{"id":"W4400776756","doi":"10.1039/d4sd00138a","title":"An automated screening platform for improving the responsiveness of genetically encoded Ca<sup>2+</sup> biosensors in mammalian cells","year":2024,"lang":"en","type":"article","venue":"Sensors & Diagnostics","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Medical Council of Canada; National Research Council Canada; University of Toronto; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Japan Society for the Promotion of Science; Canadian Institutes of Health Research; University of Toronto","keywords":"Biosensor; Fluorescent protein; Fluorescence; Computational biology; Genetically engineered; Stimulation; Green fluorescent protein; Biology; Cell biology; Chemistry; Nanotechnology; Biochemistry; Neuroscience; Materials science; Gene","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":[0.0005342035,0.0002824831,0.0002932086,0.0001795336,0.0001094598,0.0000676679,0.0002787635,0.0002973647,0.00000176567],"category_scores_gemma":[0.0006849619,0.0002168041,0.000178232,0.0003522808,0.0002316955,0.00001037323,0.00008565078,0.0001727595,0.000001978236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002702903,"about_ca_system_score_gemma":0.0001044418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009084432,"about_ca_topic_score_gemma":0.00007108138,"domain_scores_codex":[0.9981938,0.0001268731,0.0004985656,0.0005521816,0.0001965297,0.0004320438],"domain_scores_gemma":[0.9985858,0.0004401456,0.0001087904,0.0005557355,0.0002147967,0.00009473777],"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":[0.0002120915,0.00007565539,0.0001803765,0.0001048379,0.00006715887,0.00004428781,0.0002371381,0.01310973,0.9781297,0.00006899826,0.0005526891,0.007217272],"study_design_scores_gemma":[0.0002203996,0.0003644381,0.0002483101,0.00009350541,0.00009765648,0.00001521466,0.0005584089,0.1972772,0.7990503,0.00008184335,0.001723131,0.000269574],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912989,0.0005314026,0.00698806,0.0002135044,0.00007440628,0.0004936493,0.0002304086,0.0001498785,0.00001975856],"genre_scores_gemma":[0.9720686,0.0002935702,0.02699761,0.0001403336,0.0001747234,0.00002185136,0.0001897659,0.00005714716,0.00005637864],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1841675,"threshold_uncertainty_score":0.8841015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01138343770287566,"score_gpt":0.2884972106988728,"score_spread":0.2771137729959972,"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."}}