{"id":"W2908883433","doi":"10.1016/j.nano.2018.11.017","title":"Zebrafish as a predictive screening model to assess macrophage clearance of liposomes in vivo","year":2019,"lang":"en","type":"article","venue":"Nanomedicine Nanotechnology Biology and Medicine","topic":"Immune cells in cancer","field":"Immunology and Microbiology","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"In vivo; Zebrafish; Macrophage; Nanomedicine; Biodistribution; Liposome; Nanotoxicology; Nanotechnology; Drug delivery; In vitro; Computational biology; Chemistry; Biophysics; Biology; Nanoparticle; Materials science; Biotechnology; Biochemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006403707,0.001014085,0.0003891764,0.000736901,0.000353758,0.0003117839,0.000475164,0.0006812279,0.001481484],"category_scores_gemma":[0.0002828788,0.0002923219,0.0004383025,0.0001815692,0.0003533914,0.0003413759,0.0004792995,0.0008860641,0.0004600293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007147446,"about_ca_system_score_gemma":0.0006494939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003640192,"about_ca_topic_score_gemma":0.007388372,"domain_scores_codex":[0.9996802,0.000058695,0.00002690997,0.0000846284,0.00009835414,0.00005132698],"domain_scores_gemma":[0.9997597,0.00004876186,0.00007829747,0.00002670987,0.00005453372,0.00003191781],"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":[0.00005573115,0.00003224005,0.0002769462,0.00003917622,0.000004262675,0.00005690232,0.00002695219,0.0001807145,0.9981233,0.0001854811,0.00007264154,0.000945609],"study_design_scores_gemma":[0.00002105863,0.0008806918,0.0020569,0.00001569509,0.00003921945,0.0001797802,0.00003515957,0.002406283,0.9917352,0.0001142887,0.002497112,0.00001852811],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9054238,0.001937992,0.08217007,0.0003986319,0.0001274962,0.0009570766,0.002868374,0.0007421743,0.005374406],"genre_scores_gemma":[0.8903577,0.002686137,0.08886755,0.0002584589,0.00001550876,0.001526573,0.001879342,0.0002030583,0.01420561],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003640192,"threshold_uncertainty_score":0.00723803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01471690683942159,"score_gpt":0.2873846161227228,"score_spread":0.2726677092833012,"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."}}