{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001849324,0.0001558234,0.000131164,0.0001113097,0.00005346204,0.00005649711,0.0001775617,0.0001027488,0.00004313352],"category_scores_gemma":[0.0001096123,0.0001459641,0.0000305806,0.0002043509,0.0001083681,0.000008633513,0.0001812832,0.0001102337,0.00003584458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000706368,"about_ca_system_score_gemma":0.00003315533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001424919,"about_ca_topic_score_gemma":0.000004921386,"domain_scores_codex":[0.9986271,0.00007597761,0.0002109738,0.000550578,0.0001526015,0.0003827943],"domain_scores_gemma":[0.9996006,0.000008334993,0.00005648787,0.000192457,0.00003122037,0.0001108251],"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":[0.00003035332,0.00006880552,0.0002784505,0.000009660222,1.85792e-7,0.00004837887,0.00001438901,0.000002004465,0.9981307,0.00001537441,0.001246772,0.0001548827],"study_design_scores_gemma":[0.0004875563,0.0001483363,0.0008110032,0.00001506948,0.000001037617,0.0000387497,0.00001771456,0.00009133315,0.9947067,0.00007649636,0.003433202,0.0001728022],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977108,0.0001720644,0.00005226301,0.0006210548,0.0001446823,0.0003171288,0.000006634313,0.000007273976,0.0009680766],"genre_scores_gemma":[0.9981169,0.0001145467,0.0002371514,0.00105967,0.00004941071,0.0000431162,0.000002055593,0.0000145752,0.0003625611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003424044,"threshold_uncertainty_score":0.5952245,"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."}}