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Dataset results
470 results for “prediction of disease”
Data from: Landscape connectivity predicts chronic wasting disease risk in Canada
Predicting the spatial pattern of disease risk in wild animal populations is important for implementing effective control programmes. We developed a risk model predicting the probability that a deer harvested in a wild population was chronic wasting disease positive (CWD+) and evaluated the importance of landscape connectivity based on deer movements. We quantified landscape connectivity from deer 'resistance' to move across the landscape similar to the flow of electrical current across a hypothetical electronic circuit. Resistance values to deer movement were derived as the inverse of step selection function values constructed using movement data from GPS-collared deer. The top CWD risk model indicated risk increased over time was higher among mule deer Odocoileus hemionus than white-tailed deer Odocoileus virginianus, males than females, and was greater in areas with high stream density and abundant agriculture. A metric of connectivity derived from mule deer movements outperformed models including Euclidean distance, with high connectivity being associated with high CWD risk. The CWD risk model was a good predictor of CWD occurrence among an independent set of surveillance data collected in subsequent years. Synthesis and applications. We found that landscape connectivity was a major contributor to the spatial pattern of chronic wasting disease (CWD) risk on a heterogeneous landscape. For this reason, future disease surveillance programmes and models of disease spread should consider landscape connectivity. In the aspen parkland ecosystem, we recommend managers focus surveillance and control efforts along river valleys surrounded by agriculture where mule deer abound, because of the high risk of CWD infection.
Global distribution and trends of Thyroid cancer and its machine learning prediction: Comprehensive findings and questions from global burden of disease 1990–2021
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Advanced Iterative Model for Lumpy Skin Disease Prediction Using Fine-grained Feature Fusion and Adaptive Transfer Learning
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Data for "Proteome-wide prediction of mode of inheritance and molecular mechanism underlying genetic diseases using structural interactomics"
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Predicting gut microbial behavior in human diseases via community metabolic modeling and machine learning
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Cerebral small vessel disease MRI features do not improve the prediction of stroke outcome
<p class="Pardfaut"><span>Objective: To determine whether the total small vessel disease (SVD) score adds information to the prediction of stroke outcome compared to validated predictors, we tested different predictive models of outcome in stroke patients.</span></p> <p class="Pardfaut"><span>Methods: White matter hyperintensity, lacunes, perivascular spaces, microbleeds, and atrophy were quantified in two prospective datasets of 428 and 197 first-ever stroke patients, using MRI collected 24-to-72 hours after stroke onset. Functional, cognitive, and psychological status were assessed at the 3–6 month follow-up. The predictive accuracy (in terms of calibration and discrimination) of age, baseline NIHSS, and infarct volume was quantified (model-1) on dataset-1, the total SVD score was added (model-2), and the improvement in predictive accuracy was evaluated. These two models were also developed in dataset-2 for replication. Finally, in model-3, the MRI features of cerebral SVD were included rather than the total SVD score. </span></p> <p class="Pardfaut"><span>Results: Model-1 showed excellent performance for discriminating poor vs. good functional outcomes (AUC=0.915), and fair performance for identifying cognitively impaired and depressed patients (AUCs, 0.750 and 0.688 respectively). A higher SVD score was associated with a poorer outcome (odds ratio=1.30 [1.07, 1.58], p=0.0090 at best for functional outcome). However, adding the total SVD score (model-2) or individual MRI features (model-3) did not improve the prediction over model-1. Results for dataset-2 were similar. </span></p> <p class="Pardfaut"><span>Conclusions: Cerebral SVD was independently associated with functional, cognitive, and psychological outcomes, but had no clinically relevant added value to predict the individual outcomes of patients when compared to the usual predictors, such as age and baseline NIHSS.</span></p>
The impact of rising temperatures on the prevalence of coral diseases and its predictability: a global meta-analysis
<p>Coral reefs are under threat from disease as climate change alters environmental conditions. Rising temperatures exacerbate coral disease, but this relationship is likely complex as other factors also influence coral disease prevalence. To better understand this relationship, we meta-analytically examined 108 studies for changes in global coral disease over time alongside temperature, expressed using average summer sea surface temperature (SST) and cumulative heat stress as weekly sea surface temperature anomalies (WSSTAs). We found that both rising average summer SST and WSSTA were associated with global increases in the mean and variability in coral disease prevalence. Global coral disease prevalence tripled, reaching 9.92% in the 25 years examined, and the effect of ‘year’ became more stable (i.e., prevalence has lower variance over time), contrasting the effects of the two temperature stressors. Regional patterns diverged over time and differed in response to average summer SST. Our model predicted that, under the same trajectory, 76.8% of corals would be diseased globally by 2100, even assuming moderate average summer SST and WSSTA. These results highlight the need for urgent action to mitigate coral disease. Mitigating the impact of rising ocean temperatures on coral disease is a complex challenge requiring global discussion and further study.</p>
Methylation haplotypes of the insulin gene promoter in children and adolescents with type 1 diabetes: could a dimensionality reduction approach predict the disease?
<p>The aim of the present study was to identify insulin gene promoter (IGP) methyl-haplotypes among children and adolescents with T1D and suggest a predictive model for the discrimination of cases and controls according to methyl-haplotypes. Fourty individuals (20 T1D) participated. IGP-region from peripheral whole blood DNA of 40 participants (20 T1D) was sequenced by next generation sequencing, sequences were read using FASTQ files, and methylation status was calculated by python-based pipeline for targeted deep bisulfite sequenced amplicons (ampliMethProfiler). Methylation profile at 10 CpG sites proximal to transcription start site of the IGP was recorded and coded as 0 for unmethylation or 1 for methylation. A single read could result in “1111111111” methyl-haplotype (all methylated), “000000000” methyl-haplotype (all unmethylated) or any other combination.</p>
TyG Index as a Marker to Predict Severity of Coronary Artery Disease
ClinicalTrials.gov study NCT06452121. IPD Sharing: UNDECIDED. Countries: 0. Publications: 2.
Kappa Index Versus Csf Oligoclonal Bands in Diagnosis of ms and Prediction of Disease Activity
ClinicalTrials.gov study NCT06372977. IPD Sharing: Not stated. Countries: 0. Publications: 2.
Correlational Study on the Biomarkers Application to the Prediction and Diagnosis of Cardiovascular Diseases
ClinicalTrials.gov study NCT02179047. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Predicting Severity and Disease Progression in Influenza-like Illness (Including COVID-19)
ClinicalTrials.gov study NCT04664075. IPD Sharing: YES. Countries: 1. Publications: 0.
Liver Function Measured by HepQuant-SHUNT in the Prediction of Outcomes in Patients With Heart Disease
ClinicalTrials.gov study NCT02506335. IPD Sharing: NO. Countries: 0. Publications: 2.
Assessment of Speckle Tracking Strain Predictive Value for Myocardial Fibrosis in Chagas Disease
ClinicalTrials.gov study NCT02327052. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Predicting Inflammatory Skin Disease Response to IL-23 Blockade
ClinicalTrials.gov study NCT04541329. IPD Sharing: NO. Countries: 1. Publications: 0.
Neutrophil Gelatinase Associated Liocalin in Predicting AKI in Coronary Artery Disease
ClinicalTrials.gov study NCT03266367. IPD Sharing: NO. Countries: 0. Publications: 11.
Study in Advanced Parkinson's Disease Patients With Predictable Motor Fluctuations
ClinicalTrials.gov study NCT01515410. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Utility of the Superior Vena Cava Collapsibility Index (SVC-CI) to Predict Fluid Responsiveness in Patients With Coronary Artery Disease Undergoing Surgical Revascularization
ClinicalTrials.gov study NCT06645327. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Predictive Value of the "Cytocapacity Test" Patients With Lymphoproliferative Diseases and High-dose Therapy
ClinicalTrials.gov study NCT01085058. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Prediction of Severity of Liver Disease by a 13C Octanoate Breath Test (OBT)
ClinicalTrials.gov study NCT01244503. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
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.