{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001836408,0.0002449287,0.0001626283,0.0001511782,0.0000952244,0.0002453842,0.0004751862,0.000379842,0.0007885645],"category_scores_gemma":[0.0002658039,0.0001446181,0.0001711655,0.0001451842,0.0002619994,0.0002881841,0.000207091,0.0003445182,0.0002323208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008559871,"about_ca_system_score_gemma":0.0001702879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001222629,"about_ca_topic_score_gemma":0.001541609,"domain_scores_codex":[0.9999126,0.000009681724,0.000004620983,0.00002471756,0.00003616535,0.00001218453],"domain_scores_gemma":[0.9999243,0.00003091612,0.00001690777,0.000004763938,0.00001503424,0.00000804665],"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":[0.00001394284,0.000004618455,0.00006353419,0.00001301245,0.000001452789,0.0000202937,0.000008442884,0.000844816,0.997645,0.0003004068,0.00006414257,0.001020343],"study_design_scores_gemma":[0.000005602958,0.00002741002,0.0004657223,0.000001935454,0.000003218524,0.00003036395,0.0000116229,0.02507416,0.973498,0.000139247,0.0007359463,0.000006761648],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8207542,0.000487505,0.1747182,0.0004292292,0.00003902608,0.00004524515,0.0004585403,0.0006542833,0.002413735],"genre_scores_gemma":[0.9133509,0.0005301888,0.08282622,0.00007840859,0.00001003708,0.00007761049,0.0001866056,0.00006490161,0.002875145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001222629,"threshold_uncertainty_score":0.006210625,"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."}}