14670 results for "PLOS+ONE+Pennsylvania+German+ACS+language+code+pdc+open+access+study"

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• University of Utah Pelvic Organ Prolapse Disorder Study

  The overall purpose of this University of Utah Pelvic Organ Prolapse Disorder Study was to identify and localize predisposition genes contributing to pelvic organ prolapse (POP). POP cases recruited for this study were identified by one of three methods: high-risk POP pedigree cases, POP sister pairs, and surgically-treated POP cases reporting a family history of POP. High-risk POP pedigree cases were identified using the Utah Population Database (UPDB), a genealogy database of residents in Utah that has been linked to diagnostic ICD9 and CPT codes in medical records at the University of Utah and Intermountain Healthcare. We identified families with a significant excess number of POP cases compared to matched population rates and targeted these individuals for recruitment as well as any other POP cases in the family. POP sister pair cases were identified at the University of Utah Urogyncology clinic for women who had undergone POP surgery and also self-reported one or more sisters who were also surgically treated for POP. POP affection status of all sisters was confirmed either by physical examination or by chart review. Surgically treated-POP cases reporting a family history of POP were identified at the University of Utah Urogyncology clinic by self-report of a family history of POP. Efforts were made to recruit other affected family members and confirm affection status. To obtain DNA, subjects provided either a blood specimen or saliva. Medical records were reviewed by a urogynecologist and diagnostic information for pelvic organ prolapse and stress and overactive bladder were obtained. Collected DNA was genotyped and analyzed. To maintain confidentiality of the familial data, genetic data from only one subject per family has been submitted to dbGaP. Use of the University of Utah Pelvic Organ Prolapse Disorder Study data is limited to investigators studying pelvic floor disorders. These pelvic floor disorders include pelvic organ prolapse, urinary and anal incontinence, and other conditions related to weakening or injury to the muscles and connective tissue in the pelvis as a result of pelvic surgery, pregnancy, or vaginal delivery of a child. These data will be used only for research purposes related to pelvic floor disorders. They will not be used to determine the individual identity of any person or their relationship to another person or for research on non-disease traits.

  Study phs001439

• Youth-GEMs, the project using data to define the mental health trajectories of young people

  Mental health trajectories are shaped by genetic, cognitive, social, and environmental factors that are particularly influential during human development. How exactly the interplay between these factors is impacting mental health, however, is yet unknown. The project “Gene Environment interactions in Mental health trajectories of Youth (Youth-GEMs)”, funded by the European Commission under the Horizon Europe programme, aims to help reducing mental suffering among European youth. How do Youth-GEMs contribute to bettering mental health in young people? Youth-GEMs aims to identify specific neurological, (epi)genetic and environmental drivers on mental health trajectories of young people. To do so, the project is working on a public knowledge base on functional genomics of the developing human brain, between birth and 24 years old. Reliable predictive models for trajectories of onset and course of mental illness in young people of the general population in Europe will be developed. Additionally, a secure and FAIR data infrastructure in mental health is being produced. Engaging and empowering young people Youth-GEMs is also working on the engagement of young people in the scientific field of Psychiatry. For that, the project is developing initiatives such as Science Café, intended to create a learning community where ideas and stories are shared. The 10th Science Café will take place today, with the participation of Lauren Fromont from the EGA. How do the EGA contribute to Youth-GEMs? The contributions require careful data management and harmonisation: we are dealing with an extraordinary amount of data types, sources, and time points (since the studies are longitudinal). To analyse them together and to determine which factors and interactions are truly key to account for mental health trajectories, the variables need to be transformed in a common “language”, i.e., harmonised under a common data model. Further, to guarantee FAIRness of data storage and sharing for future projects that will further advance our knowledge in the mental health field, we are ensuring that data are processed securely but can also be discovered easily. Find out more information about the project in the Youth-GEMs website.

  Blog youth-gems-mental-health

• Whole transcriptome profiling of liquid biopsies from tumour xenografted mouse models: validation cohort

  While cell-free DNA (cfDNA) in liquid biopsies is widely being used and investigated, free circulating RNA (extracellular RNA, exRNA) has the potential to improve therapy response monitoring and cancer detection due to its dynamic nature. A fundamental open question hampering the initiation of large-scale liquid biopsy collections for tumour exRNA analysis is that it remains unclear in which blood subcompartment tumour-derived exRNAs primarily reside. We set out to develop a host-xenograft deconvolution framework, exRNAxeno, with cDNA mapping strategies to either a combined human-mouse reference genome or both species genomes in parallel, that can be applied to exRNA sequencing data from liquid biopsies of human xenograft mouse models, enabling to distinguish (human) tumoural RNA from (murine) host RNA, and as such to specifically analyse tumour-derived exRNA. Subsequently, the preferred exRNAxeno combination pipeline was applied to total exRNA sequencing data from blood platelets and three plasma fractions from a breast cancer patient-derived and neuroblastoma cell line-derived xenograft mouse model. We show that tumoural exRNA concentrations are not determined by plasma platelet levels, while host exRNA concentrations increase with platelet content. Furthermore, a large variability in exRNA levels and gene content across individual mice is observed. In general, the tumoural gene detectability in plasma is correlated with the RNA expression levels in the tumour tissue or cancer cell line. Our results unravel new aspects of tumour-derived exRNA biology in xenograft mouse models.

  Study EGAS00001006582

• This committee is composed on individuals who have access to the data corresponding to this project: Gut microbiome modulates response to anti PD-1 immunotherapy in melanoma patients

  Dac EGAC00001000758

• Yale Policy for head and neck cancer and HPV clinical trials

  Yale Policy for human controlled access data associated with head and neck cancer and HPV clinical trials

  Dac EGAC50000000403

• ERDERA Diagnostic Research Workstream DAC

  The ERDERA Diagnostic Research Workstream DAC decides on data access requests according to the principles defined in the ERDERA Data Sharing Framework Agreement.

  Dac EGAC50000000728

• DETECT-A NGS Data Batch 3

  This batch is a subset of the full DETECT-A dataset, containing 29294 fastq files generated from 3653 subjects. All sequencing was conducted using Illumina HiSeq 4000 and Illumina MiSeq platforms. Note that the division into batches follows no specific criteria and that the sequencing data for each subject has multiple files which may span multiple datasets. Thus, for a comprehensive analysis, it is recommended to request access to all datasets that comprise this study.

  Dataset EGAD00001008597

• DETECT-A NGS Data Batch 2

  This batch is a subset of the full DETECT-A dataset, containing 498 fastq files generated from 71 subjects. All sequencing was conducted using Illumina HiSeq 4000 and Illumina MiSeq platforms. Note that the division into batches follows no specific criteria and that the sequencing data for each subject has multiple files which may span multiple datasets. Thus, for a comprehensive analysis, it is recommended to request access to all datasets that comprise this study.

  Dataset EGAD00001008591

• DETECT-A NGS Data Batch 5

  This batch is a subset of the full DETECT-A dataset, containing 12740 fastq files generated from 3211 subjects. All sequencing was conducted using Illumina HiSeq 4000 and Illumina MiSeq platforms. Note that the division into batches follows no specific criteria and that the sequencing data for each subject has multiple files which may span multiple datasets. Thus, for a comprehensive analysis, it is recommended to request access to all datasets that comprise this study.

  Dataset EGAD00001015426

• DETECT-A NGS Data Batch 1

  This batch is a subset of the full DETECT-A dataset, containing 43963 fastq files generated from 3024 subjects. All sequencing was conducted using Illumina HiSeq 4000 and Illumina MiSeq platforms. Note that the division into batches follows no specific criteria and that the sequencing data for each subject has multiple files which may span multiple datasets. Thus, for a comprehensive analysis, it is recommended to request access to all datasets that comprise this study.

  Dataset EGAD00001008415