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458 results for “clinical research”
Data Structure of Clinical Research
<p><em>Bro</em><em>nchial asthma is one of the most common respiratory pathologies in children, characterized by rising incidence around the world. Early disease onset, severe clinical signs of bronchial asthma, the ineffectiveness of high doses of hormone therapy reduce the quality of life in patients and lead to disability. Analysis of bronchial asthma heterogeneity is now possible in virtue of computer technology and big data processing onrush.</em></p> <p><em>The information on 70 children suffering from bronchial asthma and 20 children from the control group was analyzed in this study.</em></p> <p><em>Gender, age, duration of disease, associated diseases, family history of allergic diseases, clinical blood and urine test, spirography and blood immunoassay results, total IgE, thymic stromal lymphopoietin and results of skin allergy tests were taken into account.</em></p>
Research Data for Comparative Evaluation of RT-PCR and Antigen-based Rapid Diagnostic Tests (Ag-RDTs) for SARS-CoV-2 Detection: Performance, Variant Specificity, and Clinical Implications
<p>This dataset represents laboratory findings for the comparative evaluation of the diagnostic performance of Ag-RDTs (Flourescence Immunoassay and Lateral Flow Immunoassay) with RT-PCR</p>
SMA-TB Clinical trial research team's thoughts and opinions
<p>In this video SMA-TB Clinical trial research team members from Georgia and South Africa share their thoughts and opinions regarding the SMA-TB project and its impact both at scientific and personal level. </p> <p>SMA-TB team comprises of doctors, nurses, laboratory technicians and administrative. This video gives opportunity to look at and think of SMA-TB project from different perspectives. </p> <p>SMA-TB project has received funding from the European Union's Horizon 2020 research and innovation programme under grant ageement No 847762</p>
Fabrication and characterization of a multimodal 3D printed mouse phantom for ionoacoustic quality assurance in image-guided pre-clinical proton radiation research
<p>Dataset related to the publication: "Fabrication and characterization of a multimodal 3D printed mouse phantom for ionoacoustic quality assurance in image-guided pre-clinical proton radiation research"</p>
Data related to "Effective Publication Strategies in Clinical Research"
<p>Data and supplementary material in support of "Deutz, D.B., Vlachos, E., Drongstrup, D., Dorch, B.F., Wien, C. (2019). Effective Publication Strategies in Clinical Research, PLOS ONE".</p> <p>Content:</p> <p>A README file with details regarding the purpose of the data collection, the setting and methodology and descriptions of the rest of the files, the Python script used to extract publication data from the Scopus API, the interview invitation email, the interview guidelines, the information regarding the interviews, supporting information on raw publication data, two tables as presented at the publication, and the coordinates of a plot.</p>
Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in The Brain: A Consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group
<p>Example datasets and code for the recommended implementation of Quantitative Susceptibility Mapping (QSM) in "Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in The Brain: A Consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group".</p>
Data files for manuscript "Re-evaluation and Re-analysis of 152 research exomes five years after the initial report reveals clinically relevant changes in 18%"
<p>#2023-06-16<br> #Summary<br> This ZIP-file contains the data files used for all analyses for the manuscript "Re-evaluation and Re-analysis of 152 research exomes five years after the initial report reveals clinically relevant changes in 18%".</p> <p><br> #File structure<br> README.txt This README file.<br> File S02 ("FileS2_conNDD-cohort.xlsx") All variants identified by Reuter et al. previously with reevaluated variants and addition variants identified in this <br> project togetehr with information about the families, individuals, samplesand the BAM files assessed in this project.<br> File S03 ("FileS3_conNDD-variants.xlsx") All variant data analyzed from the cohort. Including a sheet with thresholdes for in silico predictions tools used to predict effect of variants, <br> a table with exome wide homozygous variants in 4 categories (A45, LGD, Missense, Splice), a table with exome wide variants in 4 categories (A45, LGD, Missense, Splice)<br> filtered for domiant genes associated with neurodevelopmental disorders in SysID (Prime and Candidate list), a table with exome wide variants in 4 categories (A45, LGD, Missense, Splice) filtered for recessive genes associated with neurodevelopmental disorders in SysID (Prime and Candidate list), a table withcopy number (CN) calls for the cohort and a table withcalls for runs of homozygosity (RoH) regions.</p> <p>#Files and checksums<br> 29c4b2f3dd8985d268f50dd3e0265798 ./FileS2_conNDD-cohort.xlsx<br> a054334637b8b22a9bf743db1e348663 ./FileS3_conNDD-variants.xlsx<br> </p>
A concept for FAIR clinical medication data usage - From care to research with OMOP: literature list of OHDSI studies
<p>This list of papers has been reviewed for the usage of drug data and to answer the question on what drug level the study was done. </p> <p>We checked whether drug ingredient level or drug component with dose and unit was required for the studies. </p>
From Forensics to Clinical Research: Expanding the Variant Calling Pipeline for the Precision ID mtDNA Whole Genome Panel
<p>In this dataset we provide the 1000 Genomes Project's samples processed in our manuscript:</p> <p>Cortes-Figueiredo, F.; Carvalho, F.S.; Fonseca, A.C.; Paul, F.; Ferro, J.M.; Schönherr, S.; Weissensteiner, H.; Morais, V.A. From Forensics to Clinical Research: Expanding the Variant Calling Pipeline for the Precision ID mtDNA Whole Genome Panel. <em>Int. J. Mol. Sci</em>. <strong>2021</strong>, <em>22</em>, 12031. <a href="https://doi.org/10.3390/ijms222112031">https://doi.org/10.3390/ijms222112031</a><em>.</em></p> <p><strong>Abstract</strong></p> <p>Despite a multitude of methods for the sample preparation, sequencing, and data analysis of mitochondrial DNA (mtDNA), the demand for innovation remains, particularly in comparison with nuclear DNA (nDNA) research. The Applied Biosystems™ Precision ID mtDNA Whole Genome Panel (Thermo Fisher Scientific, USA) is an innovative library preparation kit suitable for degraded samples and low DNA input. However, its bioinformatic processing occurs in the enterprise Ion Torrent Suite™ Software (TSS), yielding BAM files aligned to an unorthodox version of the revised Cambridge Reference Sequence (rCRS), with a heteroplasmy threshold level of 10%. Here, we present an alternative customizable pipeline, the PrecisionCallerPipeline (PCP), for processing samples with the correct rCRS output after Ion Torrent sequencing with the Precision ID library kit. Using 18 samples (3 original samples and 15 mixtures) derived from the 1000 Genomes Project, we achieved overall improved performance metrics in comparison with the proprietary TSS, with optimal performance at a 2.5% heteroplasmy threshold. We further validated our findings with 50 samples from an ongoing independent cohort of stroke patients, with PCP finding 98.31% of TSS’s variants (TSS found 57.92% of PCP’s variants), with a significant correlation between the variant levels of variants found with both pipelines.</p> <p><br> Please refer to our the github page <a href="https://github.com/filcfig/PCP.git">filcfig/PCP</a>, for more details on running the PrecisionCalllerPipeline.</p>
Transcriptomics for clinical and experimental biology research: hang on a seq
<p>Additional R script, source data files and figures associated with the paper.</p> <p>The following source files can generate the corresponding figures using the R script (Omics_review_HC_final.R).</p> <p>Figure 1: Fig1_upset_new_V2_general.type.csv</p> <p>Figure 2 & 3: SMP_ENSG_biotypes_average_data_All_genes_V2.xlsx; FUSION_study_CPM-data_fusion_annotated.xlsx</p> <p>Extra Figures (RNA_review_figures_brain_HC.pdf) uploaded here : GSE47774_SEQC_ILM_BGI_Human_Brain_Only.xlsx; GSE47774_SEQC_ILM_BGI_Human_Brain_Only.xlsx</p> <p>Additional raw counts source files:</p> <p>RNA-seq.A: summary data is available at https://www.ebi.ac.uk/birney-srv/FUSION/. Full dataset is available at dbGaP accession phs001048.v2.p1.</p> <p>RNA-seq.B: GSE164471_GESTALT_Muscle_ENSG_counts_annotated.csv</p> <p>RNA-seq.C: GSE97084_Robinson_GeneCount_raw.tsv; GSE97084_Robinson_GeneCount_raw_2.tsv</p> <p>RNA-seq.D: GSE157585_Kulkarni_Peck_et_al_MASTERS_raw_counts.txt</p> <p>RNA-seq.E: GSE151066_Rubenstein_rsem_genes_count.csv</p> <p>HTA2.0 array: SMP191_iron_output_M_GC-HTA_ENST_Grch38_25.500.25.10_log2_2_minimum.csv; ENSG_biotypes_RNA_review_SMP_data</p> <p>Figure 5 & 6 are generated from various source files above following the R script.</p>
A Clinical Research Study to Determine Whether PD 0332991 May Be Effective in Treating Patients With Liver Cancer
ClinicalTrials.gov study NCT01356628. IPD Sharing: Not stated. Countries: 1. Publications: 3.
App-based Consent for Pediatric Clinical Research
ClinicalTrials.gov study NCT05880147. IPD Sharing: YES. Countries: 1. Publications: 7.
A Non-interventional, International, Multicentre Clinical Research Study to Build the Largest Collection of Multimodal Data (Including Clinical Data, Imaging Data and Omics Data) in Oncology
ClinicalTrials.gov study NCT06625203. IPD Sharing: YES. Countries: 4. Publications: 7.
N-MOmentum: A Clinical Research Study of Inebilizumab in Neuromyelitis Optica Spectrum Disorders
ClinicalTrials.gov study NCT02200770. IPD Sharing: YES. Countries: 25. Publications: 9.
"REDUCE" - A Clinical Research Study To Reduce The Incidence Of Prostate Cancer In Men Who Are At Increased Risk
ClinicalTrials.gov study NCT00056407. IPD Sharing: YES. Countries: 42. Publications: 25.
Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Precision Medicine Phase 2 Option 1
ClinicalTrials.gov study NCT04602806. IPD Sharing: YES. Countries: 1. Publications: 12.
Geriatric Core Dataset (G-CODE) for Clinical Research in Elderly Cancer Patients
ClinicalTrials.gov study NCT03976531. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Asthma Clinical Research Network (ACRN) Trial - Macrolides in Asthma (MIA)
ClinicalTrials.gov study NCT00318708. IPD Sharing: Not stated. Countries: 1. Publications: 1.
ClinSeq: A Large-Scale Medical Sequencing Clinical Research Pilot Study
ClinicalTrials.gov study NCT00410241. IPD Sharing: YES. Countries: 1. Publications: 5.
Sarcoma Study of MORAb-004 Utilization: Research and Clinical Evaluation
ClinicalTrials.gov study NCT01574716. IPD Sharing: Not stated. Countries: 6. Publications: 1.
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.