{"id":"W2920332083","doi":"10.1194/jlr.d090571","title":"Quantitative metabolic profiling of urinary eicosanoids for clinical phenotyping","year":2019,"lang":"en","type":"article","venue":"Journal of Lipid Research","topic":"Eicosanoids and Hypertension Pharmacology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Vetenskapsrådet; Karolinska Institutet; Hjärt-Lungfonden; Stockholms Läns Landsting; AstraZeneca Canada; AllerGen; Canada Research Chairs; Stiftelsen för Strategisk Forskning; Vårdalstiftelsen","keywords":"Isoprostanes; Eicosanoid; Inflammation; Urinary system; Urine; Medicine; Asthma; Lipid signaling; Chemistry; Pharmacology; Oxidative stress; Internal medicine; Biochemistry; Arachidonic acid; Enzyme; Lipid peroxidation","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":[0.004203243,0.00009119506,0.0004067931,0.0001834159,0.00006333331,0.0000114677,0.0003202032,0.0001604492,0.00004756625],"category_scores_gemma":[0.0007929162,0.00006983735,0.0002791719,0.0001430128,0.0001614959,0.000009264948,0.000155055,0.000427329,0.000008318677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007188497,"about_ca_system_score_gemma":0.0003652692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001248729,"about_ca_topic_score_gemma":3.820884e-7,"domain_scores_codex":[0.9980602,0.0004615556,0.0006728292,0.0002000104,0.0003137063,0.0002917319],"domain_scores_gemma":[0.9976318,0.0003578966,0.000304166,0.0001918631,0.001398536,0.00011574],"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":[0.001930506,0.0003513928,0.01259773,0.00007849546,0.0002212165,0.000004932805,0.00003379618,0.00003261178,0.9760288,0.001319419,0.005335785,0.002065297],"study_design_scores_gemma":[0.004800331,0.01310792,0.008309097,0.0001056135,0.00008431222,0.000131211,0.0007836951,0.001139259,0.8301444,0.0004515915,0.1407072,0.0002353725],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9923864,0.004959517,0.0007766705,0.0004338862,0.0006930705,0.0003153609,0.00001079997,0.000001504016,0.0004228015],"genre_scores_gemma":[0.9845658,0.002461828,0.0113439,0.0001543548,0.001080972,0.000007131875,0.000007542402,0.00002226498,0.0003561949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1458844,"threshold_uncertainty_score":0.2847884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1981013430499207,"score_gpt":0.51074520327162,"score_spread":0.3126438602216993,"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."}}