{"id":"W2789884447","doi":"10.1016/j.biopha.2018.01.172","title":"Annona muricata Linn. leaf as a source of antioxidant compounds with in vitro antidiabetic and inhibitory potential against α-amylase, α-glucosidase, lipase, non-enzymatic glycation and lipid peroxidation","year":2018,"lang":"en","type":"article","venue":"Biomedicine & Pharmacotherapy","topic":"Natural Antidiabetic Agents Studies","field":"Medicine","cited_by":169,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Institute of Genetics; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Universidade Federal de Uberlândia; Faculty of Science and Engineering, University of Manchester","keywords":"Chemistry; Methylglyoxal; DPPH; Lipid peroxidation; Annona muricata; Antioxidant; Trolox; Oxygen radical absorbance capacity; ABTS; Glycation; Gallic acid; Biochemistry; Traditional medicine; Enzyme; Medicine","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":[0.0001008949,0.000558224,0.000433769,0.0006549703,0.0003477197,0.0002063217,0.0001793157,0.0002681551,0.001678543],"category_scores_gemma":[0.0001210269,0.0001970634,0.0003656592,0.0003083398,0.0001255636,0.0004227258,0.0002644616,0.0005346661,0.0002375372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009228243,"about_ca_system_score_gemma":0.0001650717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004410339,"about_ca_topic_score_gemma":0.0008863832,"domain_scores_codex":[0.9999588,0.000009158698,0.00000485699,0.00001012381,0.00001016837,0.000006952873],"domain_scores_gemma":[0.9999428,0.00001275902,0.00001240218,0.00000490293,0.00001344107,0.00001374627],"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":[0.0009854153,0.000189771,0.0003281026,0.0003861001,0.00002988729,0.000168453,0.00006577139,0.00008943467,0.985846,0.0001634033,0.0001172759,0.01163046],"study_design_scores_gemma":[0.0001741786,0.002506213,0.0100067,0.00009963487,0.000460187,0.0008547218,0.00009385877,0.0008593393,0.9681048,0.0003417365,0.01647326,0.00002522727],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9704663,0.02036492,0.004580458,0.0004595148,0.0002072924,0.00006518322,0.0004374227,0.0001216207,0.00329726],"genre_scores_gemma":[0.9834996,0.004437637,0.005005843,0.0003443334,0.00007714071,0.00004540377,0.0005320841,0.00002250204,0.006035378],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001678543,"threshold_uncertainty_score":0.005615354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007334180918393997,"score_gpt":0.2659852605638705,"score_spread":0.2586510796454765,"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."}}