{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004057955,0.0003956095,0.0007635309,0.0006477029,0.0001744078,0.00002305732,0.0001196073,0.0001170245,0.00003872102],"category_scores_gemma":[0.00003881132,0.0002957511,0.00005381432,0.0008480473,0.001116636,0.0001990073,0.00007183309,0.0002678903,0.0000105714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006982232,"about_ca_system_score_gemma":0.0001165288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00016875,"about_ca_topic_score_gemma":0.000005624336,"domain_scores_codex":[0.9976941,0.00009757737,0.0006577562,0.0005664892,0.000557159,0.0004269016],"domain_scores_gemma":[0.9987385,0.00009516051,0.0003467151,0.0002969046,0.0002944735,0.0002282308],"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":[0.001745742,0.0004824923,0.01146012,0.0004247979,0.0001816379,0.00003053067,0.001345768,4.63353e-7,0.9529701,0.000001955321,0.0009143262,0.03044209],"study_design_scores_gemma":[0.0183307,0.002938503,0.1155463,0.001253269,0.0003608357,0.0001481287,0.0007932614,0.00356214,0.8519443,0.00001482898,0.004617639,0.0004900662],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913987,0.004087561,0.0006227699,0.002395165,0.0002256987,0.001027257,0.00001613406,0.00006367442,0.0001630334],"genre_scores_gemma":[0.9938213,0.002377894,0.001157896,0.00142009,0.0007716524,0.00003280318,0.0001205733,0.00005620345,0.0002415914],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1040862,"threshold_uncertainty_score":0.9999495,"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."}}