{"id":"W4391389443","doi":"10.1021/acscentsci.3c01250","title":"High-Performance Genetically Encoded Green Fluorescent Biosensors for Intracellular <scp>l</scp>-Lactate","year":2024,"lang":"en","type":"article","venue":"ACS Central Science","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; University of Alberta; University of Toronto","funders":"National Institute of Neurological Disorders and Stroke; Ministry of Education, Culture, Sports, Science and Technology; Japan Society for the Promotion of Science; National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; Japan Agency for Medical Research and Development","keywords":"Green fluorescent protein; Biosensor; Intracellular; Biochemistry; Glycolysis; Fluorescence; Chemistry; Extracellular; Ex vivo; Cell biology; In vitro; Biology; Biophysics; Metabolism; 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":[0.0004704522,0.0002355761,0.0001780631,0.0001057977,0.0002395245,0.0001509928,0.0006349622,0.0001231778,0.000007519776],"category_scores_gemma":[0.0001247552,0.0002063075,0.0001232843,0.0006637318,0.000508538,0.00001958362,0.0001794516,0.0001024515,0.00002096408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006619105,"about_ca_system_score_gemma":0.0002837139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001787757,"about_ca_topic_score_gemma":0.00001357668,"domain_scores_codex":[0.9974761,0.00003250622,0.0002878185,0.0008191714,0.0004040028,0.0009804167],"domain_scores_gemma":[0.9989868,0.0000322029,0.0000579681,0.0004710458,0.0001419129,0.0003100173],"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":[0.000008933196,0.00002887425,0.001012221,0.00004283978,0.00004973311,0.000005855802,0.00007254242,0.003454837,0.9847675,0.0002998432,0.001496418,0.008760383],"study_design_scores_gemma":[0.0001733287,0.0001803285,0.004403834,0.00002339899,0.00006145115,0.00001175777,0.00003008888,0.02692539,0.9449628,0.00008571984,0.02300031,0.0001416226],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942436,0.001463828,0.003077296,0.0002222783,0.000534406,0.0002955139,0.0000185107,0.00004519317,0.000099409],"genre_scores_gemma":[0.994233,0.0005982457,0.003375422,0.0001251873,0.0004793767,0.00002181198,0.00005064549,0.00002912411,0.001087193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03980475,"threshold_uncertainty_score":0.8412977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006925650539089414,"score_gpt":0.2205936737808375,"score_spread":0.2136680232417481,"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."}}