{"id":"W2088172329","doi":"10.1074/jbc.m006871200","title":"Tissue Inhibitor of Metalloproteinase (TIMP)-2 Acts Synergistically with Synthetic Matrix Metalloproteinase (MMP) Inhibitors but Not with TIMP-4 to Enhance the (Membrane Type 1)-MMP-dependent Activation of Pro-MMP-2","year":2000,"lang":"en","type":"article","venue":"Journal of Biological Chemistry","topic":"Protease and Inhibitor Mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":135,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Cancer Institute; National Institutes of Health; U.S. Department of Defense","keywords":"Matrix metalloproteinase; Matrix metalloproteinase inhibitor; Gelatinase A; Angiogenesis; Chemistry; Extracellular matrix; Metalloproteinase; Cell biology; Tissue inhibitor of metalloproteinase; Biochemistry; Cancer research; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006312046,0.0004019414,0.0006448948,0.00004820933,0.00008658065,0.00003023286,0.0005164529,0.0003794242,0.0003110164],"category_scores_gemma":[0.0005249553,0.000222744,0.0001834428,0.000274698,0.0002993247,0.00002586439,0.0001196416,0.0004129859,0.000007898496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004571406,"about_ca_system_score_gemma":0.000269166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001847757,"about_ca_topic_score_gemma":0.000002052374,"domain_scores_codex":[0.9975226,0.0001310832,0.000872655,0.0004746389,0.0006096526,0.000389365],"domain_scores_gemma":[0.9978179,0.00006312721,0.0007556605,0.000534428,0.0005591724,0.0002696528],"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":[0.00459115,0.0003829534,0.00001043634,0.0001476832,0.0002015975,0.0000549056,0.0000262288,0.0002102214,0.9920912,0.00001508677,0.00004500709,0.00222353],"study_design_scores_gemma":[0.0006742444,0.004040355,0.00002267511,0.0003401204,0.0001216558,0.0002506333,0.0000886048,0.000007468067,0.9925516,0.00003929341,0.001547487,0.0003158582],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971329,0.0003398675,0.001014949,0.0003743803,0.00004873296,0.0006258814,0.00003630306,0.00001176138,0.0004152526],"genre_scores_gemma":[0.9955803,0.0001178103,0.002984261,0.0001162337,0.0003857764,0.00004372669,0.00002485549,0.00003519264,0.0007118351],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003916905,"threshold_uncertainty_score":0.9083237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009531834222945578,"score_gpt":0.2505341931826099,"score_spread":0.2410023589596644,"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."}}