{"id":"W2907368812","doi":"10.1016/j.addr.2019.01.001","title":"Zebrafish as a preclinical in vivo screening model for nanomedicines","year":2019,"lang":"en","type":"review","venue":"Advanced Drug Delivery Reviews","topic":"Zebrafish Biomedical Research Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":152,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Freiwillige Akademische Gesellschaft","keywords":"Nanomedicine; Zebrafish; Biochemical engineering; In vivo; Computational biology; Computer science; Nanotechnology; Engineering; Biology; Biotechnology; Materials science","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001487191,0.0006136599,0.002390479,0.0001960273,0.00007651836,0.00002816314,0.001024454,0.0005135422,0.00004912087],"category_scores_gemma":[0.002186921,0.0004935926,0.001047391,0.000449167,0.0001818627,0.00001585538,0.0004215638,0.000507699,0.0001660234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007886614,"about_ca_system_score_gemma":0.0007300651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008930147,"about_ca_topic_score_gemma":0.00007081622,"domain_scores_codex":[0.9956558,0.0002930835,0.001624098,0.001322027,0.0003279406,0.0007770055],"domain_scores_gemma":[0.9971907,0.0003954411,0.0006095848,0.001291849,0.0001584981,0.0003539496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003894003,0.00008573198,0.000001019459,0.008701573,0.00006347031,0.000001700644,0.000007428387,0.00002611137,0.0002655247,0.00002141489,0.01945096,0.9713361],"study_design_scores_gemma":[0.0006466171,0.0001333881,1.740856e-7,0.009086986,0.0002108748,0.000007427889,0.000005426534,0.0009853104,0.00004342674,0.00007548061,0.9883215,0.0004834129],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00002621627,0.9875157,0.005262047,0.000117297,0.0001301266,0.00629285,0.0002517026,0.00002112792,0.0003829435],"genre_scores_gemma":[0.000005126035,0.9781857,0.01018333,0.0006562867,0.0003440613,0.004287133,0.001083331,0.0001159804,0.005139044],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9708527,"threshold_uncertainty_score":0.9997516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09726177127487146,"score_gpt":0.4253172991627582,"score_spread":0.3280555278878868,"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."}}