{"id":"W2606408485","doi":"10.1039/c6nr09182b","title":"Intrinsic functional and architectonic heterogeneity of tumor-targeted protein nanoparticles","year":2017,"lang":"en","type":"article","venue":"Nanoscale","topic":"Nanoparticle-Based Drug Delivery","field":"Materials Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Division of Materials Research; Secretaría de Estado de Investigación, Desarrollo e Innovación; Instituto de Salud Carlos III; Consejo de Seguridad Nuclear; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina; Institució Catalana de Recerca i Estudis Avançats; Agència de Gestió d'Ajuts Universitaris i de Recerca; Fundació la Marató de TV3; National Science Foundation","keywords":"Nanoparticle; Nanotechnology; Materials science; Computational biology; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.0003805644,0.0001500297,0.0002416989,0.00005887824,0.0003605102,0.0001064475,0.0002970998,0.00004911928,0.0002252328],"category_scores_gemma":[0.0002612382,0.0001355728,0.00005784245,0.00006166618,0.000494982,0.0002750593,0.0002476368,0.00008616426,0.0001368177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003051902,"about_ca_system_score_gemma":0.00008612828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00013,"about_ca_topic_score_gemma":0.000094063,"domain_scores_codex":[0.9985756,0.00009471647,0.0003093013,0.0003633012,0.000320541,0.0003365698],"domain_scores_gemma":[0.9988161,0.00005470488,0.0002462342,0.0006419717,0.0001051642,0.0001357866],"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":[0.0001508282,0.0001150212,0.01099673,0.0000371143,0.000006682513,0.00001005053,0.00003598539,0.00001639874,0.98675,0.0002774716,0.00005102531,0.001552678],"study_design_scores_gemma":[0.0007205305,0.0001288534,0.08567599,0.00003755172,0.00001411817,0.00001315001,0.00001061209,0.00009960439,0.9125183,0.0005110271,0.00013204,0.0001382741],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987504,0.0002187075,0.00004174077,0.0002338944,0.000224289,0.0002973535,0.00003660578,0.0000653964,0.000131596],"genre_scores_gemma":[0.9988137,0.000002519752,0.0009124164,0.00005402059,0.00006172423,0.00004120491,0.000001907092,0.00001622586,0.00009621359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07467927,"threshold_uncertainty_score":0.5528497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01845497126123705,"score_gpt":0.239208391245214,"score_spread":0.220753419983977,"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."}}