{"id":"W2730696354","doi":"10.1021/acs.molpharmaceut.7b00325","title":"Multiresponsive Nanogels for Targeted Anticancer Drug Delivery","year":2017,"lang":"en","type":"article","venue":"Molecular Pharmaceutics","topic":"Nanoparticle-Based Drug Delivery","field":"Materials Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"MacEwan University; University of Alberta","funders":"Grand Challenges Canada; Natural Sciences and Engineering Research Council of Canada; MacEwan University; National Natural Science Foundation of China; Canada Foundation for Innovation; University of Alberta; Alberta Innovates - Technology Futures","keywords":"Nanogel; Doxorubicin; Chemistry; Drug delivery; Concanavalin A; Cancer cell; Targeted drug delivery; Biophysics; Nanotechnology; Biochemistry; Cancer; Materials science; Biology; In vitro; Chemotherapy","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":[0.000689539,0.0002766563,0.0002954346,0.00007863811,0.0007047205,0.0003100462,0.000858149,0.00008206987,0.0002012312],"category_scores_gemma":[0.0004073597,0.0002821312,0.0001717681,0.00007015822,0.0003178434,0.0003574453,0.0002779615,0.0001390852,0.0003343079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001139429,"about_ca_system_score_gemma":0.000210524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005832554,"about_ca_topic_score_gemma":0.00001161091,"domain_scores_codex":[0.9977975,0.0001473374,0.0003403072,0.0005361214,0.0004158592,0.0007628591],"domain_scores_gemma":[0.9979398,0.0001854438,0.0002758424,0.0009726085,0.0003549347,0.0002713236],"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":[0.0003379483,0.0001086921,0.0003330892,0.00004370832,0.00003358581,0.0001307397,0.0001577724,0.0003416373,0.9942824,0.0001423873,0.001402101,0.002685926],"study_design_scores_gemma":[0.00209016,0.00004034664,0.0002319959,0.00003002678,0.0001265759,0.000005971962,0.0000321211,0.01185132,0.972205,0.0002665254,0.01275322,0.000366684],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932868,0.0009006422,0.002223986,0.001058192,0.001210105,0.0007872651,0.0002085293,0.0001406938,0.0001837926],"genre_scores_gemma":[0.9929083,0.00005439477,0.004691884,0.001734335,0.0001333093,0.0001077671,0.00001192061,0.0000644832,0.0002935776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02207736,"threshold_uncertainty_score":0.9999631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03089573800892308,"score_gpt":0.331093876553003,"score_spread":0.30019813854408,"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."}}