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109 results for “protein biomarker”
Inter-Chemical Correlation results for the study: HHEARx2017-1967 (Perfluoroalkyl and Polyfluroalkyl Substances (PFAS), Protein Biomarkers, Adiposity and Cardiometabolic Risk Factors in a 3-year Cohort of Low-Income Latino Children with Overweight and Obesity from the Stanford GOALS Randomized Controlled Trial)
Title: Perfluoroalkyl and Polyfluroalkyl Substances (PFAS), Protein Biomarkers, Adiposity and Cardiometabolic Risk Factors in a 3-year Cohort of Low-Income Latino Children with Overweight and Obesity from the Stanford GOALS Randomized Controlled Trial <br>Species: Homo sapiens <br>Number of samples: 1085 <br>Number of named analytes: 8 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=36 <br>
Summary Statistics from "Genetically regulated gene expression and proteins revealed discordant effects" (LWAS of biomarker)
<p>Summary statistics of 92 blood protein levels. The corresponding publication is currently under revision.</p> <p> The zipped txt file is tab-delimited and contains the following columns:</p> <ul> <li>protein: protein name abbreviation</li> <li>cytoband: genomic region</li> <li>gene: gene name abbreviation</li> <li>setting: either "combined" (adj. for sex & age) or sex-stratified ("males", "females"; adj. for age)</li> <li>variant_id_hg19: SNP ID according to hg19</li> <li>variant_id_hg38: SNP ID according to hg19</li> <li>chr: chromosome</li> <li>pos_hg19: base position according to hg19</li> <li>pos_hg38: base position according to hg19</li> <li>effect_allele: also known as counted allele in additive model</li> <li>other_allele: not-counted allele</li> <li>eaf: effect allele frequency</li> <li>maf: minor allele frequency</li> <li>info: imputation info score</li> <li>n_samples: number of samples</li> <li>beta: effect estimate</li> <li>se: standard error</li> <li>zscore: Z-statistic</li> <li>pvalue: p-value</li> <li>FDR: FDR by gene and setting</li> <li>BBFDR: hierarchical FDR by setting</li> <li>hierFDR: TRUE if SNP is significant after hierarchical FDR</li> </ul>
dataset related to article: " Cerebrospinal fluid neuropathological biomarkers in beta-propeller protein-associated neurodegeneration, with complicated parkinsonian phenotype"
<p>analysis sanger electropherograms in the patient's in .abi format and segregation in the family (mother; father and sister</p>
Single Cell Phenotypic Profiling to Identify a Set of Immune Cell Protein Biomarkers for Relapsed and Refractory Diffuse Large B Cell Lymphoma: A Single-Center Study
<p>Diffuse large B-cell lymphoma (DLBCL) is the most common invasive type of non-Hodgkin lymphoma. Cell-of-origin (COO) classification is related to patients’ prognoses. Primary drug resistance in treatment for DLBCL has been observed. The specific serum biomarkers in these patients who suffer from relapsed and refractory (R/R)-DLBCL remains unclear. In the current study, using single-cell RNA sequencing (scRNA-seq) and mass cytometry (CyTOF), we determined and verified immune cell biomarkers at the mRNA and protein levels in single-cell resolution from 18 diagnostic peripheral blood mononuclear cell (PBMC) specimens collected from patients with R/R DLBCL. As controls, five PBMC specimens from healthy volunteers were obtained. We identified a panel of 35 surface marker genes for the features of R/R DLBCL unique cell cluster by scRNA-seq of eight R/R DLBCL patient samples and validated its efficiency in an external cohort consisting of 10 R/R DLBCL patients by CyTOF. The cell clustering and dimension reduction were compared among R/R DLBCL samples in CyTOF Space with COO as well as the C-MYC expression designation. Immune cells from each patient occupied unique regions in the 32-dimensional phenotypic space with no apparent clustering of samples into discrete subtypes. Significant heterogeneity observed in subgroups was mainly attributed to individual differences among samples and not to expression differences in a single, homogeneous immune cell subpopulation. The marker panel showed reliability in labeling R/R DLBCL without any influence from COO stratification and C-MYC expression designation. Furthermore, we compared all the markers between R/R DLBCL and normal samples. A total of 12 biomarkers were significantly overexpressed in R/R DLBCL relative to the normal samples. Therefore, we further optimized the diagnostic biomarker panel of R/R DLBCL comprising CD82, CD55, CD36, CD63, CD59, IKZF1, CD69, CD163, CD14, CD226, CD84, and CD31. In summary, we developed a novel set of biomarkers for the diagnoses of patients with R/R DLBCL. Detections procedures at single-cell resolution provide precise biomarkers which may substantially overcome intertumoral and intratumoral heterogeneity among primary samples. The findings confirmed that each case was unique and may comprise multiple, genetically distinct subclones.</p> <p>Here we uploaded the dataset of CyTOF for external validation. For more detailed information, please contact Dr zheng (zenki_zheng@163.com)</p> <p> </p>
The Effect of Stromal Vascular Fraction (SVF) & Scaffolds Application on Fracture Healing with Bone Defect as Assessed Through Osteocalcin and Bone Morphogenetic Protein-2 (BMP-2) Biomarker Examination: Experimental Study on Murine Model
<p>This data is the raw data for the manuscript with titled The Effect of Stromal Vascular Fraction (SVF) & Scaffolds Application on Fracture Healing with Bone Defect as Assessed Through Osteocalcin and Bone Morphogenetic Protein-2 (BMP-2) Biomarker Examination: Experimental Study on Murine Model.</p>
A antibody-based array reveals a serum protein signature as biomarker for adolescent idiopathic scoliosis patients
<p>Evident adolescent idiopathic scoliosis (AIS) incurs high treatment costs, low quality of life, and many complications. Early screening of AIS is essential to avoid progressing to an evident stage. However, there is no valid serum biomarker for AIS for early screening. Antibody-based array is a large-scale study of proteins, which is expected to reveal a serum protein signature as biomarker for AIS. There are two segments of the research, including biomarkers screening and validation. In the biomarkers screening group, a total of 16 volunteers participated in this study, and we carried out differentially expressed proteins screening via protein array assay between No-AIS group and the AIS group.</p>
Protein biomarkers for early predictions of hypoxic ischemic encephalopathy
<p>Perinatal asphyxia is a temporary interruption of oxygen availability that results in neonatal morbidity and mortality. The exact burden of perinatal asphyxia is unknown due to the lack of valid and accepted diagnostic criteria applicable to resource-limited settings. After sepsis, it is the second most important cause of neonatal death. Though there are tests available that can indicate whether an infant is suffering from birth asphyxia or not, they are not the gold standard. There is a dire need for early detection biomarkers for birth asphyxia so that the outcome of birth asphyxia can be avoided. Urinary and serum proteomics has rapidly developed and standard collection and experiment protocols are now available. With the help of the ‘omics’ approach, we can speed up biomarker discovery and widely use it to facilitate diagnostic and therapeutic developments for many diseases. Some of the promising biomarkers found in urine and serum are APOC3, ORM1, SOD1, FABP1, and AZGP1.</p>
SomaScan dataset used to identify protein biomarkers for distinguishing between bacterial and viral infections in febrile children
<p>Protein profiles of children with confirmed bacterial infections (DB), confirmed viral infections (DV) in addition to healthy controls (HC). Protein profiles generated through the SomaScan 1.3k assay (SomaLogic, Colorado, USA).</p> <p>Data has been normalised already, including batch effect correction using COCONUT (https://cran.r-project.org/web/packages/COCONUT/COCONUT.pdf) and log2 transformed. </p> <p>Accompanying the protein abundance values is a separate .csv file containing information about the proteins, including UniProt ID and Entrez gene IDs associated with the proteins.</p>
Mode characterization and sensitivity evaluation of an ultra-high-frequency surface acoustic wave (UHF-SAW) resonator biosensor: application to the glial-fibrillary-acidic-protein (GFAP) biomarker detection
<p>Biosensors detect specific bio-analytes by generating a measurable signal from the interaction between the sensing element and the target molecule. Surface acoustic wave (SAW) biosensors offer unique advantages due to their high sensitivity, real-time response capability, and label-free detection. The typical SAW modes are the Rayleigh mode and the shear-horizontal mode. Both present pros and cons for biosensing applications and generally need different substrates and device geometries to be efficiently generated. This study investigates and characterizes ultra-high-frequency (UHF-) SAW resonator biosensors. It reveals the simultaneous presence of the two typical SAW modes, clearly separated in frequency, called slow and fast. The two modes are studied by numerical simulations and biosensing experiments with the glial-fibrillary-acidic-protein (GFAP) biomarker. The slow mode is generally more sensitive to changes in surface properties, such as temperature and mass changes, by a factor of about 1.4 with respect to the fast mode.</p>
Heat shock protein gene expression varies among populations but does not strongly track recent environmental conditions: implications for biomarker development
Open the record for dataset details and reuse information.
Identification and evaluation of serum protein biomarkers which differentiate psoriatic from rheumatoid arthritis
<p><span><b>Objectives</b></span></p> <p><span>To identify serum protein biomarkers which might separate early inflammatory arthritis (EIA) patients with psoriatic arthritis (PsA) from those with rheumatoid arthritis (RA) and may be used to support appropriate early intervention. </span></p> <p><span><b>Methods</b></span></p> <p><span>The serum proteome of patients with PsA and RA was interrogated using liquid chromatography mass spectrometry (LC-MS/MS) (n=64 patients), a multiplexed antibody assay (Luminex) for 48 proteins (n=64 patients) and an aptamer-based assay (SOMAscan) targeting 1,129 proteins (n=36 patients). Multiple reaction monitoring assays were developed to evaluate the performance of putative markers in the discovery cohort (n=60) as well as an independent cohort of PsA and RA patients (n=167). </span></p> <p><span><b>Results</b></span></p> <p><span>Multivariate analysis of the protein discovery data revealed that it was possible to discriminate PsA from RA patients with an area under the curve (AUC) of 0.94 for nLCMS/MS, 0.69 for Luminex based measurements; 0.73 for SOMAscan analysis. Random forest models confirmed that a subset of protein measured by MRM could differentiate PsA and RA patients with an AUC of 0.79 and 0.85 during separate evaluation and verification studies. </span></p> <p><span><b>Conclusion</b></span></p> <p><span>We report a serum protein biomarker panel which can separate EIA patients with PsA from those with RA. We suggest that the routine use of such a panel in EIA patients will improve clinical decision making. With continued evaluation and refinement on additional and larger patient cohorts including those with other arthropathies we suggest the panel identified here will contribute toward improved clinical decision making.</span></p>
Relationship Between Protein Biomarkers in Cerebrospinal Fluid and Alzheimer&Apos;s Disease in Patients With Depression
ClinicalTrials.gov study NCT02303158. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Evaluation of Multiple Protein and Molecular Biomarkers to Estimate Risk of Cancer in Gynecology Patients Presenting With a Pelvic Mass.
ClinicalTrials.gov study NCT02781272. IPD Sharing: NO. Countries: 1. Publications: 3.
Validation of Molecular and Protein Biomarkers in Sepsis
ClinicalTrials.gov study NCT04289506. IPD Sharing: NO. Countries: 1. Publications: 7.
Low Fat Versus Protein Sparing Diet for Weight Loss & Impact on Biomarkers Associated With Breast Cancer Risk
ClinicalTrials.gov study NCT01559194. IPD Sharing: Not stated. Countries: 1. Publications: 4.
iDentification and vAlidation Model of Liquid biopsY Based cfDNA Methylation and pRotEin biomArKers for Pancreatic Cancer (DAYBREAK Study)
ClinicalTrials.gov study NCT05495685. IPD Sharing: NO. Countries: 1. Publications: 49.
Evaluation of a Plasma Protein Profile as a Predictive Biomarker for Metastatic Relapse in Triple Negative Breast Cancer Patients
ClinicalTrials.gov study NCT04438681. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Early Detection of Relapse in Ovarian Cancer Using Capillary Home-sampling and a Protein Biomarker Test
ClinicalTrials.gov study NCT06445621. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Study to Identify Biomarkers for Protein Intake
ClinicalTrials.gov study NCT01314040. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Biomarker To Evaluate Protein Profiles of Neutropenic Fever/Infection With Acute or Chronic Leukemias
ClinicalTrials.gov study NCT01144793. IPD Sharing: NO. Countries: 1. Publications: 1.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.