{"id":"W4391822421","doi":"10.3390/molecules29040838","title":"Integrating High-Resolution Mass Spectral Data, Bioassays and Computational Models to Annotate Bioactives in Botanical Extracts: Case Study Analysis of C. asiatica Extract Associates Dicaffeoylquinic Acids with Protection against Amyloid-β Toxicity","year":2024,"lang":"en","type":"article","venue":"Molecules","topic":"Medicinal Plants and Neuroprotection","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Center for Complementary and Integrative Health; National Institute on Aging; National Institutes of Health; Oregon State University","keywords":"Cytotoxicity; Chemistry; Mass spectrometry; Bioassay; Computational biology; Biochemistry; Chromatography; In vitro; Biology; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004107773,0.001020345,0.0005408373,0.0006388714,0.0002862659,0.0007550608,0.0006849963,0.0009798225,0.0006836919],"category_scores_gemma":[0.0007672507,0.0003044426,0.001138888,0.0005263421,0.0003610093,0.0004931216,0.0003947688,0.0005082427,0.0001698369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004865259,"about_ca_system_score_gemma":0.0005388182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009589378,"about_ca_topic_score_gemma":0.01026021,"domain_scores_codex":[0.9999021,0.00002248182,0.000006430864,0.00003296729,0.00002220366,0.00001384032],"domain_scores_gemma":[0.9996964,0.0001924785,0.00003116498,0.00002462167,0.00004029227,0.00001502032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004284109,0.0003261478,0.01384318,0.0004127072,0.0001731956,0.0006408487,0.0001171234,0.9353958,0.03060867,0.001145007,0.0005474794,0.01636153],"study_design_scores_gemma":[0.00001162321,0.000057879,0.001711466,0.000006147316,0.00003027605,0.00004346657,0.00004219398,0.9923633,0.004742852,0.0005948424,0.000388259,0.000007693847],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9503121,0.0005572917,0.04448784,0.0002418971,0.00002291191,0.00009024183,0.001495135,0.001021642,0.001771],"genre_scores_gemma":[0.9337011,0.000462221,0.06210119,0.00009004697,0.00001086335,0.0001310673,0.002457604,0.00008948419,0.0009563353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009589378,"threshold_uncertainty_score":0.01906711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04537287835610573,"score_gpt":0.3065931366912432,"score_spread":0.2612202583351375,"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."}}