{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002248238,0.0002756846,0.000548484,0.0009885072,0.0002660612,0.0001472426,0.0004419405,0.0002126609,0.0001858753],"category_scores_gemma":[0.0004725077,0.0002034534,0.0002305941,0.0008502887,0.0001349172,0.00007511994,0.0001767531,0.0003332619,0.00002226595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001079306,"about_ca_system_score_gemma":0.0004252697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002318823,"about_ca_topic_score_gemma":0.000002856414,"domain_scores_codex":[0.9967718,0.0004422679,0.0007618308,0.0004798639,0.0008114754,0.0007328344],"domain_scores_gemma":[0.9975646,0.00002637787,0.0004887714,0.000400207,0.00108582,0.0004342191],"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":[0.00298546,0.0004127711,0.0001792618,0.00004055807,0.0002643916,0.00002876076,0.00006285375,0.000001741392,0.9953702,0.00005789422,0.000109402,0.0004867108],"study_design_scores_gemma":[0.002148553,0.004725093,0.002348332,0.00008044941,0.00004972942,0.0001410549,0.00008301675,0.00003588773,0.978165,0.00006300467,0.01189995,0.0002599555],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9645252,0.0003782481,0.03257006,0.000987543,0.000472646,0.0009320268,0.0000876593,0.00001099203,0.00003563783],"genre_scores_gemma":[0.9879307,0.002396732,0.00727991,0.0000833387,0.00196517,0.00004814039,0.00004474326,0.00005901413,0.0001922986],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02529015,"threshold_uncertainty_score":0.8296587,"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."}}