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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Full-text index only
Uncovering Cas9 PAM diversity through metagenomic mining and machine learning.
PMID 41656299 · PMC12996302 · Nature communications · 2026 · 8 claims · 6 setups
CRISPR-PAMdb is a publicly accessible database compiling Cas9 protein sequences from 3.8 million bacterial/archaeal genomes and PAM profiles from 7.4 million phage/plasmid sequences
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Has reproduction
Unlocking the microbial studies through computational approaches: how far have we reached?
PMID 36920617 · PMC10016191 · Environmental science and pollution research international · 2023 · 8 claims · 8 setups
Metagenomics enables culture-independent study of microbial communities directly from their natural environments, bypassing the need for clonal isolation.
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Has reproduction · 89
Gut-derived Lactobacillus from exceptional responders mitigates chemoradiotherapy-induced intestinal injury through methionine-driven epigenetic modulation.
PMID 40469520 · PMC12130557 · iMeta · 2025 · 8 claims · 8 setups
High pretreatment abundance of Lactobacillus (and Bifidobacterium) in gut microbiota correlates with absence of ACRIII in rectal cancer patients
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Has reproduction · 77
Electroacupuncture reshapes the microbial co-occurrence networks related to the behavioral and psychological symptoms of dementia in Alzheimer's disease.
PMID 41676443 · PMC12806058 · iMetaOmics · 2025 · 8 claims · 5 setups
Microbial keystone species and gut microbiota composition are highly variable during pathological development of BPSD in AD
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Full-text index only
Gut microbiome signatures associated with depression and obesity.
PMID 41615149 · PMC13011431 · mSystems · 2026 · 8 claims · 4 setups
Taxonomic gut microbiome profiles classify depressed vs non-depressed subjects with a balanced accuracy of 0.90 using machine learning