{"id":"W4399715349","doi":"10.1016/j.cell.2024.05.035","title":"A metabolomics pipeline highlights microbial metabolism in bloodstream infections","year":2024,"lang":"en","type":"article","venue":"Cell","topic":"Antimicrobial Peptides and Activities","field":"Immunology and Microbiology","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research; National Institute of General Medical Sciences; National Sleep Foundation; Howard Hughes Medical Institute; Division of Chemistry; Canadian HIV Trials Network, Canadian Institutes of Health Research; National Science Foundation","keywords":"Biology; Metabolomics; Context (archaeology); Microbial metabolism; Microbiology; Metabolism; Antibiotic resistance; Bacteria; Antibiotics; Computational biology; Bioinformatics; Biochemistry; Genetics","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":[0.00160807,0.001481916,0.001121843,0.004214587,0.0007977274,0.002850155,0.0007306569,0.001288077,0.009816284],"category_scores_gemma":[0.00142316,0.0006377572,0.00131667,0.002874277,0.0003670702,0.001882918,0.001980989,0.002162818,0.004098526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001176815,"about_ca_system_score_gemma":0.002112189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0024717,"about_ca_topic_score_gemma":0.005936422,"domain_scores_codex":[0.9994437,0.00007532491,0.00002940335,0.0001229691,0.0002486238,0.0000799519],"domain_scores_gemma":[0.9991868,0.0001490383,0.00007042758,0.00007593557,0.0003924496,0.0001253049],"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":[0.00149481,0.000282119,0.01066054,0.0034448,0.0005707378,0.001107434,0.0004456181,0.004549218,0.5436152,0.008188336,0.09600961,0.3296315],"study_design_scores_gemma":[0.0001680449,0.0007750197,0.03720516,0.000860975,0.0005086234,0.001783611,0.0005525009,0.01825075,0.2434125,0.02087169,0.6752514,0.0003597192],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1253977,0.1221081,0.4010211,0.03276151,0.003011258,0.002139694,0.1693022,0.0513363,0.09292226],"genre_scores_gemma":[0.2246567,0.09302965,0.533527,0.007685286,0.00163517,0.001216716,0.1037749,0.003609624,0.03086499],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009816284,"threshold_uncertainty_score":0.03283876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007316151901708792,"score_gpt":0.214393100170915,"score_spread":0.2070769482692062,"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."}}