{"id":"W4385238213","doi":"10.1101/2023.07.21.549589","title":"FLECS Technology for High-Throughput Screening of Hypercontractile Cellular Phenotypes in Fibrosis: A Function-First Approach to Anti-Fibrotic Drug Discovery","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"California NanoSystems Institute; Canadian Institutes of Health Research","keywords":"Myofibroblast; Ex vivo; Drug discovery; Fibrosis; Phenotype; In vivo; Downregulation and upregulation; Contractility; Drug; Pharmacology; Cancer research; Phenotypic screening; Computational biology; High-throughput screening; Function (biology); Biology; Cell biology; Bioinformatics; Medicine; Pathology; Gene; Biochemistry; Endocrinology; Genetics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001003948,0.0008521269,0.001021594,0.0009700477,0.0003268951,0.0006754794,0.0005176069,0.0005493564,0.00399342],"category_scores_gemma":[0.0007943734,0.0002826249,0.0004793036,0.0005116136,0.000414155,0.0004917739,0.0004462702,0.001022991,0.002355008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004827745,"about_ca_system_score_gemma":0.0004904376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001130968,"about_ca_topic_score_gemma":0.002106005,"domain_scores_codex":[0.9992571,0.0001725593,0.00004046259,0.0001137857,0.0003552346,0.00006072058],"domain_scores_gemma":[0.9995826,0.0001763579,0.00005218155,0.00005728678,0.00009645618,0.00003517475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001563479,0.0000592439,0.0003838205,0.00009618737,0.00002245893,0.000057103,0.00001926871,0.0008032872,0.9884278,0.0003814386,0.0008100354,0.00878289],"study_design_scores_gemma":[0.00003820917,0.0003221755,0.001523784,0.00001216579,0.00002265427,0.0001352089,0.00001343518,0.005362512,0.9850681,0.0001749564,0.00730835,0.00001840531],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5757479,0.004956176,0.3801875,0.00175306,0.0002787297,0.001215011,0.01630815,0.007813255,0.01174011],"genre_scores_gemma":[0.7547395,0.003663653,0.1937815,0.0007868098,0.0001359327,0.002088172,0.01739258,0.0007729267,0.02663894],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00399342,"threshold_uncertainty_score":0.01335931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01438103056943254,"score_gpt":0.2209817203525028,"score_spread":0.2066006897830703,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}