Experiments
Searchable full-text extractions: founding hypothesis, core claims, experimental setups, key results and statistics — pulled out of each paper as structure. Search a cell line, an assay or an entity (e.g. HUH7) and find every paper that worked with it. This corpus stands on its own: most entries carry no reproduction assessment (yet).
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Spatial transcriptomics identifies differentiation, lipid metabolism, and retinoid pathway alterations in acne vulgaris.
PMID 41657309 · PMC12892907 · JCI insight · 2026 · 8 claims · 6 setups
A custom KRT5-directed segmentation pipeline improves transcript assignment accuracy in the spatially complex sebaceous gland compared with standard multimodal or nuclei-expansion segmentation
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Oxidative stress-associated genes TPPP3 and VEGFA in COPD revealed by bulk and single-cell sequencing analysis.
PMID 41620539 · PMC12916760 · Scientific reports · 2026 · 8 claims · 9 setups
76 overlapping genes were identified between COPD-associated DEGs and oxidative stress-related genes.
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IgVH genes from different anatomical regions, with different histopathological patterns, of a rheumatoid arthritis patient suggest cyclic re-entry of mature synovial B-cells in the hypermutation process.
PMID 11056671 · PMC17813 · Arthritis research · 2000 · 8 claims · 5 setups
Somatically mutated IgVH genes with amino acid deletions and mixed IgV molecules were found in all three anatomical regions, suggesting a novel pathway for generating (auto)antibody specificities
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Genetic variants of adiponectin receptor 2 are associated with increased adiponectin levels and decreased triglyceride/VLDL levels in patients with metabolic syndrome.
PMID 16700915 · PMC1482678 · Cardiovascular diabetology · 2006 · 8 claims · 6 setups
A haplotype of three AdipoR2 variants (+795G/A, +870C/A, +963C/T) in perfect linkage disequilibrium is associated with higher plasma adiponectin levels and lower fasting triglyceride, VLDL-triglyceride, and VLDL-cholesterol levels
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sCellST predicts single-cell gene expression from H& E images.
PMID 41513659 · PMC12858858 · Nature communications · 2026 · 7 claims · 6 setups
sCellST is a weakly supervised (Multiple Instance Learning) deep learning framework that predicts single-cell gene expression from H&E images alone, trained using paired spatial transcriptomics (Visium) and H&E slides