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1,274 results for “disease models”
Dietary ketosis improves circadian dysfunction as well as motor symptoms in the BACHD mouse model of Huntington’s disease
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Dataset related to article "Intracerebral Injection of Extracellular Vesicles from Mesenchymal Stem Cells Exerts Reduced Aβ Plaque Burden in Early Stages of a Preclinical Model of Alzheimer's Disease."
<p>Bone marrow Mesenchymal Stem Cells (BM-MSCs), due to their strong protective and anti-inflammatory abilities, have been widely investigated in the context of several diseases for their possible therapeutic role, based on the release of a highly proactive secretome composed of soluble factors and Extracellular Vesicles (EVs). BM-MSC-EVs, in particular, convey many of the beneficial features of parental cells, including direct and indirect β-amyloid degrading-activities, immunoregulatory and neurotrophic abilities. Therefore, EVs represent an extremely attractive tool for therapeutic purposes in neurodegenerative diseases, including Alzheimer's disease (AD). We examined the therapeutic potential of BM-MSC-EVs injected intracerebrally into the neocortex of APPswe/PS1dE9 AD mice at 3 and 5 months of age, a time window in which the cognitive behavioral phenotype is not yet detectable or has just started to appear. We demonstrate that BM-MSC-EVs are effective at reducing the Aβ plaque burden and the amount of dystrophic neurites in both the cortex and hippocampus. The presence of Neprilysin on BM-MSC-EVs, opens the possibility of a direct β-amyloid degrading action. Our results indicate a potential role for BM-MSC-EVs already in the early stages of AD, suggesting the possibility of intervening before overt clinical manifestations.</p>
A multi-state occupancy modeling framework for robust estimation of disease prevalence in multi-tissue disease systems
<p>1. Given the public health, economic, and conservation implications of zoonotic diseases, their effective surveillance is of paramount importance. The traditional approach to estimating pathogen prevalence as the proportion of infected individuals in the population is biased because it fails to account for imperfect detection. A statistically robust way to reduce bias in prevalence estimates is to obtain repeated samples (or sample many tissues in multi-tissue disease systems) and to apply statistical methods that account for imperfect detection and permit the interdependence of the infection process across multiple tissues.</p> <p>2. We developed a multi-state occupancy modeling framework which considers two scenarios about the infection process, one where no assumptions about the dependencies among the tissues are made (general), and another where dependence among tissues is not permitted (constrained).</p> <p>3. We applied this model to pseudorabies virus (PrV) DNA detection data obtained from whole blood; and oral, nasal, and genital mucosa of 510 feral swine (Sus scrofa) during the years 2014-2016 in Florida, USA.</p> <p>4. The constrained model was better supported by data. Estimated PrV prevalence varied among tissues, ranging from to 0.06 (CI: 0.02-0.14) in genital to 0.54 (CI: 0.14-0.82) in nasal tissue. Probability of PrV detection ranged from 0.11 (CI: 0.06-0.18) in nasal to 0.51 (CI: 0.21-0.81) in genital tissue. Estimates of PrV prevalence after accounting for imperfect detection were higher than the naïve estimates for all four tissues.</p> <p>5. PrV prevalence was not affected by the age or sex of the animal or the year of sampling, but prevalence increased as drought severity increased.</p> <p>6. The conditional probability of detecting PrV given infection in at least one tissue type within an individual was highest for nasal tissue, suggesting that nasal is the best tissue to sample for PrV surveillance if only one tissue can be sampled, at least for systems with tissue-specific prevalence and detection probabilities similar to ours.</p> <p>7. We found that pathogen prevalence in multi-tissue disease systems can vary across tissues. Our results emphasize the importance of sampling multiple tissues, and the application of robust statistical models to account for imperfect detection in the surveillance of systemic diseases. The multi-state modeling framework is broadly applicable to the surveillance of pathogens that infect multiple tissues and where the infection status or detection of the pathogen in one tissue may depend on the infection status of the pathogen in other tissues). 29-Jul-2020</p>
Experimental evidence of warming-induced disease emergence and its prediction by a trait-based mechanistic model
<p>Predicting the effects of seasonality and climate change on the emergence and spread of infectious disease remains difficult, in part because of poorly understood connections between warming and the mechanisms driving disease. Trait-based mechanistic models combined with thermal performance curves arising from the Metabolic Theory of Ecology (MTE) have been highlighted as a promising approach going forward; however, this framework has not been tested under controlled experimental conditions that isolate the role of gradual temporal warming on disease dynamics and emergence. Here, we provide experimental evidence that a slowly warming host – parasite system can be pushed through a critical transition into an epidemic state. We then show that a trait-based mechanistic model with MTE functional forms can predict the critical temperature for disease emergence, subsequent disease dynamics through time, and final infection prevalence in an experimentally warmed system of <i>Daphnia </i>and a microsporidian parasite. Our results serve as a proof of principle that trait-based mechanistic models using MTE sub-functions can predict warming-induced disease emergence in data-rich systems – a critical step towards generalizing the approach to other systems.</p>
Proteinuric chronic kidney disease mouse model RNAseq
<p>RNA seq in renal cortex from proteinuric CKD mouse model</p>
Data from: Gut microbiota regulate motor deficits and neuroinflammation in a model of Parkinson's disease
The intestinal microbiota influence neurodevelopment, modulate behavior, and contribute to neurological disorders. However, a functional link between gut bacteria and neurodegenerative diseases remains unexplored. Synucleinopathies are characterized by aggregation of the protein α-synuclein (αSyn), often resulting in motor dysfunction as exemplified by Parkinson's disease (PD). Using mice that overexpress αSyn, we report herein that gut microbiota are required for motor deficits, microglia activation, and αSyn pathology. Antibiotic treatment ameliorates, while microbial re-colonization promotes, pathophysiology in adult animals, suggesting that postnatal signaling between the gut and the brain modulates disease. Indeed, oral administration of specific microbial metabolites to germ-free mice promotes neuroinflammation and motor symptoms. Remarkably, colonization of αSyn-overexpressing mice with microbiota from PD-affected patients enhances physical impairments compared to microbiota transplants from healthy human donors. These findings reveal that gut bacteria regulate movement disorders in mice and suggest that alterations in the human microbiome represent a risk factor for PD.
Modelling the impact of antibody-dependent enhancement on disease severity of ZIKV and DENV sequential and co-infection
<p>Human infections with viruses of the genus <em>Flavivirus</em>, including dengue virus (DENV) and Zika virus (ZIKV), are of increasing global importance. Due to antibody dependent enhancement, secondary infection with one <em>Flavivirus</em> following primary infection with another {\it Flavivirus} can result in a significantly larger peak viral load with a much higher risk of severe disease. Although several mathematical models have been developed to quantify the virus dynamics in the primary and secondary infections of DENV, little progress has been made regarding secondary infection of DENV after a primary infection of ZIKV, or DENV-ZIKV co-infection. Here, we address this critical gap by developing compartmental models of virus dynamics. We first fitted the models to published data on dengue viral loads of the primary and secondary infections with the observation that the primary infection reaches its peak much more gradually than the secondary infection. We then quantitatively show that antibody dependent enhancement (ADE) is the key factor determining a sharp increase/decrease of viral load near the peak time in the secondary infection. In comparison, our simulations of DENV and ZIKV co-infection (simultaneous rather than sequential) show that ADE has very limited influence on the peak DENV viral load. This indicates pre-existing immunity to ZIKV is the determinant of a high level of ADE effect. Our numerical simulations show that 1) in the absence of ADE effect, a subsequent co-infection is beneficial to the second virus; 2) if ADE is feasible, then a subsequent co-infection can induce greater damage to the host with a higher peak viral load and a much earlier peak time for the second virus, and for the second peak for the first virus.</p>
Data from: Detection error influences both temporal seroprevalence predictions and risk factors associations in wildlife disease models
Understanding the prevalence of pathogens in invasive species is essential to guide efforts to prevent transmission to agricultural animals, wildlife, and humans. Pathogen prevalence can be difficult to estimate for wild species due to imperfect sampling and testing (pathogens may not be detected in infected individuals and erroneously detected in individuals that are not infected). The invasive wild pig (Sus scrofa, also referred to as wild boar and feral swine) is one of the most widespread hosts of domestic animal and human pathogens in North America. We developed hierarchical Bayesian models that account for imperfect detection to estimate the seroprevalence of five pathogens (porcine reproductive and respiratory syndrome virus, pseudorabies virus, Influenza A virus in swine, Hepatitis E virus, and Brucella spp.) in wild pigs in the United States using a dataset of over 50,000 samples across nine years. To assess the effect of incorporating detection error in models, we also evaluated models that ignored detection error. Both sets of models included effects of demographic parameters on seroprevalence. We compared our predictions of seroprevalence to 40 published studies, only one of which accounted for imperfect detection. We found a range of seroprevalence among the pathogens with a high seroprevalence of pseudorabies virus, indicating significant risk to livestock and wildlife. Demographics had mostly weak effects, indicating that other variables may have greater effects in predicting seroprevalence. Models that ignored detection error led to different predictions of seroprevalence as well as different inferences on the effects of demographic parameters. Our results highlight the importance of incorporating detection error in models of seroprevalence and demonstrate that ignoring such error may lead to erroneous conclusions about the risk associated with pathogen transmission. When using opportunistic sampling data to model seroprevalence and evaluate risk factors, detection error should be included.
DATA of Effect of different iterative reconstruction algorithms on ultra-low dose CT of inflammatory bowel disease in a rabbit model
<p>This file contains the experimental data of the scientific paper titled: " <strong>Effect of different iterative reconstruction algorithms on ultra-low dose CT of inflammatory bowel disease in a rabbit model"</strong></p>
Supplemental Data for "Modeled Fetal Risk of Genetic Diseases Identified by Expanded Carrier Screening"
<p>Data file accompanying: Haque IS, Lazarin GA, Kang HP, Evans EA, Goldberg JD, Wapner RJ. Modeled Fetal Risk of Genetic Diseases Identified by Expanded Carrier Screening. <em>JAMA. </em>2016;316(7):734-742. doi:10.1001/jama.2016.11139</p> <p>(SRC = self-reported racial/ethnic category; TG = targeted genotyping; NGS = next-generation sequencing)</p> <p>Data file includes:</p> <ul> <li><strong>Couple Data: </strong>Number of self-identified reproductive couples, separated by tandem or sequential screening status and by (mother SRC, father SRC)</li> <li><strong>Disease Severity</strong>: List of all diseases tested with severity rating as used in the manuscript.</li> <li><strong>Allele Data</strong>: Listing of all alleles considered pathogenic in manuscript's data analysis, with number of observations and number of tested chromosomes in each SRC.</li> <li><strong>Chromosome Frequencies</strong>: for each disease in each SRC: <ul> <li>Effective total chromosome count (effective sample size after integrating TG and NGS-only alleles).</li> <li>Beta posterior a,b: parameters a, b for the best-fit beta distribution approximating the probability that a random chromosome in this SRC carries a pathogenic allele (integrating both TG and NGS alleles).</li> <li># Chromosomes total/positive for TG alleles</li> <li># Chromosomes total/positive for NGS alleles</li> <li># Chromosomes total/positive for individuals tested by TG</li> <li># Chromosomes total/positive for individuals tested by NGS</li> </ul> </li> <li><strong>Disease Risks</strong>: for each disease in each pairing of SRCs <ul> <li>Father/Mother computed carrier frequency: probability that a random individual from father/mother's SRC is a carrier for the given disease</li> <li>Computed risk of affected conceptus (mean, 2.5, 97.5 percentiles): mean and CI of the posterior distribution over the probability that a random conceptus arising from the racial/ethnic pairing indicated would be homozygous or compound heterozygous for pathogenic alleles for the indicated disease.</li> <li>Computed carrier couple frequency: probability that a random couple from the given SRCs would be a carrier couple for the indicated disease (ie, that both members of the couple would be carriers for the indicated disease)</li> <li>Total couples: number of tandemly-tested couples of the indicated SRC pairing who both had the "routine carrier testing" indication for testing and were both tested for the given disease</li> <li>Number of carrier couple: from the set of "Total couples", the number of couples in which both members were carriers for the indicated disease</li> <li>Number of carrier couples expected: based on computed carrier couple frequency and number of tested couples, the expected number of carrier couples under the model described in sections 4.3.2 and 4.4 of the supplement.</li> <li>P-value: probability that the number of observed carrier couples or a more extreme count would have occurred by chance, given the posterior distribution over carrier couple counts (see section 4.4 of the supplement). One-tailed p-value.</li> </ul> </li> </ul>
Supplemental Materials - Performance Figures for "A Model for Predicting the (re)-occurrence of a ≥40% eGFR Decline in a large Population-based cohort of Persons with or At-Risk of Chronic Kidney Disease " paper
<p>The zip file contains performance metrics figures for each dynamic Bayesian Network (DBN) model, stratified by comorbidities, race, CKD stages, and ethnicity.</p> <p>Contains:</p> <ul> <li>Stratified: Bootstrapping of 1000 iterations and 1000 samples with stratified proportions (as in the original population of the test set) of rapid eGFR decliners and non-decliners.</li> </ul> <p> </p> <p>Second zip contains DBN structures as matrices for 2 periods study entry to entry period and entry period to year 1 for all sites in 2 excel files.</p>
Reproducible network changes occur in a mouse model of temporal lobe epilepsy but do not correlate with disease severity
<p><strong>Dataset for the publication: 'Reproducible network changes occur in a mouse model of temporal lobe epilepsy but do not correlate with disease severity '</strong><br><strong>Rigoni et al. 2023, Neurobiology of Disease, doi: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.nbd.2023.106382" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.nbd.2023.106382</span></a></strong></p> <p><strong>Dataset description</strong></p> <p><em>Data\data2publish\sub- </em>: 50 epochs of raw epicranial EEG data (31 x 8001 x 50, channels x time x n_epochs,<em> </em>Fs=4k Hz). The epochs are available for 29 mice, on different sessions (ses-d0, ses-d28, ses-d29) depending on the animal </p> <p><em>Data\data2publish\EA_info.xlsx</em>: number of epileptiform activities automatically detected for each animal at d28 and d29</p> <p><em>Data\data2publish\derivatives\eeg_preprocessing: </em>results of the script A_EEG_preprocessing.m, for each animal and session</p> <p><em>Data\data2publish\derivatives\elec_layout: </em>different layouts used to plot results. Mouse_layout_modif is the one used in Fig 4</p> <p><em>Data\data2publish\derivatives\network_metrics</em>_<em>wpli: </em>results of network analyses (script C_network_analyses.m)</p> <p><em>Data\data2publish\derivatives\wpli</em>: connectivity matrices (30 x 30) obtained with the script B_connectivity_wpli.m for each animal, in each session, for each frequency band wit</p> <p><strong>Code for analyses available here: </strong> <a href="https://github.com/IsottaR/ir_mice_project_Zenodo">https://github.com/IsottaR/ir_mice_project_Zenodo </a></p> <p>Abbreviations:</p> <p>EEG= electroencephalography</p>
Evolution of Castanea in North America: RADseq and ecological modeling reveal a history of radiation, range shifts, and disease
<p><b>Premise of the Study: </b>Chestnuts and chinquapins are some of the best known and most widely loved of any plants in North America. Despite the fame of this clade, relatively little genomic sequencing has been done, and much is still unknown about their evolution. </p> <p><b>Methods: </b>Here we use ddRAD data to infer the species-level phylogeny for <i>Castanea </i>and assess the phylogeography of the North American species using samples collected from populations that span the full extent of the species' ranges. We also construct species distribution models using digitized herbarium specimens and observational data from field surveys. </p> <p><b>Key Results: </b>We identified strong population structure within <i>Castanea dentata</i> (American Chestnut) that reflects a stepwise northern migration since the last glacial maximum. Our species distribution models further confirm this scenario and match closely with the <i>Castanea</i> fossil pollen record. We also found significant structure within the <i>Castanea pumila</i> lineage, most notably a genetic cluster that corresponds to the frequently recognized "<i>Castanea pumila var. ozarkensis</i>."</p> <p><b>Conclusions: </b>The two North American <i>Castanea</i> species have contrasting patterns of population structure, but each is typical of plant phylogeography in North America. Within the <i>C. pumila</i> complex we find novel genetic structure that provides new insights to <i>C. pumila</i> taxonomy. Our results also identify a series of distinctive populations that will be valuable in on going efforts to conserve and restore the Chestnuts and Chinquapins in North America.</p>
Modeling management strategies for chronic disease in wildlife: predictions for the control of respiratory disease in bighorn sheep
<p>1. Controlling persistent infectious disease in wildlife populations is an on-going challenge for wildlife managers and conservationists worldwide.</p> <p>2. Here, we develop a dynamic pathogen transmission model capturing key features of M. ovipneumoniae infection, a major cause of population declines in North American bighorn sheep (Ovis canadensis). We explore the effects of model assumptions and parameter values on disease dynamics, including density versus frequency dependent transmission, the inclusion of a carrier class versus a longer infectious period, host survival rates, disease-induced mortality and recovery rates, and the epidemic growth rate.</p> <p>3. We compare the effectiveness of a suite of management actions following an epidemic, including test-and-remove, depopulation-and-reintroduction, range expansion, herd augmentation, and density reduction.</p> <p>4. Our results suggest that test-and-remove, depopulation-and-reintroduction, and range expansion have the potential to facilitate recovery of persistently infected bighorn sheep herds post-epidemic. By contrast, augmentation could lead to worse outcomes than those expected in the absence of management. Management that improves host survival or reduces disease-induced mortality are also likely to improve population size and persistence of chronically infected herds.</p> <p>5. Dynamic transmission models like the one employed here offer a structured, logical approach towards exploring hypotheses and can serve as a basis for planning field experiments and adaptive management. Models should be used iteratively with the field empirical approaches to triangulate on better approaches to wildlife management.</p>
Melanocortin 1 receptor activation protects against alpha-synuclein pathologies in models of Parkinson's disease
<p>Raw data for the manuscript "Melanocortin 1 receptor activation protects against alpha-synuclein pathologies in models of Parkinson’s disease"</p>
The appendix for dynamic model of respiratory infectious disease transmission by population mobility based on city network
<p>First, a scale-free city network was established, and the shortest path between any two nodes was determined. Second, the movement path of tourists was designed based on the shortest path. Subsequently, every infected person's information, such as the city, infection time, onset, and hospitalisation, was confirmed based on their movement path. Third, the features of the transmission path and time distribution of the epidemic were characterised after summarising the information. Finally, the reliability of the model was verified.</p>
The effects of exploratory behavior on physical activity in a common animal model of human disease, zebrafish (Danio rerio)
<p>Zebrafish (Danio rerio) are widely accepted as a multidisciplinary vertebrate model for neurobehavioral and clinical studies, and more recently have become established as a model for exercise physiology and behavior. Individual differences in activity level (e.g., exploration) have been characterized in zebrafish, however, how different levels of exploration correspond to differences in motivation to engage in swimming behavior has not yet been explored. We screened individual zebrafish in two tests of exploration: the open field and novel tank diving tests. The fish were then exposed to a tank in which they could choose to enter a compartment with a flow of water (as a means of testing voluntary motivation to exercise). After a 2-day habituation period, behavioral observations were conducted. We used correlative analyses to investigate the robustness of the different exploration tests. Due to the complexity of dependent behavioral variables, we used machine learning to determine the personality variables that were best at predicting swimming behavior. Our results show that contrary to our predictions, the correlation between novel tank diving test variables and open field test variables was relatively weak. Novel tank diving variables were more correlated with themselves than open field variables were to each other. Males exhibited stronger relationships between behavioral variables than did females. In terms of swimming behavior, fish that spent more time in the swimming zone spent more time actively swimming, however, swimming behavior was inconsistent across the time of the study. All relationships between swimming variables and exploration tests were relatively weak, though novel tank diving test variables had stronger correlations. Machine learning showed that three novel tank diving variables (entries top/bottom, movement rate, average top entry duration) and one open field variable (proportion of time spent frozen) were the best predictors of swimming behavior, demonstrating that the novel tank diving test is a powerful tool to investigate exploration. Increased knowledge about how individual differences in exploration may play a role in swimming behavior in zebrafish is fundamental to their utility as a model of exercise physiology and behavior.</p>
Multiscale Entropy Analysis of Retinal Signals Reveals Reduced Complexity in a Mouse Model of Alzheimer's Disease
<p>MEA recordings from wild-type and 5xFAD mice retinas used for the analyses in the manuscript "Multiscale Entropy Analysis of Retinal Signals Reveals Reduced Complexity in a Mouse Model of Alzheimer's Disease".</p>
Developing epidemiological preparedness for a probable plant disease invasion: modelling citrus huánglóngbìng in the European Union
<p>Video 1: Spread of the vector in a single simulation in Region A (Valencia). Corresponds to Fig S10 in supplementary material. Maps show the measure of vector density within each cell and light grey shows initial exposure.</p> <p>Video 2: Spread of the pathogen in a single simulation in Region A (Valencia). Corresponds to Fig 3 in main text. Both vector and pathogen are introduced simultaneously at t=0 into a single 1km x 1km cell. Maps showing the density of infected citrus host units (E+C+I) within each cell at different times after introduction.</p> <p>Video 3: Spread of the pathogen in a single simulation in Region A (Valencia) using baseline parameters for detection and control. Corresponds to Fig 6 in main text. Maps show densities of infected citrus (E+C+I) in each 1km x 1km cell</p> <p>Video 4: Spread of the vector in a single simulation in Region B (Andalusia). Corresponds to Fig S13 in supplementary material. Maps show the measure of vector density within each cell and light grey shows initial exposure.</p> <p>Video 5: Spread of the pathogen in a single simulation in Region B (Andalusia). Corresponds to Fig S14 in supplementary material. Both vector and pathogen are introduced simultaneously at t=0 into a single 1km x 1km cell. Maps showing the density of infected citrus host units (E+C+I) within each cell at different times after introduction.</p> <p>Video 6: Spread of the pathogen in a single simulation in Region B (Andalusia) using baseline parameters for detection and control. Corresponds to Fig S17 in supplementary material. Maps show densities of infected citrus (E+C+I) in each 1km x 1km cell</p>
Codes: An early warning indicator trained on stochastic disease-spreading models with different noises
<p>This dataset contains the training data (Version V1) and all the codes (Version V2) of the paper entitled "An early warning indicator trained on stochastic disease-spreading models with different noises." </p> <p>Time series and corresponding residuals from white noise (equation 2.5), environmental noise (equation 2.8), and demographic noise (equation 2.9) are stored in the training_data_WhiteN, training_data_EnvN, and training_data_DemN folders, respectively. All residuals of the time series are contained in the training_resids folder, which also includes labels and groups of the training data. For details on the data generation process, please refer to section 3.1 in the paper. </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.