{"id":"W2092933242","doi":"10.1016/j.biomaterials.2013.09.097","title":"Differentiation of neuronal stem cells into motor neurons using electrospun poly-l-lactic acid/gelatin scaffold","year":2013,"lang":"en","type":"article","venue":"Biomaterials","topic":"Nerve injury and regeneration","field":"Neuroscience","cited_by":138,"is_retracted":false,"has_abstract":false,"ca_institutions":"New World Laboratories; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gelatin; Neurite; Neural stem cell; Neural tissue engineering; Materials science; Scaffold; Stem cell; Cellular differentiation; Cell biology; Tissue engineering; Biomedical engineering; Biology; Biochemistry; Medicine; In vitro","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002001117,0.0003602758,0.0001475574,0.0002814643,0.0001406125,0.0002881075,0.000150074,0.0002786124,0.0007549436],"category_scores_gemma":[0.0001235299,0.0001735125,0.0002136198,0.0001819118,0.0001855489,0.0002942906,0.0001915533,0.0002302919,0.0002224866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001352668,"about_ca_system_score_gemma":0.0002564413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006374713,"about_ca_topic_score_gemma":0.002130034,"domain_scores_codex":[0.9999101,0.000006193971,0.00001424819,0.00002030607,0.00002771961,0.00002127701],"domain_scores_gemma":[0.9999107,0.00001763012,0.00002481638,0.000009057449,0.00001577767,0.00002200991],"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":[0.00002438772,0.000009063595,0.00007188534,0.00003226679,0.00000240333,0.00005448347,0.00002154613,0.00009857368,0.9985539,0.00006982013,0.00001164375,0.001049938],"study_design_scores_gemma":[0.000008090598,0.0000931793,0.001000094,0.000006101662,0.000009267526,0.0001011456,0.00002245531,0.0006632687,0.9970186,0.00002694542,0.001048399,0.000002498311],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9879684,0.00075095,0.007760981,0.00004256393,0.00004223198,0.00004593603,0.0001153916,0.00009937239,0.003174101],"genre_scores_gemma":[0.9882509,0.0005869842,0.007962862,0.0000238267,0.000007426434,0.00003509363,0.000115632,0.00002092884,0.002996434],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007549436,"threshold_uncertainty_score":0.002525568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03193074536627756,"score_gpt":0.2570307639888173,"score_spread":0.2251000186225398,"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."}}