{"id":"W4380606669","doi":"10.1101/2023.06.14.545014","title":"Predicting T cell activation based on intracellular calcium fluctuations","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"CAR-T cell therapy research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Hôpital Maisonneuve-Rosemont","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Fonds de Recherche du Québec - Santé; Radboud Universiteit","keywords":"T-cell receptor; Antigen; Cytotoxic T cell; Biology; T cell; Cell biology; Polyclonal antibodies; Streptamer; Intracellular; Molecular biology; In vitro; Immunology; Immune system; 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":[0.0002109502,0.0005950942,0.0002942238,0.0006239798,0.0001443792,0.0005003421,0.0002530174,0.0005805417,0.0008031747],"category_scores_gemma":[0.0008352419,0.0001436069,0.0001907944,0.0003088385,0.0001772795,0.0003078681,0.0002268986,0.0004882954,0.0002288948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004742583,"about_ca_system_score_gemma":0.0003503997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002563221,"about_ca_topic_score_gemma":0.002722548,"domain_scores_codex":[0.9999092,0.000007568233,0.000005752547,0.00004084455,0.00002091375,0.00001579788],"domain_scores_gemma":[0.99979,0.00006871873,0.00005228529,0.00001281719,0.00004462089,0.00003152487],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001014733,0.0001853374,0.06863388,0.000125777,0.00008645011,0.000209385,0.00003952735,0.4245812,0.4264167,0.0007732952,0.001579056,0.07635455],"study_design_scores_gemma":[0.000005653959,0.00003560251,0.005001646,0.000002718362,0.000006688308,0.00002195728,0.000005511735,0.9689654,0.02560795,0.0002364066,0.0001047392,0.000005887081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9120427,0.0002720362,0.08444043,0.0002283565,0.00004939856,0.00003936369,0.0007025262,0.000990802,0.001234425],"genre_scores_gemma":[0.9910489,0.00007313544,0.008196744,0.00002431255,0.000009269779,0.00001423467,0.0001974041,0.00001322405,0.000422801],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002563221,"threshold_uncertainty_score":0.005096614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03854715687238681,"score_gpt":0.2796330752602993,"score_spread":0.2410859183879125,"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."}}