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1,274
datasets available to search
ShareScore release 0.9.0
Dataset results
1,274 results for “Disease Model”
Brain pathology and cerebellar Purkinje cell loss in a mouse model of chronic neuronopathic Gaucher disease
GEO Series GSE157992. Mus musculus. 35 samples. Type: Expression profiling by high throughput sequencing.
hPSC-derived Sacral Neural Crest Enables Rescue in a Severe Model of Hirschsprung’s Disease
GEO Series GSE199441. Homo sapiens. 35 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Supplementary processed data for Chandler et al. Single-cell transcriptomics identifies aberrant glomerular angiogenic signalling in the early stages of a murine model of WT1 kidney disease
<p><strong><em>Summary datafiles for Chandler et al.</em></strong></p> <ul> <li>Processed single-cell RNA sequencing data, derived from murine glomeruli (isolated using the dynabead technique) from <em>n = 2</em> wild-type <em>Wt1<sup>+/+</sup></em> (Ctrl) and <em>n = 2</em> mutant <em>Wt1<sup>R394W/+</sup></em> (Mut) littermates of WT1 glomerulopathy, followed by 10x Genomics Chromium v3 platform.</li> <li>Please refer to associated scripts for analysis of the data: <a href="https://github.com/daniyal-jafree1995/collaborations/blob/main/Chandleretal_2022_WT1glomerulopathyscRNAseq.R">https://github.com/daniyal-jafree1995/collaborations/blob/main/Chandleretal_2022_WT1glomerulopathyscRNAseq.R</a> </li> <li>File descriptions as below: <ul> <li>barcodes.tsv.gz - barcodes file required for input to Read10X function in Seurat</li> <li>features.tsv.gz - features file required for input to Read10X function in Seurat</li> <li>matrix.mtx.gz - matrix file required for input to Read10X function in Seurat</li> <li>WT1_scRNAseq.rds - RDS file containing processed and annotated Seurat object for downstream analysis</li> </ul> </li> </ul>
Transcriptome profiling in knock-in mouse models of Huntington's disease [cerebellum_mRNA]
GEO Series GSE73468. Mus musculus. 166 samples. Type: Expression profiling by high throughput sequencing.
A rewiring of the earliest immune events leading to T-cell mediated disease following intestinal microbial infection in a PINK1KO mouse model of Parkinson’s disease
GEO Series GSE271210. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
hPSC-derived Sacral Neural Crest Enables Rescue in a Severe Model of Hirschsprung’s Disease [RNA-seq]
GEO Series GSE199439. Homo sapiens. 21 samples. Type: Expression profiling by high throughput sequencing.
APP and its intracellular domain modulate Alzheimer’s disease risk gene networks in transgenic APPsw and PSEN1M146I porcine models [hippocampus]
GEO Series GSE254548. Sus scrofa. 12 samples. Type: Expression profiling by high throughput sequencing.
Distinct transcriptomic and epigenomic responses of mature oligodendrocytes during disease progression in a mouse model of multiple sclerosis [RNA-seq]
GEO Series GSE283086. Mus musculus. 15 samples. Type: Expression profiling by high throughput sequencing.
Single-cell transcriptomics revealed molecular vulnerability in a human midbrain-like organoid model of Parkinson's Disease [RNA-Seq]
GEO Series GSE271115. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing.
Dysregulated ac4C modification of mRNA in a mouse model of early stage Alzheimer's disease [RNA-seq]
GEO Series GSE286035. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
The impact of reduced PLCG2 expression on microglial biology and disease pathology in a murine model of Alzheimer’s disease
GEO Series GSE196502. Mus musculus. 48 samples. Type: Expression profiling by array.
Data for paper: Fine scale infectious disease modeling using satellite-derived data: application to pandemic influenza in Rwanda
<p>Dataset used by code for the paper: Fine scale infectious disease modeling using satellite-derived data: application to pandemic influenza in RwandaFine scale infectious disease modeling using satellite-derived data: application to pandemic influenza in Rwanda</p>
Data set from Spinelli D, Marconi S, Caruso R, Conti M, Benedetto F, De Beaufort HW, Auricchio F, Trimarchi S. 3D printing of aortic models as a teaching tool for improving understanding of aortic disease. J Cardiovasc Surg (Torino). 2019 Oct;60(5):582-588. doi: 10.23736/S0021-9509.19.10841-5. Epub 2019 Jun 26. PMID: 31256581.
<p>Data set from Spinelli D, Marconi S, Caruso R, Conti M, Benedetto F, De Beaufort HW, Auricchio F, Trimarchi S. 3D printing of aortic models as a teaching tool for improving understanding of aortic disease. J Cardiovasc Surg (Torino). 2019 Oct;60(5):582-588. doi: 10.23736/S0021-9509.19.10841-5. Epub 2019 Jun 26. PMID: 31256581.</p> <p> </p> <p>This is the abstract:</p> <p><strong>Background: </strong>A geometrical understanding of the individual patient's disease morphology is crucial in aortic surgery. The aim of our study was to validate a questionnaire addressing understanding of aortic disease and use this questionnaire to investigate the value of 3D printing as a teaching tool for surgical trainees.</p> <p><strong>Methods: </strong>Anonymized CT-angiography images of six different patients were selected as didactic cases of aortic disease and made into 3D models of transparent rigid resin with the Vat-photopolymerization technique. The 3D aortic models, which could be disassembled and reassembled, were displayed to 37 surgical trainees, immediately after a seminar on aortic disease. A questionnaire was developed to compare the trainees' understanding before (T0) and after (T1) demonstration of the 3D printed models.</p> <p><strong>Results: </strong>A panel of 15 experts participated in evaluating face and content validity of the questionnaire. The questionnaire validity was established and therefore the information investigated by the questionnaire could be synthetized using the mean of the items to indicate the understanding. The participants (mean age 28 years, range 26-34, male 59%) showed a significant improvement in understanding from T0 (median=7.25; IQR=1.50) to T1 (median=8.00; IQR=1.50; P=0.002).</p> <p><strong>Conclusions: </strong>Preliminary data suggest that the use of 3D-printed aortic models as a teaching tool was feasible and improved the understanding of aortic disease among surgical trainees.</p>
Data set from Flocco SF, Dellafiore F, Caruso R, Giamberti A, Micheletti A, Negura DG, Piazza L, Carminati M, Chessa M. Improving health perception through a transition care model for adolescents with congenital heart disease. J Cardiovasc Med (Hagerstown). 2019 Apr;20(4):253-260. doi: 10.2459/JCM.0000000000000770. PMID: 30676496.
<p>Data set from Flocco SF, Dellafiore F, Caruso R, Giamberti A, Micheletti A, Negura DG, Piazza L, Carminati M, Chessa M. Improving health perception through a transition care model for adolescents with congenital heart disease. J Cardiovasc Med (Hagerstown). 2019 Apr;20(4):253-260. doi: 10.2459/JCM.0000000000000770. PMID: 30676496.</p> <p> </p> <p>this is the abstract:</p> <p><strong>Aims: </strong>The aim of this study was to assess the impact of a transition clinic model on adolescent congenital heart disease (CHD) patients' health perception outcomes. The transition clinic model consists of multidisciplinary standardized interventions to educate and support CHD patients and represents a key element in the adequate delivery of care to these individuals during their transition from childhood to adulthood. Currently, empirical data regarding the impact of transition clinic models on the improvement of health perceptions in CHD adolescent patients are lacking.</p> <p><strong>Methods: </strong>A quasi-experimental design was employed. Quality of life, satisfaction, health perceptions and knowledge were assessed at the time of enrolment (T0) and a year after enrolment (T1), respectively. During the follow-up period, the patients enrolled (aged 11-18 years) were involved in the CHD-specific transition clinic model (CHD-TC).</p> <p><strong>Results: </strong>A sample of 224 CHD adolescents was enrolled (60.7% boys; mean age: 14.84 ± 1.78 years). According to Warnes' classification, 22% of patients had simple heart defect, 56% showed moderate complexity and 22% demonstrated severe complexity. The overall results suggested a good impact of the CHD-TC on adolescents' outcomes, detailing in T1 the occurrence of a reduction of pain (P < 0.001) and anxiety (P < 0.001) and an improvement of knowledge (P < 0.001), life satisfaction (P < 0.001), perception of health status (P < 0.001) and quality of life (P < 0.001).</p> <p><strong>Conclusion: </strong>The CHD-TC seems to provide high-quality care to the patient by way of a multidisciplinary team. The results of the present study are encouraging and confirm the need to create multidisciplinary standardized interventions in order to educate and support the delivery of care for CHD adolescents and their families.</p>
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.