{"id":"W4409348483","doi":"10.1016/j.cell.2025.03.017","title":"Engineering TCR-controlled fuzzy logic into CAR T cells enhances therapeutic specificity","year":2025,"lang":"en","type":"article","venue":"Cell","topic":"CAR-T cell therapy research","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Chinese American Medical Society; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Kennedy Trust for Rheumatology Research; Aspen Center for Physics; Fonds Québécois de la Recherche sur la Nature et les Technologies; Kennedy Memorial Trust; National Cancer Institute; National Institutes of Health; National Science Foundation","keywords":"T-cell receptor; Chimeric antigen receptor; Biology; Antigen; Cancer immunotherapy; Immunotherapy; Cancer research; Crosstalk; T cell; Receptor; Tumor microenvironment; Cell biology; Immunology; Immune system; Tumor cells; Biochemistry","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":[0.0001190741,0.0002254669,0.0001164309,0.0001953038,0.0001567356,0.0004244045,0.0002179857,0.0003447935,0.002888464],"category_scores_gemma":[0.000318776,0.0000953447,0.0001524094,0.0001208974,0.0002142096,0.0002309546,0.0001540264,0.0004124913,0.000430654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002345304,"about_ca_system_score_gemma":0.0001948798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004671312,"about_ca_topic_score_gemma":0.0006010793,"domain_scores_codex":[0.9998862,0.00001337915,0.000004759832,0.00002488972,0.00004544912,0.00002538825],"domain_scores_gemma":[0.9999022,0.0000271943,0.0000193266,0.000008223727,0.00002908954,0.00001397463],"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":[0.0001470455,0.00005401873,0.0002335955,0.00004952345,0.000005899877,0.0001115413,0.00003036966,0.0047942,0.9769253,0.004226551,0.0001580989,0.01326384],"study_design_scores_gemma":[0.00003380937,0.0002659267,0.0004466228,0.00001004465,0.00002465466,0.000310885,0.00004592986,0.05316038,0.9402525,0.001299153,0.004138028,0.00001201652],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7929539,0.000563471,0.1848464,0.0003099244,0.0001649318,0.00007000141,0.0001014577,0.0006320921,0.02035784],"genre_scores_gemma":[0.9822518,0.00009436371,0.01440979,0.00006661101,0.000008003713,0.00001031062,0.00002931249,0.0000179348,0.003111929],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002888464,"threshold_uncertainty_score":0.009662926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0187410059740537,"score_gpt":0.2878649279099915,"score_spread":0.2691239219359378,"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."}}