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Additional file 1 of Ethnicity-specific blood pressure thresholds based on cardiovascular and renal complications: a prospective study in the UK Biobank
No full textAdditional file 1: Figure S1. Flowchart for inclusion in the analytic sample of participants in the UK biobank. Table S1. Manual blood pressure measurement distribution and values compared to automated measurements. Table S2. Outcome definitions and corresponding ICD 9/10 codes. Table S3. Number of cases and the crude incidence rate for the composite outcomes. Table S4. Ethnicity-specific thresholds of systolic blood pressure after adjusting for immigration history. Table S5. Ethnicity-specific thresholds of systolic blood pressure in different age groups. Table S6. Ethnicity-specific thresholds of systolic blood pressure by sex groups. Table S7. Population attributable fraction of the composite outcome associated with grade 1 hypertension defined by ethnicity-specific thresholds vs. ESH-recommended thresholds
Additional file 1 of Symptoms of depression and risk of emergency department visits among people aged 70 years and over
No full textSupplementary Material
Additional file 1 of The impact of selective HDAC inhibitors on the transcriptome of early mouse embryos
No full textSupplementary Material
Additional file 1 of Process evaluation of the Belgian one-month-without alcohol campaign ‘Tournée Minérale’: a mixed method approach
No full textSupplementary Material
Additional file 1 of Non-COVID-19 hospitalization and mortality during the COVID-19 pandemic in Iran: a longitudinal assessment of 41 million people in 2019–2022
No full textAdditional file 1
Additional file 1 of Interaction between Wnt/β-catenin signaling pathway and EMT pathway mediates the mechanism of sunitinib resistance in renal cell carcinoma
No full textSupplementary Material
Multicellular ecotypes shape progression of lung adenocarcinoma from ground-glass opacity towards advanced stages
No full textSingle-cell RNA sequencing and read processing The cell suspension was loaded into Chromium microfluidic chips with 3’(v3) chemistry and barcoded with a 10× Chromium Controller (10X Genomics). RNA from the barcoded cells was subsequently reverse-transcribed and sequencing libraries constructed with reagents from a Chromium Single Cell 3’ v3 reagent kit (10X Genomics) according to the manufacturer’s instructions. Sequencing was performed with Illumina Novaseq 6000, according to the manufacturer’s instructions (Illumina). The 10X Genomics CellRanger software pipeline (v 5.0.1) was used to demultiplex cell barcodes and reads were mapped to the hg38 human genome using STAR aligner (v2.7.10a)(Dobin et al., 2013).Filtering, normalization, integration and clustering of scRNA-seq data Seurat (v3.1.0) was used for filtering, selecting variable gene, dataset integration, dimensionality reduction, clustering, cell type annotation, differential expression, and visualization. We applied quality measures on raw gene-cell-barcode matrix for each cell: mitochondrial genes (≤20%, unique molecular identifiers (UMIs), and gene count (ranging from 200 to 6000). We excluded genes with min.cells < 3 and removed mitochondrial as well as ribosomal genes in the subsequent analysis. For the remaining cells and genes, we defined relative expression by centering the gene count through using the ‘ScaleData’ function. In the integration step, function ‘SelectIntegrationFeatures’ was used to select features, which were used to scaled (function ' ScaleData’) and compute the principal component (PCs, function ‘RunPCA’). When identifying integration anchors, one sample from each clinical stage was randomly selected as reference (function ‘FindIntegrationAnchors’, reduction = "rpca"), Then all scRNAseq datasets were integrated using above anchor (function ‘IntegrateData’). Cell clustering, tSNE visualization and UMAP visualization were performed using the FindClusters, RunTSNE and RunUMAP functions, respectively. The annotations of cell identity on each cluster were defined by the expression of known marker genes, including: EPCAM, KRT19, KRT18, CDH1 for epithelial cells; CD3D, CD3E, CD3G for T cells; TRAC, LYZ, MARCO, CD68, FCGR3A for myeloid cells; CD79A for B cells; DCN, THY1, COL1A1, COL1A2 for fibroblasts, PECAM1, FLT1 for endothelial cells; KIT, MS4A2, GATA2 for MAST cells; NKG7, NCAM1, KLRD1 for NK cells.</p
Addressing Rare Disease Data FAIRness in the Disease Ontology Knowledgebase (DO-KB)
No full textMaking human disease knowledge FAIR and TRUST-worthy are the hallmarks of the Human Disease Ontology (DO) project. Coordination of key biomedical data across large-scale biomedical resources strengthens the foundation of knowledge. The Human Disease Ontology serves as the nomenclature and classification standard for human diseases by providing a stable, etiology-based structure; integrating mechanistic drivers of human disease; including rare, common, and complex diseases; and cross-referencing clinical vocabularies – all combined in an expertly curated resource of over 11,000 diseases linking disease concepts through more than 35,000 vocabulary cross mappings.</p
