6377 results for "11+1双色球多少钱一注"

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• Targeted sequencing data of peripheral blood mononuclear cells obtained from 4 ATL patients and 10 HTLV-1-infected asymptomatic carriers.

  Targeted capture sequencing data of peripheral blood mononuclear cells (PBMCs) obtained from 4 ATL patients (6 samples) and 10 HTLV-1-infected asymptomatic carriers.

  Dataset EGAD00001007031

• RNAseq dataset for MALT1 in MCL

  Manuscript Title: Co-targeting of BTK and MALT1 overcomes resistance to BTK inhibitors in mantle cell lymphoma Journal: Journal of Clinical Investigation Authors Vivian Changying Jiang1, Yang Liu1, Junwei Lian1, Shengjian Huang1, Alexa Jordan1, Qingsong Cai1, Fangfang Yan3, Joseph Mitchell McIntosh1, Yijing Li1, Yuxuan Che1, Zhihong Chen1, Jovanny Vargas1, Maria Badillo1, JohnNelson Bigcal1, Heng-Huan Lee1, Wei Wang1, Yixin Yao1, Lei Nie1, Christopher Flowers1, and Michael Wang1, 2* Abstract Bruton’s tyrosine kinase (BTK) is a proven target in mantle cell lymphoma (MCL), an aggressive subtype of non-Hodgkin lymphoma. However, resistance to BTK inhibitors is a major clinical challenge. We here report that MALT1 is one of the top overexpressed genes in ibrutinib-resistant MCL cells, while expression of CARD11, which is upstream of MALT1, is decreased. MALT1 genetic knockout or inhibition produced dramatic defects in MCL cell growth regardless of ibrutinib sensitivity. Conversely, CARD11 knockout cells showed anti-tumor effects only in ibrutinib-sensitive cells, suggesting that MALT1 overexpression could drive ibrutinib resistance via bypassing BTK-CARD11 signaling. Additionally, BTK knockdown and MALT1 knockout markedly impaired MCL tumor migration and dissemination, and MALT1 pharmacological inhibition decreased MCL cell viability, adhesion, and migration by suppressing NF-κB, PI3K-ATK-mTOR, and integrin signaling. Importantly, co-targeting MALT1 with safimaltib and BTK with pirtobrutinib induced potent anti-MCL activity in ibrutinib-resistant MCL cell lines and patient-derived xenografts. Therefore, we conclude that MALT1 overexpression associates with resistance to BTK inhibitors in MCL, targeting abnormal MALT1 activity could be a promising therapeutic strategy to overcome BTK inhibitor resistance, and co-targeting of MALT1 and BTK should improve MCL treatment efficacy and durability as well as patient outcomes. Dataset description: The bulk RNA-seq dataset was generated for the cell lines below and used for two major purposes: 1. DEG analysis and GSEA analysis comparing IBN-R and IBN-S cells 2. DEG analysis and GSEA analysis comparing MCL cells with/without MI-2 treatment. sample Cell MI-2 Ibrutinib (IBN) Venetoclax (VEN) Used for IBN-R vs IBN-S comparison Used for MI-2 vs untreated (DMSO) H9 Granta519 - R S yes H21 Granta519 - R S yes H33 Granta519 - R S yes H10 Granta519-VEN-R - R R yes H22 Granta519-VEN-R - R R yes H34 Granta519-VEN-R - R R yes H3 JeKo BTK KD_1 - R R yes yes H15 JeKo BTK KD_1 - R R yes yes H27 JeKo BTK KD_1 - R R yes yes H5 JeKo BTK KD_2 - R R yes yes H17 JeKo BTK KD_2 - R R yes yes H29 JeKo BTK KD_2 - R R yes yes H1 JeKo-1 - S R yes yes H13 JeKo-1 - S R yes yes H25 JeKo-1 - S R yes yes H7 Mino - S S yes H19 Mino - S S yes H31 Mino - S S yes H8 Mino-VEN-R - S R yes H20 Mino-VEN-R - S R yes H32 Mino-VEN-R - S R yes H11 Rec-1 - S S yes H23 Rec-1 - S S yes H12 Rec-VEN-R - S S yes H24 Rec-VEN-R - S R yes H36 Rec-VEN-R - S R yes H35 Rec-1 -- S R yes H4 JeKo BTK KD_1 + MI-2 + yes H16 JeKo BTK KD_1 + MI-2 + yes H28 JeKo BTK KD_1 + MI-2 + yes H6 JeKo BTK KD_2 + MI-2 + yes H18 JeKo BTK KD_2 + MI-2 + yes H30 JeKo BTK KD_2 + MI-2 + yes H2 JeKo-1 + MI-2 + yes H14 JeKo-1 + MI-2 + yes H26 JeKo-1 + MI-2 + yes

  Dataset EGAD00001009771

• Search for Susceptibility Genes for Diabetic Nephropathy in Type 1 Diabetes (GoKinD study participants and parents), NIDDK

  Genetics of Kidneys in Diabetes (GoKinD) study is an initiative aimed at identifying susceptibility genes for diabetic nephropathy in type 1 diabetes. A large number of individuals with type 1 diabetes were screened to identify two subsets, one with clear-cut kidney disease and another with normal renal status despite long-term diabetes. Those who met additional entry criteria and consented to participate were enrolled. When possible, both parents were also enrolled to form family trios. Altogether, GoKinD includes 3043 participants comprising 931 cases, 944 singletons, 268 pairs of parents of cases, and 316 pairs of parents of control. Accessible as a GAIN database are 905 of the cases, 890 of the controls, 10 pairs of parents of cases and 10 pairs of parents of controls (dbGaP phs000018 Search for Susceptibility Genes for Diabetic Nephropathy in Type 1 Diabetes (GoKinD study participants), GAIN). The other parents and the remaining cases and controls are available by a separate application process through NIDDK. Interested investigators may request the DNA collection and corresponding clinical data for GoKinD participants using the instructions and application form available at http://www.niddkrepository.org or by contacting the Juvenile Diabetes Research Foundation.

  Study phs000088

• Search for Susceptibility Genes for Diabetic Nephropathy in Type 1 Diabetes (GoKinD study participants), GAIN

  Genetics of Kidneys in Diabetes (GoKinD) study is an initiative aimed at identifying susceptibility genes for diabetic nephropathy in type 1 diabetes. A large number of individuals with type 1 diabetes were screened to identify two subsets, one with clear-cut kidney disease and another with normal renal status despite long-term diabetes. Those who met additional entry criteria and consented to participate were enrolled. When possible, both parents were also enrolled to form family trios. Altogether, GoKinD includes 3043 participants comprising 931 cases, 944 singletons, 268 pairs of parents of cases, and 316 pairs of parents of control. Accessible as a GAIN database are 905 of the cases, 890 of the controls, 10 pairs of parents of cases and 10 pairs of parents of controls. The other parents and the remaining cases and controls are available by a separate application process through NIDDK (dbGaP phs000088 Search for Susceptibility Genes for Diabetic Nephropathy in Type 1 Diabetes (GoKinD study participants and parents), NIDDK). Interested investigators may request the DNA collection and corresponding clinical data for GoKinD participants using the instructions and application form available at Juvenile Diabetes Research Foundation. Consent groups and participant set Diabetic complications only (DCO): 1825 (904 cases, 881 controls, 40 others)

  Study phs000018

• ScRNA-seq of human kidney immune cells of patients with ANCA-associated glomerulonephritis, Lupus Nephritis against a "healthy" nephrectomy control

  ANCA-associated glomerulonephritis (AGN) associates with a high risk of end-stage kidney disease. The role of kidney immune cells in local inflammation remains unclear. Here, we investigate kidney immune cell diversity and function. Kidney tissue from AGN patients (n=5) and a lupus nephritis (LN) patient (n=1) were aquired during a biopsy procedure for a clinical indication. Needle-core biopsies were obtained for histopathological examination, and an additional pass was performed to retrieve kidney tissue for scRNA-seq. Healthy kidney tissue (n=1) was obtained from a kidney that was surgically removed do tue due to a (non-invasive) papillary urothelial carcinoma. Immediately after collection, kidney tissue was processed into a single-cell suspension and sorted using a 4-color flow cytometry panel to isolate living, CD45+ immune cells. To aid in the multi-omic characterization, surface markers and T and B cell repertoires were sequenced in 2 samples (1 AGN patient and the nephrectomy control). These samples were incubated with an oligo-antibody TotalSeq-C cocktail containing 130 unique cell surface antigens.

  Study EGAS50000000159

• Medulloblastoma PDX Primary tumor

  Dataset EGAD00001009484

• mRNA and T cell receptor sequencing of patients with Pandemrix-associated narcolepsy type 1

  Increased risk of Narcolepsy type 1 (NT1) was reported among children and adolescents vaccinated with AS03-adjuvanted pandemic influenza A vaccine (H1N1; Pandemrix®). We hypothesized that viral T cell epitope(s) in Pandemrix mimicked self-epitope(s), and that recognition of these cross-reactive epitopes contributed to disease-specific autoimmunity. We demonstrate that pediatric, Pandemrix-associated NT1 patients had enhanced T-cell immunity against dominant T-cell epitopes of Pandemrix vaccine viral proteins neuraminidase (NA) and nucleoprotein (NP), and also responded against a self-epitope in brain-expressed protein-O-mannosyltransferase 1 (POMT1). Here, the gene expression profiles and TCR repertoire of PBMC from patients and healthy controls were compared. PBMC samples were stimulated with peptides NA-175-189 or POMT1-675-589, and bulk RNA isolated and sequenced (RNAseq and TCRseq).

  Study EGAS00001004886

• Dental Caries: Whole Genome Association and Gene x Environment Studies

  Dental caries (also known as tooth decay) remains the most common chronic disease of childhood, five times more common than asthma and seven times more common than environmental allergies, with more than 40% of children exhibiting caries when they enter kindergarten. In 2005, it was estimated that dental health care costs were approximately $84 billion, of which 60% or about $50 billion were related to treatment of dental caries. Although overall caries prevalence has declined over the last 40 years, dental caries in the primary dentition and mean caries rates in children ages 2-11 has increased markedly over the past 12 years. Childhood caries is a serious public health issue because of associated health problems and because disparities in oral health have led to substantially higher average disease prevalence among children in poverty and in under-served racial and ethnic groups. These issues are of such concern that in 2005, the American Academy of Pediatrics made children's oral health one of their top areas of focus, as it is for the majority of the NIDCR "Disparities Centers". The etiology of dental caries has been studied for many years. Multiple factors contribute to a person's risk for caries, including: 1) environmental factors such as diet, oral hygiene, fluoride exposure and the level of colonization of cariogenic bacteria and 2) host factors such as salivary flow, salivary buffering capacity, position of teeth relative to each other, surface characteristics of tooth enamel and depth of occlusal fissures on posterior teeth. In spite of all that is known about this disease, there are still individuals who appear to be more susceptible to caries and those who are extremely resistant, regardless of the environmental risk factors to which they are exposed, implying that genetic factors also play an important role in caries etiology. This conclusion is supported by studies in both humans and animals, with the most compelling evidence coming from studies of twins reared apart in which investigators found significant resemblance within monozygotic (MZ) but not dizygotic (DZ) twin pairs for percentage of teeth and surfaces restored or carious and estimated the genetic contribution to caries as 40%. Other recent studies of twins reared together estimated the heritability for caries, adjusted for age and gender, as ranging from 45-64%. Despite the strong evidence of a genetic component to risk for dental caries, there have been only a few studies of candidate genes in caries, and no published genome-wide scans. A comprehensive genome wide search is the only approach that will allow us to identify those genetic regions likely to harbor genes increasing the risk for dental caries, and eventually to identify the etiologic genes and to explore the interaction of those genes with microbiological, dietary, fluoride, and behavioral factors that are known to be associated with caries risk and progression. Therefore, the goal of this study is to perform genome-wide association (GWA) studies of dental caries with a large panel of SNP's (610,000) in families and individuals ascertained through multiple US sites (University of Pittsburgh and University of Iowa). The v2 release of this study includes 96 additional individuals who were genotyped with the CCDG: Dental Caries and CL/P in Guatemala project (dbGaP accession number phs000440) to augment the data initially presented here. These subjects were genotyped on the Illumina 610 platform to make their data comparable. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org), which was developed through the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to dental caries through large-scale genome-wide association studies of well-characterized families and individuals at multiple sites in the U.S. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). The study was supported by the National Institute of Dental and Craniofacial Research (NIDCR, U01-DE018903). Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington. Samples of some participants in this study were selected for further genomic analysis. These results will be available in the dbGaP CIDR study: Center for Oral Health Research in Appalachia (COHRA) Genomic Studies of Oral Health and Disease (phs001591).

  Study phs000095

• Genetic Basis of Early Onset Bicuspid Aortic Valve Disease

  The study protocol was approved by the Committee for the Protection of Human Subjects at the University of Texas Health Science Center at Houston (HSC-MS-11-0185). Study recruitment began on July 1, 2017, and concluded on March 30, 2022. After written informed consent, we enrolled probands with early onset BAV disease (EBAV), which we defined as individuals with BAV who were under the age of 30 at the time of first clinical event. Clinical events were defined as aortic replacement, aortic valve surgery, aortic dissection, moderate or severe aortic stenosis or aortic regurgitation, large aneurysm (Z > 4.5), or intervention for BAV-related conditions. Those with hypoplastic left heart, known genetic mutations, genetic syndromes, or complex congenital heart disease were excluded. Samples were collected and genotyped as previously reported. For comparison, we analyzed a cohort of older individuals of European ancestry with sporadic BAV disease selected from the International BAV Consortium (BAVGWAS). Phenotypes were derived from record review with confirmation of image data whenever possible [25-26]. The computational pipeline for CNV analysis of Illumina single nucleotide polymorphism (SNP) array data included three independent CNV detection algorithms.GenomeStudio was used to exclude samples with indeterminate sex or more than 5% missing genotypes, and single nucleotide polymorphisms (SNPs) with GenTrain = 0. Principal component analysis was used to remove outliers that did not cluster with European ancestry. Prior to CNV analysis, each dataset was trimmed by selecting a common set of 650,000 SNPs that were genotyped on each of the microarrays used in this study.Three independent algorithms (PennCNV, cnvPartition, and QuantiSNP) were used to generate CNV calls and sample-level quality statistics from SNP intensity data. PennCNV and QuantiSNP were run on Unix clusters and cnvPartition data were exported from GenomeStudio. The analysis was run using default configurations. PennCNV was used to generate QC data and remove CNV calls that intersect with polymorphic genomic regions. Samples that met any of the following criteria were excluded, standard deviation of the LogR ratio (obtained from PennCNV) > 0.35 or number of CNVs > 2 standard deviations above the mean for each data set. CNV calls less than 20 Kilobases in length and/or spanned by fewer than 6 probes were excluded. The overlap function for rare CNVs in PLINK was used to construct CNV regions (CNVRs) after adjacent regions were merged using PennCNV. LogR ratio (LRR) and B allele frequency (BAF) data at CNVRs and calls of interest were visualized in GenomeStudio for validation. For segregation analysis, GenomeStudio was used to determine the presence of CNVs in relatives.A total of 22,014 unselected control Illumina Genotypes obtained from the Database of Genotypes and Phenotypes were analyzed using identical methods (S1 Table). The Wisconsin Longitudinal Study (WLS) includes data on a cohort of 10,300 individuals who graduated from Wisconsin high schools in 1957. The Health and Retirement Study (HRS) includes data on 37,000 individuals aged 50 above from 23,000 households across the United States. Principal component analysis was used to select European ancestry genotypes from these datasets for analysis. Datasets were paired for case-control analysis based on the concordance of log-transformed sample-level quality control statistics (number of CNV calls and standard deviation of logR ratios). Chi-squared or Fisher exact tests were used to compare CNV frequencies in cases and controls.Rare CNV functions in PLINK (v1.7) were used to perform permutation-based burden tests or gene set-based enrichment tests. Case control burden tests were restricted to CNVs that were longer than 110 Kb and less than 0.1% in frequency. CNV overlap functions in PLINK were used to identify rare CNVs that intersect between datasets or involve specific BAV or CHD genes . The list of candidate genes included 190 CHD genes that have strong cumulative evidence to cause BAV or related congenital malformations from human or animal model data. Genome Reference Consortium Human Build 37 was used for CNV annotation [34].

  Study phs003705

• Genetic Basis of Cryptorchidism

  Non-syndromic cryptorchidism is a common malformation, but the genetic loci that increase susceptibility to cryptorchidism remain unknown. A genome-wide association study (GWAS) was completed to determine whether common allelic variants are associated with susceptibility to cryptorchidism. Two sets of discovery groups were genotyped. Group 1 (559 cases and 1772 controls) was genotyped with HumanHap550v1.0, HumanHap550v3.0 or Human610-quad v1.0. Group 2 (353 cases and 1149 controls) was genotyped with HumanOmniExpress-12v1 or HumanOmniExpress-12v1-1. The sample and marker QCs were performed separately using PLINK (v1.07; http://pngu.mgh.harvard.edu/purcell/plink/) and logistic regression analyses were performed with first and second multidimensional scaling (MDS) components as covariates.

  Study phs000986