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470
datasets available to search
ShareScore release 0.9.0
Dataset results
470 results for “prediction of disease”
Predictive Biomarkers For Pediatric Chronic Graft-Versus-Host Disease
ClinicalTrials.gov study NCT02067832. IPD Sharing: Not stated. Countries: 3. Publications: 2.
Minimal Residual Disease as a Possible Predictive Factor for Relapse in Patients With AL Amyloidosis
ClinicalTrials.gov study NCT02555969. IPD Sharing: Not stated. Countries: 1. Publications: 15.
Validation of Multimodal Evoked Potentials (mmEP) for Predicting Disease Progression in Multiple Sclerosis
ClinicalTrials.gov study NCT03632473. IPD Sharing: Not stated. Countries: 1. Publications: 4.
AI-enabled Endoscopic Prediction of Post-operative Recurrence in Crohn's Disease
ClinicalTrials.gov study NCT06505304. IPD Sharing: NO. Countries: 6. Publications: 7.
Evaluation of Digestive Damage and Associated Predictive Factors in Crohn's Disease 5 to 10 Years After Diagnosis
ClinicalTrials.gov study NCT02549976. IPD Sharing: Not stated. Countries: 1. Publications: 2.
PreDiction and Validation of Clinical CoursE of Coronary Artery DiSease With CT-Derived Non-Invasive HemodYnamic Phenotyping and Plaque Characterization (DESTINY Study)
ClinicalTrials.gov study NCT04794868. IPD Sharing: UNDECIDED. Countries: 2. Publications: 2.
PREDICT: Thinking About Pregnancy Risk in Women With Kidney Disease
ClinicalTrials.gov study NCT04370769. IPD Sharing: NO. Countries: 1. Publications: 1.
A Study to Characterize Multidimensional Model to Predict the Course of Crohn's Disease (CD)
ClinicalTrials.gov study NCT03668249. IPD Sharing: YES. Countries: 1. Publications: 1.
Non-invasive Methods for Predicting Decompensation of Chronic Liver Disease
ClinicalTrials.gov study NCT06741904. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
The Genetics and Vascular Health Check Study (GENVASC) Aims to Help Determine Whether Gathering Genetic Information Can Improve the Prediction of Risk of Coronary Artery Disease (CAD)
ClinicalTrials.gov study NCT04417387. IPD Sharing: YES. Countries: 1. Publications: 1.
Data from: Axial symptoms predict mortality in patients with Parkinson disease with subthalamic stimulation
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Data from: MHC class II DRB diversity predicts antigen recognition and is associated with disease severity in California sea lions naturally infected with Leptospira interrogans
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Data from: Detection error influences both temporal seroprevalence predictions and risk factors associations in wildlife disease models
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Data from: Accuracy in the prediction of disease epidemics when ensembling simple but highly correlated models
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Modeling management strategies for chronic disease in wildlife: predictions for the control of respiratory disease in bighorn sheep
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Experimental evidence of warming-induced disease emergence and its prediction by a trait-based mechanistic model
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Predictive value of clinical indices for intravenous immunoglobulin resistance and coronary artery lesion in Kawasaki disease
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Data from: Accurate genomic predictions for chronic wasting disease in U.S. white-tailed deer
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Data: 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>
Phylogenetic relatedness among Cladosporium leaf endophytes predicts their ability to reduce the severity of a poplar leaf rust disease
<p>More closely related organisms are expected to function more similarly than distantly related organisms due to shared ancestry and functional trait heritability. However, there have been few tests of this hypothesis for fungal leaf endophytes, which can modify host plant disease severity by a variety of mechanisms. We tested whether phylogenetic relatedness within <i>Cladosporium</i>, a genus including many common fungal leaf endophyte species, predicts endophyte effects on cottonwood leaf rust disease severity caused by <i>Melampsora ×</i> <i>columbiana</i>. First, we used multilocus sequence typing to infer phylogenetic relationships among 96 <i>Cladosporium </i>isolates collected from wild cottonwood trees growing in Pacific Northwest of North America. Next, we conducted a double-inoculation leaf-disk assay (endophyte inoculated first, then rust pathogen) for a subset of 50 <i>Cladosporium </i>isolates to characterize disease modification for the endophyte isolates; data on endophytes parasitizing rust was collected simultaneously for each isolate. We used generalized linear models to link disease modification and mycoparisitic ability to endophyte phylogeny, while accounting for endophyte geographic origin. We recognized 17 distinct species of <i>Cladosporium</i>; all fifty isolates of <i>Cladosporium</i> reduced rust disease severity in our leaf disk assay (by as much as 79% and as little as 45%). <i>Cladosporium </i>phylogeny was a significant predictor of rust disease severity and was also correlated with mycoparasitism. The geographic origin of the isolates explained only a small amount of the overall variation in disease reduction. Our results demonstrate that fungal endophyte phylogenetic relatedness can help predict differences in endophyte function.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.