{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000286434,0.000858294,0.0006125639,0.0004519144,0.0001577685,0.0004534102,0.0002718896,0.0002584856,0.003204432],"category_scores_gemma":[0.0002219177,0.0002418986,0.0005732605,0.0003176835,0.0002224543,0.0002670753,0.0002825053,0.0008059799,0.0007873976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002024384,"about_ca_system_score_gemma":0.0003187468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002951706,"about_ca_topic_score_gemma":0.0006717565,"domain_scores_codex":[0.999671,0.00006478447,0.00003699759,0.00004620383,0.00007896547,0.0001021185],"domain_scores_gemma":[0.9997975,0.0000433969,0.00003802526,0.00002440701,0.00001833777,0.00007826192],"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":[0.0003002518,0.0001025699,0.0001537306,0.00009516897,0.0000228412,0.00009877639,0.000009176157,0.00005302495,0.997234,0.00004765195,0.0001271986,0.001755548],"study_design_scores_gemma":[0.00005287508,0.001195306,0.002248509,0.000006553014,0.00004446684,0.000455811,0.00001578551,0.0004128369,0.9911035,0.0000249389,0.004433448,0.000005955746],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9743124,0.01000094,0.006520831,0.0003959603,0.0005293221,0.000190936,0.000747444,0.0003877857,0.006914353],"genre_scores_gemma":[0.9804114,0.003788084,0.007332805,0.0001759757,0.0001483076,0.0001565787,0.001017068,0.00003715948,0.006932682],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003204432,"threshold_uncertainty_score":0.0107199,"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."}}