{"id":"W2222803489","doi":"10.1039/c5nr08463f","title":"Improving nanoparticle diffusion through tumor collagen matrix by photo-thermal gold nanorods","year":2016,"lang":"en","type":"article","venue":"Nanoscale","topic":"Nanoplatforms for cancer theranostics","field":"Engineering","cited_by":119,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Prostate Cancer Canada","keywords":"Nanorod; Colloidal gold; Materials science; Nanoparticle; Diffusion; Matrix (chemical analysis); Nanotechnology; Chemical engineering; Composite material","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":[0.0001592852,0.0002707258,0.0001216771,0.0000732054,0.00009015728,0.000178593,0.000162437,0.0002090911,0.0004522717],"category_scores_gemma":[0.0001430981,0.0001506574,0.0001439268,0.00004987684,0.0001869966,0.0001777346,0.0001133739,0.0002144859,0.0001314305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003436628,"about_ca_system_score_gemma":0.0001590981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001154822,"about_ca_topic_score_gemma":0.002120948,"domain_scores_codex":[0.9999142,0.000009333294,0.000006828761,0.00003231351,0.00001943612,0.00001798333],"domain_scores_gemma":[0.9998877,0.00003603483,0.00003898264,0.00001120158,0.00001273294,0.00001331877],"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":[0.000008944422,0.00000310904,0.00001862754,0.000008636982,6.590245e-7,0.00000527954,0.000004973987,0.00006367986,0.9996051,0.00001961548,0.000005492419,0.000255809],"study_design_scores_gemma":[0.000002745982,0.00002914877,0.0001459688,6.847293e-7,0.00000147717,0.00001260761,0.000003110709,0.0006229846,0.9989938,0.000007116132,0.0001786224,0.000001707147],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920847,0.000407308,0.006358291,0.00005913745,0.00002247976,0.00001919101,0.00004662168,0.00007194423,0.0009302785],"genre_scores_gemma":[0.9889733,0.000290482,0.009121933,0.00003596878,0.000004958526,0.00002152395,0.00004192771,0.00003005324,0.001479895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001154822,"threshold_uncertainty_score":0.002493501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005969169392837805,"score_gpt":0.2106586890927888,"score_spread":0.204689519699951,"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."}}