{"id":"W4385335504","doi":"10.1021/acs.nanolett.3c02186","title":"Toward Predicting Nanoparticle Distribution in Heterogeneous Tumor Tissues","year":2023,"lang":"en","type":"article","venue":"Nano Letters","topic":"Nanoparticle-Based Drug Delivery","field":"Materials Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; NanoMedicines Innovation Network","keywords":"Nanoparticle; Computer science; Identification (biology); Distribution (mathematics); Biological system; Nanotechnology; Tumor cells; Tumor heterogeneity; Materials science; Biology; Mathematics; Cancer; Cancer research","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":[0.0004528182,0.0005410652,0.0004727379,0.000798923,0.000193544,0.0007115683,0.000517435,0.000616109,0.0003404148],"category_scores_gemma":[0.001914808,0.0003675126,0.0003847682,0.000499377,0.0003699703,0.0004341974,0.0003188335,0.0003955296,0.0001786564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005435608,"about_ca_system_score_gemma":0.0007297975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005883935,"about_ca_topic_score_gemma":0.005397792,"domain_scores_codex":[0.9998832,0.0000326896,0.000005722588,0.00003855753,0.00002956068,0.00001023149],"domain_scores_gemma":[0.9995412,0.0002810226,0.00005637505,0.00004045125,0.00005971697,0.00002130082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006362808,0.0000340285,0.004627413,0.00001646435,0.00001686579,0.00006063727,0.00001608142,0.9705846,0.01551307,0.0006540311,0.0001312279,0.008281977],"study_design_scores_gemma":[0.000001790378,0.000005179078,0.000267218,6.215525e-7,0.000001561637,0.000007297615,0.000004261315,0.9966847,0.002643224,0.0003441706,0.00003830195,0.000001600124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5077831,0.0001753675,0.4893335,0.0001745636,0.00001525147,0.00006651145,0.0003378842,0.0009706442,0.001143232],"genre_scores_gemma":[0.929859,0.0001177469,0.06921755,0.00003840408,0.000007701676,0.00005334716,0.0002099204,0.00005747957,0.0004388827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005883935,"threshold_uncertainty_score":0.01169938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01864570233999735,"score_gpt":0.2430981055986143,"score_spread":0.2244524032586169,"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."}}