{"id":"W2072616362","doi":"10.1007/s12264-012-1296-5","title":"Antisense MMP-9 RNA inhibits malignant glioma cell growth in vitro and in vivo","year":2013,"lang":"en","type":"article","venue":"Neuroscience Bulletin","topic":"Protease and Inhibitor Mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Moncton","funders":"National Key Research and Development Program of China; Program for New Century Excellent Talents in University; Natural Science Foundation of Tianjin City; National Natural Science Foundation of China","keywords":"Glioma; Lipofectamine; In vivo; Cancer research; Transfection; Endostatin; MMP9; Biology; Cell growth; Antisense RNA; Small interfering RNA; Matrix metalloproteinase; Gene knockdown; Cell; Cell culture; Genetic enhancement; Molecular biology; Pathology; Medicine; RNA; Downregulation and upregulation; Angiogenesis; Vector (molecular 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":[0.0002087294,0.0006035704,0.0003676742,0.0005121046,0.0003134733,0.0003433867,0.0003835045,0.0003528198,0.002377916],"category_scores_gemma":[0.0001748203,0.0002460085,0.0003017146,0.0002260957,0.0003812599,0.0002304098,0.0001288722,0.001157872,0.0005922426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003681588,"about_ca_system_score_gemma":0.0004340954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001956391,"about_ca_topic_score_gemma":0.002978984,"domain_scores_codex":[0.9997965,0.0000342855,0.00001685194,0.00003474229,0.00005344934,0.00006429102],"domain_scores_gemma":[0.999743,0.0001053314,0.00004153114,0.00003134666,0.00002228372,0.00005647351],"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":[0.0002679347,0.000137798,0.00006690674,0.0000207959,0.000005957472,0.00002744496,0.00001433982,0.00006978558,0.9981493,0.0001435421,0.00008638392,0.001009787],"study_design_scores_gemma":[0.00002387733,0.0005654186,0.0004745949,0.000001308492,0.00001153147,0.00004980832,0.00001151309,0.0004194719,0.9977283,0.000032871,0.000679404,0.000001934427],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9875005,0.001970986,0.004810547,0.0003613363,0.0002479329,0.00005318667,0.0004866911,0.0003052939,0.004263392],"genre_scores_gemma":[0.988822,0.00127278,0.002275787,0.00005619241,0.0000558793,0.00005671312,0.0005351503,0.00005939308,0.006866248],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002377916,"threshold_uncertainty_score":0.007954955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006264845345377384,"score_gpt":0.1983002718185036,"score_spread":0.1920354264731262,"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."}}