12023 results for "1m coins fc 26 Visit Buyfc26coins.com for latest FC 26 coins news..e3gh"

in 67.18 milliseconds.

• Sickle Cell Disease Natural History Data Resource (SCD NHDR)

  Data Access NOTE Please refer to the “Authorized Access” section below for information about how access to the data from this accession differs from many other dbGaP accessions. BackgroundThe NHLBI initiated the Sickle Cell Disease Natural History Data Resource (SCD NHDR) to leverage existing SCD studies to build a data resource to provide phenotypic data on contemporaneous control cohorts for gene therapy trials and other studies. The potential uses of the NHDR are broad, including providing matched controls for clinical trials, observing a cohort of untreated patients as part of a natural history study, or conducting comparative effectiveness research on a subpopulation with selected co-morbidities or drug treatment profiles. The SCD NHDR includes two multi-center SCD longitudinal cohort studies with extensive annual clinical data and patient surveys, the Sickle Cell Disease Implementation Consortium (SCDIC-I and SCDIC-II) and the Globin Research Network for Data and Discovery (GRNDaD). The SCDIC-I, initiated in 2017 and concluded in June 2022, enrolled over 2400 patients with SCD from 8 US treatment centers. The SCDIC-II reconsented 1220 of the SCDIC-I patients as well as enrolling an additional 450 new patients from the same 8 centers between September 2022 and October 2023, for a total of 1670 in SCDIC-II. Only two of the many GRNDaD centers are submitting data to the SCD NHDR for deposit into BDC (approximately 122 patients). Data submissions from the SCD NHDR to BDC will be annually starting in early 2024. Description of the Study Data All data obtained for the SCD NHDR are considered common data elements (CDEs) important for studies of SCD. These CDEs were identified from several workgroups of SCD experts convened under the NHLBI's Cure-SC Initiative (https://curesickle.org/cde-catalog). The following types of data elements were abstracted from the medical record – variables that describe the SCD diagnosis, SCD co-morbidities and other clinical complications, elements from the physical examination, medications and transfusions, insurance type, laboratory measurements, healthcare utilization, history of SCD-related procedures, and measurements from cardiac procedures. The following types of data were self-reported on the patient survey – demographics, diagnosis information, pain experience, social and mental health information, other patient reported outcome domains (e.g., sleep, fatigue), and alcohol and smoking history. Standard PRO measures were used from ASCQ-Me, PROMIS and Neuro-QoL. Patient surveys were completed approximately annually. Under SCDIC-I, two medical record abstractions and labs were reported at enrollment and approximately 3 years later. Under SCDIC-II and GRNDaD, all data are collected annually. Patients may refuse to complete the survey.

  Study phs003529

• Providing safe access to sensitive human data across borders: Federated EGA becomes a reality

  Enabling discovery and access to sensitive data across national boundaries is vital for improving human health. It enables more powerful and efficient research by increasing the volume and diversity of data available for analysis. It allows us to better understand the causes of diseases – cancer, rare diseases, infectious diseases like COVID-19 – and develop new medicines and treatments. Sensitive human omics data are typically generated by research initiatives and shared using specialist repositories which provide services for data submission, discovery, and access. The EGA is one such repository. Established in 2008 at EMBL’s European Bioinformatics Institute (EMBL-EBI) in the UK, since 2012 the EGA (“Central EGA”) has been jointly managed by EMBL-EBI and the Centre for Genomic Regulation (CRG) in Spain. Many countries have emerging personalised medicine programmes which generate data from national initiatives. These programmes are driving a transition in human genomics from being research-driven to receiving funding through healthcare. Data generated in a clinical context are subject to stricter governance than research data and must follow national data protection legislation. To solve these challenges, the Federated EGA provides a network of connected resources to enable transnational discovery of and access to human omics data for research, while also respecting jurisdictional data protection regulations. In this way, the Federated EGA infrastructure supports the goals of European initiatives such as the 1+ Million Genomes initiative (1+MG), the European Health Data Space, and a number of EU-funded 1+MG implementation projects including Beyond 1 Million Genomes. The Federated EGA is made up of “Nodes” – typically nationally funded and operated – which store and manage data locally while allowing global discovery within the Federated EGA network. Since 2016, multiple parallel efforts, supported by ELIXIR and other transnational and national initiatives, have created technical and legal frameworks for establishing the Federated EGA. In 2022, the first five Nodes – Finland, Germany, Norway, Spain, and Sweden – officially joined the Federated EGA by signing Federated EGA Collaboration Agreements with Central EGA. The Finnish FEGA Node is operated by CSC - IT Center for Science and provides data management services according to national laws and the requirements of the EU General Data Protection Regulation (GDPR). These services provide tools and support for the whole life-cycle of sensitive research data from collating to analysis, publication, and authorised re-use. The development of the services has been a joint effort with the other Nordic nodes within NeIC's Tryggve and Heilsa projects, and funded by Finnish Ministry of Education and Culture and projects coordinated by ELIXIR Finland. Read more about Finnish FEGA signing. The German Human Genome-Phenome Archive (GHGA) strives to provide a national infrastructure as well as an ethical and legal framework that balances FAIR omics data usage and data protection needs for Germany. As a Germany-wide consortium funded by the German Research Foundation under the umbrella of the NFDI association, GHGA combines the expertise of 21 universities and research institutions to form a federated national infrastructure. Read more about GHGA signing. The German Human Genome-Phenome Archive (GHGA) strives to provide a national infrastructure as well as an ethical and legal framework that balances FAIR omics data usage and data protection needs for Germany. As a Germany-wide consortium funded by the German Research Foundation under the umbrella of the NFDI association, GHGA combines the expertise of 21 universities and research institutions to form a federated national infrastructure. Read more about GHGA signing. In Norway, a key component of the infrastructure is the services of The Sensitive Data Service (TSD) offered by USIT at University of Oslo. The Federated EGA Norway Node is developed by ELIXIR Norway and operated by the University of Oslo as the responsible legal entity. Core software modules are developed jointly with the other Nordic nodes in the NeIC Tryggve and Heilsa projects. Read more about FEGA Norway signing. The Spanish FEGA (es-FEGA) is a national service for storing sensitive biomedical data in Spain. Supported by the Spanish Institute of Bioinformatics (INB) in collaboration with Central EGA, sensitive research datasets are primarily hosted at the Barcelona Supercomputing Centre facilities. The Swedish Sensitive Data Archive is a secure data archive and sharing platform for sensitive datasets. It was developed by the National Bioinformatics Infrastructure Sweden (NBIS) in collaboration with other Nordic ELIXIR Nodes through the Tryggve and Heilsa projects funded by NeIC and coordinated with Central EGA through ELIXIR. Read more about the Swedish Node signing. The Swedish Sensitive Data Archive is a secure data archive and sharing platform for sensitive datasets. It was developed by the National Bioinformatics Infrastructure Sweden (NBIS) in collaboration with other Nordic ELIXIR Nodes through the Tryggve and Heilsa projects funded by NeIC and coordinated with Central EGA through ELIXIR. Read more about the Swedish Node signing. By providing a solution for secure and efficient management of human omics data, the Federated EGA aims to foster data reuse, enable reproducibility, accelerate biomedical research, and improve human health. Find out more Interested in setting up your own Federated EGA Node? Check out the FEGA Onboarding Knowledge Base for more information. The ELIXIR Federated Human Data Community is a great entry point for anyone interested in learning more about the Federated EGA. You can: Join the ELIXIR Federated Human Data Community mailing list (select “Human Data”) Attend the ELIXIR Federated Human Data Community calls

  Blog safe-access-to-sensitive-human-data-federated-ega

• From research to data sharing: exploring EGA user's experiences

  The Cell Plasticity and Regeneration Group at the Bellvitge Biomedical Research Institute-IDIBELL focuses on the process of recruitment of macrophages that takes place in the small intestine during injury and healing. They recently published a paper titled “Mucosal Macrophages Govern Intestinal Regeneration in Response to Injury" in Gastroenterology Journal. As part of the research, some experiments were conducted using human intestinal organoid lines. These cells were processed for RNA sequencing, and the sequencing data were deposited at the EGA to be made available to the scientific community. When dealing with human genomic information, repositories must ensure the availability of the datasets while ensuring data protection. In this context, the European Genome-phenome Archive stands as a service for secure archiving and sharing of genetic, phenotypic and clinical data resulting from biomedical research. Following the recent publication of their paper, we took the opportunity to talk to Ilias Moraitis, first author, and Jordi Guiu, group leader, to find out about their experience with data sharing. Could you explain the focus of your research? In the lab, we study intestinal regeneration and how immune cells participate in this process. We use several techniques: engineered mouse models, image tracing, as well as mouse and human cells intestinal organoids. What challenges do you face regarding data management? We didn’t have a lot of problems. Always the informatic part, the data analysis, can give problems. But everything was smooth and working. We think it’s very important to deposit the data in repositories. For the mouse data it is very straightforward, but for us it was the first time depositing human data, which is sensitive because it comes from patients, and this is legally regulated. That’s why we thought about the EGA, and it was our first time. Why do you think it is important to submit data to repositories such as EGA? Because we think data is important for the science. Nowadays, we are sequencing a lot everywhere worldwide and having these resources shared with the scientific community it’s not only good for science, but it is also saving money and reducing costs. And there is so much data in there that can be used for other projects and for other questions. And it saves time! How was the process of submitting the data to the EGA? The communication worked very well but it was slower than we would like. But I think this is something we learn on the way. People usually wait until the last minute to do this before publication but if you know it in advance, you can start the process earlier. If you had to repeat the process, would you do anything differently? It’s like everything, isn’t it? For the first time you don’t know, you aren’t sure, but for the second time you know the steps, you know what to do and it’s faster. Of course, we would to do it earlier. Also, there were some things related to the control access that we didn’t know how to manage, but it was something more internal to us. Who is going to receive the communications and when, is our ethical committee going to evaluate this? All these technicalities that we didn’t know before starting this process, so we had to think about all these things. Next time it will be faster on our side. Do you have any suggestions for us to improve the submission process? Maybe we’ll need to ask our bioinformatician who did it! He was in charge of this part. Besides that, we think if the process were faster, it would be better for everyone. Also, in science a lot of times you have to do a lot of things at the very last minute, because of the nature of the experiments, or the need to accomplish for submitting a paper. Being faster in the process would be a plus. On your side, we went through the process and were able to find some information, but there were aspects we weren't aware of and weren't sure how to handle. I'm not sure if other institutes have different protocols for managing data requests or how they handle these internally. We believe the main issue lies in how the forms are filled out. Do you have any recommendations for other submitters? The main advice is to do this with enough time. Submit the data when you have the sequences, and don’t wait until the last minute. And then it can be under embargo, so you don’t need to make it public at that moment. As soon as you sequence, upload it and then it will be there for whenever you need to publish. How do you think we could encourage other researchers to submit their data? There are different layers here. One is that it is mandatory. You have to do this. The other is that we belong to a research community, to the same community and sharing this is making our research community stronger and more efficient. And then, also because this gives you visibility. The data is there, other people can analyse your data, and this also will bring citations to your papers. It has many advantages.

  Blog from-research-to-data-sharing

• Evaluating potential drug candidates for the treament of Henamgiopericytoma on patient derived cell line models

  Solitary fibrous tumor/Hemangiopericytoma (SFT/HPC) is a rare subtype of soft tissue sarcoma associated with NAB2-STAT6 gene fusions. This study established and characterized a novel SFT/HPC patient-derived cell line called SFT-S1. Potential drug candidates that could be repurposed for the treatment of SFT/HPC were screened.

  Study EGAS50000000027

• HCC cfMeDIP-seq

  This dataset comprises 406 raw fastq files derived from 203 plasma cfDNA samples from 90 patients diagnosed with hepatocellular carcinoma (HCC). The samples include 90 baseline HCC (b-HCC) samples, collected during liver transplant or resection surgeries, and 113 postoperative follow-up (f-HCC) samples. For each sample, 10ng of cfDNA was used for library preparation utilizing cfMeDIP-seq technology based on the 5mC antibody-immunoprecipitation strategy. Libraries were validated via Bioanalyzer trace analysis and sequenced on Illumina NovaSeq 6000 or HiSeq 2500 platform with paired-end 150-bp (NovaSeq) or 125-bp (HiSeq) model for ~100 million reads per sample.

  Dataset EGAD50000000651

• RNA sequencing of Notch inhibitor treated HCC PDX models across timepoints

  To study the effect of Notch inhibition on the transcriptional profiles our PDX models, we generated bulk RNA-seq data for two Notch inhibition sensitive and three Notch inhibition insensitive models across multiple timepoints (8h, 24h, 48h, 7d, 17d) using anti-JAG1, anti-Notch2, anti-Notch1 or anti-gD antibodies. 3-5 samples were collected for each treatment condition, resulting in a total of 96 samples. Isolated RNA was used as input for library preparation using TruSeq RNA Sample Preparation Kit v2 (Illumina). The libraries were multiplexed and sequenced on Illumina HiSeq 2500 (Illumina) to produce FASTQ files.

  Dataset EGAD50000000737

• Transcriptomics for IMMU-SCCHN1 cohort

  The goal of IMMUcan is to understand how the immune system interacts with tumors and how therapeutic interventions influence this interaction. This cohort includes patients with confirmed locally recurrent and/or metastatic squamous cell carcinoma of the head and neck (SCCHN) who receive first-line treatment with immune checkpoint inhibitors (ICI) or ICI combined with chemotherapy. This dataset contains transcriptomics data processed with the STAR pipeline. Two sequencing kits were employed: Roche KAPA for high-quality RNA samples and Takara SMART-Seq Stranded for samples with limited RNA quantities.

  Dataset EGAD50000002206

• 10X single-cell Multiome (RNA+ATAC) and single-cell RNAseq of xenograft-derived HSPC, progenitors and myeloid progeny

  10X genomics chromium single-cell ATAC+RNA (Multiome) was use to prepare single-nucleus RNA- and ATAC-seq libraries for human HSPC sorted from xenografts recovered from inflammatory challenge. 10X genomics 3' kits were used to prepare single-cell RNA-seq libraries for committed progenitors and myeloid cells from the same xenografts. These were sequenced on an Illumina NovaSea 6000 platform; BAM files are provided. Samples from 3 conditions were sequenced for each cell type; 3 chromatin accessibility runs and 9 gene expression runs are provided.

  Dataset EGAD50000002328

• Variant calling for LUNG-NSCLC2 cohort

  The goal of IMMUcan is to understand how the immune system interacts with tumors and how therapeutic interventions influence this interaction. This cohort includes patients with non-small cell lung cancer who had surgical resection. This is a subcohort of the EORTC SPECTAlung study. This dataset contains whole exome sequencing data from tumor samples processed with the Vegan pipeline. In the absence of matching germline samples, variant calling is performed using two panel of normals (one for female and one for male). It includes SNP and CNV calling.

  Dataset EGAD50000002235

• Longitudinal profiling of circulating tumour DNA for tracking tumour dynamics in pancreatic cancer

  The utility of circulating tumour DNA (ctDNA) for longitudinal tumour monitoring in pancreatic ductal adenocarcinoma (PDAC) has not been explored beyond mutations in the KRAS proto-oncogene. We aim to characterise and track patient-specific somatic ctDNA variants, to assess longitudinal changes in disease burden and explore the landscape of actionable alterations.

  Study EGAS00001005981