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• Congenital Heart Disease in UK Families

  This project aims to identify highly penetrant coding variants increasing the risk of Congenital Heart Disease (CHD) performing whole exome sequencing on DNA samples from 23 affected individuals, selected from 10 families with presumed Autosomal Recessive Inheritance. This is a collaboration with Prof. Eamonn Maher and Dr. Chirag Patel from the Department of Medical and Molecular Genetics, University of Birmingham plans to sequence 23 indexed Agilent whole exome pulldown libraries on 75Bp PE HiSeq (Illumina).

  Study EGAS00001000066

• Detection of clinically relevant genetic and transcriptomic landscape in DLBCL uniformly treated by R-CHOP

  Recent studies using next-generation sequencing strategies have described the landscape of genetic alterations in diffuse large B-cell lymphoma (DLBCL). However, little is known about the clinical relevance of recurrent mutations and copy number alterations and their transcriptional footprints. This study examines the frequency, interaction and clinical impact of recurrent genetic aberrations in DLBCL using high-resolution technologies in a large population-based cohort.

  Study EGAS00001002657

• SOFT_study___sequencing_premenopausal_breast_cancer

  Our project will examine the role of PIK3CA mutations and their sensitivity to endocrine therapies and its role, with the addition of complete ovarian suppression. We plan to test our hypotheses using tumour samples collected from patients enrolled in the SOFT/IBCSG24-02 clinical study (Suppression of Ovarian Function Trial – (NCT00066690). SOFT is a phase III trial that randomised 3066 premenopausal women to evaluate if adding ovarian suppression to adjuvant endocrine therapy will improve clinical outcomes.

  Study EGAS00001000983

• Whole genome sequencing of ASD quartet families

  Autism spectrum disorder (ASD) is genetically heterogeneous with >100 susceptibility genes known. We used whole-genome sequencing (WGS) of 85 quartet families (two parents and two ASD-affected siblings) to comprehensively examine mutation characteristics. Our results emphasize using WGS to maximize the detection of all classes of mutations potentially involved in autism, and to enable the interpretation of that data in confirmatory and predictive diagnosis in different individuals in a family.

  Study EGAS00001001023

• Breast Cancer - immune clusters - RNA-seq

  In this study we use expression data from breast cancer tumors to define immune clusters in breast cancer. Immune clusters have gradual levels of immune infiltration. In the intermediate immune infiltration cluster, we found a worse prognosis which is independent of known clinicopathological features. We also found the immune clusters associated with treatment response. Further using gene expression data and deconvolution algorithms to dissect the immune contexture of the clusters.

  Study EGAS00001003631

• Genetic subclone heterogeneity of tumor-initiating cells in human colorectal cancer

  A subset of tumor-initiating cells (TIC) drives colorectal cancer (CRC) progression in serial mouse xenografts. The TIC compartment itself is heterogeneous and comprises hierarchically organized long-term TIC, tumor transient amplifying cells, and delayed contributing TIC. To address the genomic heterogeneity of the CRC TIC compartment, genome-wide high-coverage whole genome sequencing was performed on patient tumors (n=3), corresponding serially passaged TIC-enriched spheres, and serial xenografts.HIPO P002

  Study EGAS00001001857

• Single-cell RNA sequencing of 6 follicular lymphoma tumors

  Follicular lymphoma (FL) is an indolent cancer of mature B-cells but carries increased risk of transformation to a more aggressive histology over time. We present here comprehensive profiling both tumor and immune compartments in 6 diagnostic FL biopsies by single-cell RNA sequencing. This confirmed results from 155 FL tumors characterized by mass cytometry (CyTOF) which revealed two distinct evolutionary trajectories with disparate risk of transformation and alternate biologies.

  Study EGAS00001005257

• Dual-mTOR inhibitor Rapalink-1 reduces prostate cancer patient-derived xenograft growth and alters tumor heterogeneity

  Bone metastasis is the leading cause of prostate cancer (PCa) mortality, frequently marking the progression to castration-resistant PCa. In this study, we compared the molecular pathways enriched in a set of bone metastasis from breast and prostate cancer from snap-frozen tissue. To further model PCa drug resistance mechanisms, we used two patient-derived xenografts (PDX) models of bone-metastatic PCa, BM18 and LAPC9.

  Study EGAS00001004431

• UROMOL 2020 - SNP data

  The molecular landscape in non-muscle-invasive bladder cancer (NMIBC) is characterized by large biological heterogeneity with variable clinical outcomes. Here, we performed a large integrative multi-omics analysis of patients diagnosed with NMIBC (n=834). Transcriptomic analysis identifies four classes (1, 2a, 2b and 3) reflecting tumor biology and disease aggressiveness. Both transcriptome-based subtyping and the level of chromosomal instability provide independent prognostic value beyond established prognostic clinicopathological parameters.

  Study EGAS00001004862

• RNA-sequencing of mechanical stress induced osteoarthritis-like damage in aged human cartilage explants treated with the anti-deiodinase iopanoic acid

  The current study aimed to confirm chondroprotective effects of the D2 inhibitor iopanoic acid (IOP) in an ex vivo aged human osteochondral explant model in which injurious mechanical stress is applied to inflict OA-like damage. This dataset contains RNA-seq (Illumina HiSeq X ten) of osteochondral explant cartilage from aged human patients that underwent a joint replacement surgery due to OA (N=10 donors; n=21 explants).

  Study EGAS00001006242

• Mapping genetic effects on cell type-specific chromatin accessibility using single nucleus ATAC-seq

  Gene regulation is highly cell type-specific and understanding the function of non-coding genetic variants associated with complex traits requires molecular phenotyping at cell type resolution. In this study we performed single nucleus ATAC-seq (snATAC-seq) and genotyping in peripheral blood mononuclear cells from 13 individuals. We mapped chromatin accessibility QTLs (caQTLs) in each immune cell type and sub-type in a subset of 10 samples of European genetic ancestry.

  Study EGAS00001006184

• Pre-diagnostic saliva microbiota of Finnish children with autoimmune diseases

  Pre-diagnostic saliva microbiota samples of Finnish children (aged 11/12 years). This is a case-control study, where case refers to the children who developed Type 1 DM or IBD later in life and control refers to the children who were free from these diseases. The aim of the study was to find biomarkers in saliva microbiota that may help us predict DM or IBD before the onset of these diseases.

  Study EGAS00001006949

• GWAS on covid-19 severity and susceptibility in the province of Bergamo, Italy

  This is a case-control GWAS that involved the voluntary recruitment of inhabitants of the Bergamo province via an online questionnaire. We collected detailed clinical data and verified all information through personal interviews. Cases (severe COVID-19; G1) and controls (mild or asymptomatic COVID-19, G2; non-infected, G3) were matched by age, sex and risk factors

  Study EGAS00001007310

• The cellular state space of AML unveils novel NPM1 subtypes with distinct clinical outcomes and immune evasion properties

  Acute myeloid leukemia (AML) is a genetically and cellularly heterogeneous disease. We characterized 120 AMLs using bulk genomic and transcriptomic analyses (WES, MP-WGS, RNA-seq), and single-cell RNA sequencing (scRNA-seq). Our results reveal an extensive cellular heterogeneity that distorts the bulk transcriptomic profiles. Notably, NPM1 mutated AML could be stratified into two novel, clinically relevant classes (NPM1class I and NPM1class II).

  Study EGAS50000001084

• Oral microbiome metagenomic sequencing - Human Phenotype Project (HPP)

  This study characterizes the oral microbiome of participants enrolled in the Weizmann Institute Human Phenotype Project (HPP), a longitudinal cohort study. Oral swab samples were collected and subjected to shallow whole-genome sequencing using the Gencove low-pass sequencing platform. Raw reads include residual human host sequences in addition to microbial sequences. The dataset comprises metagenomic FASTQ files from [N] participants. Data access is restricted in accordance with participant informed consent and Weizmann IRB approval

  Study EGAS50000001763

• USZ Dermatology and UZH DQBM DAC

  This DAC is responsible for datasets arising from the collaboration between USZ Department of Dermatology and UZH Department of Quantitative Biomedicine.

  Dac EGAC50000000853

• Comprehensive genomic analysis for AYA with acute lymphoblastic leukemia

  AYA with B-ALL showed extremely inferior outcomes when compared to children. Genetic basis undelying AYA B-ALL is largely unknown. We performed integrated analysis of RNA-seq (hum0045���JGAS000047) and target DNA-seq.

  Study JGAS000276

• Swarm Learning to identify COVID-19, tuberculosis and leukemia patients based on blood transcriptomes

  Identification of patients with life-threatening diseases including leukemias or infections such as tuberculosis or COVID-19 is an important goal of modern precision medicine. However, there is an increasing divide between what is technically possible and what is allowed because of privacy legislation. We have recently illustrated that classical machine learning can identify leukemia patients based on their blood transcriptomes. To facilitate integration of any omics data from any data owner world-wide without violating privacy laws, we here introduce Swarm Learning (SL), a decentralized machine learning approach uniting edge computing, artificial intelligence (AI), blockchain and privacy protection without the need for a central coordinator thereby going beyond federated learning. To illustrate its feasibility, using more than 12,000 transcriptomes from peripheral blood mononuclear cells and more than 2,000 peripheral blood transcriptomes we demonstrate that SL of omics data distributed across different individual sites leads to disease classifiers that outperform those developed at individual sites. Yet, SL completely protects local privacy regulations by design. We propose this approach to noticeably accelerate the introduction of precision medicine.

  Dataset EGAD00001006231

• A Single-Cell and Spatial Atlas of Early Human Olfactory Development

  The human nasal region is a complex structure derived from neural crest and placodal lineages, but its development remains poorly understood due to limited fetal tissue access and its intricate architecture. To address this, we created a single-nucleus and spatial transcriptomic atlas of the human fetal nasal region using tissue from 10 fetuses between 7 and 12 post-conceptional weeks. Single-nucleus RNA sequencing (snRNA-seq) revealed 34 distinct cell types, including epithelial, cartilaginous, immune, neuronal, glial, muscular, and vascular cells. By integrating snRNA-seq with multiplexed error-robust fluorescence in situ hybridization (MERFISH), we tracked dynamic changes in cell composition and gene expression over time in the olfactory epithelium (OE) and surrounding nasal regions. We identified novel markers of olfactory sensory neuron development and key pathways regulating epithelial patterning and OE morphogenesis. Notably, we observed early molecular signatures consistent with the "one neuron-one receptor" model, with spatially organized expression of 171 olfactory receptor genes. Together, this study presents the first integrated molecular and spatial framework of early human nasal development. It offers an unprecedented molecular blueprint of olfactory system formation and serves as a foundational resource for embryologists and developmental biologists studying sensory neurogenesis, epithelial patterning, and congenital disorders.

  Dataset EGAD50000001712

• Systematic Growth Factor Profiling Platform for 3D Tumor Models Reveals EstradiolResponsive Cellular Mechanisms of Immunotherapy Resistance

  Epithelial ovarian cancer (EOC) exhibits significant molecular heterogeneity, which is thought to be influenced by the tumor microenvironment (TME). However, the specific factors driving this heterogeneity remain poorly understood. In this study, we integrated growth factoromics and genomic data to identify TME factors contributing to EOC heterogeneity and immunosuppression. Our analysis revealed distinct subgroups of EOC samples with differential responsiveness to specific growth factors, particularly estradiol and R-spondin. Notably, samples with high responsiveness to estradiol and Wnt (C1 cluster) were enriched in the Mesenchymal (MES) molecular subtype, while samples with high responsiveness to R-spondin (C2 cluster) were associated with the Differentiated (DIF) and Immunoreactive (IMR) subtypes. We discovered that the G protein-coupled estrogen receptor (GPER), rather than the classical estrogen receptor alpha (ERα), was the key mediator of estrogen responsiveness and a determinant of molecular subtype classification. Single-cell sequencing analysis identified subsets of estrogen-responsive malignant cell (MAL.PDCD5) and cancer-associated fibroblast (FB.TNFSF10), which were associated with immunosuppressive patterns in the TCGA cohort. Importantly, the interaction between these cell populations significantly increased upon estrogen treatment, suggesting a potential mechanism by which estrogen signaling may contribute to the development of an immunosuppressive TME. Our findings highlight the critical role of estrogen signaling in shaping the molecular heterogeneity and immunosuppressive TME of EOC and provide novel insights into potential therapeutic targets for overcoming immunosuppression in this malignancy.

  Study EGAS50000000422

• Multi-omic dataset utilized in the analysis of a phase II study of epigenetic priming followed by nivolumab in lung cancer

  This dataset includes sequence files from whole exome sequencing and bulk RNA sequencing of tissue and blood biospecimens from a phase II clinical trial investigating epigenetic priming followed by immune checkpoint blockade in non-small cell lung cancer (NSCLC; NCT01928576). There are 78 whole exome sequencing files and 36 bulk RNA sequencing files included in this dataset.

  Dataset EGAD50000001336

• pan-cancer plasma cfRNA

  Dataset contains mRNA capture sequencing data from plasma of 266 different human donors. The first, pan-cancer, cohort covers 25 high-grade to metastatic cancer types (8 cancer patients per type) and a control group (8 healthy donors). The validation cohort comprises additional plasma samples from ovarian, prostate and uterine cancer patients (12 per type) as well as additional samples from controls (22 new and 8 repeated). Samples were sequenced on a NovaSeq 6000 and are provided in FASTQ format.

  Dataset EGAD00001009713

• Multi-omics Profiling of Airway Neutrophils and Microbiome in Stable Bronchiectasis

  This dataset comprises raw sequencing data generated from a multi-omics study investigating airway neutrophils and the microbiome in patients with stable bronchiectasis. Samples consist of multiplexed pools derived from blood and sputum collected from multiple subjects. Each pool represents a combination of biological materials from different individuals, and therefore individual-level metadata such as sex and disease stage are not attributable at the sample level. The dataset aims to characterize immune and microbial profiles associated with chronic airway inflammation in bronchiectasis.

  Dataset EGAD50000002607

• Genomic landscapes of endometrioid and mucinous ovarian cancers and morphologically similar tumor types

  While endometrioid and mucinous ovarian cancers represent nearly a quarter of ovarian cancers, their molecular characteristics and pathologic origins are poorly understood. To characterize the genomic and epigenomic alterations of these ovarian cancer subtypes and evaluate links to morphologically similar tumors from other sites, we performed sequence, copy number, mutation signature, rearrangement, and methylation analyses from tumor samples and matched normal tissues of 133 patients. These analyses included patients with ovarian endometrioid (n=44), ovarian mucinous (n=43), uterine endometrial (n=15), and gastrointestinal mucinous cancers (n=31). In addition to identifying genes previously known to be involved in these tumors, we identified alterations in RAD51C, NOTCH4, SMARCA1/4 and JAK1 in ovarian endometrioid, ESR1 in uterine endometrioid, and SMARCA4 in ovarian mucinous cancers. Whole genome sequencing revealed rearrangements involving PTEN, NF1, and NF2 in ovarian endometrioid tumors, and NF1 and MED1 in ovarian mucinous cancers. The number of alterations and affected genes in ovarian and uterine endometrioid cancers were similar as were genome-wide methylation profiles, suggesting that these tumors may have a similar tissue of origin. By contrast, methylation patterns in ovarian mucinous cancers were more similar to other gastrointestinal mucinous cancers, including mucinous cancers of the stomach, colon, and pancreas. These analyses provide insights into the genomic landscapes and origins of mucinous and endometrioid ovarian carcinomas, providing new avenues for early clinical intervention and management of patients with these cancers.

  Study EGAS50000000523

• Germline variants collaborate with somatic mutations and initiate and/or drive disease in primary myelodysplastic syndrome (MDS) and therapy-related myeloid neoplasms (t-MN)

  Therapy-related myeloid neoplasms (T-MN) are poorly characterized secondary hematological malignancies following chemotherapy/radiotherapy exposure. We compared the clinical and mutational characteristics of T-MN (n=129) and primary myelodysplastic syndrome (P-MDS, n=108) patients. Although the somatic mutation frequency was similar between T-MN and P-MDS patients (93% in both groups), the pattern was distinct. TP53 mutations were more frequent in T-MN (29.5% versus 7%), while spliceosomal complex mutations were more common in P-MDS (56.5% versus 25.6%).In contrast to P-MDS, the ring sideroblasts (RS) phenotype was not associated with better survival in T-MN, most probably due to genetic association with TP53 mutations. SF3B1 was mutated in 96% of P-MDS with ≥15% RS, but in only 32% T-MN. TP53 mutations were detected in 92% T-MN with ≥15% RS and SF3B1 wild-type cases. Interestingly, T-MN and P-MDS patients with “Very low” or “Low” Revised International Prognostic Scoring System (IPSS-R) showed similar biological and clinical characteristics. In a Cox-regression analysis, TP53 mutation was a poor prognostic factor in T-MN, independent of IPSS-R cytogenetic, disease-modifying therapy and NRAS mutation.Our data have direct implications for T-MN management and provide evidence that, in addition to conventional disease parameters, mutational analysis should be incorporated in T-MN risk stratification.

  Study EGAS00001003547