1351 results for "26 credit fc Visitez le site Buyfc26coins.com Source fiable et approuvée pour les FC 26 coins.Gkre"

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• Dopaminergic neuron differentiation of human embryonic stem cells

  2 replicates of dopaminergic neuron differentiation of human embryonic stem cells from timepoints day 17 and day 28, performed with 10X Genomics single cell RNA-seq. Part of the Ásgrimsdottir et al. (Arenas Lab).

  Study EGAS50000001099

• RNA seq of MPNST tumour samples

  RNA sequencing of 10 MPNST tumour samples as part of a larger study of MPNST. Article: Lyskjær et al, 2020, J Pathol, "H3K27me3 expression and methylation status in histological variants of malignant peripheral nerve sheath tumours".

  Study EGAS00001004528

• RNA-Seq data from 34 CAF-S3 subset in human breast and ovarian cancers

  This dataset is related to publications Costa et al. Cancer Cell 2018 and Givel et al. Nat. Commun. 2018 which describe the identification of 4 Cancer Associated Fibroblasts (CAF) in breast and ovarian cancer. This dataset contains transcriptomic profiles obtained by RNA-Seq of 34 CAF-S3 samples from breast and ovarian Tumors.

  Dataset EGAD00001004810

• RNA-seq data of breast cancer HCI011 model from Ros et al (2020)

  RNA-seq data of the HCI011 and HCI011R models, GDC032 treated and control (total of 21 samples), from the paper: FOXM1 is a biomarker of resistance to PI3Kα inhibition in ER+ breast cancer that is detectable using metabolic imaging (Ros et al, 2020)

  Dataset EGAD00001006245

• Healthy Never Smokers

  Bronchial brushing dataset from healthy never-smokers after exposure to diesel exhaust. Include 18 samples from 9 research participants who underwent bronchoscopy after controlled exposure to diesel exhaust. Main study design described in detail in Ryu et al 2022 AJRCCM (PMID: 35202552). This dataset was used in Hill et al Nature 2023.

  Dataset EGAD00001010024

• Single cell RNAseq of stenotic, inflamed and non-inflamed transmural lesions from patients with Crohn's disease

  This dataset contains a gene-cell matrix derived from single-cell RNA sequencing (scRNA-seq) data of ileal tissue from Crohn's disease (CD) patients and colorectal cancer (CRC) patients. It includes: Crohn's Disease Patients: A trio of transmural lesions (stenotic, inflamed, and non-inflamed) from each patient. Colorectal Cancer Patients: Unaffected ileal tissue used as external non-inflamed control. Cell Level Metadata: The dataset includes relevant cell-level metadata such as cell type annotations used in the study. Experimental Details: Platform: 10x Genomics Chromium Single Cell 3' GEX Sequencing: Illumina NovaSeq Processing: Data processed with Cell Ranger software. Resulting count matrices were merged for downstream analysis, including integration and dimensionality reduction. Dataset Composition: Crohn's Disease Patients: 10 patients with 3 samples each (non-inflamed, inflamed, stenotic), totaling 30 samples. Colorectal Cancer Patients: 5 patients with 1 sample each of unaffected tissue, totaling 5 samples. Data Provided: Merged Raw Count Matrix: The final merged raw count matrix used for downstream analysis. Cell Metadata File: Contains details of sample, tissue, and patient for each cell in the count matrix. Barcodes File: Indicate each cell barcode which also encodes the sample, tissue, and patient details for each cell. CD.S_Inf: Stenotic Corhn's disease inflamed samples CD.S_Sten: Stenotic CD patient stenosis sample CD.S_Prox: Stenotic CD Patient - proximal non-inflamed sample CC.C_Prox: CRC Patient proximal unaffected sample eg: A barcode 'CC.C_1_Prox_AAGTCGTAGACCCTTA' indicates CRC Patient unaffected proximal sampe from CRC Patient no.1 and the nucleic acid sequence indicate a unique cell from this sample. Total Samples: Crohn's Disease (CD) Patients: 30 samples Colorectal Cancer (CRC) Patients: 5 samples Patient_no Sample Sample_type 1 CC.C_1 CC.C_1_Prox CC.C_Prox 2 CD.S_1 CD.S_1_Prox CD.S_Prox 3 CD.S_1 CD.S_1_Infl CD.S_Infl 4 CD.S_1 CD.S_1_Sten CD.S_Sten 5 CC.C_2 CC.C_2_Prox CC.C_Prox 6 CD.S_2 CD.S_2_Prox CD.S_Prox 7 CD.S_2 CD.S_2_Infl CD.S_Infl 8 CD.S_2 CD.S_2_Sten CD.S_Sten 9 CC.C_3 CC.C_3_Prox CC.C_Prox 10 CC.C_4 CC.C_4_Prox CC.C_Prox 11 CD.S_3 CD.S_3_Prox CD.S_Prox 12 CD.S_3 CD.S_3_Infl CD.S_Infl 13 CD.S_3 CD.S_3_Sten CD.S_Sten 14 CD.S_4 CD.S_4_Prox CD.S_Prox 15 CD.S_4 CD.S_4_Infl CD.S_Infl 16 CD.S_4 CD.S_4_Sten CD.S_Sten 17 CC.C_5 CC.C_5_Prox CC.C_Prox 18 CD.S_5 CD.S_5_Prox CD.S_Prox 19 CD.S_5 CD.S_5_Infl CD.S_Infl 20 CD.S_5 CD.S_5_Sten CD.S_Sten 21 CD.S_6 CD.S_6_Prox CD.S_Prox 22 CD.S_6 CD.S_6_Infl CD.S_Infl 23 CD.S_6 CD.S_6_Sten CD.S_Sten 24 CD.S_7 CD.S_7_Prox CD.S_Prox 25 CD.S_7 CD.S_7_Infl CD.S_Infl 26 CD.S_7 CD.S_7_Sten CD.S_Sten 27 CD.S_8 CD.S_8_Prox CD.S_Prox 28 CD.S_8 CD.S_8_Infl CD.S_Infl 29 CD.S_8 CD.S_8_Sten CD.S_Sten 30 CD.S_9 CD.S_9_Prox CD.S_Prox 31 CD.S_9 CD.S_9_Infl CD.S_Infl 32 CD.S_9 CD.S_9_Sten CD.S_Sten 33 CD.S_10 CD.S_10_Prox CD.S_Prox 34 CD.S_10 CD.S_10_Infl CD.S_Infl 35 CD.S_10 CD.S_10_Sten CD.S_Sten

  Dataset EGAD50000000559

• Platelet DNA study Murphy et al 2023 DAC

  Dac EGAC00001003034

• OSCC WES + WGS Boot et al. 2020

  Study EGAS00001004376

• Thirant et al Nature Communications 2023 Data Access Commitee

  Dac EGAC00001003192

• DAC Bonaguro et al. 2021, STAT1 AM RNAseq

  Dac EGAC00001001963

• Kutanan and Liu et al 2021 Thai study DAC

  Dac EGAC00001002553

• Breast Cancer PDTX Encyclopaedia

  Molecular data from the collection of PDTX breast cancer models described in Bruna et al, 2016. Cell. It includes whole exome sequencing, shallow whole genome sequencing, expression arrays and reduced bisulfite representation sequencing (RRBS).

  Study EGAS00001001913

• ChIP-seq data of Hodgkin lymphoma cell line L-428

  ChIP-seq data (H3K4Me3, H3K27Ac histone modifications) of Hodgkin lymphoma cell line L-428. Samples were processed as previously described (Sud et al., 2018). The files are in bam format, aligned to build 37.

  Study EGAS00001003033

• Liu et al Taiwan genome-wide data DAC

  Dac EGAC00001003038

• Thirant, Peltier et al Data Access Commitee

  Dac EGAC00001001789

• NIH RECOVER-Pediatric: Understanding the Long-Term Impact of COVID on Children and Families

  The NIH Researching COVID to Enhance Recovery (RECOVER) initiative comprises a set of three combined retrospective and prospective, longitudinal and observational meta-cohort studies with nested case-control studies designed to define and characterize the epidemiology, natural history, clinical spectrum, and underlying mechanisms of post-acute effects of SARS-CoV-2 infection (PASC or Post-COVID syndrome) in a diverse study population representative of the general COVID-19 population in the US. Enrolled patients, with and without known SARS-CoV-2 infection, will be observed for clinical signs and symptoms of PASC and will be assessed for risk and resiliency factors and potential mediating factors associated with the severity and progression of PASC. The objective of the RECOVER initiative is to enhance knowledge of recovery from SARS-CoV-2 infections in order to support development of novel diagnostic and therapeutic interventions. Overarching scientific objectives are as follows:Characterize the incidence and prevalence of sequelae of SARS-CoV-2 infection.Characterize the spectrum of clinical symptoms, subclinical organ dysfunction, natural history, and distinct phenotypes identified as sequelae of SARS-CoV-2 infection. Define the biological mechanisms underlying pathogenesis of the sequelae of SARS-CoV-2 infection. The RECOVER observational studies comprise three cohorts across the lifespan (adult, pediatric, and tissue pathology (autopsy)). The data collection and data analysis plans for each cohort have been harmonized to use common data elements where feasible. Brief descriptions of each cohort are provided in the following paragraphs: 1) NIH RECOVER: A Multi-site Observational Study of Post-Acute Sequelae of SARS-CoV-2 Infection in Adults (see phs003463)The RECOVER adult cohort study is a combined retrospective and prospective, longitudinal, observational meta-cohort of individuals who will enter the cohort with and without SARS-CoV-2 infection and at varying stages before and after infection. Individuals with and without SARS-CoV-2 infection, and with or without PASC symptoms, will be followed to identify risk factors and occurrence of PASC. This study will be conducted in the United States and subjects will be recruited through inpatient, outpatient, and community-based settings. Study data including age, demographics, social determinants of health, medical history, vaccination history, details of acute SARS-CoV-2 infection, overall health and physical function, and PASC symptom screen will be reported by subjects or collected from the electronic health record using a case report form at specified intervals. Biologic specimens will be collected at specified intervals, with some tests performed in local clinical laboratories and others performed by centralized research centers or banked in the Biospecimen Repository. Advanced clinical examinations and radiologic examinations will be performed at local study sites with cross-site standardization. 2) A Multi-Center Observational Study: The RECOVER Post Acute Sequelae of SARS-CoV-2 (PASC) Pediatric Cohort Study (this study)The RECOVER pediatric study is a combined retrospective and prospective, longitudinal, observational meta-cohort of individuals ages newborn-25 years who will enter the cohort with and without SARS-CoV-2 infection at varying stages before and after infection. Individuals with and without SARS-CoV-2 infection and with or without PASC symptoms will be followed to identify risk factors and occurrence of PASC. This study recruit participants inpatient, outpatient, and community-based settings in the United States. Study data including age, demographics, social determinants of health, medical history, vaccination history, details of acute SARS-CoV-2 infection, overall health and physical function, and PASC symptoms will be reported by participants or collected from the electronic health record using a case report form at specified intervals. Biologic specimens will be collected at specified intervals, with some tests performed in local clinical laboratories and others performed by centralized research centers or banked in the Biospecimen Repository. Advanced clinical and radiologic examinations will be performed at local study sites with cross-site standardization. 3) NIH RECOVER: A Multi-site Pathology Study of Post-Acute Sequelae of SARS-CoV-2 Infection The RECOVER tissue pathology study is a cross-sectional study designed to define and characterize the epidemiology, natural history, clinical spectrum, and underlying mechanisms of post-acute effects of SARS-CoV-2 infection in a diverse population representative of the general COVID-19 population in the US. The autopsy study will characterize the pathology of PASC in (i) non-hospitalized patients who die 30 days or later from symptom onset of COVID-19, and (ii) hospitalized patients who die 30 days or later after discharge from a hospitalization for COVID-19. The study will include decedents who had previously fully recovered from SARS-CoV-2 infection (i.e., >30 days from onset in non-hospitalized, or >30 days from discharge in hospitalized patients), and decedents who meet clinical criteria of PASC as defined by the recent World Health Organization publication (see Section 5.4 below). The autopsy study will also explore the pathology of acute SARS-CoV-2 infection in a smaller subset of patients who died 15-30 days from symptom onset. This protocol defines the common set of clinical data elements, autopsy procedures for tissue collection, core measures, pathology protocols, shared pathology tissues, data elements, and methodology. Each investigator site is expected to perform autopsies on the decedents to address the pathophysiology of the potential long-term effects of SARS-CoV-2 infection on human health. The Consortium analysis plan aims to address research questions by incorporating: 1) tissue obtained from autopsies performed at each Phase II participant's site; and 2) tissue available from other pathology investigators/autopsy sites within the Consortium.

  Study phs003461

• Large B cell lymphomas with CCND1 rearrangement have different immunoglobulin gene breakpoints and genomic profile than mantle cell lymphoma

  Next-generation sequencig data including whole-genome sequencing, whole-exome sequencing, and capture-based gene panels of large B cell lymphomas with CCND1 rearrangementas described in Özoğul,Montaner, Pol et al 2024.

  Study EGAS50000000564

• SJCRH Patient-derived orthotopic xenografts of pediatric brain tumors

  Primary patient pediatric brain tumors from patients treated at SJCRH were implanted and amplified only in the brain of immunocompromised mice without tissue culture steps and referred as patient-derived orthotopic xenografts (Smith et al, Acta Neuropathologica, 2020).

  Study EGAS00001005533

• HIPO016 - glioblastoma tumour methylation microarray profiling

  Methylation microarray profiling (Illumina Human Methylation 450k and EPIC platforms) of 60 adult glioblastomas. Tumours were subtyped using the approach from Sturm et al. (https://doi.org/10.1016/j.ccr.2012.08.024): 12 IDH, 18 MES, 12 RTK I, 18 RTK II. DNA was prepared, assayed on the microarrays, and raw data computationally processed as described in Capper et al., "DNA methylation-based classification of central nervous system tumours": https://www.nature.com/articles/nature26000

  Dataset EGAD00010001797

• Sequencing data for oesophageal and related samples - Alex Frankell et al (RNA)

  Data supporting: "The landscape of selection in 551 Esophageal Adenocarcinomas defines genomic biomarkers for the clinic." Frankell et al. RNAseq (BAM files) 116 tumours

  Dataset EGAD00001004423

• Analysis for oesophageal and related samples - Nowicki-Osuch, Zhuang et al (scRNA analysis)

  Data supporting: “Deep molecular phenotyping reveals the identity of Barrett’s esophagus and its malignant transition.” Nowicki-Osuch, Zhuang et al. Single cell metadata and analysis 38 Barrett's and normals

  Dataset EGAD00001005455

• He et al. WES data

  This dataset are the bam files of WES data from the paper by He et al.

  Dataset EGAD00001007134

• Dataset-linking-WGS-samples-in-ega-box-81-via-README-for-study-EGAS00001002923

  This dataset contains 318 Tumor and Control WGS files submitted in another EGA box for samples for Gerhauser et al.,Cancer Cell, 2018, 34:996-1011. WGS and sequencing protocol was earlier described in Weischenfeldt et al, Cancer Cell, 2013.

  Dataset EGAD00001007861

• Sequencing data for oesophageal and related samples - Nowicki-Osuch, Zhuang et al (scRNA)

  Data supporting: “Single-cell RNA sequencing unifies developmental programs of Esophageal and Gastric Intestinal Metaplasia.” Nowicki-Osuch, Zhuang et al. scRNAseq (FASTQ files) 59 samples

  Dataset EGAD00001010074

• Sequencing data for oesophageal and related samples - Black et al (WGS)

  Data supporting: "Understanding the malignant potential of gastric metaplasia of the oesophagus and its relevance to Barrett’s Oesophagus surveillance: individual-level data analysis" Black et al (WGS BOs/normals)

  Dataset EGAD00001011255