{"id":"W4321083453","doi":"10.1101/2023.02.14.528498","title":"Optical tools for visualizing and controlling human GLP-1 receptor activation with high spatiotemporal resolution","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Receptor Mechanisms and Signaling","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"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":"Liraglutide; Receptor; Glucagon-like peptide 1 receptor; G protein-coupled receptor; Fluorescence; Chemistry; Derivative (finance); HEK 293 cells; Förster resonance energy transfer; Biophysics; Cell biology; Biology; Biochemistry; Endocrinology; Physics; 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":[0.0002365459,0.0002924666,0.0001071019,0.0002073834,0.000121633,0.0003477457,0.0002723192,0.0003651175,0.001161893],"category_scores_gemma":[0.0002295577,0.0002146148,0.0001678906,0.00008991877,0.000280582,0.0002185057,0.0002937104,0.0004281711,0.0004563542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004057813,"about_ca_system_score_gemma":0.0001614329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003641092,"about_ca_topic_score_gemma":0.0004337833,"domain_scores_codex":[0.9998957,0.00001406153,0.000004968515,0.0000271824,0.00004254081,0.00001554121],"domain_scores_gemma":[0.9999059,0.00004027935,0.00002227469,0.00001225722,0.000008906922,0.00001022954],"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":[0.00001589848,0.000006259023,0.00003632818,0.00002522488,0.000002117082,0.00002750957,0.00001178807,0.0001586762,0.9969995,0.0005385134,0.0001506422,0.002027503],"study_design_scores_gemma":[0.00001182166,0.00002910541,0.0002877509,0.000004894056,0.000004132252,0.0001321483,0.000009744342,0.002588719,0.9903579,0.0001796737,0.006387685,0.000006499252],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6617489,0.004063833,0.3197435,0.0009034168,0.0002588576,0.0001527498,0.0007028882,0.001771987,0.01065385],"genre_scores_gemma":[0.7474569,0.002633657,0.240185,0.0003252798,0.00005834399,0.0002556642,0.0004605485,0.000230586,0.008393951],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001161893,"threshold_uncertainty_score":0.003886938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02596998865305265,"score_gpt":0.252898774034841,"score_spread":0.2269287853817884,"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."}}