{"id":"W1585830496","doi":"","title":"Tissue inhibitor of metalloproteinases-4 inhibits but does not support the activation of gelatinase A via efficient inhibition of membrane type 1-matrix metalloproteinase.","year":2001,"lang":"en","type":"article","venue":"PubMed","topic":"Protease and Inhibitor Mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":144,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Gelatinase A; Hemopexin; Matrix metalloproteinase; Gelatinase; Concanavalin A; Fibroblast activation protein, alpha; Chemistry; Angiogenesis; Tissue inhibitor of metalloproteinase; Matrix metalloproteinase inhibitor; Molecular biology; Transfection; Extracellular matrix; Midkine; Zymogen; Metalloproteinase; Cell biology; Biochemistry; Cancer research; Biology; Enzyme; Receptor; In vitro; Growth factor; Cancer","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":[0.0002428639,0.0005023585,0.000488456,0.0002552716,0.0001126181,0.0004757767,0.0004129815,0.0003206374,0.003533236],"category_scores_gemma":[0.0004949209,0.0001569009,0.0004147743,0.0002018642,0.0002418311,0.0003953079,0.0002485545,0.000680362,0.00183205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003661244,"about_ca_system_score_gemma":0.000415782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006201169,"about_ca_topic_score_gemma":0.0007375831,"domain_scores_codex":[0.9996318,0.00006384975,0.00002950203,0.00005379797,0.0001357309,0.0000852994],"domain_scores_gemma":[0.9996817,0.00008819861,0.00008286402,0.00005651482,0.00004285518,0.00004784199],"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":[0.0001792437,0.00006369425,0.0004105392,0.0002517605,0.00004721358,0.00008891119,0.00001528573,0.0001016184,0.9894708,0.0003802588,0.0006187613,0.008371805],"study_design_scores_gemma":[0.00005142517,0.000482284,0.004340576,0.0000291965,0.00004941598,0.001317373,0.00002097093,0.001198617,0.9581686,0.00020672,0.03412354,0.00001126524],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8805098,0.07170414,0.02447566,0.001290054,0.001079885,0.00021066,0.002582956,0.001087839,0.01705904],"genre_scores_gemma":[0.9628936,0.00927115,0.008680793,0.000277995,0.0001031057,0.0001236164,0.002549403,0.00007887499,0.01602145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003533236,"threshold_uncertainty_score":0.0118199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01209238608909871,"score_gpt":0.2282845917597288,"score_spread":0.21619220567063,"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."}}