{"id":"W3039005864","doi":"10.1016/j.jpba.2020.113458","title":"Characterization of CGK012 in rat plasma by high performance liquid chromatography and mass spectrometry (HPLC–MS/MS): Application to a pharmacokinetic study","year":2020,"lang":"en","type":"article","venue":"Journal of Pharmaceutical and Biomedical Analysis","topic":"Wnt/β-catenin signaling in development and cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"National Research Foundation of Korea","keywords":"Chemistry; Protein precipitation; Bioanalysis; Chromatography; Pharmacokinetics; Formic acid; High-performance liquid chromatography; Acetonitrile; Mass spectrometry; Liquid chromatography–mass spectrometry; Tandem mass spectrometry; Pharmacology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003249386,0.0001397372,0.0003572251,0.0002734976,0.00003060517,0.00001636331,0.0001477621,0.00007809456,0.00008291358],"category_scores_gemma":[0.00002199498,0.0001090234,0.00008023092,0.001187243,0.0001038126,0.00001278218,0.00007118563,0.0001490417,8.9426e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001057617,"about_ca_system_score_gemma":0.00002783573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003985154,"about_ca_topic_score_gemma":6.728022e-7,"domain_scores_codex":[0.9986591,0.00006714511,0.000539294,0.0002423818,0.0003241555,0.0001679626],"domain_scores_gemma":[0.9993407,0.00001551091,0.0001919898,0.00005886131,0.0000828017,0.0003101869],"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":[0.0007357593,0.0002287246,0.03592443,0.0000524453,0.0005093199,0.000004487308,0.00009853621,0.00001248206,0.9592381,8.640361e-7,0.0001093866,0.00308551],"study_design_scores_gemma":[0.002401556,0.002115584,0.03027856,0.00002645742,0.0009102391,0.00001073021,0.00006452123,0.004720529,0.9517209,0.00000350704,0.007508983,0.0002384396],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9921567,0.0003851546,0.005973844,0.001271218,0.0000418344,0.0001469012,0.00001433308,0.000003227903,0.000006826113],"genre_scores_gemma":[0.9976057,0.001289518,0.0004269632,0.0004165652,0.0001848513,0.000008973602,0.00004711367,0.000007837431,0.00001246638],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007517159,"threshold_uncertainty_score":0.4445846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008483014895963713,"score_gpt":0.268884306866161,"score_spread":0.2604012919701973,"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."}}