{"id":"W2263590892","doi":"10.1021/acs.jproteome.5b00992","title":"Cinnamaldehyde Characterization as an Antibacterial Agent toward <i>E. coli</i> Metabolic Profile Using 96-Blade Solid-Phase Microextraction Coupled to Liquid Chromatography–Mass Spectrometry","year":2016,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chemistry; Chromatography; Metabolomics; Metabolome; Mass spectrometry; Escherichia coli; Cinnamaldehyde; Metabolic pathway; Solid-phase microextraction; Proteomics; Bacteria; Lipidome; Biochemistry; Metabolism; Gas chromatography–mass spectrometry; Lipid metabolism; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001918529,0.0005323754,0.0002591007,0.0005605043,0.0001756325,0.0003573436,0.0002578771,0.0003262765,0.0006168672],"category_scores_gemma":[0.0003025941,0.0001151798,0.0004457495,0.0004574934,0.0001949055,0.0002808705,0.0001410841,0.0003117684,0.0001966692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002872928,"about_ca_system_score_gemma":0.000340015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002234324,"about_ca_topic_score_gemma":0.003351051,"domain_scores_codex":[0.9997918,0.00003092753,0.00001856361,0.00004828483,0.00007617607,0.00003422797],"domain_scores_gemma":[0.9998494,0.00002879159,0.00003765086,0.00001286371,0.00005690578,0.00001433532],"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":[0.00003985505,0.00001431585,0.0001964457,0.0000265099,0.000004052701,0.00001512489,0.000006142099,0.00005065999,0.9986803,0.00001270246,0.000006473912,0.000947393],"study_design_scores_gemma":[0.000001356228,0.0001008757,0.003008781,0.000002696246,0.00001420419,0.00003224463,0.00001395783,0.0003106991,0.9961606,0.00001226547,0.0003377762,0.000004464715],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9819509,0.001929769,0.01266696,0.0001333325,0.00003052051,0.0001052505,0.001432839,0.0001056455,0.001644769],"genre_scores_gemma":[0.9763282,0.001750489,0.01804462,0.00007861402,0.00001310861,0.00008072459,0.001720052,0.00004476482,0.001939345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002234324,"threshold_uncertainty_score":0.004442692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04479268537566546,"score_gpt":0.3910235481289593,"score_spread":0.3462308627532939,"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."}}