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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Genome-wide microRNA profiling in human fetal nervous tissues by oligonucleotide microarray.
PMID 16983573 · PMC1705512 · Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2006 · 8 claims · 5 setups
72-83% of assayed miRNAs are expressed across human fetal organs, with G24w cerebrum showing the most miRNAs expressed
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Has reproduction · 85
Chromosome-level genome assembly of Lilford's wall lizard, Podarcis lilfordi (Günther, 1874) from the Balearic Islands (Spain).
PMID 37137526 · PMC10214862 · DNA research : an international journal for rapid publication of reports on genes and genomes · 2023 · 8 claims · 8 setups
First high-quality chromosome-level genome assembly and annotation of P. lilfordi, generated via a mixed sequencing strategy (10X linked reads, ONT long reads, Hi-C) plus RNAseq/Iso-Seq
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Fusion of short telomeres in human cells is characterized by extensive deletion and microhomology, and can result in complex rearrangements.
PMID 20026586 · PMC2847243 · Nucleic acids research · 2010 · 8 claims · 5 setups
Telomere fusion in human cells is characterized by extensive sub-telomeric deletion of at least one telomere, extending up to 5.6-6.1 kb
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Has reproduction · 83
Mural cell-derived chemokines provide a protective niche to safeguard vascular macrophages and limit chronic inflammation.
PMID 37652021 · PMC10588993 · Immunity · 2023 · 7 claims · 8 setups
Vascular macrophages form a spatiotemporal niche in direct contact with mural cells across microvascular and macrovascular beds
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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