{"id":"W4378800761","doi":"10.7554/elife.86628.3","title":"Optical tools for visualizing and controlling human GLP-1 receptor activation with high spatiotemporal resolution","year":2023,"lang":"en","type":"article","venue":"eLife","topic":"Receptor Mechanisms and Signaling","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Université de Genève; University of Toronto; European Commission; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Receptor; Liraglutide; Glucagon-like peptide 1 receptor; G protein-coupled receptor; Glucose homeostasis; Glucagon receptor; Peptide; HEK 293 cells; Förster resonance energy transfer; Glucagon; Chemistry; Fluorescence; Cell biology; Biophysics; Biology; Biochemistry; Hormone; Endocrinology; Insulin; Physics; Insulin resistance; Type 2 diabetes; Agonist","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003041438,0.0004541308,0.0001525082,0.0002658964,0.0001733179,0.0003475054,0.0004353831,0.0004544875,0.001144262],"category_scores_gemma":[0.0002923782,0.0003224107,0.0002463507,0.0001247166,0.000339557,0.0002995932,0.0004195771,0.0006860243,0.0004834876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004504886,"about_ca_system_score_gemma":0.0002584099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004768219,"about_ca_topic_score_gemma":0.0007756323,"domain_scores_codex":[0.9998444,0.00002323374,0.00000731009,0.00003930696,0.00006049661,0.00002519189],"domain_scores_gemma":[0.9998585,0.00005232359,0.0000405333,0.00001886542,0.00001294717,0.0000167414],"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":[0.0000226532,0.00001075576,0.0000499739,0.00005210209,0.000003795939,0.00004006007,0.00001869778,0.0001857484,0.9952318,0.0007066786,0.000204978,0.003472711],"study_design_scores_gemma":[0.00001458251,0.00004957138,0.0003494451,0.000008553367,0.000006976749,0.0002219805,0.00001480025,0.002297453,0.9879305,0.0001976828,0.008897685,0.00001078728],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4563784,0.006029201,0.5195618,0.001159237,0.0002938298,0.0002479225,0.0009313808,0.002211485,0.01318664],"genre_scores_gemma":[0.5746887,0.004797227,0.4121734,0.0004575789,0.00005707125,0.0004895906,0.0005832403,0.0002309713,0.006522088],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001144262,"threshold_uncertainty_score":0.003827929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02763649230142507,"score_gpt":0.287836440873332,"score_spread":0.260199948571907,"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."}}