual accrues an average net present value of $3456 and 3·57 DALYs. Globally, the annual burden is $627·46 million in health-care costs and 806,170 DALYs. The global net present value of currently infected individuals is $24·73 billion in health-care costs and 29,385,250 DALYs. Conversion of this burden into costs results in annual per-person costs of $4660 and lifetime per-person costs of $27,684. Global costs are $7·19 billion per year and $188·80 billion per lifetime. More than 10% of these costs emanate from the USA and Canada, where Chagas disease has not been traditionally endemic. A substantial proportion of the burden emerges from lost productivity from cardiovascular disease-induced early mortality. The economic burden of Chagas disease is similar to or exceeds those

Data-driven models of dominantly-inherited Alzheimer's disease progression.

PubMed

Oxtoby, Neil P; Young, Alexandra L; Cash, David M; Benzinger, Tammie L S; Fagan, Anne M; Morris, John C; Bateman, Randall J; Fox, Nick C; Schott, Jonathan M; Alexander, Daniel C

2018-05-01

See Li and Donohue (doi:10.1093/brain/awy089) for a scientific commentary on this article.Dominantly-inherited Alzheimer's disease is widely hoped to hold the key to developing interventions for sporadic late onset Alzheimer's disease. We use emerging techniques in generative data-driven disease progression modelling to characterize dominantly-inherited Alzheimer's disease progression with unprecedented resolution, and without relying upon familial estimates of years until symptom onset. We retrospectively analysed biomarker data from the sixth data freeze of the Dominantly Inherited Alzheimer Network observational study, including measures of amyloid proteins and neurofibrillary tangles in the brain, regional brain volumes and cortical thicknesses, brain glucose hypometabolism, and cognitive performance from the Mini-Mental State Examination (all adjusted for age, years of education, sex, and head size, as appropriate). Data included 338 participants with known mutation status (211 mutation carriers in three subtypes: 163 PSEN1, 17 PSEN2, and 31 APP) and a baseline visit (age 19-66; up to four visits each, 1.1 ± 1.9 years in duration; spanning 30 years before, to 21 years after, parental age of symptom onset). We used an event-based model to estimate sequences of biomarker changes from baseline data across disease subtypes (mutation groups), and a differential equation model to estimate biomarker trajectories from longitudinal data (up to 66 mutation carriers, all subtypes combined). The two models concur that biomarker abnormality proceeds as follows: amyloid deposition in cortical then subcortical regions (∼24 ± 11 years before onset); phosphorylated tau (17 ± 8 years), tau and amyloid-β changes in cerebrospinal fluid; neurodegeneration first in the putamen and nucleus accumbens (up to 6 ± 2 years); then cognitive decline (7 ± 6 years), cerebral hypometabolism (4 ± 4 years), and further regional neurodegeneration. Our models predicted symptom onset more

Genetic enhancement of macroautophagy in vertebrate models of neurodegenerative diseases.

PubMed

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.

Leptospirosis disease mapping with standardized morbidity ratio and Poisson-Gamma model: An analysis of Leptospirosis disease in Kelantan, Malaysia

NASA Astrophysics Data System (ADS)

Che Awang, Aznida; Azah Samat, Nor

2017-09-01

Leptospirosis is a disease caused by the infection of pathogenic species from the genus of Leptospira. Human can be infected by the leptospirosis from direct or indirect exposure to the urine of infected animals. The excretion of urine from the animal host that carries pathogenic Leptospira causes the soil or water to be contaminated. Therefore, people can become infected when they are exposed to contaminated soil and water by cut on the skin as well as open wound. It also can enter the human body by mucous membrane such nose, eyes and mouth, for example by splashing contaminated water or urine into the eyes or swallowing contaminated water or food. Currently, there is no vaccine available for the prevention or treatment of leptospirosis disease but this disease can be treated if it is diagnosed early to avoid any complication. The disease risk mapping is important in a way to control and prevention of disease. Using a good choice of statistical model will produce a good disease risk map. Therefore, the aim of this study is to estimate the relative risk for leptospirosis disease based initially on the most common statistic used in disease mapping called Standardized Morbidity Ratio (SMR) and Poisson-gamma model. This paper begins by providing a review of the SMR method and Poisson-gamma model, which we then applied to leptospirosis data of Kelantan, Malaysia. Both results are displayed and compared using graph, tables and maps. The result shows that the second method Poisson-gamma model produces better relative risk estimates compared to the SMR method. This is because the Poisson-gamma model can overcome the drawback of SMR where the relative risk will become zero when there is no observed leptospirosis case in certain regions. However, the Poisson-gamma model also faced problems where the covariate adjustment for this model is difficult and no possibility for allowing spatial correlation between risks in neighbouring areas. The problems of this model have

A Framework for Modeling Emerging Diseases to Inform Management

PubMed Central

Katz, Rachel A.; Richgels, Katherine L.D.; Walsh, Daniel P.; Grant, Evan H.C.

2017-01-01

The rapid emergence and reemergence of zoonotic diseases requires the ability to rapidly evaluate and implement optimal management decisions. Actions to control or mitigate the effects of emerging pathogens are commonly delayed because of uncertainty in the estimates and the predicted outcomes of the control tactics. The development of models that describe the best-known information regarding the disease system at the early stages of disease emergence is an essential step for optimal decision-making. Models can predict the potential effects of the pathogen, provide guidance for assessing the likelihood of success of different proposed management actions, quantify the uncertainty surrounding the choice of the optimal decision, and highlight critical areas for immediate research. We demonstrate how to develop models that can be used as a part of a decision-making framework to determine the likelihood of success of different management actions given current knowledge. PMID:27983501

A framework for modeling emerging diseases to inform management

USGS Publications Warehouse

Russell, Robin E.; Katz, Rachel A.; Richgels, Katherine L. D.; Walsh, Daniel P.; Grant, Evan H. Campbell

2017-01-01

The rapid emergence and reemergence of zoonotic diseases requires the ability to rapidly evaluate and implement optimal management decisions. Actions to control or mitigate the effects of emerging pathogens are commonly delayed because of uncertainty in the estimates and the predicted outcomes of the control tactics. The development of models that describe the best-known information regarding the disease system at the early stages of disease emergence is an essential step for optimal decision-making. Models can predict the potential effects of the pathogen, provide guidance for assessing the likelihood of success of different proposed management actions, quantify the uncertainty surrounding the choice of the optimal decision, and highlight critical areas for immediate research. We demonstrate how to develop models that can be used as a part of a decision-making framework to determine the likelihood of success of different management actions given current knowledge.

A Framework for Modeling Emerging Diseases to Inform Management.

PubMed

Russell, Robin E; Katz, Rachel A; Richgels, Katherine L D; Walsh, Daniel P; Grant, Evan H C

2017-01-01

The rapid emergence and reemergence of zoonotic diseases requires the ability to rapidly evaluate and implement optimal management decisions. Actions to control or mitigate the effects of emerging pathogens are commonly delayed because of uncertainty in the estimates and the predicted outcomes of the control tactics. The development of models that describe the best-known information regarding the disease system at the early stages of disease emergence is an essential step for optimal decision-making. Models can predict the potential effects of the pathogen, provide guidance for assessing the likelihood of success of different proposed management actions, quantify the uncertainty surrounding the choice of the optimal decision, and highlight critical areas for immediate research. We demonstrate how to develop models that can be used as a part of a decision-making framework to determine the likelihood of success of different management actions given current knowledge.

models of congenital heart disease.

PubMed

Biglino, Giovanni; Capelli, Claudio; Leaver, Lindsay-Kay; Schievano, Silvia; Taylor, Andrew M; Wray, Jo

2015-01-01

To develop a participatory approach in the evaluation of 3D printed patient-specific models of congenital heart disease (CHD) with different stakeholders who would potentially benefit from the technology (patients, parents, clinicians and nurses). Workshops, focus groups and teaching sessions were organised, targeting different stakeholders. Sessions involved displaying and discussing different 3D models of CHD. Model evaluation involved response counts from questionnaires and thematic analysis of audio-recorded discussions and written feedback. Stakeholders’ responses indicated the scope and potential for clinical translation of 3D models. As tangible, three-dimensional artefacts, these can have a role in communicative processes. Their patient-specific quality is also important in relation to individual characteristics of CHD. Patients indicated that 3D models can help them visualise ‘what’s going on inside’. Parents agreed that models can spark curiosity in young people. Clinicians indicated that teaching might be the most relevant application. Nurses agreed that 3D models improved their learning experience during a CHD course. Engagement of different stakeholders to evaluate 3D printing technology for CHD identified the potential of the models for improving patient– doctor communication, patient empowerment and training. A participatory approach could benefit the clinical evaluation and translation of 3D printing technology.

A mathematical model of Chagas disease transmission

NASA Astrophysics Data System (ADS)

Hidayat, Dayat; Nugraha, Edwin Setiawan; Nuraini, Nuning

2018-03-01

Chagas disease is a parasitic infection caused by protozoan Trypanosoma cruzi which is transmitted to human by insects of the subfamily Triatominae, including Rhodnius prolixus. This disease is a major problem in several countries of Latin America. A mathematical model of Chagas disease with separate vector reservoir and a neighboring human resident is constructed. The basic reproductive ratio is obtained and stability analysis of the equilibria is shown. We also performed sensitivity populations dynamics of infected humans and infected insects based on migration rate, carrying capacity, and infection rate parameters. Our findings showed that the dynamics of the infected human and insect is mostly affected by carrying capacity insect in the settlement.

Modelling impacts of climate change on arable crop diseases: progress, challenges and applications.

PubMed

Newbery, Fay; Qi, Aiming; Fitt, Bruce Dl

2016-08-01

Combining climate change, crop growth and crop disease models to predict impacts of climate change on crop diseases can guide planning of climate change adaptation strategies to ensure future food security. This review summarises recent developments in modelling climate change impacts on crop diseases, emphasises some major challenges and highlights recent trends. The use of multi-model ensembles in climate change modelling and crop modelling is contributing towards measures of uncertainty in climate change impact projections but other aspects of uncertainty remain largely unexplored. Impact assessments are still concentrated on few crops and few diseases but are beginning to investigate arable crop disease dynamics at the landscape level. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Parathyroid diseases and animal models.

PubMed

Imanishi, Yasuo; Nagata, Yuki; Inaba, Masaaki

2012-01-01

CIRCULATING CALCIUM AND PHOSPHATE ARE TIGHTLY REGULATED BY THREE HORMONES: the active form of vitamin D (1,25-dihydroxyvitamin D), fibroblast growth factor (FGF)-23, and parathyroid hormone (PTH). PTH acts to stimulate a rapid increment in serum calcium and has a crucial role in calcium homeostasis. Major target organs of PTH are kidney and bone. The oversecretion of the hormone results in hypercalcemia, caused by increased intestinal calcium absorption, reduced renal calcium clearance, and mobilization of calcium from bone in primary hyperparathyroidism. In chronic kidney disease, secondary hyperparathyroidism of uremia is observed in its early stages, and this finally develops into the autonomous secretion of PTH during maintenance hemodialysis. Receptors in parathyroid cells, such as the calcium-sensing receptor, vitamin D receptor, and FGF receptor (FGFR)-Klotho complex have crucial roles in the regulation of PTH secretion. Genes such as Cyclin D1, RET, MEN1, HRPT2, and CDKN1B have been identified in parathyroid diseases. Genetically engineered animals with these receptors and the associated genes have provided us with valuable information on the patho-physiology of parathyroid diseases. The application of these animal models is significant for the development of new therapies.

Modelling the effect of urbanization on the transmission of an infectious disease.

PubMed

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.

Modelling the propagation of social response during a disease outbreak

PubMed Central

Fast, Shannon M.; González, Marta C.; Wilson, James M.; Markuzon, Natasha

2015-01-01

Epidemic trajectories and associated social responses vary widely between populations, with severe reactions sometimes observed. When confronted with fatal or novel pathogens, people exhibit a variety of behaviours from anxiety to hoarding of medical supplies, overwhelming medical infrastructure and rioting. We developed a coupled network approach to understanding and predicting social response. We couple the disease spread and panic spread processes and model them through local interactions between agents. The social contagion process depends on the prevalence of the disease, its perceived risk and a global media signal. We verify the model by analysing the spread of disease and social response during the 2009 H1N1 outbreak in Mexico City and 2003 severe acute respiratory syndrome and 2009 H1N1 outbreaks in Hong Kong, accurately predicting population-level behaviour. This kind of empirically validated model is critical to exploring strategies for public health intervention, increasing our ability to anticipate the response to infectious disease outbreaks. PMID:25589575

Route prediction model of infectious diseases for 2018 Winter Olympics in Korea

NASA Astrophysics Data System (ADS)

Kim, Eungyeong; Lee, Seok; Byun, Young Tae; Kim, Jae Hun; Lee, Hyuk-jae; Lee, Taikjin

2014-03-01

There are many types of respiratory infectious diseases caused by germs, virus, mycetes and parasites. Researchers recently have tried to develop mathematical models to predict the epidemic of infectious diseases. However, with the development of ground transportation system in modern society, the spread of infectious diseases became faster and more complicated in terms of the speed and the pathways. The route of infectious diseases during Vancouver Olympics was predicted based on the Susceptible-Infectious-Recovered (SIR) model. In this model only the air traffic as an essential factor for the intercity migration of infectious diseases was involved. Here, we propose a multi-city transmission model to predict the infection route during 2018 Winter Olympics in Korea based on the pre-existing SIR model. Various types of transportation system such as a train, a car, a bus, and an airplane for the interpersonal contact in both inter- and intra-city are considered. Simulation is performed with assumptions and scenarios based on realistic factors including demographic, transportation and diseases data in Korea. Finally, we analyze an economic profit and loss caused by the variation of the number of tourists during the Olympics.

An image-based model of brain volume biomarker changes in Huntington's disease.

PubMed

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.

Models to capture the potential for disease transmission in domestic sheep flocks.

PubMed

Schley, David; Whittle, Sophie; Taylor, Michael; Kiss, Istvan Zoltan

2012-09-15

Successful control of livestock diseases requires an understanding of how they spread amongst animals and between premises. Mathematical models can offer important insight into the dynamics of disease, especially when built upon experimental and/or field data. Here the dynamics of a range of epidemiological models are explored in order to determine which models perform best in capturing real-world heterogeneities at sufficient resolution. Individual based network models are considered together with one- and two-class compartmental models, for which the final epidemic size is calculated as a function of the probability of disease transmission occurring during a given physical contact between two individuals. For numerical results the special cases of a viral disease with a fast recovery rate (foot-and-mouth disease) and a bacterial disease with a slow recovery rate (brucellosis) amongst sheep are considered. Quantitative results from observational studies of physical contact amongst domestic sheep are applied and results from the differently structured flocks (ewes with newborn lambs, ewes with nearly weaned lambs and ewes only) compared. These indicate that the breeding cycle leads to significant changes in the expected basic reproduction ratio of diseases. The observed heterogeneity of contacts amongst animals is best captured by full network simulations, although simple compartmental models describe the key features of an outbreak but, as expected, often overestimate the speed of an outbreak. Here the weights of contacts are heterogeneous, with many low weight links. However, due to the well-connected nature of the networks, this has little effect and differences between models remain small. These results indicate that simple compartmental models can be a useful tool for modelling real-world flocks; their applicability will be greater still for more homogeneously mixed livestock, which could be promoted by higher intensity farming practices. Copyright © 2012

Tissue Chips to aid drug development and modeling for rare diseases

PubMed Central

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

Big Data for Infectious Disease Surveillance and Modeling.

PubMed

Bansal, Shweta; Chowell, Gerardo; Simonsen, Lone; Vespignani, Alessandro; Viboud, Cécile

2016-12-01

We devote a special issue of the Journal of Infectious Diseases to review the recent advances of big data in strengthening disease surveillance, monitoring medical adverse events, informing transmission models, and tracking patient sentiments and mobility. We consider a broad definition of big data for public health, one encompassing patient information gathered from high-volume electronic health records and participatory surveillance systems, as well as mining of digital traces such as social media, Internet searches, and cell-phone logs. We introduce nine independent contributions to this special issue and highlight several cross-cutting areas that require further research, including representativeness, biases, volatility, and validation, and the need for robust statistical and hypotheses-driven analyses. Overall, we are optimistic that the big-data revolution will vastly improve the granularity and timeliness of available epidemiological information, with hybrid systems augmenting rather than supplanting traditional surveillance systems, and better prospects for accurate infectious diseases models and forecasts. Published by Oxford University Press for the Infectious Diseases Society of America 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.

CRISPR-engineered genome editing for the next generation neurological disease modeling.

PubMed

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.

Tesevatinib ameliorates progression of polycystic kidney disease in rodent models of autosomal recessive polycystic kidney disease

PubMed Central

Sweeney, William E; Frost, Philip; Avner, Ellis D

2017-01-01

AIM To investigate the therapeutic potential of tesevatinib (TSV), a unique multi-kinase inhibitor currently in Phase II clinical trials for autosomal dominant polycystic kidney disease (ADPKD), in well-defined rodent models of autosomal recessive polycystic kidney disease (ARPKD). METHODS We administered TSV in daily doses of 7.5 and 15 mg/kg per day by I.P. to the well characterized bpk model of polycystic kidney disease starting at postnatal day (PN) 4 through PN21 to assess efficacy and toxicity in neonatal mice during postnatal development and still undergoing renal maturation. We administered TSV by oral gavage in the same doses to the orthologous PCK model (from PN30 to PN90) to assess efficacy and toxicity in animals where developmental processes are complete. The following parameters were assessed: Body weight, total kidney weight; kidney weight to body weight ratios; and morphometric determination of a cystic index and a measure of hepatic disease. Renal function was assessed by: Serum BUN; creatinine; and a 12 h urinary concentrating ability. Validation of reported targets including the level of angiogenesis and inhibition of angiogenesis (active VEGFR2/KDR) was assessed by Western analysis. RESULTS This study demonstrates that: (1) in vivo pharmacological inhibition of multiple kinase cascades with TSV reduced phosphorylation of key mediators of cystogenesis: EGFR, ErbB2, c-Src and KDR; and (2) this reduction of kinase activity resulted in significant reduction of renal and biliary disease in both bpk and PCK models of ARPKD. The amelioration of disease by TSV was not associated with any apparent toxicity. CONCLUSION The data supports the hypothesis that this multi-kinase inhibitor TSV may provide an effective clinical therapy for human ARPKD. PMID:28729967

Disease model curation improvements at Mouse Genome Informatics

PubMed Central

Bello, Susan M.; Richardson, Joel E.; Davis, Allan P.; Wiegers, Thomas C.; Mattingly, Carolyn J.; Dolan, Mary E.; Smith, Cynthia L.; Blake, Judith A.; Eppig, Janan T.

2012-01-01

Optimal curation of human diseases requires an ontology or structured vocabulary that contains terms familiar to end users, is robust enough to support multiple levels of annotation granularity, is limited to disease terms and is stable enough to avoid extensive reannotation following updates. At Mouse Genome Informatics (MGI), we currently use disease terms from Online Mendelian Inheritance in Man (OMIM) to curate mouse models of human disease. While OMIM provides highly detailed disease records that are familiar to many in the medical community, it lacks structure to support multilevel annotation. To improve disease annotation at MGI, we evaluated the merged Medical Subject Headings (MeSH) and OMIM disease vocabulary created by the Comparative Toxicogenomics Database (CTD) project. Overlaying MeSH onto OMIM provides hierarchical access to broad disease terms, a feature missing from the OMIM. We created an extended version of the vocabulary to meet the genetic disease-specific curation needs at MGI. Here we describe our evaluation of the CTD application, the extensions made by MGI and discuss the strengths and weaknesses of this approach. Database URL: http://www.informatics.jax.org/ PMID:22434831

Glycogen storage disease type Ia in canines: a model for human metabolic and genetic liver disease.

PubMed

Specht, Andrew; Fiske, Laurie; Erger, Kirsten; Cossette, Travis; Verstegen, John; Campbell-Thompson, Martha; Struck, Maggie B; Lee, Young Mok; Chou, Janice Y; Byrne, Barry J; Correia, Catherine E; Mah, Cathryn S; Weinstein, David A; Conlon, Thomas J

2011-01-01

A canine model of Glycogen storage disease type Ia (GSDIa) is described. Affected dogs are homozygous for a previously described M121I mutation resulting in a deficiency of glucose-6-phosphatase-α. Metabolic, clinicopathologic, pathologic, and clinical manifestations of GSDIa observed in this model are described and compared to those observed in humans. The canine model shows more complete recapitulation of the clinical manifestations seen in humans including "lactic acidosis", larger size, and longer lifespan compared to other animal models. Use of this model in preclinical trials of gene therapy is described and briefly compared to the murine model. Although the canine model offers a number of advantages for evaluating potential therapies for GSDIa, there are also some significant challenges involved in its use. Despite these challenges, the canine model of GSDIa should continue to provide valuable information about the potential for generating curative therapies for GSDIa as well as other genetic hepatic diseases.

Modeling the spatial spread of infectious diseases: the GLobal Epidemic and Mobility computational model

PubMed Central

Balcan, Duygu; Gonçalves, Bruno; Hu, Hao; Ramasco, José J.; Colizza, Vittoria

2010-01-01

Here we present the Global Epidemic and Mobility (GLEaM) model that integrates sociodemographic and population mobility data in a spatially structured stochastic disease approach to simulate the spread of epidemics at the worldwide scale. We discuss the flexible structure of the model that is open to the inclusion of different disease structures and local intervention policies. This makes GLEaM suitable for the computational modeling and anticipation of the spatio-temporal patterns of global epidemic spreading, the understanding of historical epidemics, the assessment of the role of human mobility in shaping global epidemics, and the analysis of mitigation and containment scenarios. PMID:21415939

Toxin-Induced Models of Parkinson's Disease

PubMed Central

Bové, Jordi; Prou, Delphine; Perier, Céline; Przedborski, Serge

2005-01-01

Summary: Parkinson's disease (PD) is a common neurodegenerative disease that appears essentially as a sporadic condition. It results mainly from the death of dopaminergic neurons in the substantia nigra. PD etiology remains mysterious, whereas its pathogenesis begins to be understood as a multifactorial cascade of deleterious factors. Most insights into PD pathogenesis come from investigations performed in experimental models of PD, especially those produced by neurotoxins. Although a host of natural and synthetic molecules do exert deleterious effects on dopaminergic neurons, only a handful are used in living laboratory animals to recapitulate some of the hallmarks of PD. In this review, we discuss what we believe are the four most popular parkinsonian neurotoxins, namely 6-hydroxydopamine (6-OHDA), 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), rotenone, and paraquat. The main goal is to provide an updated summary of the main characteristics of each of these four neurotoxins. However, we also try to provide the reader with an idea about the various strengths and the weaknesses of these neurotoxic models. PMID:16389312

Mixture models for undiagnosed prevalent disease and interval-censored incident disease: applications to a cohort assembled from electronic health records.

PubMed

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.

Investigating Interventions in Alzheimer's Disease with Computer Simulation Models

PubMed Central

Proctor, Carole J.; Boche, Delphine; Gray, Douglas A.; Nicoll, James A. R.

2013-01-01

Progress in the development of therapeutic interventions to treat or slow the progression of Alzheimer's disease has been hampered by lack of efficacy and unforeseen side effects in human clinical trials. This setback highlights the need for new approaches for pre-clinical testing of possible interventions. Systems modelling is becoming increasingly recognised as a valuable tool for investigating molecular and cellular mechanisms involved in ageing and age-related diseases. However, there is still a lack of awareness of modelling approaches in many areas of biomedical research. We previously developed a stochastic computer model to examine some of the key pathways involved in the aggregation of amyloid-beta (Aβ) and the micro-tubular binding protein tau. Here we show how we extended this model to include the main processes involved in passive and active immunisation against Aβ and then demonstrate the effects of this intervention on soluble Aβ, plaques, phosphorylated tau and tangles. The model predicts that immunisation leads to clearance of plaques but only results in small reductions in levels of soluble Aβ, phosphorylated tau and tangles. The behaviour of this model is supported by neuropathological observations in Alzheimer patients immunised against Aβ. Since, soluble Aβ, phosphorylated tau and tangles more closely correlate with cognitive decline than plaques, our model suggests that immunotherapy against Aβ may not be effective unless it is performed very early in the disease process or combined with other therapies. PMID:24098635

The Danger Model Approach to the Pathogenesis of the Rheumatic Diseases

PubMed Central

Pacheco-Tena, César; González-Chávez, Susana Aideé

2015-01-01

The danger model was proposed by Polly Matzinger as complement to the traditional self-non-self- (SNS-) model to explain the immunoreactivity. The danger model proposes a central role of the tissular cells' discomfort as an element to prime the immune response processes in opposition to the traditional SNS-model where foreignness is a prerequisite. However recent insights in the proteomics of diverse tissular cells have revealed that under stressful conditions they have a significant potential to initiate, coordinate, and perpetuate autoimmune processes, in many cases, ruling over the adaptive immune response cells; this ruling potential can also be confirmed by observations in several genetically manipulated animal models. Here, we review the pathogenesis of rheumatic diseases such as systemic lupus erythematous, rheumatoid arthritis, spondyloarthritis including ankylosing spondylitis, psoriasis, and Crohn's disease and provide realistic approaches based on the logic of the danger model. We assume that tissular dysfunction is a prerequisite for chronic autoimmunity and propose two genetically conferred hypothetical roles for the tissular cells causing the disease: (A) the Impaired cell and (B) the paranoid cell. Both roles are not mutually exclusive. Some examples in human disease and in animal models are provided based on current evidence. PMID:25973436

Disease modeling using human induced pluripotent stem cells: lessons from the liver.

PubMed

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.

Big Data for Infectious Disease Surveillance and Modeling

PubMed Central

Bansal, Shweta; Chowell, Gerardo; Simonsen, Lone; Vespignani, Alessandro; Viboud, Cécile

2016-01-01

We devote a special issue of the Journal of Infectious Diseases to review the recent advances of big data in strengthening disease surveillance, monitoring medical adverse events, informing transmission models, and tracking patient sentiments and mobility. We consider a broad definition of big data for public health, one encompassing patient information gathered from high-volume electronic health records and participatory surveillance systems, as well as mining of digital traces such as social media, Internet searches, and cell-phone logs. We introduce nine independent contributions to this special issue and highlight several cross-cutting areas that require further research, including representativeness, biases, volatility, and validation, and the need for robust statistical and hypotheses-driven analyses. Overall, we are optimistic that the big-data revolution will vastly improve the granularity and timeliness of available epidemiological information, with hybrid systems augmenting rather than supplanting traditional surveillance systems, and better prospects for accurate infectious diseases models and forecasts. PMID:28830113

A model for ubiquitous care of noncommunicable diseases.

PubMed

Vianna, Henrique Damasceno; Barbosa, Jorge Luis Victória

2014-09-01

The ubiquitous computing, or ubicomp, is a promising technology to help chronic diseases patients managing activities, offering support to them anytime, anywhere. Hence, ubicomp can aid community and health organizations to continuously communicate with patients and to offer useful resources for their self-management activities. Communication is prioritized in works of ubiquitous health for noncommunicable diseases care, but the management of resources is not commonly employed. We propose the UDuctor, a model for ubiquitous care of noncommunicable diseases. UDuctor focuses the resources offering, without losing self-management and communication supports. We implemented a system and applied it in two practical experiments. First, ten chronic patients tried the system and filled out a questionnaire based on the technology acceptance model. After this initial evaluation, an alpha test was done. The system was used daily for one month and a half by a chronic patient. The results were encouraging and show potential for implementing UDuctor in real-life situations.

Endometriosis research: animal models for the study of a complex disease.

PubMed

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.

Disease Spreading Model with Partial Isolation

NASA Astrophysics Data System (ADS)

Chakraborty, Abhijit; Manna, S. S.

2013-08-01

The effect of partial isolation has been studied in disease spreading processes using the framework of susceptible-infected-susceptible (SIS) and susceptible-infected-recovered (SIR) models. The partial isolation is introduced by imposing a restriction: each infected individual can probabilistically infect up to a maximum number n of his susceptible neighbors, but not all. It has been observed that the critical values of the spreading rates for endemic states are non-zero in both models and decrease as 1/n with n, on all graphs including scale-free graphs. In particular, the SIR model with n = 2 turned out to be a special case, characterized by a new bond percolation threshold on square lattice.

Understanding disease mechanisms with models of signaling pathway activities.

PubMed

Sebastian-Leon, Patricia; Vidal, Enrique; Minguez, Pablo; Conesa, Ana; Tarazona, Sonia; Amadoz, Alicia; Armero, Carmen; Salavert, Francisco; Vidal-Puig, Antonio; Montaner, David; Dopazo, Joaquín

2014-10-25

Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is one of the main challenges in the analysis of genomic data and is on the basis of the future implementation of precision medicine. Here we propose a simple probabilistic model in which signaling pathways are separated into elementary sub-pathways or signal transmission circuits (which ultimately trigger cell functions) and then transforms gene expression measurements into probabilities of activation of such signal transmission circuits. Using this model, differential activation of such circuits between biological conditions can be estimated. Thus, circuit activation statuses can be interpreted as biomarkers that discriminate among the compared conditions. This type of mechanism-based biomarkers accounts for cell functional activities and can easily be associated to disease or drug action mechanisms. The accuracy of the proposed model is demonstrated with simulations and real datasets. The proposed model provides detailed information that enables the interpretation disease mechanisms as a consequence of the complex combinations of altered gene expression values. Moreover, it offers a framework for suggesting possible ways of therapeutic intervention in a pathologically perturbed system.

Animal models of gastrointestinal and liver diseases. Animal models of cystic fibrosis: gastrointestinal, pancreatic, and hepatobiliary disease and pathophysiology

PubMed Central

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

Predictive models for ocular chronic graft-versus-host disease diagnosis and disease activity in transplant clinical practice.

PubMed

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

Predictive models for ocular chronic graft-versus-host disease diagnosis and disease activity in transplant clinical practice

PubMed Central

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

Organ allocation for chronic liver disease: model for end-stage liver disease and beyond.

PubMed

Asrani, Sumeet K; Kim, W Ray

2010-05-01

Implementation of the model for end-stage liver disease (MELD) score has led to a reduction in waiting list registration and waitlist mortality. Prognostic models have been proposed to either refine or improve the current MELD-based liver allocation. The model for end-stage liver disease - sodium (MELDNa) incorporates serum sodium and has been shown to improve the predictive accuracy of the MELD score. However, laboratory variation and manipulation of serum sodium is a concern. Organ allocation in the United Kingdom is now based on a model that includes serum sodium. An updated MELD score is associated with a lower relative weight for serum creatinine coefficient and a higher relative weight for bilirubin coefficient, although the contribution of reweighting coefficients as compared with addition of variables is unclear. The D-MELD, the arithmetic product of donor age and preoperative MELD, proposes donor-recipient matching; however, inappropriate transplantation of high-risk donors is a concern. Finally, the net benefit model ranks patients according to the net survival benefit that they would derive from the transplant. However, complex statistical models are required and unmeasured characteristics may unduly affect the model. Despite their limitations, efforts to improve the current MELD-based organ allocation are encouraging.

How predictive quantitative modelling of tissue organisation can inform liver disease pathogenesis.

PubMed

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.

Pharmacokinetic/pharmacodynamic modelling approaches in paediatric infectious diseases and immunology☆

PubMed Central

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

Modeling human mobility responses to the large-scale spreading of infectious diseases.

PubMed

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.

Human Environmental Disease Network: A computational model to assess toxicology of contaminants.

PubMed

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.

Incorporating individual health-protective decisions into disease transmission models: a mathematical framework.

PubMed

Durham, David P; Casman, Elizabeth A

2012-03-07

It is anticipated that the next generation of computational epidemic models will simulate both infectious disease transmission and dynamic human behaviour change. Individual agents within a simulation will not only infect one another, but will also have situational awareness and a decision algorithm that enables them to modify their behaviour. This paper develops such a model of behavioural response, presenting a mathematical interpretation of a well-known psychological model of individual decision making, the health belief model, suitable for incorporation within an agent-based disease-transmission model. We formalize the health belief model and demonstrate its application in modelling the prevalence of facemask use observed over the course of the 2003 Hong Kong SARS epidemic, a well-documented example of behaviour change in response to a disease outbreak.

Incorporating individual health-protective decisions into disease transmission models: a mathematical framework

PubMed Central

Durham, David P.; Casman, Elizabeth A.

2012-01-01

It is anticipated that the next generation of computational epidemic models will simulate both infectious disease transmission and dynamic human behaviour change. Individual agents within a simulation will not only infect one another, but will also have situational awareness and a decision algorithm that enables them to modify their behaviour. This paper develops such a model of behavioural response, presenting a mathematical interpretation of a well-known psychological model of individual decision making, the health belief model, suitable for incorporation within an agent-based disease-transmission model. We formalize the health belief model and demonstrate its application in modelling the prevalence of facemask use observed over the course of the 2003 Hong Kong SARS epidemic, a well-documented example of behaviour change in response to a disease outbreak. PMID:21775324

Agent-Based Modeling of Chronic Diseases: A Narrative Review and Future Research Directions.

PubMed

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.

Neuronal glycogen synthesis contributes to physiological aging.

PubMed

Sinadinos, Christopher; Valles-Ortega, Jordi; Boulan, Laura; Solsona, Estel; Tevy, Maria F; Marquez, Mercedes; Duran, Jordi; Lopez-Iglesias, Carmen; Calbó, Joaquim; Blasco, Ester; Pumarola, Marti; Milán, Marco; Guinovart, Joan J

2014-10-01

Glycogen is a branched polymer of glucose and the carbohydrate energy store for animal cells. In the brain, it is essentially found in glial cells, although it is also present in minute amounts in neurons. In humans, loss-of-function mutations in laforin and malin, proteins involved in suppressing glycogen synthesis, induce the presence of high numbers of insoluble polyglucosan bodies in neuronal cells. Known as Lafora bodies (LBs), these deposits result in the aggressive neurodegeneration seen in Lafora's disease. Polysaccharide-based aggregates, called corpora amylacea (CA), are also present in the neurons of aged human brains. Despite the similarity of CA to LBs, the mechanisms and functional consequences of CA formation are yet unknown. Here, we show that wild-type laboratory mice also accumulate glycogen-based aggregates in the brain as they age. These structures are immunopositive for an array of metabolic and stress-response proteins, some of which were previously shown to aggregate in correlation with age in the human brain and are also present in LBs. Remarkably, these structures and their associated protein aggregates are not present in the aged mouse brain upon genetic ablation of glycogen synthase. Similar genetic intervention in Drosophila prevents the accumulation of glycogen clusters in the neuronal processes of aged flies. Most interestingly, targeted reduction of Drosophila glycogen synthase in neurons improves neurological function with age and extends lifespan. These results demonstrate that neuronal glycogen accumulation contributes to physiological aging and may therefore constitute a key factor regulating age-related neurological decline in humans. © 2014 The Authors. Aging cell published by the Anatomical Society and John Wiley & Sons Ltd.

Developing an active implementation model for a chronic disease management program

PubMed Central

Smidth, Margrethe; Christensen, Morten Bondo; Olesen, Frede; Vedsted, Peter

2013-01-01

Background 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. Methods 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. Results 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

Developing an active implementation model for a chronic disease management program.

PubMed

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

Modelling the propagation of social response during a disease outbreak.

PubMed

Fast, Shannon M; González, Marta C; Wilson, James M; Markuzon, Natasha

2015-03-06

Epidemic trajectories and associated social responses vary widely between populations, with severe reactions sometimes observed. When confronted with fatal or novel pathogens, people exhibit a variety of behaviours from anxiety to hoarding of medical supplies, overwhelming medical infrastructure and rioting. We developed a coupled network approach to understanding and predicting social response. We couple the disease spread and panic spread processes and model them through local interactions between agents. The social contagion process depends on the prevalence of the disease, its perceived risk and a global media signal. We verify the model by analysing the spread of disease and social response during the 2009 H1N1 outbreak in Mexico City and 2003 severe acute respiratory syndrome and 2009 H1N1 outbreaks in Hong Kong, accurately predicting population-level behaviour. This kind of empirically validated model is critical to exploring strategies for public health intervention, increasing our ability to anticipate the response to infectious disease outbreaks. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Transgenic Monkey Model of the Polyglutamine Diseases Recapitulating Progressive Neurological Symptoms

PubMed Central

Ishibashi, Hidetoshi; Minakawa, Eiko N.; Motohashi, Hideyuki H.; Takayama, Osamu; Popiel, H. Akiko; Puentes, Sandra; Owari, Kensuke; Nakatani, Terumi; Nogami, Naotake; Yamamoto, Kazuhiro; Yonekawa, Takahiro; Tanaka, Yoko; Fujita, Naoko; Suzuki, Hikaru; Aizawa, Shu; Nagano, Seiichi; Yamada, Daisuke; Wada, Keiji; Kohsaka, Shinichi

2017-01-01

Abstract Age-associated neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, and the polyglutamine (polyQ) diseases, are becoming prevalent as a consequence of elongation of the human lifespan. Although various rodent models have been developed to study and overcome these diseases, they have limitations in their translational research utility owing to differences from humans in brain structure and function and in drug metabolism. Here, we generated a transgenic marmoset model of the polyQ diseases, showing progressive neurological symptoms including motor impairment. Seven transgenic marmosets were produced by lentiviral introduction of the human ataxin 3 gene with 120 CAG repeats encoding an expanded polyQ stretch. Although all offspring showed no neurological symptoms at birth, three marmosets with higher transgene expression developed neurological symptoms of varying degrees at 3–4 months after birth, followed by gradual decreases in body weight gain, spontaneous activity, and grip strength, indicating time-dependent disease progression. Pathological examinations revealed neurodegeneration and intranuclear polyQ protein inclusions accompanied by gliosis, which recapitulate the neuropathological features of polyQ disease patients. Consistent with neuronal loss in the cerebellum, brain MRI analyses in one living symptomatic marmoset detected enlargement of the fourth ventricle, which suggests cerebellar atrophy. Notably, successful germline transgene transmission was confirmed in the second-generation offspring derived from the symptomatic transgenic marmoset gamete. Because the accumulation of abnormal proteins is a shared pathomechanism among various neurodegenerative diseases, we suggest that this new marmoset model will contribute toward elucidating the pathomechanisms of and developing clinically applicable therapies for neurodegenerative diseases. PMID:28374014

Towards a Hybrid Agent-based Model for Mosquito Borne Disease.

PubMed

Mniszewski, S M; Manore, C A; Bryan, C; Del Valle, S Y; Roberts, D

2014-07-01

Agent-based models (ABM) are used to simulate the spread of infectious disease through a population. Detailed human movement, demography, realistic business location networks, and in-host disease progression are available in existing ABMs, such as the Epidemic Simulation System (EpiSimS). These capabilities make possible the exploration of pharmaceutical and non-pharmaceutical mitigation strategies used to inform the public health community. There is a similar need for the spread of mosquito borne pathogens due to the re-emergence of diseases such as chikungunya and dengue fever. A network-patch model for mosquito dynamics has been coupled with EpiSimS. Mosquitoes are represented as a "patch" or "cloud" associated with a location. Each patch has an ordinary differential equation (ODE) mosquito dynamics model and mosquito related parameters relevant to the location characteristics. Activities at each location can have different levels of potential exposure to mosquitoes based on whether they are inside, outside, or somewhere in-between. As a proof of concept, the hybrid network-patch model is used to simulate the spread of chikungunya through Washington, DC. Results are shown for a base case, followed by varying the probability of transmission, mosquito count, and activity exposure. We use visualization to understand the pattern of disease spread.

Modeling infectious diseases dissemination through online role-playing games.

PubMed

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.

Biology and therapy of inherited retinal degenerative disease: insights from mouse models

PubMed Central

Veleri, Shobi; Lazar, Csilla H.; Chang, Bo; Sieving, Paul A.; Banin, Eyal; Swaroop, Anand

2015-01-01

Retinal neurodegeneration associated with the dysfunction or death of photoreceptors is a major cause of incurable vision loss. Tremendous progress has been made over the last two decades in discovering genes and genetic defects that lead to retinal diseases. The primary focus has now shifted to uncovering disease mechanisms and designing treatment strategies, especially inspired by the successful application of gene therapy in some forms of congenital blindness in humans. Both spontaneous and laboratory-generated mouse mutants have been valuable for providing fundamental insights into normal retinal development and for deciphering disease pathology. Here, we provide a review of mouse models of human retinal degeneration, with a primary focus on diseases affecting photoreceptor function. We also describe models associated with retinal pigment epithelium dysfunction or synaptic abnormalities. Furthermore, we highlight the crucial role of mouse models in elucidating retinal and photoreceptor biology in health and disease, and in the assessment of novel therapeutic modalities, including gene- and stem-cell-based therapies, for retinal degenerative diseases. PMID:25650393

Agent-Based Modeling of Chronic Diseases: A Narrative Review and Future Research Directions

PubMed Central

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

Mathematical modeling of zika virus disease with nonlinear incidence and optimal control

NASA Astrophysics Data System (ADS)

Goswami, Naba Kumar; Srivastav, Akhil Kumar; Ghosh, Mini; Shanmukha, B.

2018-04-01

The Zika virus was first discovered in a rhesus monkey in the Zika Forest of Uganda in 1947, and it was isolated from humans in Nigeria in 1952. Zika virus disease is primarily a mosquito-borne disease, which is transmitted to human primarily through the bite of an infected Aedes species mosquito. However, there is documented evidence of sexual transmission of this disease too. In this paper, a nonlinear mathematical model for Zika virus by considering nonlinear incidence is formulated and analyzed. The equilibria and the basic reproduction number (R0) of the model are found. The stability of the different equilibria of the model is discussed in detail. When the basic reproduction number R0 < 1, the disease-free equilibrium is locally and globally stable i.e. in this case disease dies out. For R0 > 1, we have endemic equilibrium which is locally stable under some restriction on parameters. Further this model is extended to optimal control model and is analyzed by using Pontryagin’s Maximum Principle. It has been observed that optimal control plays a significant role in reducing the number of zika infectives. Finally, numerical simulation is performed to illustrate the analytical findings.

Materials for Neural Differentiation, Trans-Differentiation, and Modeling of Neurological Disease.

PubMed

Gong, Lulu; Cao, Lining; Shen, Zhenmin; Shao, Li; Gao, Shaorong; Zhang, Chao; Lu, Jianfeng; Li, Weida

2018-04-01

Neuron regeneration from pluripotent stem cells (PSCs) differentiation or somatic cells trans-differentiation is a promising approach for cell replacement in neurodegenerative diseases and provides a powerful tool for investigating neural development, modeling neurological diseases, and uncovering the mechanisms that underlie diseases. Advancing the materials that are applied in neural differentiation and trans-differentiation promotes the safety, efficiency, and efficacy of neuron regeneration. In the neural differentiation process, matrix materials, either natural or synthetic, not only provide a structural and biochemical support for the monolayer or three-dimensional (3D) cultured cells but also assist in cell adhesion and cell-to-cell communication. They play important roles in directing the differentiation of PSCs into neural cells and modeling neurological diseases. For the trans-differentiation of neural cells, several materials have been used to make the conversion feasible for future therapy. Here, the most current applications of materials for neural differentiation for PSCs, neuronal trans-differentiation, and neurological disease modeling is summarized and discussed. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The Zebrafish Model Organism Database: new support for human disease models, mutation details, gene expression phenotypes and searching

PubMed Central

Howe, Douglas G.; Bradford, Yvonne M.; Eagle, Anne; Fashena, David; Frazer, Ken; Kalita, Patrick; Mani, Prita; Martin, Ryan; Moxon, Sierra Taylor; Paddock, Holly; Pich, Christian; Ramachandran, Sridhar; Ruzicka, Leyla; Schaper, Kevin; Shao, Xiang; Singer, Amy; Toro, Sabrina; Van Slyke, Ceri; Westerfield, Monte

2017-01-01

The Zebrafish Model Organism Database (ZFIN; http://zfin.org) is the central resource for zebrafish (Danio rerio) genetic, genomic, phenotypic and developmental data. ZFIN curators provide expert manual curation and integration of comprehensive data involving zebrafish genes, mutants, transgenic constructs and lines, phenotypes, genotypes, gene expressions, morpholinos, TALENs, CRISPRs, antibodies, anatomical structures, models of human disease and publications. We integrate curated, directly submitted, and collaboratively generated data, making these available to zebrafish research community. Among the vertebrate model organisms, zebrafish are superbly suited for rapid generation of sequence-targeted mutant lines, characterization of phenotypes including gene expression patterns, and generation of human disease models. The recent rapid adoption of zebrafish as human disease models is making management of these data particularly important to both the research and clinical communities. Here, we describe recent enhancements to ZFIN including use of the zebrafish experimental conditions ontology, ‘Fish’ records in the ZFIN database, support for gene expression phenotypes, models of human disease, mutation details at the DNA, RNA and protein levels, and updates to the ZFIN single box search. PMID:27899582

Canine mammary tumors as a model for human disease.

PubMed

Abdelmegeed, Somaia M; Mohammed, Sulma

2018-06-01

Animal models for examining human breast cancer (HBC) carcinogenesis have been extensively studied and proposed. With the recent advent of immunotherapy, significant attention has been focused on the dog as a model for human cancer. Dogs develop mammary tumors and other cancer types spontaneously with an intact immune system, which exhibit a number of clinical and molecular similarities to HBC. In addition to the spontaneous tumor presentation, the clinical similarities between human and canine mammary tumors (CMT) include the age at onset, hormonal etiology and course of the diseases. Furthermore, factors that affect the disease outcome, including tumor size, stage and lymph node invasion, are similar in HBC and CMT. Similarly, the molecular characteristics of steroid receptor, epidermal growth factor, proliferation marker, metalloproteinase and cyclooxygenase expression, and the mutation of the p53 tumor suppressor gene in CMT, mimic HBC. Furthermore, ductal carcinomas in situ in human and canine mammary glands are particularly similar in their pathological, molecular and visual characteristics. These CMT characteristics and their similarities to HBC indicate that the dog could be an excellent model for the study of human disease. These similarities are discussed in detail in the present review, and are compared with the in vitro and other in vivo animal models available.

Mathematical Modeling of Protein Misfolding Mechanisms in Neurological Diseases: A Historical Overview.

PubMed

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.

Genetically engineered pigs as models for human disease

PubMed Central

Perleberg, Carolin; Kind, Alexander

2018-01-01

ABSTRACT Genetically modified animals are vital for gaining a proper understanding of disease mechanisms. Mice have long been the mainstay of basic research into a wide variety of diseases but are not always the most suitable means of translating basic knowledge into clinical application. The shortcomings of rodent preclinical studies are widely recognised, and regulatory agencies around the world now require preclinical trial data from nonrodent species. Pigs are well suited to biomedical research, sharing many similarities with humans, including body size, anatomical features, physiology and pathophysiology, and they already play an important role in translational studies. This role is set to increase as advanced genetic techniques simplify the generation of pigs with precisely tailored modifications designed to replicate lesions responsible for human disease. This article provides an overview of the most promising and clinically relevant genetically modified porcine models of human disease for translational biomedical research, including cardiovascular diseases, cancers, diabetes mellitus, Alzheimer's disease, cystic fibrosis and Duchenne muscular dystrophy. We briefly summarise the technologies involved and consider the future impact of recent technical advances. PMID:29419487

Describing and Modeling Workflow and Information Flow in Chronic Disease Care

PubMed Central

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

Development of model infectious disease protocols for fire and EMS personnel.

PubMed

Miller, Nancy L; Gudmestad, Tom; Eisenberg, Mickey S

2005-01-01

To develop model infectious disease exposure plans for emergency medical services agencies in King County, Washington. All fire departments in King County, Washington, were surveyed to determine their pathogen exposure policies. After these agencies were surveyed, model response plans were developed for both bloodborne and airborne pathogen exposure. Twenty-four of the 35 fire departments in King County submitted infectious disease exposure policies. There was diversity among the plans, and not all were deemed able to provide prophylaxis in a timely fashion. Based on this lack of uniformity among response plans, model response plans were developed for bloodborne and airborne infectious disease pathogens. Great variety was present throughout the exposure plans currently in use throughout King County, Washington. Model plans would likely universalize response to pathogen exposure and help to ensure prompt and appropriate postexposure prophylaxis.

Stability of equilibrium points in intraguild predation model with disease with SI model

NASA Astrophysics Data System (ADS)

Hassan, Aimi Nuraida binti Ali; Bujang, Noriham binti; Mahdi, Ahmad Faisal Bin

2017-04-01

Intraguild Predation (IGP) is classified as killing and eating among potential competitors. Intraguild Predation is a universal interaction, differing from competition or predation. Lotka Volterra competition model and Intraguild predation model has been analyze. The assumption for this model is no any immigration or migration involves. This paper is only considered IGP model for susceptible and infective (SI) only. The analysis of stability of the equilibrium points of Intraguild Predation Models with disease using Routh Hurwitz criteria will be illustrated using some numerical example.

Occurrence of spontaneous periodontal disease in the SAMP1/YitFc murine model of Crohn disease.

PubMed

Pietropaoli, Davide; Del Pinto, Rita; Corridoni, Daniele; Rodriguez-Palacios, Alexander; Di Stefano, Gabriella; Monaco, Annalisa; Weinberg, Aaron; Cominelli, Fabio

2014-12-01

Oral involvement is often associated with inflammatory bowel disease (IBD). Recent evidence suggests a high incidence of periodontal disease in patients with Crohn disease (CD). To the best of the authors' knowledge, no animal model of IBD that displays associated periodontal disease was reported previously. The aim of this study is to investigate the occurrence and progression of periodontal disease in SAMP1/YitFc (SAMP) mice that spontaneously develop a CD-like ileitis. In addition, the temporal correlation between the onset and progression of periodontal disease and the onset of ileitis in SAMP mice was studied. At different time points, SAMP and parental AKR/J (AKR) control mice were sacrificed, and mandibles were prepared for stereomicroscopy and histology. Terminal ilea were collected for histologic assessment of inflammation score. Periodontal status, i.e., alveolar bone loss (ABL) and alveolar bone crest, was examined by stereomicroscopy and histomorphometry, respectively. ABL increased in both strains with age. SAMP mice showed greater ABL compared with AKR mice by 12 weeks of age, with maximal differences observed at 27 weeks of age. AKR control mice did not show the same severity of periodontal disease. Interestingly, a strong positive correlation was found between ileitis severity and ABL in SAMP mice, independent of age. The present results demonstrate the occurrence of periodontal disease in a mouse model of progressive CD-like ileitis. In addition, the severity of periodontitis strongly correlated with the severity of ileitis, independent of age, suggesting that common pathogenic mechanisms, such as abnormal immune response and dysbiosis, may be shared between these two phenotypes.

Concise review: Patient-derived olfactory stem cells: new models for brain diseases.

PubMed

Mackay-Sim, Alan

2012-11-01

Traditional models of brain diseases have had limited success in driving candidate drugs into successful clinical translation. This has resulted in large international pharmaceutical companies moving out of neuroscience research. Cells are not brains, obviously, but new patient-derived stem models have the potential to elucidate cell biological aspects of brain diseases that are not present in worm, fly, or rodent models, the work horses of disease investigations and drug discovery. Neural stem cells are present in the olfactory mucosa, the organ of smell in the nose. Patient-derived olfactory mucosa has demonstrated disease-associated differences in a variety of brain diseases and recently olfactory mucosa stem cells have been generated from patients with schizophrenia, Parkinson's disease, and familial dysautonomia. By comparison with cells from healthy controls, patient-derived olfactory mucosa stem cells show disease-specific alterations in gene expression and cell functions including: a shorter cell cycle and faster proliferation in schizophrenia, oxidative stress in Parkinson's disease, and altered cell migration in familial dysautonomia. Olfactory stem cell cultures thus reveal patient-control differences, even in complex genetic diseases such as schizophrenia and Parkinson's disease, indicating that multiple genes of small effect can converge on shared cell signaling pathways to present as a disease-specific cellular phenotype. Olfactory mucosa stem cells can be maintained in homogeneous cultures that allow robust and repeatable multiwell assays suitable for screening libraries of drug candidate molecules. Copyright © 2012 AlphaMed Press.

Disease mapping based on stochastic SIR-SI model for Dengue and Chikungunya in Malaysia

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

Samat, N. A.; Ma'arof, S. H. Mohd Imam

This paper describes and demonstrates a method for relative risk estimation which is based on the stochastic SIR-SI vector-borne infectious disease transmission model specifically for Dengue and Chikungunya diseases in Malaysia. Firstly, the common compartmental model for vector-borne infectious disease transmission called the SIR-SI model (susceptible-infective-recovered for human populations; susceptible-infective for vector populations) is presented. This is followed by the explanations on the stochastic SIR-SI model which involve the Bayesian description. This stochastic model then is used in the relative risk formulation in order to obtain the posterior relative risk estimation. Then, this relative estimation model is demonstrated using Denguemore » and Chikungunya data of Malaysia. The viruses of these diseases are transmitted by the same type of female vector mosquito named Aedes Aegypti and Aedes Albopictus. Finally, the findings of the analysis of relative risk estimation for both Dengue and Chikungunya diseases are presented, compared and displayed in graphs and maps. The distribution from risk maps show the high and low risk area of Dengue and Chikungunya diseases occurrence. This map can be used as a tool for the prevention and control strategies for both diseases.« less

Disease mapping based on stochastic SIR-SI model for Dengue and Chikungunya in Malaysia

NASA Astrophysics Data System (ADS)

Samat, N. A.; Ma'arof, S. H. Mohd Imam

2014-12-01

This paper describes and demonstrates a method for relative risk estimation which is based on the stochastic SIR-SI vector-borne infectious disease transmission model specifically for Dengue and Chikungunya diseases in Malaysia. Firstly, the common compartmental model for vector-borne infectious disease transmission called the SIR-SI model (susceptible-infective-recovered for human populations; susceptible-infective for vector populations) is presented. This is followed by the explanations on the stochastic SIR-SI model which involve the Bayesian description. This stochastic model then is used in the relative risk formulation in order to obtain the posterior relative risk estimation. Then, this relative estimation model is demonstrated using Dengue and Chikungunya data of Malaysia. The viruses of these diseases are transmitted by the same type of female vector mosquito named Aedes Aegypti and Aedes Albopictus. Finally, the findings of the analysis of relative risk estimation for both Dengue and Chikungunya diseases are presented, compared and displayed in graphs and maps. The distribution from risk maps show the high and low risk area of Dengue and Chikungunya diseases occurrence. This map can be used as a tool for the prevention and control strategies for both diseases.

Persistent estrus rat models of polycystic ovary disease: an update.

PubMed

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.

Spatiotemporal multivariate mixture models for Bayesian model selection in disease mapping.

PubMed

Lawson, A B; Carroll, R; Faes, C; Kirby, R S; Aregay, M; Watjou, K

2017-12-01

It is often the case that researchers wish to simultaneously explore the behavior of and estimate overall risk for multiple, related diseases with varying rarity while accounting for potential spatial and/or temporal correlation. In this paper, we propose a flexible class of multivariate spatio-temporal mixture models to fill this role. Further, these models offer flexibility with the potential for model selection as well as the ability to accommodate lifestyle, socio-economic, and physical environmental variables with spatial, temporal, or both structures. Here, we explore the capability of this approach via a large scale simulation study and examine a motivating data example involving three cancers in South Carolina. The results which are focused on four model variants suggest that all models possess the ability to recover simulation ground truth and display improved model fit over two baseline Knorr-Held spatio-temporal interaction model variants in a real data application.

Animal models of Helicobacter-induced disease: methods to successfully infect the mouse.

PubMed

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.

Decision analytic models for Alzheimer's disease: state of the art and future directions.

PubMed

Cohen, Joshua T; Neumann, Peter J

2008-05-01

Decision analytic policy models for Alzheimer's disease (AD) enable researchers and policy makers to investigate questions about the costs and benefits of a wide range of existing and potential screening, testing, and treatment strategies. Such models permit analysts to compare existing alternatives, explore hypothetical scenarios, and test the strength of underlying assumptions in an explicit, quantitative, and systematic way. Decision analytic models can best be viewed as complementing clinical trials both by filling knowledge gaps not readily addressed by empirical research and by extrapolating beyond the surrogate markers recorded in a trial. We identified and critiqued 13 distinct AD decision analytic policy models published since 1997. Although existing models provide useful insights, they also have a variety of limitations. (1) They generally characterize disease progression in terms of cognitive function and do not account for other distinguishing features, such as behavioral symptoms, functional performance, and the emotional well-being of AD patients and caregivers. (2) Many describe disease progression in terms of a limited number of discrete states, thus constraining the level of detail that can be used to characterize both changes in patient status and the relationships between disease progression and other factors, such as residential status, that influence outcomes of interest. (3) They have focused almost exclusively on evaluating drug treatments, thus neglecting other disease management strategies and combinations of pharmacologic and nonpharmacologic interventions. Future AD models should facilitate more realistic and compelling evaluations of various interventions to address the disease. An improved model will allow decision makers to better characterize the disease, to better assess the costs and benefits of a wide range of potential interventions, and to better evaluate the incremental costs and benefits of specific interventions used in

Using the Gravity Model to Estimate the Spatial Spread of Vector-Borne Diseases

PubMed Central

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

Using the gravity model to estimate the spatial spread of vector-borne diseases.

PubMed

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.

Feeding difficulties, a key feature of the Drosophila NDUFS4 mitochondrial disease model

PubMed Central

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

Vector-borne diseases models with residence times - A Lagrangian perspective.

PubMed

Bichara, Derdei; Castillo-Chavez, Carlos

2016-11-01

A multi-patch and multi-group modeling framework describing the dynamics of a class of diseases driven by the interactions between vectors and hosts structured by groups is formulated. Hosts' dispersal is modeled in terms of patch-residence times with the nonlinear dynamics taking into account the effective patch-host size. The residence times basic reproduction number R 0 is computed and shown to depend on the relative environmental risk of infection. The model is robust, that is, the disease free equilibrium is globally asymptotically stable (GAS) if R 0 ≤1 and a unique interior endemic equilibrium is shown to exist that is GAS whenever R 0 >1 whenever the configuration of host-vector interactions is irreducible. The effects of patchiness and groupness, a measure of host-vector heterogeneous structure, on the basic reproduction number R 0 , are explored. Numerical simulations are carried out to highlight the effects of residence times on disease prevalence. Copyright © 2016 Elsevier Inc. All rights reserved.

Fibrocalcific aortic valve disease: Opportunity to understand disease mechanisms using mouse models

PubMed Central

Weiss, Robert M.; Miller, Jordan D.; Heistad, Donald D.

2013-01-01

Studies in vitro and in vivo continue to identify complex regulated mechanisms leading to overt fibrocalcific aortic valve disease (FCAVD). Assessment of the functional impact of those processes requires careful studies of models of FCAVD in vivo. Although the genetic basis for FCVAD is unknown for most patients with FCAVD, several disease-associated genes have been identified in humans and mice. Some gene products which regulate valve development in utero also protect against fibro-calcific disease during postnatal aging. Valve calcification can occur via processes that resemble bone formation. But valve calcification can also occur by non-osteogenic mechanisms, such as formation of calcific apoptotic nodules. Anti-calcific interventions might preferentially target either osteogenic or non-osteogenic calcification. Although FCAVD and atherosclerosis share several risk factors and mechanisms, there are fundamental differences between arteries and the aortic valve, with respect to disease mechanisms and responses to therapeutic interventions. Both innate and acquired immunity are likely to contribute to FCAVD. Angiogenesis is a feature of inflammation, but may also contribute independently to progression of FCAVD, possibly by actions of pericytes that are associated with new blood vessels. Several therapeutic interventions appear to be effective in attenuating development of FCAVD in mice. Therapies which are effective early in the course of FCAVD, however, are not necessarily effective in established disease. PMID:23833295

Clozapine and GABA transmission in schizophrenia disease models: establishing principles to guide treatments.

PubMed

O'Connor, William T; O'Shea, Sean D

2015-06-01

Schizophrenia disease models are necessary to elucidate underlying changes and to establish new therapeutic strategies towards a stage where drug efficacy in schizophrenia (against all classes of symptoms) can be predicted. Here we summarise the evidence for a GABA dysfunction in schizophrenia and review the functional neuroanatomy of five pathways implicated in schizophrenia, namely the mesocortical, mesolimbic, ventral striopallidal, dorsal striopallidal and perforant pathways including the role of local GABA transmission and we describe the effect of clozapine on local neurotransmitter release. This review also evaluates psychotropic drug-induced, neurodevelopmental and environmental disease models including their compatibility with brain microdialysis. The validity of disease models including face, construct, etiological and predictive validity and how these models constitute theories about this illness is also addressed. A disease model based on the effect of the abrupt withdrawal of clozapine on GABA release is also described. The review concludes that while no single animal model is entirely successful in reproducing schizophreniform symptomatology, a disease model based on an ability to prevent and/or reverse the abrupt clozapine discontinuation-induced changes in GABA release in brain regions implicated in schizophrenia may be useful for hypothesis testing and for in vivo screening of novel ligands not limited to a single pharmacological class. Copyright © 2015 Elsevier Inc. All rights reserved.

Modeling congenital disease and inborn errors of development in Drosophila melanogaster

PubMed Central

Moulton, Matthew J.; Letsou, Anthea

2016-01-01

ABSTRACT Fly models that faithfully recapitulate various aspects of human disease and human health-related biology are being used for research into disease diagnosis and prevention. Established and new genetic strategies in Drosophila have yielded numerous substantial successes in modeling congenital disorders or inborn errors of human development, as well as neurodegenerative disease and cancer. Moreover, although our ability to generate sequence datasets continues to outpace our ability to analyze these datasets, the development of high-throughput analysis platforms in Drosophila has provided access through the bottleneck in the identification of disease gene candidates. In this Review, we describe both the traditional and newer methods that are facilitating the incorporation of Drosophila into the human disease discovery process, with a focus on the models that have enhanced our understanding of human developmental disorders and congenital disease. Enviable features of the Drosophila experimental system, which make it particularly useful in facilitating the much anticipated move from genotype to phenotype (understanding and predicting phenotypes directly from the primary DNA sequence), include its genetic tractability, the low cost for high-throughput discovery, and a genome and underlying biology that are highly evolutionarily conserved. In embracing the fly in the human disease-gene discovery process, we can expect to speed up and reduce the cost of this process, allowing experimental scales that are not feasible and/or would be too costly in higher eukaryotes. PMID:26935104

Endophenotype Network Models: Common Core of Complex Diseases

PubMed Central

Ghiassian, Susan Dina; Menche, Jörg; Chasman, Daniel I.; Giulianini, Franco; Wang, Ruisheng; Ricchiuto, Piero; Aikawa, Masanori; Iwata, Hiroshi; Müller, Christian; Zeller, Tania; Sharma, Amitabh; Wild, Philipp; Lackner, Karl; Singh, Sasha; Ridker, Paul M.; Blankenberg, Stefan; Barabási, Albert-László; Loscalzo, Joseph

2016-01-01

Historically, human diseases have been differentiated and categorized based on the organ system in which they primarily manifest. Recently, an alternative view is emerging that emphasizes that different diseases often have common underlying mechanisms and shared intermediate pathophenotypes, or endo(pheno)types. Within this framework, a specific disease’s expression is a consequence of the interplay between the relevant endophenotypes and their local, organ-based environment. Important examples of such endophenotypes are inflammation, fibrosis, and thrombosis and their essential roles in many developing diseases. In this study, we construct endophenotype network models and explore their relation to different diseases in general and to cardiovascular diseases in particular. We identify the local neighborhoods (module) within the interconnected map of molecular components, i.e., the subnetworks of the human interactome that represent the inflammasome, thrombosome, and fibrosome. We find that these neighborhoods are highly overlapping and significantly enriched with disease-associated genes. In particular they are also enriched with differentially expressed genes linked to cardiovascular disease (risk). Finally, using proteomic data, we explore how macrophage activation contributes to our understanding of inflammatory processes and responses. The results of our analysis show that inflammatory responses initiate from within the cross-talk of the three identified endophenotypic modules. PMID:27278246

Endophenotype Network Models: Common Core of Complex Diseases

NASA Astrophysics Data System (ADS)

Ghiassian, Susan Dina; Menche, Jörg; Chasman, Daniel I.; Giulianini, Franco; Wang, Ruisheng; Ricchiuto, Piero; Aikawa, Masanori; Iwata, Hiroshi; Müller, Christian; Zeller, Tania; Sharma, Amitabh; Wild, Philipp; Lackner, Karl; Singh, Sasha; Ridker, Paul M.; Blankenberg, Stefan; Barabási, Albert-László; Loscalzo, Joseph

2016-06-01

Historically, human diseases have been differentiated and categorized based on the organ system in which they primarily manifest. Recently, an alternative view is emerging that emphasizes that different diseases often have common underlying mechanisms and shared intermediate pathophenotypes, or endo(pheno)types. Within this framework, a specific disease’s expression is a consequence of the interplay between the relevant endophenotypes and their local, organ-based environment. Important examples of such endophenotypes are inflammation, fibrosis, and thrombosis and their essential roles in many developing diseases. In this study, we construct endophenotype network models and explore their relation to different diseases in general and to cardiovascular diseases in particular. We identify the local neighborhoods (module) within the interconnected map of molecular components, i.e., the subnetworks of the human interactome that represent the inflammasome, thrombosome, and fibrosome. We find that these neighborhoods are highly overlapping and significantly enriched with disease-associated genes. In particular they are also enriched with differentially expressed genes linked to cardiovascular disease (risk). Finally, using proteomic data, we explore how macrophage activation contributes to our understanding of inflammatory processes and responses. The results of our analysis show that inflammatory responses initiate from within the cross-talk of the three identified endophenotypic modules.

A coupled hidden Markov model for disease interactions

PubMed Central

Sherlock, Chris; Xifara, Tatiana; Telfer, Sandra; Begon, Mike

2013-01-01

To investigate interactions between parasite species in a host, a population of field voles was studied longitudinally, with presence or absence of six different parasites measured repeatedly. Although trapping sessions were regular, a different set of voles was caught at each session, leading to incomplete profiles for all subjects. We use a discrete time hidden Markov model for each disease with transition probabilities dependent on covariates via a set of logistic regressions. For each disease the hidden states for each of the other diseases at a given time point form part of the covariate set for the Markov transition probabilities from that time point. This allows us to gauge the influence of each parasite species on the transition probabilities for each of the other parasite species. Inference is performed via a Gibbs sampler, which cycles through each of the diseases, first using an adaptive Metropolis–Hastings step to sample from the conditional posterior of the covariate parameters for that particular disease given the hidden states for all other diseases and then sampling from the hidden states for that disease given the parameters. We find evidence for interactions between several pairs of parasites and of an acquired immune response for two of the parasites. PMID:24223436

Early-life stress origins of gastrointestinal disease: animal models, intestinal pathophysiology, and translational implications

PubMed Central

Pohl, Calvin S.; Medland, Julia E.

2015-01-01

Early-life stress and adversity are major risk factors in the onset and severity of gastrointestinal (GI) disease in humans later in life. The mechanisms by which early-life stress leads to increased GI disease susceptibility in adult life remain poorly understood. Animal models of early-life stress have provided a foundation from which to gain a more fundamental understanding of this important GI disease paradigm. This review focuses on animal models of early-life stress-induced GI disease, with a specific emphasis on translational aspects of each model to specific human GI disease states. Early postnatal development of major GI systems and the consequences of stress on their development are discussed in detail. Relevant translational differences between species and models are highlighted. PMID:26451004

Application of a theoretical model to evaluate COPD disease management.

PubMed

Lemmens, Karin M M; Nieboer, Anna P; Rutten-Van Mölken, Maureen P M H; van Schayck, Constant P; Asin, Javier D; Dirven, Jos A M; Huijsman, Robbert

2010-03-26

Disease management programmes are heterogeneous in nature and often lack a theoretical basis. An evaluation model has been developed in which theoretically driven inquiries link disease management interventions to outcomes. The aim of this study is to methodically evaluate the impact of a disease management programme for patients with chronic obstructive pulmonary disease (COPD) on process, intermediate and final outcomes of care in a general practice setting. A quasi-experimental research was performed with 12-months follow-up of 189 COPD patients in primary care in the Netherlands. The programme included patient education, protocolised assessment and treatment of COPD, structural follow-up and coordination by practice nurses at 3, 6 and 12 months. Data on intermediate outcomes (knowledge, psychosocial mediators, self-efficacy and behaviour) and final outcomes (dyspnoea, quality of life, measured by the CRQ and CCQ, and patient experiences) were obtained from questionnaires and electronic registries. Implementation of the programme was associated with significant improvements in dyspnoea (p < 0.001) and patient experiences (p < 0.001). No significant improvement was found in mean quality of life scores. Improvements were found in several intermediate outcomes, including investment beliefs (p < 0.05), disease-specific knowledge (p < 0.01; p < 0.001) and medication compliance (p < 0.01). Overall, process improvement was established. The model showed associations between significantly improved intermediate outcomes and improvements in quality of life and dyspnoea. The application of a theory-driven model enhances the design and evaluation of disease management programmes aimed at improving health outcomes. This study supports the notion that a theoretical approach strengthens the evaluation designs of complex interventions. Moreover, it provides prudent evidence that the implementation of COPD disease management programmes can positively influence outcomes of care.

Application of a theoretical model to evaluate COPD disease management

PubMed Central

2010-01-01

Background Disease management programmes are heterogeneous in nature and often lack a theoretical basis. An evaluation model has been developed in which theoretically driven inquiries link disease management interventions to outcomes. The aim of this study is to methodically evaluate the impact of a disease management programme for patients with chronic obstructive pulmonary disease (COPD) on process, intermediate and final outcomes of care in a general practice setting. Methods A quasi-experimental research was performed with 12-months follow-up of 189 COPD patients in primary care in the Netherlands. The programme included patient education, protocolised assessment and treatment of COPD, structural follow-up and coordination by practice nurses at 3, 6 and 12 months. Data on intermediate outcomes (knowledge, psychosocial mediators, self-efficacy and behaviour) and final outcomes (dyspnoea, quality of life, measured by the CRQ and CCQ, and patient experiences) were obtained from questionnaires and electronic registries. Results Implementation of the programme was associated with significant improvements in dyspnoea (p < 0.001) and patient experiences (p < 0.001). No significant improvement was found in mean quality of life scores. Improvements were found in several intermediate outcomes, including investment beliefs (p < 0.05), disease-specific knowledge (p < 0.01; p < 0.001) and medication compliance (p < 0.01). Overall, process improvement was established. The model showed associations between significantly improved intermediate outcomes and improvements in quality of life and dyspnoea. Conclusions The application of a theory-driven model enhances the design and evaluation of disease management programmes aimed at improving health outcomes. This study supports the notion that a theoretical approach strengthens the evaluation designs of complex interventions. Moreover, it provides prudent evidence that the implementation of COPD disease management programmes can

EpiModel: An R Package for Mathematical Modeling of Infectious Disease over Networks.

PubMed

Jenness, Samuel M; Goodreau, Steven M; Morris, Martina

2018-04-01

Package EpiModel provides tools for building, simulating, and analyzing mathematical models for the population dynamics of infectious disease transmission in R. Several classes of models are included, but the unique contribution of this software package is a general stochastic framework for modeling the spread of epidemics on networks. EpiModel integrates recent advances in statistical methods for network analysis (temporal exponential random graph models) that allow the epidemic modeling to be grounded in empirical data on contacts that can spread infection. This article provides an overview of both the modeling tools built into EpiModel , designed to facilitate learning for students new to modeling, and the application programming interface for extending package EpiModel , designed to facilitate the exploration of novel research questions for advanced modelers.

Rapamycin enhances survival in a Drosophila model of mitochondrial disease.

PubMed

Wang, Adrienne; Mouser, Jacob; Pitt, Jason; Promislow, Daniel; Kaeberlein, Matt

2016-12-06

Pediatric mitochondrial disorders are a devastating category of diseases caused by deficiencies in mitochondrial function. Leigh Syndrome (LS) is the most common of these diseases with symptoms typically appearing within the first year of birth and progressing rapidly until death, usually by 6-7 years of age. Our lab has recently shown that genetic inhibition of the mechanistic target of rapamycin (TOR) rescues the short lifespan of yeast mutants with defective mitochondrial function, and that pharmacological inhibition of TOR by administration of rapamycin significantly rescues the shortened lifespan, neurological symptoms, and neurodegeneration in a mouse model of LS. However, the mechanism by which TOR inhibition exerts these effects, and the extent to which these effects can extend to other models of mitochondrial deficiency, are unknown. Here, we probe the effects of TOR inhibition in a Drosophila model of complex I deficiency. Treatment with rapamycin robustly suppresses the lifespan defect in this model of LS, without affecting behavioral phenotypes. Interestingly, this increased lifespan in response to TOR inhibition occurs in an autophagy-independent manner. Further, we identify a fat storage defect in the ND2 mutant flies that is rescued by rapamycin, supporting a model that rapamycin exerts its effects on mitochondrial disease in these animals by altering metabolism.

Effects of global climate on infectious disease: the cholera model.

PubMed

Lipp, Erin K; Huq, Anwar; Colwell, Rita R

2002-10-01

Recently, the role of the environment and climate in disease dynamics has become a subject of increasing interest to microbiologists, clinicians, epidemiologists, and ecologists. Much of the interest has been stimulated by the growing problems of antibiotic resistance among pathogens, emergence and/or reemergence of infectious diseases worldwide, the potential of bioterrorism, and the debate concerning climate change. Cholera, caused by Vibrio cholerae, lends itself to analyses of the role of climate in infectious disease, coupled to population dynamics of pathogenic microorganisms, for several reasons. First, the disease has a historical context linking it to specific seasons and biogeographical zones. In addition, the population dynamics of V. cholerae in the environment are strongly controlled by environmental factors, such as water temperature, salinity, and the presence of copepods, which are, in turn, controlled by larger-scale climate variability. In this review, the association between plankton and V. cholerae that has been documented over the last 20 years is discussed in support of the hypothesis that cholera shares properties of a vector-borne disease. In addition, a model for environmental transmission of cholera to humans in the context of climate variability is presented. The cholera model provides a template for future research on climate-sensitive diseases, allowing definition of critical parameters and offering a means of developing more sophisticated methods for prediction of disease outbreaks.

Kidney disease models: tools to identify mechanisms and potential therapeutic targets

PubMed Central

Bao, Yin-Wu; Yuan, Yuan; Chen, Jiang-Hua; Lin, Wei-Qiang

2018-01-01

Acute kidney injury (AKI) and chronic kidney disease (CKD) are worldwide public health problems affecting millions of people and have rapidly increased in prevalence in recent years. Due to the multiple causes of renal failure, many animal models have been developed to advance our understanding of human nephropathy. Among these experimental models, rodents have been extensively used to enable mechanistic understanding of kidney disease induction and progression, as well as to identify potential targets for therapy. In this review, we discuss AKI models induced by surgical operation and drugs or toxins, as well as a variety of CKD models (mainly genetically modified mouse models). Results from recent and ongoing clinical trials and conceptual advances derived from animal models are also explored. PMID:29515089

A three-dimensional human neural cell culture model of Alzheimer's disease.

PubMed

Choi, Se Hoon; Kim, Young Hye; Hebisch, Matthias; Sliwinski, Christopher; Lee, Seungkyu; D'Avanzo, Carla; Chen, Hechao; Hooli, Basavaraj; Asselin, Caroline; Muffat, Julien; Klee, Justin B; Zhang, Can; Wainger, Brian J; Peitz, Michael; Kovacs, Dora M; Woolf, Clifford J; Wagner, Steven L; Tanzi, Rudolph E; Kim, Doo Yeon

2014-11-13

Alzheimer's disease is the most common form of dementia, characterized by two pathological hallmarks: amyloid-β plaques and neurofibrillary tangles. The amyloid hypothesis of Alzheimer's disease posits that the excessive accumulation of amyloid-β peptide leads to neurofibrillary tangles composed of aggregated hyperphosphorylated tau. However, to date, no single disease model has serially linked these two pathological events using human neuronal cells. Mouse models with familial Alzheimer's disease (FAD) mutations exhibit amyloid-β-induced synaptic and memory deficits but they do not fully recapitulate other key pathological events of Alzheimer's disease, including distinct neurofibrillary tangle pathology. Human neurons derived from Alzheimer's disease patients have shown elevated levels of toxic amyloid-β species and phosphorylated tau but did not demonstrate amyloid-β plaques or neurofibrillary tangles. Here we report that FAD mutations in β-amyloid precursor protein and presenilin 1 are able to induce robust extracellular deposition of amyloid-β, including amyloid-β plaques, in a human neural stem-cell-derived three-dimensional (3D) culture system. More importantly, the 3D-differentiated neuronal cells expressing FAD mutations exhibited high levels of detergent-resistant, silver-positive aggregates of phosphorylated tau in the soma and neurites, as well as filamentous tau, as detected by immunoelectron microscopy. Inhibition of amyloid-β generation with β- or γ-secretase inhibitors not only decreased amyloid-β pathology, but also attenuated tauopathy. We also found that glycogen synthase kinase 3 (GSK3) regulated amyloid-β-mediated tau phosphorylation. We have successfully recapitulated amyloid-β and tau pathology in a single 3D human neural cell culture system. Our unique strategy for recapitulating Alzheimer's disease pathology in a 3D neural cell culture model should also serve to facilitate the development of more precise human neural cell

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.

Variability in results from negative binomial models for Lyme disease measured at different spatial scales.

PubMed

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.

An Integrated Framework for Process-Driven Model Construction in Disease Ecology and Animal Health

PubMed Central

Mancy, Rebecca; Brock, Patrick M.; Kao, Rowland R.

2017-01-01

Process models that focus on explicitly representing biological mechanisms are increasingly important in disease ecology and animal health research. However, the large number of process modelling approaches makes it difficult to decide which is most appropriate for a given disease system and research question. Here, we discuss different motivations for using process models and present an integrated conceptual analysis that can be used to guide the construction of infectious disease process models and comparisons between them. Our presentation complements existing work by clarifying the major differences between modelling approaches and their relationship with the biological characteristics of the epidemiological system. We first discuss distinct motivations for using process models in epidemiological research, identifying the key steps in model design and use associated with each. We then present a conceptual framework for guiding model construction and comparison, organised according to key aspects of epidemiological systems. Specifically, we discuss the number and type of disease states, whether to focus on individual hosts (e.g., cows) or groups of hosts (e.g., herds or farms), how space or host connectivity affect disease transmission, whether demographic and epidemiological processes are periodic or can occur at any time, and the extent to which stochasticity is important. We use foot-and-mouth disease and bovine tuberculosis in cattle to illustrate our discussion and support explanations of cases in which different models are used to address similar problems. The framework should help those constructing models to structure their approach to modelling decisions and facilitate comparisons between models in the literature. PMID:29021983

An Integrated Framework for Process-Driven Model Construction in Disease Ecology and Animal Health.

PubMed

Mancy, Rebecca; Brock, Patrick M; Kao, Rowland R

2017-01-01

Process models that focus on explicitly representing biological mechanisms are increasingly important in disease ecology and animal health research. However, the large number of process modelling approaches makes it difficult to decide which is most appropriate for a given disease system and research question. Here, we discuss different motivations for using process models and present an integrated conceptual analysis that can be used to guide the construction of infectious disease process models and comparisons between them. Our presentation complements existing work by clarifying the major differences between modelling approaches and their relationship with the biological characteristics of the epidemiological system. We first discuss distinct motivations for using process models in epidemiological research, identifying the key steps in model design and use associated with each. We then present a conceptual framework for guiding model construction and comparison, organised according to key aspects of epidemiological systems. Specifically, we discuss the number and type of disease states, whether to focus on individual hosts (e.g., cows) or groups of hosts (e.g., herds or farms), how space or host connectivity affect disease transmission, whether demographic and epidemiological processes are periodic or can occur at any time, and the extent to which stochasticity is important. We use foot-and-mouth disease and bovine tuberculosis in cattle to illustrate our discussion and support explanations of cases in which different models are used to address similar problems. The framework should help those constructing models to structure their approach to modelling decisions and facilitate comparisons between models in the literature.

An individual-based model of rabbit viral haemorrhagic disease on European wild rabbits (Oryctolagus cuniculus)

USGS Publications Warehouse

Fa, John E.; Sharples, Colin M.; Bell, Diana J.; DeAngelis, Donald L.

2001-01-01

We developed an individual-based model of Rabbit Viral Hemorrhagic Disease (RVHD) for European wild rabbits (Oryctolagus cuniculus L.), representing up to 1000 rabbits in four hectares. Model output for productivity and recruitment matched published values. The disease was density-dependent and virulence affected outcome. Strains that caused death after several days produced greater overall mortality than strains in which rabbits either died or recovered very quickly. Disease effect also depended on time of year. We also elaborated a larger scale model representing 25 km2 and 100,000+ rabbits, split into a number of grid-squares. This was a more traditional model that did not represent individual rabbits, but employed a system of dynamic equations for each grid-square. Disease spread depended on probability of transmission between neighboring grid-squares. Potential recovery from a major population crash caused by the disease relied on disease virulence and frequency of recurrence. The model's dependence on probability of disease transmission between grid-squares suggests the way that the model represents the spatial distribution of the population affects simulation. Although data on RVHD in Europe are lacking, our models provide a basis for describing the disease in realistic detail and for assessing influence of various social and spatial factors on spread.

Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression

PubMed Central

Liu, Yu-Ying; Li, Shuang; Li, Fuxin; Song, Le; Rehg, James M.

2016-01-01

The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progression due to its ability to describe noisy observations arriving irregularly in time. However, the lack of an efficient parameter learning algorithm for CT-HMM restricts its use to very small models or requires unrealistic constraints on the state transitions. In this paper, we present the first complete characterization of efficient EM-based learning methods for CT-HMM models. We demonstrate that the learning problem consists of two challenges: the estimation of posterior state probabilities and the computation of end-state conditioned statistics. We solve the first challenge by reformulating the estimation problem in terms of an equivalent discrete time-inhomogeneous hidden Markov model. The second challenge is addressed by adapting three approaches from the continuous time Markov chain literature to the CT-HMM domain. We demonstrate the use of CT-HMMs with more than 100 states to visualize and predict disease progression using a glaucoma dataset and an Alzheimer’s disease dataset. PMID:27019571

Inter-model comparison of the landscape determinants of vector-borne disease: implications for epidemiological and entomological risk modeling.

PubMed

Lorenz, Alyson; Dhingra, Radhika; Chang, Howard H; Bisanzio, Donal; Liu, Yang; Remais, Justin V

2014-01-01

Extrapolating landscape regression models for use in assessing vector-borne disease risk and other applications requires thoughtful evaluation of fundamental model choice issues. To examine implications of such choices, an analysis was conducted to explore the extent to which disparate landscape models agree in their epidemiological and entomological risk predictions when extrapolated to new regions. Agreement between six literature-drawn landscape models was examined by comparing predicted county-level distributions of either Lyme disease or Ixodes scapularis vector using Spearman ranked correlation. AUC analyses and multinomial logistic regression were used to assess the ability of these extrapolated landscape models to predict observed national data. Three models based on measures of vegetation, habitat patch characteristics, and herbaceous landcover emerged as effective predictors of observed disease and vector distribution. An ensemble model containing these three models improved precision and predictive ability over individual models. A priori assessment of qualitative model characteristics effectively identified models that subsequently emerged as better predictors in quantitative analysis. Both a methodology for quantitative model comparison and a checklist for qualitative assessment of candidate models for extrapolation are provided; both tools aim to improve collaboration between those producing models and those interested in applying them to new areas and research questions.

Modelling the economic impact of three lameness causing diseases using herd and cow level evidence.

PubMed

Ettema, Jehan; Østergaard, Søren; Kristensen, Anders Ringgaard

2010-06-01

Diseases to the cow's hoof, interdigital skin and legs are highly prevalent and of large economic impact in modern dairy farming. In order to support farmer's decisions on preventing and treating lameness and its underlying causes, decision support models can be used to predict the economic profitability of such actions. An existing approach of modelling lameness as one health disorder in a dynamic, stochastic and mechanistic simulation model has been improved in two ways. First of all, three underlying diseases causing lameness were modelled: digital dermatitis, interdigital hyperplasia and claw horn diseases. Secondly, the existing simulation model was set-up in way that it uses hyper-distributions describing diseases risk of the three lameness causing diseases. By combining information on herd level risk factors with prevalence of lameness or prevalence of underlying diseases among cows, marginal posterior probability distributions for disease prevalence in the specific herd are created in a Bayesian network. Random draws from these distributions are used by the simulation model to describe disease risk. Hereby field data on prevalence is used systematically and uncertainty around herd specific risk is represented. Besides the fact that estimated profitability of halving disease risk depended on the hyper-distributions used, the estimates differed for herds with different levels of diseases risk and reproductive efficiency. (c) 2010 Elsevier B.V. All rights reserved.

A small nonhuman primate model for filovirus-induced disease.

PubMed

Carrion, Ricardo; Ro, Youngtae; Hoosien, Kareema; Ticer, Anysha; Brasky, Kathy; de la Garza, Melissa; Mansfield, Keith; Patterson, Jean L

2011-11-25

Ebolavirus and Marburgvirus are members of the filovirus family and induce a fatal hemorrhagic disease in humans and nonhuman primates with 90% case fatality. To develop a small nonhuman primate model for filovirus disease, common marmosets (Callithrix jacchus) were intramuscularly inoculated with wild type Marburgvirus Musoke or Ebolavirus Zaire. The infection resulted in a systemic fatal disease with clinical and morphological features closely resembling human infection. Animals experienced weight loss, fever, high virus titers in tissue, thrombocytopenia, neutrophilia, high liver transaminases and phosphatases and disseminated intravascular coagulation. Evidence of a severe disseminated viral infection characterized principally by multifocal to coalescing hepatic necrosis was seen in EBOV animals. MARV-infected animals displayed only moderate fibrin deposition in the spleen. Lymphoid necrosis and lymphocytic depletion observed in spleen. These findings provide support for the use of the common marmoset as a small nonhuman primate model for filovirus induced hemorrhagic fever. Copyright © 2011 Elsevier Inc. All rights reserved.

Strategies, models and biomarkers in experimental non-alcoholic fatty liver disease research

PubMed Central

Willebrords, Joost; Pereira, Isabel Veloso Alves; Maes, Michaël; Yanguas, Sara Crespo; Colle, Isabelle; Van Den Bossche, Bert; Da silva, Tereza Cristina; Oliveira, Cláudia P; Andraus, Wellington; Alves, Venâncio Avancini Ferreira; Cogliati, Bruno; Vinken, Mathieu

2015-01-01

Non-alcoholic fatty liver disease encompasses a spectrum of liver diseases, including simple steatosis, steatohepatitis, liver fibrosis and cirrhosis and hepatocellular carcinoma. Non-alcoholic fatty liver disease is currently the most dominant chronic liver disease in Western countries due to the fact that hepatic steatosis is associated with insulin resistance, type 2 diabetes mellitus, obesity, metabolic syndrome and drug-induced injury. A variety of chemicals, mainly drugs, and diets is known to cause hepatic steatosis in humans and rodents. Experimental non-alcoholic fatty liver disease models rely on the application of a diet or the administration of drugs to laboratory animals or the exposure of hepatic cell lines to these drugs. More recently, genetically modified rodents or zebrafish have been introduced as non-alcoholic fatty liver disease models. Considerable interest now lies in the discovery and development of novel non-invasive biomarkers of non-alcoholic fatty liver disease, with specific focus on hepatic steatosis. Experimental diagnostic biomarkers of non-alcoholic fatty liver disease, such as (epi)genetic parameters and ‘-omics’-based read-outs are still in their infancy, but show great promise. . In this paper, the array of tools and models for the study of liver steatosis is discussed. Furthermore, the current state-of-art regarding experimental biomarkers such as epigenetic, genetic, transcriptomic, proteomic and metabonomic biomarkers will be reviewed. PMID:26073454

Environmental and socio-economic risk modelling for Chagas disease in Bolivia.

PubMed

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.

Improving Disease Prediction by Incorporating Family Disease History in Risk Prediction Models with Large-Scale Genetic Data.

PubMed

Gim, Jungsoo; Kim, Wonji; Kwak, Soo Heon; Choi, Hosik; Park, Changyi; Park, Kyong Soo; Kwon, Sunghoon; Park, Taesung; Won, Sungho

2017-11-01

Despite the many successes of genome-wide association studies (GWAS), the known susceptibility variants identified by GWAS have modest effect sizes, leading to notable skepticism about the effectiveness of building a risk prediction model from large-scale genetic data. However, in contrast to genetic variants, the family history of diseases has been largely accepted as an important risk factor in clinical diagnosis and risk prediction. Nevertheless, the complicated structures of the family history of diseases have limited their application in clinical practice. Here, we developed a new method that enables incorporation of the general family history of diseases with a liability threshold model, and propose a new analysis strategy for risk prediction with penalized regression analysis that incorporates both large numbers of genetic variants and clinical risk factors. Application of our model to type 2 diabetes in the Korean population (1846 cases and 1846 controls) demonstrated that single-nucleotide polymorphisms accounted for 32.5% of the variation explained by the predicted risk scores in the test data set, and incorporation of family history led to an additional 6.3% improvement in prediction. Our results illustrate that family medical history provides valuable information on the variation of complex diseases and improves prediction performance. Copyright © 2017 by the Genetics Society of America.

Early-life stress origins of gastrointestinal disease: animal models, intestinal pathophysiology, and translational implications.

PubMed

Pohl, Calvin S; Medland, Julia E; Moeser, Adam J

2015-12-15

Early-life stress and adversity are major risk factors in the onset and severity of gastrointestinal (GI) disease in humans later in life. The mechanisms by which early-life stress leads to increased GI disease susceptibility in adult life remain poorly understood. Animal models of early-life stress have provided a foundation from which to gain a more fundamental understanding of this important GI disease paradigm. This review focuses on animal models of early-life stress-induced GI disease, with a specific emphasis on translational aspects of each model to specific human GI disease states. Early postnatal development of major GI systems and the consequences of stress on their development are discussed in detail. Relevant translational differences between species and models are highlighted. Copyright © 2015 the American Physiological Society.

Stochastic Models of Emerging Infectious Disease Transmission on Adaptive Random Networks

PubMed Central

Pipatsart, Navavat; Triampo, Wannapong

2017-01-01

We presented adaptive random network models to describe human behavioral change during epidemics and performed stochastic simulations of SIR (susceptible-infectious-recovered) epidemic models on adaptive random networks. The interplay between infectious disease dynamics and network adaptation dynamics was investigated in regard to the disease transmission and the cumulative number of infection cases. We found that the cumulative case was reduced and associated with an increasing network adaptation probability but was increased with an increasing disease transmission probability. It was found that the topological changes of the adaptive random networks were able to reduce the cumulative number of infections and also to delay the epidemic peak. Our results also suggest the existence of a critical value for the ratio of disease transmission and adaptation probabilities below which the epidemic cannot occur. PMID:29075314

Stereotaxical Infusion of Rotenone: A Reliable Rodent Model for Parkinson's Disease

PubMed Central

Xiong, Nian; Huang, Jinsha; Zhang, Zhentao; Zhang, Zhaowen; Xiong, Jing; Liu, Xingyuan; Jia, Min; Wang, Fang; Chen, Chunnuan; Cao, Xuebing; Liang, Zhihou; Sun, Shenggang; Lin, Zhicheng; Wang, Tao

2009-01-01

A clinically-related animal model of Parkinson's disease (PD) may enable the elucidation of the etiology of the disease and assist the development of medications. However, none of the current neurotoxin-based models recapitulates the main clinical features of the disease or the pathological hallmarks, such as dopamine (DA) neuron specificity of degeneration and Lewy body formation, which limits the use of these models in PD research. To overcome these limitations, we developed a rat model by stereotaxically (ST) infusing small doses of the mitochondrial complex-I inhibitor, rotenone, into two brain sites: the right ventral tegmental area and the substantia nigra. Four weeks after ST rotenone administration, tyrosine hydroxylase (TH) immunoreactivity in the infusion side decreased by 43.7%, in contrast to a 75.8% decrease observed in rats treated systemically with rotenone (SYS). The rotenone infusion also reduced the DA content, the glutathione and superoxide dismutase activities, and induced alpha-synuclein expression, when compared to the contralateral side. This ST model displays neither peripheral toxicity or mortality and has a high success rate. This rotenone-based ST model thus recapitulates the slow and specific loss of DA neurons and better mimics the clinical features of idiopathic PD, representing a reliable and more clinically-related model for PD research. PMID:19924288

Time series modeling of pathogen-specific disease probabilities with subsampled data.

PubMed

Fisher, Leigh; Wakefield, Jon; Bauer, Cici; Self, Steve

2017-03-01

Many diseases arise due to exposure to one of multiple possible pathogens. We consider the situation in which disease counts are available over time from a study region, along with a measure of clinical disease severity, for example, mild or severe. In addition, we suppose a subset of the cases are lab tested in order to determine the pathogen responsible for disease. In such a context, we focus interest on modeling the probabilities of disease incidence given pathogen type. The time course of these probabilities is of great interest as is the association with time-varying covariates such as meteorological variables. In this set up, a natural Bayesian approach would be based on imputation of the unsampled pathogen information using Markov Chain Monte Carlo but this is computationally challenging. We describe a practical approach to inference that is easy to implement. We use an empirical Bayes procedure in a first step to estimate summary statistics. We then treat these summary statistics as the observed data and develop a Bayesian generalized additive model. We analyze data on hand, foot, and mouth disease (HFMD) in China in which there are two pathogens of primary interest, enterovirus 71 (EV71) and Coxackie A16 (CA16). We find that both EV71 and CA16 are associated with temperature, relative humidity, and wind speed, with reasonably similar functional forms for both pathogens. The important issue of confounding by time is modeled using a penalized B-spline model with a random effects representation. The level of smoothing is addressed by a careful choice of the prior on the tuning variance. © 2016, The International Biometric Society.

Animal in vivo models of EBV-associated lymphoproliferative diseases: special references to rabbit models.

PubMed

Hayashi, K; Teramoto, N; Akagi, T

2002-10-01

Animal models of human EBV-associated diseases are essential to elucidate the pathogenesis of EBV-associated diseases. Here we review those previous models using EBV or EBV-like herpesviruses and describe the details on our two newly-developed rabbit models of lymphoproliferative diseases (LPD) induced by simian EBV-like viruses. The first is Cynomolgus-EBV-induced T-cell lymphomas in rabbits inoculated intravenously (77-90%) and orally (82-89%) during 2-5 months. EBV-DNA was detected in peripheral blood by PCR from 2 days after oral inoculation, while anti-EBV-VCA IgG was raised 3 weeks later. Rabbit lymphomas and their cell lines contained EBV-DNA and expressed EBV-encoded RNA-1 (EBER-1). Rabbit lymphoma cell lines, most of which have specific chromosomal abnormality, showed tumorigenicity in nude mice. The second is the first animal model for EBV-infected T-cell LPD with virus-associated hemophagocytic syndrome (VAHS), using rabbits infected with an EBV-like herpesvirus, Herpesvirus papio (HVP). Rabbits inoculated intravenously with HVP-producing cells showed increased anti-EBV-VCA-IgG titers, and most (85%) subsequently died of fatal LPD and VAHS, with bleeding and hepatosplenomegaly, during 22-105 days. Peroral spray of cell-free HVP induced viral infection with seroconversion in 3 out of 5 rabbits, with 2 of the 3 infected rabbits dying of LPD with VAHS. Atypical T lymphocytes containing HVP-DNA and expressing EBER-1 were observed in many organs. Hemophagocytic histiocytosis was observed in the lymph nodes, spleen, bone marrow, and thymus. These rabbit models are also useful and inexpensive alternative experimental model systems for studying the biology and pathogenesis of EBV, and prophylactic and therapeutic regimens.

EpiModel: An R Package for Mathematical Modeling of Infectious Disease over Networks

PubMed Central

Jenness, Samuel M.; Goodreau, Steven M.; Morris, Martina

2018-01-01

Package EpiModel provides tools for building, simulating, and analyzing mathematical models for the population dynamics of infectious disease transmission in R. Several classes of models are included, but the unique contribution of this software package is a general stochastic framework for modeling the spread of epidemics on networks. EpiModel integrates recent advances in statistical methods for network analysis (temporal exponential random graph models) that allow the epidemic modeling to be grounded in empirical data on contacts that can spread infection. This article provides an overview of both the modeling tools built into EpiModel, designed to facilitate learning for students new to modeling, and the application programming interface for extending package EpiModel, designed to facilitate the exploration of novel research questions for advanced modelers. PMID:29731699

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.

A knowledge based approach to matching human neurodegenerative disease and animal models

PubMed Central

Maynard, Sarah M.; Mungall, Christopher J.; Lewis, Suzanna E.; Imam, Fahim T.; Martone, Maryann E.

2013-01-01

Neurodegenerative diseases present a wide and complex range of biological and clinical features. Animal models are key to translational research, yet typically only exhibit a subset of disease features rather than being precise replicas of the disease. Consequently, connecting animal to human conditions using direct data-mining strategies has proven challenging, particularly for diseases of the nervous system, with its complicated anatomy and physiology. To address this challenge we have explored the use of ontologies to create formal descriptions of structural phenotypes across scales that are machine processable and amenable to logical inference. As proof of concept, we built a Neurodegenerative Disease Phenotype Ontology (NDPO) and an associated Phenotype Knowledge Base (PKB) using an entity-quality model that incorporates descriptions for both human disease phenotypes and those of animal models. Entities are drawn from community ontologies made available through the Neuroscience Information Framework (NIF) and qualities are drawn from the Phenotype and Trait Ontology (PATO). We generated ~1200 structured phenotype statements describing structural alterations at the subcellular, cellular and gross anatomical levels observed in 11 human neurodegenerative conditions and associated animal models. PhenoSim, an open source tool for comparing phenotypes, was used to issue a series of competency questions to compare individual phenotypes among organisms and to determine which animal models recapitulate phenotypic aspects of the human disease in aggregate. Overall, the system was able to use relationships within the ontology to bridge phenotypes across scales, returning non-trivial matches based on common subsumers that were meaningful to a neuroscientist with an advanced knowledge of neuroanatomy. The system can be used both to compare individual phenotypes and also phenotypes in aggregate. This proof of concept suggests that expressing complex phenotypes using formal

Mouse models of ageing and their relevance to disease.

PubMed

Kõks, Sulev; Dogan, Soner; Tuna, Bilge Guvenc; González-Navarro, Herminia; Potter, Paul; Vandenbroucke, Roosmarijn E

2016-12-01

Ageing is a process that gradually increases the organism's vulnerability to death. It affects different biological pathways, and the underlying cellular mechanisms are complex. In view of the growing disease burden of ageing populations, increasing efforts are being invested in understanding the pathways and mechanisms of ageing. We review some mouse models commonly used in studies on ageing, highlight the advantages and disadvantages of the different strategies, and discuss their relevance to disease susceptibility. In addition to addressing the genetics and phenotypic analysis of mice, we discuss examples of models of delayed or accelerated ageing and their modulation by caloric restriction. Copyright © 2016 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Model construction for the intention to use telecare in patients with chronic diseases.

PubMed

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.

Impact of the gut microbiota on rodent models of human disease.

PubMed

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.

Modeling human diseases: an education in interactions and interdisciplinary approaches.

PubMed

Zon, Leonard

2016-06-01

Traditionally, most investigators in the biomedical arena exploit one model system in the course of their careers. Occasionally, an investigator will switch models. The selection of a suitable model system is a crucial step in research design. Factors to consider include the accuracy of the model as a reflection of the human disease under investigation, the numbers of animals needed and ease of husbandry, its physiology and developmental biology, and the ability to apply genetics and harness the model for drug discovery. In my lab, we have primarily used the zebrafish but combined it with other animal models and provided a framework for others to consider the application of developmental biology for therapeutic discovery. Our interdisciplinary approach has led to many insights into human diseases and to the advancement of candidate drugs to clinical trials. Here, I draw on my experiences to highlight the importance of combining multiple models, establishing infrastructure and genetic tools, forming collaborations, and interfacing with the medical community for successful translation of basic findings to the clinic. © 2016. Published by The Company of Biologists Ltd.

InterSpread Plus: a spatial and stochastic simulation model of disease in animal populations.

PubMed

Stevenson, M A; Sanson, R L; Stern, M W; O'Leary, B D; Sujau, M; Moles-Benfell, N; Morris, R S

2013-04-01

We describe the spatially explicit, stochastic simulation model of disease spread, InterSpread Plus, in terms of its epidemiological framework, operation, and mode of use. The input data required by the model, the method for simulating contact and infection spread, and methods for simulating disease control measures are described. Data and parameters that are essential for disease simulation modelling using InterSpread Plus are distinguished from those that are non-essential, and it is suggested that a rational approach to simulating disease epidemics using this tool is to start with core data and parameters, adding additional layers of complexity if and when the specific requirements of the simulation exercise require it. We recommend that simulation models of disease are best developed as part of epidemic contingency planning so decision makers are familiar with model outputs and assumptions and are well-positioned to evaluate their strengths and weaknesses to make informed decisions in times of crisis. Copyright © 2012 Elsevier B.V. All rights reserved.

Disease progression model in subjects with mild cognitive impairment from the Alzheimer's disease neuroimaging initiative: CSF biomarkers predict population subtypes

PubMed Central

Samtani, Mahesh N; Raghavan, Nandini; Shi, Yingqi; Novak, Gerald; Farnum, Michael; Lobanov, Victor; Schultz, Tim; Yang, Eric; DiBernardo, Allitia; Narayan, Vaibhav A

2013-01-01

AIM The objective is to develop a semi-mechanistic disease progression model for mild cognitive impairment (MCI) subjects. The model aims to describe the longitudinal progression of ADAS-cog scores from the Alzheimer's disease neuroimaging initiative trial that had data from 198 MCI subjects with cerebrospinal fluid (CSF) information who were followed for 3 years. METHOD Various covariates were tested on disease progression parameters and these variables fell into six categories: imaging volumetrics, biochemical, genetic, demographic, cognitive tests and CSF biomarkers. RESULTS CSF biomarkers were associated with both baseline disease score and disease progression rate in subjects with MCI. Baseline disease score was also correlated with atrophy measured using hippocampal volume. Progression rate was also predicted by executive functioning as measured by the Trail B-test. CONCLUSION CSF biomarkers have the ability to discriminate MCI subjects into sub-populations that exhibit markedly different rates of disease progression on the ADAS-cog scale. These biomarkers can therefore be utilized for designing clinical trials enriched with subjects that carry the underlying disease pathology. PMID:22534009

Translation of Real-Time Infectious Disease Modeling into Routine Public Health Practice

PubMed Central

Chughtai, Abrar A.; Heywood, Anita; Gardner, Lauren M.; Heslop, David J.; MacIntyre, C. Raina

2017-01-01

Infectious disease dynamic modeling can support outbreak emergency responses. We conducted a workshop to canvas the needs of stakeholders in Australia for practical, real-time modeling tools for infectious disease emergencies. The workshop was attended by 29 participants who represented government, defense, general practice, and academia stakeholders. We found that modeling is underused in Australia and its potential is poorly understood by practitioners involved in epidemic responses. The development of better modeling tools is desired. Ideal modeling tools for operational use would be easy to use, clearly indicate underlying parameterization and assumptions, and assist with policy and decision making. PMID:28418309

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.

Impact Assessment of Pine Wilt Disease Using the Species Distribution Model and the CLIMEX Model

NASA Astrophysics Data System (ADS)

KIM, J. U.; Jung, H.

2016-12-01

The plant disease triangle consists of the host plant, pathogen and environment, but their interaction has not been considered in climate change adaptation policy. Our objectives are to predict the changes of a coniferous forest, pine wood nematodes (Bursaphelenchus xylophilus) and pine sawyer beetles (Monochamus spp.), which is a cause of pine wilt disease in the Republic of Korea. We analyzed the impact of pine wilt disease on climate change by using the species distribution model (SDM) and the CLIMEX model. Area of coniferous forest will decline and move to northern and high-altitude area. But pine wood nematodes and pine sawyer beetles are going to spread because they are going to be in a more favorable environment in the future. Coniferous forests are expected to have high vulnerability because of the decrease in area and the increase in the risk of pine wilt disease. Such changes to forest ecosystems will greatly affect climate change in the future. If effective and appropriate prevention and control policies are not implemented, coniferous forests will be severely damaged. An adaptation policy should be created in order to protect coniferous forests from the viewpoint of biodiversity. Thus we need to consider the impact assessment of climate change for establishing an effective adaptation policy. The impact assessment of pine wilt disease using a plant disease triangle drew suitable results to support climate change adaptation policy.

An ethical assessment model for digital disease detection technologies.

PubMed

Denecke, Kerstin

2017-09-20

Digital epidemiology, also referred to as digital disease detection (DDD), successfully provided methods and strategies for using information technology to support infectious disease monitoring and surveillance or understand attitudes and concerns about infectious diseases. However, Internet-based research and social media usage in epidemiology and healthcare pose new technical, functional and formal challenges. The focus of this paper is on the ethical issues to be considered when integrating digital epidemiology with existing practices. Taking existing ethical guidelines and the results from the EU project M-Eco and SORMAS as starting point, we develop an ethical assessment model aiming at providing support in identifying relevant ethical concerns in future DDD projects. The assessment model has four dimensions: user, application area, data source and methodology. The model supports in becoming aware, identifying and describing the ethical dimensions of DDD technology or use case and in identifying the ethical issues on the technology use from different perspectives. It can be applied in an interdisciplinary meeting to collect different viewpoints on a DDD system even before the implementation starts and aims at triggering discussions and finding solutions for risks that might not be acceptable even in the development phase. From the answers, ethical issues concerning confidence, privacy, data and patient security or justice may be judged and weighted.

Aspergillus in chronic lung disease: Modeling what goes on in the airways.

PubMed

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.

Malaria Disease Mapping in Malaysia based on Besag-York-Mollie (BYM) Model

NASA Astrophysics Data System (ADS)

Azah Samat, Nor; Mey, Liew Wan

2017-09-01

Disease mapping is the visual representation of the geographical distribution which give an overview info about the incidence of disease within a population through spatial epidemiology data. Based on the result of map, it helps in monitoring and planning resource needs at all levels of health care and designing appropriate interventions, tailored towards areas that deserve closer scrutiny or communities that lead to further investigations to identify important risk factors. Therefore, the choice of statistical model used for relative risk estimation is important because production of disease risk map relies on the model used. This paper proposes Besag-York-Mollie (BYM) model to estimate the relative risk for Malaria in Malaysia. The analysis involved using the number of Malaria cases that obtained from the Ministry of Health Malaysia. The outcomes of analysis are displayed through graph and map, including Malaria disease risk map that constructed according to the estimation of relative risk. The distribution of high and low risk areas of Malaria disease occurrences for all states in Malaysia can be identified in the risk map.

A customizable model for chronic disease coordination: Lessons learned from the coordinated chronic disease program

DOE PAGES

Voetsch, Karen; Sequeira, Sonia; Chavez, Amy Holmes

2016-03-31

In 2012, the Centers for Disease Control and Prevention provided funding and technical assistance to all states and territories to implement the Coordinated Chronic Disease Program, marking the first time that all state health departments had federal resources to coordinate chronic disease prevention and control programs. This article describes lessons learned from this initiative and identifies key elements of a coordinated approach. We analyzed 80 programmatic documents from 21 states and conducted semistructured interviews with 7 chronic disease directors. Six overarching themes emerged: 1) focused agenda, 2) identification of functions, 3) comprehensive planning, 4) collaborative leadership and expertise, 5) managedmore » resources, and 6) relationship building. Furthermore, these elements supported 4 essential activities: 1) evidence-based interventions, 2) strategic use of staff, 3) consistent communication, and 4) strong program infrastructure. On the basis of these elements and activities, we propose a conceptual model that frames overarching concepts, skills, and strategies needed to coordinate state chronic disease prevention and control programs.« less

A customizable model for chronic disease coordination: Lessons learned from the coordinated chronic disease program

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

Voetsch, Karen; Sequeira, Sonia; Chavez, Amy Holmes

In 2012, the Centers for Disease Control and Prevention provided funding and technical assistance to all states and territories to implement the Coordinated Chronic Disease Program, marking the first time that all state health departments had federal resources to coordinate chronic disease prevention and control programs. This article describes lessons learned from this initiative and identifies key elements of a coordinated approach. We analyzed 80 programmatic documents from 21 states and conducted semistructured interviews with 7 chronic disease directors. Six overarching themes emerged: 1) focused agenda, 2) identification of functions, 3) comprehensive planning, 4) collaborative leadership and expertise, 5) managedmore » resources, and 6) relationship building. Furthermore, these elements supported 4 essential activities: 1) evidence-based interventions, 2) strategic use of staff, 3) consistent communication, and 4) strong program infrastructure. On the basis of these elements and activities, we propose a conceptual model that frames overarching concepts, skills, and strategies needed to coordinate state chronic disease prevention and control programs.« less

Clinical profiles associated with influenza disease in the ferret model.

PubMed

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.

Lower vertebrate and invertebrate models of Alzheimer's disease - A review.

PubMed

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.

Rabbit and Mouse Models of HSV-1 Latency, Reactivation, and Recurrent Eye Diseases

PubMed Central

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

Data-model fusion to better understand emerging pathogens and improve infectious disease forecasting.

PubMed

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.

A transition-based joint model for disease named entity recognition and normalization.

PubMed

Lou, Yinxia; Zhang, Yue; Qian, Tao; Li, Fei; Xiong, Shufeng; Ji, Donghong

2017-08-01

Disease named entities play a central role in many areas of biomedical research, and automatic recognition and normalization of such entities have received increasing attention in biomedical research communities. Existing methods typically used pipeline models with two independent phases: (i) a disease named entity recognition (DER) system is used to find the boundaries of mentions in text and (ii) a disease named entity normalization (DEN) system is used to connect the mentions recognized to concepts in a controlled vocabulary. The main problems of such models are: (i) there is error propagation from DER to DEN and (ii) DEN is useful for DER, but pipeline models cannot utilize this. We propose a transition-based model to jointly perform disease named entity recognition and normalization, casting the output construction process into an incremental state transition process, learning sequences of transition actions globally, which correspond to joint structural outputs. Beam search and online structured learning are used, with learning being designed to guide search. Compared with the only existing method for joint DEN and DER, our method allows non-local features to be used, which significantly improves the accuracies. We evaluate our model on two corpora: the BioCreative V Chemical Disease Relation (CDR) corpus and the NCBI disease corpus. Experiments show that our joint framework achieves significantly higher performances compared to competitive pipeline baselines. Our method compares favourably to other state-of-the-art approaches. Data and code are available at https://github.com/louyinxia/jointRN. dhji@whu.edu.cn. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Modeling Insights into Haemophilus influenzae Type b Disease, Transmission, and Vaccine Programs

PubMed Central

Rose, Charles E.; Cohn, Amanda; Coronado, Fatima; Clark, Thomas A.; Wenger, Jay D.; Bulkow, Lisa; Bruce, Michael G.; Messonnier, Nancy E.; Hennessy, Thomas W.

2012-01-01

In response to the 2007–2009 Haemophilus influenzae type b (Hib) vaccine shortage in the United States, we developed a flexible model of Hib transmission and disease for optimizing Hib vaccine programs in diverse populations and situations. The model classifies population members by age, colonization/disease status, and antibody levels, with movement across categories defined by differential equations. We implemented the model for the United States as a whole, England and Wales, and the Alaska Native population. This model accurately simulated Hib incidence in all 3 populations, including the increased incidence in England/Wales beginning in 1999 and the change in Hib incidence in Alaska Natives after switching Hib vaccines in 1996. The model suggests that a vaccine shortage requiring deferral of the booster dose could last 3 years in the United States before loss of herd immunity would result in increasing rates of invasive Hib disease in children <5 years of age. PMID:22257582

New transgenic models of Parkinson's disease using genome editing technology.

PubMed

Cota-Coronado, J A; Sandoval-Ávila, S; Gaytan-Dávila, Y P; Diaz, N F; Vega-Ruiz, B; Padilla-Camberos, E; Díaz-Martínez, N E

2017-11-28

Parkinson's disease (PD) is the second most common neurodegenerative disorder. It is characterised by selective loss of dopaminergic neurons in the substantia nigra pars compacta, which results in dopamine depletion, leading to a number of motor and non-motor symptoms. In recent years, the development of new animal models using nuclease-based genome-editing technology (ZFN, TALEN, and CRISPR/Cas9 nucleases) has enabled the introduction of custom-made modifications into the genome to replicate key features of PD, leading to significant advances in our understanding of the pathophysiology of the disease. We review the most recent studies on this new generation of in vitro and in vivo PD models, which replicate the most relevant symptoms of the disease and enable better understanding of the aetiology and mechanisms of PD. This may be helpful in the future development of effective treatments to halt or slow disease progression. Copyright © 2017 Sociedad Española de Neurología. Publicado por Elsevier España, S.L.U. All rights reserved.

Gait analysis in a mouse model resembling Leigh disease.

PubMed

de Haas, Ria; Russel, Frans G; Smeitink, Jan A

2016-01-01

Leigh disease (LD) is one of the clinical phenotypes of mitochondrial OXPHOS disorders and also known as sub-acute necrotizing encephalomyelopathy. The disease has an incidence of 1 in 77,000 live births. Symptoms typically begin early in life and prognosis for LD patients is poor. Currently, no clinically effective treatments are available. Suitable animal and cellular models are necessary for the understanding of the neuropathology and the development of successful new therapeutic strategies. In this study we used the Ndufs4 knockout (Ndufs4(-/-)) mouse, a model of mitochondrial complex I deficiency. Ndusf4(-/-) mice exhibit progressive neurodegeneration, which closely resemble the human LD phenotype. When dissecting behavioral abnormalities in animal models it is of great importance to apply translational tools that are clinically relevant. To distinguish gait abnormalities in patients, simple walking tests can be assessed, but in animals this is not easy. This study is the first to demonstrate automated CatWalk gait analysis in the Ndufs4(-/-) mouse model. Marked differences were noted between Ndufs4(-/-) and control mice in dynamic, static, coordination and support parameters. Variation of walking speed was significantly increased in Ndufs4(-/-) mice, suggesting hampered and uncoordinated gait. Furthermore, decreased regularity index, increased base of support and changes in support were noted in the Ndufs4(-/-) mice. Here, we report the ability of the CatWalk system to sensitively assess gait abnormalities in Ndufs4(-/-) mice. This objective gait analysis can be of great value for intervention and drug efficacy studies in animal models for mitochondrial disease. Copyright © 2015 Elsevier B.V. All rights reserved.

Establishment of hydrochloric acid/lipopolysaccharide-induced pelvic inflammatory disease model.

PubMed

Oh, Yeonsu; Lee, Jaehun; Kim, Hyeon-Cheol; Hahn, Tae-Wook; Yoon, Byung-Il; Han, Jeong-Hee; Kwon, Yong-Soo; Park, Joung Jun; Koo, Deog-Bon; Rhee, Ki-Jong; Jung, Bae Dong

2016-09-30

Pelvic inflammatory disease (PID), which is one of the most problematic complications experienced by women with sexually transmitted diseases, frequently causes secondary infections after reproductive abnormalities in veterinary animals. Although the uterus is self-protective, it becomes fragile during periods or pregnancy. To investigate PID, bacteria or lipopolysaccharide (LPS) extracted from gram negative bacteria has been used to induce the disease in several animal models. However, when LPS is applied to the peritoneum, it often causes systemic sepsis leading to death and the PID was not consistently demonstrated. Hydrochloric acid (HCl) has been used to induce inflammation in the lungs and stomach but not tested for reproductive organs. In this study, we developed a PID model in mice by HCl and LPS sequential intracervical (i.c.) administration. The proinflammatory cytokines, interleukin (IL)-1β, IL-6 and tumor necrosis factor-α, were detected in the mouse uterus by western blot analysis and cytokine enzyme-linked immunosorbent assay after HCl (25 mg/kg) administration i.c. followed by four LPS (50 mg/kg) treatments. Moreover, mice exhibited increased infiltration of neutrophils in the endometrium and epithelial layer. These results suggest that ic co-administration of HCl and LPS induces PID in mice. This new model may provide a consistent and reproducible PID model for future research.

Modeling Alzheimer's disease with human induced pluripotent stem (iPS) cells.

PubMed

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.

Animal models of Parkinson's disease: a source of novel treatments and clues to the cause of the disease

PubMed Central

Duty, Susan; Jenner, Peter

2011-01-01

Animal models of Parkinson's disease (PD) have proved highly effective in the discovery of novel treatments for motor symptoms of PD and in the search for clues to the underlying cause of the illness. Models based on specific pathogenic mechanisms may subsequently lead to the development of neuroprotective agents for PD that stop or slow disease progression. The array of available rodent models is large and ranges from acute pharmacological models, such as the reserpine- or haloperidol-treated rats that display one or more parkinsonian signs, to models exhibiting destruction of the dopaminergic nigro-striatal pathway, such as the classical 6-hydroxydopamine (6-OHDA) rat and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) mouse models. All of these have provided test beds in which new molecules for treating the motor symptoms of PD can be assessed. In addition, the emergence of abnormal involuntary movements (AIMs) with repeated treatment of 6-OHDA-lesioned rats with L-DOPA has allowed for examination of the mechanisms responsible for treatment-related dyskinesia in PD, and the detection of molecules able to prevent or reverse their appearance. Other toxin-based models of nigro-striatal tract degeneration include the systemic administration of the pesticides rotenone and paraquat, but whilst providing clues to disease pathogenesis, these are not so commonly used for drug development. The MPTP-treated primate model of PD, which closely mimics the clinical features of PD and in which all currently used anti-parkinsonian medications have been shown to be effective, is undoubtedly the most clinically-relevant of all available models. The MPTP-treated primate develops clear dyskinesia when repeatedly exposed to L-DOPA, and these parkinsonian animals have shown responses to novel dopaminergic agents that are highly predictive of their effect in man. Whether non-dopaminergic drugs show the same degree of predictability of response is a matter of debate. As our

Animal models of Parkinson's disease: a source of novel treatments and clues to the cause of the disease.

PubMed

Duty, Susan; Jenner, Peter

2011-10-01

Animal models of Parkinson's disease (PD) have proved highly effective in the discovery of novel treatments for motor symptoms of PD and in the search for clues to the underlying cause of the illness. Models based on specific pathogenic mechanisms may subsequently lead to the development of neuroprotective agents for PD that stop or slow disease progression. The array of available rodent models is large and ranges from acute pharmacological models, such as the reserpine- or haloperidol-treated rats that display one or more parkinsonian signs, to models exhibiting destruction of the dopaminergic nigro-striatal pathway, such as the classical 6-hydroxydopamine (6-OHDA) rat and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) mouse models. All of these have provided test beds in which new molecules for treating the motor symptoms of PD can be assessed. In addition, the emergence of abnormal involuntary movements (AIMs) with repeated treatment of 6-OHDA-lesioned rats with L-DOPA has allowed for examination of the mechanisms responsible for treatment-related dyskinesia in PD, and the detection of molecules able to prevent or reverse their appearance. Other toxin-based models of nigro-striatal tract degeneration include the systemic administration of the pesticides rotenone and paraquat, but whilst providing clues to disease pathogenesis, these are not so commonly used for drug development. The MPTP-treated primate model of PD, which closely mimics the clinical features of PD and in which all currently used anti-parkinsonian medications have been shown to be effective, is undoubtedly the most clinically-relevant of all available models. The MPTP-treated primate develops clear dyskinesia when repeatedly exposed to L-DOPA, and these parkinsonian animals have shown responses to novel dopaminergic agents that are highly predictive of their effect in man. Whether non-dopaminergic drugs show the same degree of predictability of response is a matter of debate. As our

A Gaussian random field model for similarity-based smoothing in Bayesian disease mapping.

PubMed

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.

Applicability of the SMART Model of Transition Readiness for Sickle-Cell Disease

PubMed Central

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

A guide to using functional magnetic resonance imaging to study Alzheimer's disease in animal models.

PubMed

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.

A guide to using functional magnetic resonance imaging to study Alzheimer's disease in animal models

PubMed Central

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

A Reaction-Diffusion Model of Vector-Borne Disease with Periodic Delays

NASA Astrophysics Data System (ADS)

Wu, Ruiwen; Zhao, Xiao-Qiang

2018-06-01

A vector-borne disease is caused by a range of pathogens and transmitted to hosts through vectors. To investigate the multiple effects of the spatial heterogeneity, the temperature sensitivity of extrinsic incubation period and intrinsic incubation period, and the seasonality on disease transmission, we propose a nonlocal reaction-diffusion model of vector-borne disease with periodic delays. We introduce the basic reproduction number R_0 for this model and then establish a threshold-type result on its global dynamics in terms of R_0 . In the case where all the coefficients are constants, we also prove the global attractivity of the positive constant steady state when R_0>1 . Numerically, we study the malaria transmission in Maputo Province, Mozambique.

LITTLE FISH, BIG DATA: ZEBRAFISH AS A MODEL FOR CARDIOVASCULAR AND METABOLIC DISEASE.

PubMed

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.

Reasoning over genetic variance information in cause-and-effect models of neurodegenerative diseases

PubMed Central

Naz, Mufassra; Kodamullil, Alpha Tom

2016-01-01

The work we present here is based on the recent extension of the syntax of the Biological Expression Language (BEL), which now allows for the representation of genetic variation information in cause-and-effect models. In our article, we describe, how genetic variation information can be used to identify candidate disease mechanisms in diseases with complex aetiology such as Alzheimer’s disease and Parkinson’s disease. In those diseases, we have to assume that many genetic variants contribute moderately to the overall dysregulation that in the case of neurodegenerative diseases has such a long incubation time until the first clinical symptoms are detectable. Owing to the multilevel nature of dysregulation events, systems biomedicine modelling approaches need to combine mechanistic information from various levels, including gene expression, microRNA (miRNA) expression, protein–protein interaction, genetic variation and pathway. OpenBEL, the open source version of BEL, has recently been extended to match this requirement, and we demonstrate in our article, how candidate mechanisms for early dysregulation events in Alzheimer’s disease can be identified based on an integrative mining approach that identifies ‘chains of causation’ that include single nucleotide polymorphism information in BEL models. PMID:26249223

Future cardiovascular disease in China: Markov model and risk factor scenario projections from the Coronary Heart Disease Policy Model-China

PubMed Central

Moran, Andrew; Gu, Dongfeng; Zhao, Dong; Coxson, Pamela; Wang, Y. Claire; Chen, Chung-Shiuan; Liu, Jing; Cheng, Jun; Bibbins-Domingo, Kirsten; Shen, Yu-Ming; He, Jiang; Goldman, Lee

2010-01-01

Background The relative effects of individual and combined risk factor trends on future cardiovascular disease in China have not been quantified in detail. Methods and Results Future risk factor trends in China were projected based on prior trends. Cardiovascular disease (coronary heart disease and stroke) in adults ages 35 to 84 years was projected from 2010 to 2030 using the Coronary Heart Disease Policy Model–China, a Markov computer simulation model. With risk factor levels held constant, projected annual cardiovascular events increased by >50% between 2010 and 2030 based on population aging and growth alone. Projected trends in blood pressure, total cholesterol, diabetes (increases), and active smoking (decline) would increase annual cardiovascular disease events by an additional 23%, an increase of approximately 21.3 million cardiovascular events and 7.7 million cardiovascular deaths over 2010 to 2030. Aggressively reducing active smoking in Chinese men to 20% prevalence in 2020 and 10% prevalence in 2030 or reducing mean systolic blood pressure by 3.8 mm Hg in men and women would counteract adverse trends in other risk factors by preventing cardiovascular events and 2.9 to 5.7 million total deaths over 2 decades. Conclusions Aging and population growth will increase cardiovascular disease by more than a half over the coming 20 years, and projected unfavorable trends in blood pressure, total cholesterol, diabetes, and body mass index may accelerate the epidemic. National policy aimed at controlling blood pressure, smoking, and other risk factors would counteract the expected future cardiovascular disease epidemic in China. PMID:20442213

Drosophila Models of Parkinson's Disease: Discovering Relevant Pathways and Novel Therapeutic Strategies

PubMed Central

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

ERAIZDA: a model for holistic annotation of animal infectious and zoonotic diseases.

PubMed

Buza, Teresia M; Jack, Sherman W; Kirunda, Halid; Khaitsa, Margaret L; Lawrence, Mark L; Pruett, Stephen; Peterson, Daniel G

2015-01-01

There is an urgent need for a unified resource that integrates trans-disciplinary annotations of emerging and reemerging animal infectious and zoonotic diseases. Such data integration will provide wonderful opportunity for epidemiologists, researchers and health policy makers to make data-driven decisions designed to improve animal health. Integrating emerging and reemerging animal infectious and zoonotic disease data from a large variety of sources into a unified open-access resource provides more plausible arguments to achieve better understanding of infectious and zoonotic diseases. We have developed a model for interlinking annotations of these diseases. These diseases are of particular interest because of the threats they pose to animal health, human health and global health security. We demonstrated the application of this model using brucellosis, an infectious and zoonotic disease. Preliminary annotations were deposited into VetBioBase database (http://vetbiobase.igbb.msstate.edu). This database is associated with user-friendly tools to facilitate searching, retrieving and downloading of disease-related information. Database URL: http://vetbiobase.igbb.msstate.edu. © The Author(s) 2015. Published by Oxford University Press.

ERAIZDA: a model for holistic annotation of animal infectious and zoonotic diseases

PubMed Central

Buza, Teresia M.; Jack, Sherman W.; Kirunda, Halid; Khaitsa, Margaret L.; Lawrence, Mark L.; Pruett, Stephen; Peterson, Daniel G.

2015-01-01

There is an urgent need for a unified resource that integrates trans-disciplinary annotations of emerging and reemerging animal infectious and zoonotic diseases. Such data integration will provide wonderful opportunity for epidemiologists, researchers and health policy makers to make data-driven decisions designed to improve animal health. Integrating emerging and reemerging animal infectious and zoonotic disease data from a large variety of sources into a unified open-access resource provides more plausible arguments to achieve better understanding of infectious and zoonotic diseases. We have developed a model for interlinking annotations of these diseases. These diseases are of particular interest because of the threats they pose to animal health, human health and global health security. We demonstrated the application of this model using brucellosis, an infectious and zoonotic disease. Preliminary annotations were deposited into VetBioBase database (http://vetbiobase.igbb.msstate.edu). This database is associated with user-friendly tools to facilitate searching, retrieving and downloading of disease-related information. Database URL: http://vetbiobase.igbb.msstate.edu PMID:26581408

Modeling treatment of ischemic heart disease with partially observable Markov decision processes.

PubMed

Hauskrecht, M; Fraser, H

1998-01-01

Diagnosis of a disease and its treatment are not separate, one-shot activities. Instead they are very often dependent and interleaved over time, mostly due to uncertainty about the underlying disease, uncertainty associated with the response of a patient to the treatment and varying cost of different diagnostic (investigative) and treatment procedures. The framework of Partially observable Markov decision processes (POMDPs) developed and used in operations research, control theory and artificial intelligence communities is particularly suitable for modeling such a complex decision process. In the paper, we show how the POMDP framework could be used to model and solve the problem of the management of patients with ischemic heart disease, and point out modeling advantages of the framework over standard decision formalisms.

Model of two infectious diseases in nettle caterpillar population

NASA Astrophysics Data System (ADS)

Firdausi, F. Z.; Nuraini, N.

2016-04-01

Palm oil is a vital commodity to the economy of Indonesia. The area of oil palm plantations in Indonesia has increased from year to year. However, the effectiveness of palm oil production is reduced by pest infestation. One of the pest which often infests oil palm plantations is nettle caterpillar. The pest control used in this study is biological control, viz. biological agents given to oil palm trees. This paper describes a mathematical model of two infectious diseases in nettle caterpillar population. The two infectious diseases arise due to two biological agents, namely Bacillus thuringiensis bacterium and parasite which usually attack nettle caterpillars. The derivation of the model constructed in this paper is obtained from ordinary differential equations without time delay. The equilibrium points are analyzed. Two of three equilibrium points are stable if the Routh-Hurwitz criteria are fulfilled. In addition, this paper also presents the numerical simulation of the model which has been constructed.

Roles of amino acids in preventing and treating intestinal diseases: recent studies with pig models.

PubMed

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.

Gene therapy in large animal models of human cardiovascular genetic disease.

PubMed

Sleeper, Meg M; Bish, Lawrence T; Sweeney, H Lee

2009-01-01

Several naturally occurring animal models for human genetic heart diseases offer an excellent opportunity to evaluate potential novel therapies, including gene therapy. Some of these diseases--especially those that result in a structural defect during development (e.g., patent ductus arteriosus, pulmonic stenosis)--would likely be difficult to treat with a therapeutic gene transfer approach. However, the ability to transduce a significant proportion of the myocardial cells should make the various forms of inherited cardiomyopathy amenable to a therapeutic gene transfer approach. Adeno-associated virus may be the ideal vector for cardiac gene therapy since its low immunogenicity allows for stable transgene expression, a crucial factor when considering treatment of a chronic disease. Cardiomyopathies are a major cause of morbidity and mortality in both children and adults, and large animal models are available for the major forms of inherited cardiomyopathy (dilated cardiomyopathy, hypertrophic cardiomyopathy, and arrhythmogenic right ventricular cardiomyopathy). One of these animal models, juvenile dilated cardiomyopathy of Portuguese water dogs, offers an effective means to assess the efficacy of therapeutic gene transfer to alter the course of cardiomyopathy and heart failure. Correction of the abnormal metabolic processes that occur with heart failure (e.g., calcium metabolism, apoptosis) could normalize diseased myocardial function. Gene therapy may offer a promising new approach for the treatment of cardiac disease in both veterinary and human clinical settings.

Modelling the influence of human behaviour on the spread of infectious diseases: a review.

PubMed

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.

Modelling the influence of human behaviour on the spread of infectious diseases: a review

PubMed Central

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

Forecasting high-priority infectious disease surveillance regions: a socioeconomic model.

PubMed

Chan, Emily H; Scales, David A; Brewer, Timothy F; Madoff, Lawrence C; Pollack, Marjorie P; Hoen, Anne G; Choden, Tenzin; Brownstein, John S

2013-02-01

Few researchers have assessed the relationships between socioeconomic inequality and infectious disease outbreaks at the population level globally. We use a socioeconomic model to forecast national annual rates of infectious disease outbreaks. We constructed a multivariate mixed-effects Poisson model of the number of times a given country was the origin of an outbreak in a given year. The dataset included 389 outbreaks of international concern reported in the World Health Organization's Disease Outbreak News from 1996 to 2008. The initial full model included 9 socioeconomic variables related to education, poverty, population health, urbanization, health infrastructure, gender equality, communication, transportation, and democracy, and 1 composite index. Population, latitude, and elevation were included as potential confounders. The initial model was pared down to a final model by a backwards elimination procedure. The dependent and independent variables were lagged by 2 years to allow for forecasting future rates. Among the socioeconomic variables tested, the final model included child measles immunization rate and telephone line density. The Democratic Republic of Congo, China, and Brazil were predicted to be at the highest risk for outbreaks in 2010, and Colombia and Indonesia were predicted to have the highest percentage of increase in their risk compared to their average over 1996-2008. Understanding socioeconomic factors could help improve the understanding of outbreak risk. The inclusion of the measles immunization variable suggests that there is a fundamental basis in ensuring adequate public health capacity. Increased vigilance and expanding public health capacity should be prioritized in the projected high-risk regions.

2D versus 3D human induced pluripotent stem cell-derived cultures for neurodegenerative disease modelling.

PubMed

Centeno, Eduarda G Z; Cimarosti, Helena; Bithell, Angela

2018-05-22

Neurodegenerative diseases, such as Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD) and amyotrophic lateral sclerosis (ALS), affect millions of people every year and so far, there are no therapeutic cures available. Even though animal and histological models have been of great aid in understanding disease mechanisms and identifying possible therapeutic strategies, in order to find disease-modifying solutions there is still a critical need for systems that can provide more predictive and physiologically relevant results. One possible avenue is the development of patient-derived models, e.g. by reprogramming patient somatic cells into human induced pluripotent stem cells (hiPSCs), which can then be differentiated into any cell type for modelling. These systems contain key genetic information from the donors, and therefore have enormous potential as tools in the investigation of pathological mechanisms underlying disease phenotype, and progression, as well as in drug testing platforms. hiPSCs have been widely cultured in 2D systems, but in order to mimic human brain complexity, 3D models have been proposed as a more advanced alternative. This review will focus on the use of patient-derived hiPSCs to model AD, PD, HD and ALS. In brief, we will cover the available stem cells, types of 2D and 3D culture systems, existing models for neurodegenerative diseases, obstacles to model these diseases in vitro, and current perspectives in the field.

Adapting a scenario tree model for freedom from disease as surveillance progresses: the Canadian notifiable avian influenza model.

PubMed

Christensen, Jette; El Allaki, Farouk; Vallières, André

2014-05-01

Scenario tree models with temporal discounting have been applied in four continents to support claims of freedom from animal disease. Recently, a second (new) model was developed for the same population and disease. This is a natural development because surveillance is a dynamic process that needs to adapt to changing circumstances - the difficulty is the justification for, documentation of, presentation of and the acceptance of the changes. Our objective was to propose a systematic approach to present changes to an existing scenario tree model for freedom from disease. We used the example of how we adapted the deterministic Canadian Notifiable Avian Influenza scenario tree model published in 2011 to a stochastic scenario tree model where the definition of sub-populations and the estimation of probability of introduction of the pathogen were modified. We found that the standardized approach by Vanderstichel et al. (2013) with modifications provided a systematic approach to make and present changes to an existing scenario tree model. We believe that the new 2013 CanNAISS scenario tree model is a better model than the 2011 model because the 2013 model included more surveillance data. In particular, the new data on Notifiable Avian Influenza in Canada from the last 5 years were used to improve input parameters and model structure. Crown Copyright © 2014. Published by Elsevier B.V. All rights reserved.

The use of modelling to evaluate and adapt strategies for animal disease control.

PubMed

Saegerman, C; Porter, S R; Humblet, M F

2011-08-01

Disease is often associated with debilitating clinical signs, disorders or production losses in animals and/or humans, leading to severe socio-economic repercussions. This explains the high priority that national health authorities and international organisations give to selecting control strategies for and the eradication of specific diseases. When a control strategy is selected and implemented, an effective method of evaluating its efficacy is through modelling. To illustrate the usefulness of models in evaluating control strategies, the authors describe several examples in detail, including three examples of classification and regression tree modelling to evaluate and improve the early detection of disease: West Nile fever in equids, bovine spongiform encephalopathy (BSE) and multifactorial diseases, such as colony collapse disorder (CCD) in the United States. Also examined are regression modelling to evaluate skin test practices and the efficacy of an awareness campaign for bovine tuberculosis (bTB); mechanistic modelling to monitor the progress of a control strategy for BSE; and statistical nationwide modelling to analyse the spatio-temporal dynamics of bTB and search for potential risk factors that could be used to target surveillance measures more effectively. In the accurate application of models, an interdisciplinary rather than a multidisciplinary approach is required, with the fewest assumptions possible.

G-protein signaling modulator 1 deficiency accelerates cystic disease in an orthologous mouse model of autosomal dominant polycystic kidney disease

PubMed Central

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

Connecting the dots between genes, biochemistry, and disease susceptibility: systems biology modeling in human genetics.