{"id":"W2026822060","doi":"10.1021/acsnano.5b01077","title":"A PEGylation-Free Biomimetic Porphyrin Nanoplatform for Personalized Cancer Theranostics","year":2015,"lang":"en","type":"article","venue":"ACS Nano","topic":"Nanoplatforms for cancer theranostics","field":"Engineering","cited_by":171,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research; Canada Foundation for Innovation; National Natural Science Foundation of China; Ontario Institute for Cancer Research; Prostate Cancer Canada; Congressionally Directed Medical Research Programs; Natural Sciences and Engineering Research Council of Canada; Princess Margaret Cancer Foundation","keywords":"PEGylation; Photodynamic therapy; Fluorescence-lifetime imaging microscopy; Biodistribution; Positron emission tomography; Drug delivery; Molecular imaging; Chemistry; Nanotechnology; Biophysics; Materials science; Fluorescence; Polyethylene glycol; Medicine; In vivo; Biochemistry; Nuclear medicine; In vitro; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002139519,0.0003272122,0.0003462355,0.0001581096,0.00009503983,0.00007006185,0.0004568396,0.0001899851,0.00008575127],"category_scores_gemma":[0.000179539,0.0003063713,0.0001320833,0.0003672478,0.00007833881,0.000301577,0.00005171203,0.0001273026,0.00007163759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003826756,"about_ca_system_score_gemma":0.0001823775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006075027,"about_ca_topic_score_gemma":0.00007384732,"domain_scores_codex":[0.9984789,0.000007272281,0.0003716275,0.0002525718,0.0003592429,0.00053034],"domain_scores_gemma":[0.9987811,0.0001697194,0.00007457363,0.0005368236,0.0002229554,0.0002148013],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000973977,0.0001994671,0.00246536,0.0006702051,0.001271725,0.00002699477,0.006798079,0.01310216,0.8042377,0.01632583,0.1130468,0.04088169],"study_design_scores_gemma":[0.01812912,0.0005428388,0.0002890719,0.0002352354,0.0004684443,0.00003972193,0.000597749,0.02265561,0.3374729,0.03591459,0.58161,0.002044691],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9107788,0.01483753,0.05529533,0.0006709445,0.00672523,0.002684768,0.001121764,0.001729332,0.006156346],"genre_scores_gemma":[0.9889947,0.0003958174,0.006279869,0.0002587065,0.000535807,0.0003544126,0.0001011608,0.0002093443,0.002870152],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4685632,"threshold_uncertainty_score":0.9999388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03418861684974758,"score_gpt":0.2583933910528881,"score_spread":0.2242047742031405,"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."}}