{"id":"W7116088642","doi":"10.1016/j.isci.2025.114498","title":"Identification of skeletal muscle stem cell adhesion motifs using spot-synthesis-based peptide arrays","year":2025,"lang":"en","type":"article","venue":"iScience","topic":"Muscle Physiology and Disorders","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier Universitaire de Sherbrooke; Université de Sherbrooke","funders":"Fonds de Recherche du Québec - Santé; Université de Sherbrooke; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Fonds de recherche du Québec – Nature et technologies; Deutsche Forschungsgemeinschaft; Muscular Dystrophy Canada","keywords":"Extracellular matrix; Skeletal muscle; Peptide; Cell adhesion; Adhesion; Phenotype; Peptide library; Cell adhesion molecule","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001913637,0.00008027429,0.00008722016,0.00005449625,0.0001088528,0.00001009223,0.0002303549,0.0000729126,0.000009073826],"category_scores_gemma":[0.00004750125,0.00007795324,0.00006461952,0.0001714154,0.0001908958,0.000006475411,0.00005166857,0.00003752667,0.000004526653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008707077,"about_ca_system_score_gemma":0.0001022799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001568968,"about_ca_topic_score_gemma":0.000005631041,"domain_scores_codex":[0.9992433,0.00005138612,0.0001801375,0.0002916936,0.00008913737,0.0001443233],"domain_scores_gemma":[0.9994911,0.00002397676,0.0001030099,0.000299464,0.00005549111,0.00002695696],"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":[0.000014867,0.0000596054,0.0004990071,0.00003420311,0.000002363258,1.855815e-7,0.00001687049,0.00243482,0.9935178,0.00004675293,0.00007671821,0.003296869],"study_design_scores_gemma":[0.0001228388,0.00003718063,0.01129749,0.00001833235,0.00001063309,1.707818e-7,0.0001135978,0.004845036,0.9829909,0.00008806408,0.0003968364,0.00007893034],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9774466,0.0002721146,0.02123342,0.00004447772,0.0001236413,0.00008806962,0.000006889296,0.00000688779,0.0007779191],"genre_scores_gemma":[0.999302,0.00001314376,0.000348397,0.00008381196,0.00001531192,0.000007647976,0.000005527542,0.000004211664,0.0002199596],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02185541,"threshold_uncertainty_score":0.3178841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0123963040723078,"score_gpt":0.2558792063646604,"score_spread":0.2434829022923526,"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."}}