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44 results for “blood count”
Curated GWAS summary statistics on African ancestry on 19 blood count traits and glycemic traits (hg38)
<p>Genome wide curated summary statistics on 19 blood count traits and glycemic traits</p> <p>File format is the inittable format intended to be used with the Joint Analysis of Summary Statistics (JASS), which allows to perform multi-trait GWAS:</p> <p>https://gitlab.pasteur.fr/statistical-genetics/jass</p> <p>GWAS of hematological traits originate from Chen et al paper and were downloaded from the GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/32888493#study_panel">https://www.ebi.ac.uk/gwas/publications/32888493#study_panel</a>). GWAS of glycemic traits come from the <a href="https://www.zotero.org/google-docs/?S1MIfx">(18)</a> study downloadable from GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/34059833">https://www.ebi.ac.uk/gwas/publications/34059833</a>).</p> <p> </p>
Curated GWAS summary statistics on East Asian ancestry on 19 blood count traits and glycemic traits
<p>Genome wide curated summary statistics on 19 blood count traits and glycemic traits</p> <p>File format is the inittable format intended to be used with the Joint Analysis of Summary Statistics (JASS), which allows to perform multi-trait GWAS:</p> <p>https://gitlab.pasteur.fr/statistical-genetics/jass</p> <p>GWAS of hematological traits originate from Chen et al paper and were downloaded from the GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/32888493#study_panel">https://www.ebi.ac.uk/gwas/publications/32888493#study_panel</a>). GWAS of glycemic traits come from the <a href="https://www.zotero.org/google-docs/?S1MIfx">(18)</a> study downloadable from GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/34059833">https://www.ebi.ac.uk/gwas/publications/34059833</a>).</p> <p>Full description of the method used to derive this dataset can be found in </p>
Dataset: Whole blood count, used in: "AIDeveloper: deep learning image classification in life science and beyond"
<p>Real-time deformability cytometry (RT-DC) data of whole blood measurements.<br> Data was used to train and validate a neural net to perform a blood count based on brightfield images of RT-DC.</p> <p>01_Model: Contains the final model as well as an AIDeveloper meta-file that allows to reproduce the training procedure. The metafile preciesely defines which dataset was used for training and which for validation as well as all parameters that were set in AIDeveloper.</p> <p>The following folders contain data that was used for training (and validation):</p> <ul> <li>Cambr</li> <li>KIK</li> <li>20190306_DextranBlood_AI_DataSet</li> <li>Gs_Blood_Train</li> </ul> <p>Testing data is stored on figshare:<br> https://figshare.com/articles/Krater_et_al_2020_Data_zip/9902636</p>
Using Romiplostim to Treat Low Platelet Counts Following Chemotherapy and Autologous Hematopoietic Cell Transplantation in People With Blood Cancer
ClinicalTrials.gov study NCT04478123. IPD Sharing: YES. Countries: 1. Publications: 1.
Study of a New Medication for Childhood Chronic Immune Thrombocytopenia (ITP), a Blood Disorder of Low Platelet Counts That Can Lead to Bruising Easily, Bleeding Gums, and/or Bleeding Inside the Body.
ClinicalTrials.gov study NCT01520909. IPD Sharing: Not stated. Countries: 13. Publications: 2.
Reference Intervals of Complete Blood Count and Coagulation Tests in Pregnant Women at Hung Vuong Hospital
ClinicalTrials.gov study NCT05929326. IPD Sharing: YES. Countries: 1. Publications: 5.
Data from: Complete blood count reference intervals from a healthy adult urban population in Kenya
Background: There are racial, ethnic and geographical differences in complete blood count (CBC) reference intervals (RIs) and therefore it is necessary to establish RIs that are population specific. Several studies have been carried out in Africa to derive CBC RIs but many were not conducted with the rigor recommended for RI studies hence limiting the adoption and generalizability of the results. Method: By use of a Beckman Coulter ACT 5 DIFF CP analyser, we measured CBC parameters in samples collected from 528 healthy black African volunteers in a largely urban population. The latent abnormal values exclusion (LAVE) method was used for secondary exclusion of individuals who may have had sub-clinical diseases. The RIs were derived by both parametric and non-parametric methods with and without LAVE for comparative purposes. Results: Haemoglobin (Hb) levels were lower while platelet counts were higher in females across the 4 age stratifications. The lower limits for Hb and red blood cell parameters significantly increased after applying the LAVE method which eliminated individuals with latent anemia and inflammation. We adopted RIs by parametric method because 90% confidence intervals of the RI limits were invariably narrower than those by the non-parametric method. The male and female RIs for Hb after applying the LAVE method were 14.5−18.7 g/dL and 12.0−16.5 g/dL respectively while the platelet count RIs were 133−356 and 152−443 x103 per µL respectively. Conclusion: Consistent with other studies from Sub-Saharan Africa, Hb and neutrophil counts were lower than Caucasian values. Our finding of higher Hb and lower eosinophil counts compared to other studies conducted in rural Kenya most likely reflects the strict recruitment criteria and healthier reference population after secondary exclusion of individuals with possible sub-clinical diseases.
Dataset BD "Inflammation Biomarkers in Blood as Mortality Predictors in Community-Acquired Pneumonia Admitted Patients: Importance of comparison with Neutrophil Count Percentage or Neutrophil-Lymphocyte Ratio."
<p>Dataset of the article tittled: <strong>Inflammation Biomarkers in Blood as Mortality Predictors in Community-Acquired Pneumonia Admitted Patients: Importance of comparison with Neutrophil Count Percentage or Neutrophil-Lymphocyte Ratio.</strong></p> <p> </p>
Blood cell differential count discretization modeling predicts survival in adults reporting to the emergency room: a retrospective cohort study
<p><strong>Objectives</strong>: to assess survival predictivity of baseline blood cell differential count (BCDC), discretized according to two different methods, in adults visiting the Emergency Room (ER) for illness or trauma over one-year. </p> <p><strong>Design</strong>: Retrospective cohort study of hospital records. </p> <p><strong>Setting</strong>: Tertiary care public hospital in northern Italy. </p> <p><strong>Participants</strong>: 11052 patients aged > 18 years, consecutively admitted to the ER in one year, and for whom BCDC collection was indicated by ER medical staff at first presentation.</p> <p><strong>Primary outcome</strong>: Survival was the referral outcome for explorative model development. Automated BCDC analysis at baseline assessed hemoglobin, red cell mean volume (MCV) and distribution-width (RDW), platelet distribution-width (PDW), plateletcrit (PCT), absolute red blood cells, white blood cells, neutrophils, lymphocytes, monocytes, eosinophils, basophils, and platelets. Discretization cutoffs were defined by Benchmark and Tailored methods. Benchmark cutoffs were stated on laboratory reference values (CLSI). Tailored cutoffs for linear, sigmoid-shaped and for U-shaped distributed variables were discretized by Maximally Selected Rank Statistics and by Optimal-Equal Hazard Ratio respectively. Explanatory variables (age, gender, ER admission during SARS-CoV2 surges, in-hospital admission) were analyzed using Cox multivariable regression. ROC curves were drawn by sum of Cox-significant variables for each method.</p> <p><strong>Results</strong>: Of 11052 patients (median age 67 years, IQR 51–81, 48% female), 59% (n=6489) were discharged and 41% (n=4563) were admitted in hospital. After a 306-day median follow up (IQR 208–417 days), 9455 (86%) patients were alive and 1597 (14%) deceased. Increased HRs were associated with age >73-years (HR=4.6 CI=4.0–5.2), in-hospital admission (HR=2.2 CI=1.9–2.4), ER admission during SARS-CoV2 surges (Wave-I HR=1.7 CI=1.5–1.9); Wave-II HR=1.2 CI=1.0–1.3). Gender, hemoglobin, MCV, RDW, PDW, neutrophils, lymphocytes and eosinophils counts were significant in overall. Benchmark-BCDC model included basophils and platelet count (AUROC 0.74). Tailored-BCDC model included monocyte counts and plateletcrit (AUROC 0.79).</p> <p><strong>Conclusions</strong>: baseline discretized BCDC provides meaningful insight regarding Emergency Room patients survival.</p>
Comparison of The Effects Of General Anesthesia and PECS Block Methods on Blood Counts in Patients With Breast Cancer
ClinicalTrials.gov study NCT06151639. IPD Sharing: NO. Countries: 1. Publications: 8.
Correlation Between TILs and Blood Cell Counts in Triple Negative Breast Cancer Patients
ClinicalTrials.gov study NCT04068623. IPD Sharing: NO. Countries: 1. Publications: 1.
Using the Blood Eosinophil Count to Guide Systemic Corticosteroid Treatment in Asthma Exacerbations
ClinicalTrials.gov study NCT05417906. IPD Sharing: NO. Countries: 1. Publications: 1.
Usefulness of White Blood Cell Count (WBCC) During Infection in Geriatric Patient
ClinicalTrials.gov study NCT03943277. IPD Sharing: NO. Countries: 1. Publications: 2.
Effect of Type of General Anesthesia Maintenance on Exhaled Nitric Oxide and Eosinophil Blood Count
ClinicalTrials.gov study NCT02065635. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Blood cell differential count discretization modeling predicts survival in adults reporting to the emergency room: a retrospective cohort study
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Data from: Complete blood count reference intervals from a healthy adult urban population in Kenya
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Gene expression counts from blood, strand-specific, BCM UDN
<p><strong>File description:</strong></p> <ol> <li> <p>Gene-level counts using the gtf file from the release 34 of GENCODE <a href="https://www.gencodegenes.org/human/release_34">https://www.gencodegenes.org/human/release_34</a></p> </li> <li> <p>Split counts spanning from one exon to another using an annotation-free algorithm, therefore capturing new splice sites</p> </li> <li> <p>Non-split counts covering exon-intron boundaries</p> </li> <li> <p>Sample annotation describing each sample from the dataset</p> </li> <li> <p>Description file with global information from the dataset</p> </li> </ol> <p><strong>Use: </strong>The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired end specifications match. Afterwards, the DROP pipeline can be used to compute expression and splicing outliers (<a href="https://github.com/gagneurlab/drop">https://github.com/gagneurlab/drop</a>).</p> <p><strong>Maintainer: </strong>Vicente A. Yépez, <a href="mailto:yepez@in.tum.de">yepez@in.tum.de</a></p> <p><strong>URL:</strong> <a href="https://github.com/gagneurlab/drop/">https://github.com/gagneurlab/drop/</a><br> </p> <p><strong>Title</strong>: BCM UDN Whole Blood<br> <strong>Number of samples</strong>: 125<br> <strong>Tissue</strong>: Peripheral blood<br> <strong>Organism</strong>: Homo sapiens<br> <strong>Genome assembly:</strong> hg19<br> <strong>Gene annotation</strong>: gencode34<br> <strong>Disease (ICD-10: N)</strong>: : 1, D84: 10, E34: 2, H57: 1, K92: 4, M25: 17, NONE: 53, Q20: 3, Q33: 2, Q89: 6, R62: 26<br> <strong>Strand specific</strong>: TRUE<br> <strong>Paired end: </strong>TRUE<br> <strong>Protocol: </strong>poly(A) enrichment, no globin depletion<br> <strong>Dataset contact: </strong>David Murdock, <a href="mailto:david.murdock@bcm.edu">david.murdock@bcm.edu</a></p> <p><strong>Citation:</strong> Cite both the resource using Zenodo's citation and the publication under References</p>
Raw count data with annotation in h5ad format of scRNAseq of blood and matched temporal artery in Giant Cell Arteritis
<p>Raw count data with annotation in h5ad format of scRNAseq of blood and matched temporal artery in Giant Cell Arteritis</p>
Curated GWAS summary statistics on European ancestry on 19 blood count traits and glycemic traits (hg38)
<p>Genome wide curated summary statistics on 19 blood count traits and glycemic traits</p> <p>File format is the inittable format intended to be used with the Joint Analysis of Summary Statistics (JASS), which allows to perform multi-trait GWAS:</p> <p>https://gitlab.pasteur.fr/statistical-genetics/jass</p> <p>GWAS of hematological traits originate from Chen et al paper and were downloaded from the GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/32888493#study_panel">https://www.ebi.ac.uk/gwas/publications/32888493#study_panel</a>). GWAS of glycemic traits come from the <a href="https://www.zotero.org/google-docs/?S1MIfx">(18)</a> study downloadable from GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/34059833">https://www.ebi.ac.uk/gwas/publications/34059833</a>).</p>
White Blood Cell Counts and Onset of Cardiovascular Diseases: a CALIBER Study
ClinicalTrials.gov study NCT02014610. IPD Sharing: Not stated. Countries: 0. Publications: 4.
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