{"id":"W2518722611","doi":"10.1016/j.actbio.2016.08.026","title":"Neural tissue engineering: Bioresponsive nanoscaffolds using engineered self-assembling peptides","year":2016,"lang":"en","type":"review","venue":"Acta Biomaterialia","topic":"Supramolecular Self-Assembly in Materials","field":"Materials Science","cited_by":82,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Institute for Nanotechnology; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Advanced Education and Technology; Women and Children's Health Research Institute","keywords":"Neural tissue engineering; Materials science; Neural engineering; Nanotechnology; Tissue engineering; Drug delivery; Peptide; Self-assembling peptide; Computer science; Biomedical engineering; Chemistry; Artificial intelligence; Engineering; Biochemistry","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":["metaepi_narrow","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.001253417,0.001950587,0.003729691,0.001000772,0.0002699829,0.001410549,0.002021881,0.0013845,0.000877133],"category_scores_gemma":[0.0004351823,0.001534373,0.0006494738,0.0007453744,0.0001055631,0.0007062655,0.0008553662,0.0001672888,0.001615793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007166585,"about_ca_system_score_gemma":0.0005614451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007292207,"about_ca_topic_score_gemma":0.000001030715,"domain_scores_codex":[0.9925044,0.000955748,0.002182447,0.00174642,0.0008389684,0.001771997],"domain_scores_gemma":[0.9957196,0.0004290772,0.001274038,0.001874749,0.000247227,0.0004552718],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002817917,0.0000631777,2.538912e-7,0.006600796,0.0002066709,0.0002370997,0.00007785131,0.000002678007,0.9874195,0.00006042837,0.0005494439,0.004753881],"study_design_scores_gemma":[0.0003557973,0.0001159263,0.00000172031,0.006914348,0.0008150658,0.0004508603,0.00000780369,0.00004757723,0.4360729,0.000008220291,0.553586,0.001623753],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.2025066,0.7669936,0.0002197982,0.00003662174,0.0248252,0.002229827,0.0009739758,0.002193697,0.00002061237],"genre_scores_gemma":[0.02375306,0.9553958,0.01506934,0.00005218054,0.003667639,0.0004681042,0.0003553068,0.001093782,0.0001447589],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.5530366,"threshold_uncertainty_score":0.9999119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03468813570782028,"score_gpt":0.3209570427831933,"score_spread":0.286268907075373,"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."}}