{"id":"W2067885678","doi":"10.1007/s10856-006-0687-4","title":"Differentiation of preosteoblasts using a delivery system with BMPs and bioactive glass microspheres","year":2007,"lang":"en","type":"article","venue":"Journal of Materials Science Materials in Medicine","topic":"Bone Tissue Engineering Materials","field":"Engineering","cited_by":81,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Food and Drug Administration; Université de Sherbrooke","keywords":"Bone morphogenetic protein 2; Matrix metalloproteinase; Chemistry; Bone morphogenetic protein; Alkaline phosphatase; Bioactive glass; Delivery system; Matrix (chemical analysis); Cell biology; Biomedical engineering; Molecular biology; Biochemistry; In vitro; Biology; Dentistry; Enzyme; Medicine; Chromatography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003526977,0.0002308587,0.0008373295,0.0004878915,0.00004273785,0.00007236852,0.0002550583,0.00008458286,0.00005160032],"category_scores_gemma":[0.0001075623,0.0001655387,0.00001440468,0.0003552905,0.0004161345,0.0004524636,0.00005537088,0.00006944603,8.325096e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002342663,"about_ca_system_score_gemma":0.0000552987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001178245,"about_ca_topic_score_gemma":0.000008826111,"domain_scores_codex":[0.9976959,0.00006590887,0.001194654,0.0001691109,0.0005184159,0.0003560253],"domain_scores_gemma":[0.9988534,0.00008728191,0.000555523,0.0001690925,0.000222114,0.0001125552],"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":[0.0002706914,0.00001369084,0.000235384,0.0006766865,0.00002312499,0.00004010505,0.0006068609,0.002343846,0.9956232,0.00005949446,0.000003165853,0.000103711],"study_design_scores_gemma":[0.0009691227,0.0002771442,0.01360602,0.002119266,0.00004567779,0.0003699777,0.0008330126,0.0002267161,0.9813823,0.00001466734,0.000003094303,0.0001529964],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925558,0.0002441033,0.004520088,0.00001054572,0.002380593,0.0002138402,0.00001699725,0.00003397078,0.00002406699],"genre_scores_gemma":[0.994951,0.00004954256,0.004659194,0.000003333199,0.0003007709,0.00000170677,9.851268e-7,0.00003185442,0.000001682383],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01424094,"threshold_uncertainty_score":0.6750473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009768835484776287,"score_gpt":0.2311416925891476,"score_spread":0.2213728571043714,"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."}}