{"id":"W2070363546","doi":"10.1155/2013/252531","title":"Systemic siRNA Delivery via Peptide-Tagged Polymeric Nanoparticles, Targeting PLK1 Gene in a Mouse Xenograft Model of Colorectal Cancer","year":2013,"lang":"en","type":"article","venue":"International Journal of Biomaterials","topic":"RNA Interference and Gene Delivery","field":"Biochemistry, Genetics and Molecular Biology","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Gene knockdown; Polyethylene glycol; Chemistry; Biodistribution; Gene delivery; Cytotoxicity; PLK1; In vivo; Cancer research; Nanoparticle; PEG ratio; Peptide; In vitro; Transfection; Nanotechnology; Medicine; Cell; Materials science; Biochemistry; Apoptosis; Biology; Gene; Cell cycle","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":[],"consensus_categories":[],"category_scores_codex":[0.0002650986,0.000152125,0.0002885713,0.0001772948,0.00001963399,0.00004977009,0.0004210817,0.0001067439,0.00009284489],"category_scores_gemma":[0.00004837306,0.000132949,0.0001331926,0.00005871495,0.00006069951,0.00003670474,0.0001085638,0.0000379366,0.00001260419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005642993,"about_ca_system_score_gemma":0.0001343484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005041226,"about_ca_topic_score_gemma":0.00001718123,"domain_scores_codex":[0.9984123,0.00008306737,0.0008658571,0.0001668222,0.0002751185,0.0001968305],"domain_scores_gemma":[0.9986237,0.00001176867,0.0006322325,0.0000956236,0.0005660409,0.00007059983],"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":[0.0003868744,0.00009230232,0.002816635,0.00001530936,0.0002020063,0.00001144156,0.0001109445,0.001524829,0.9938246,0.000001713839,0.0004822232,0.0005310949],"study_design_scores_gemma":[0.0007073606,0.0002027628,0.0008122292,0.0001003667,0.00001565958,0.0001008041,0.0001019213,0.00150198,0.9962736,0.00003232448,0.00001520231,0.0001357874],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99749,0.001216729,0.0003237404,0.00007858265,0.0006646159,0.0001262211,0.00008108262,0.00000426384,0.00001477464],"genre_scores_gemma":[0.9980942,0.0006395878,0.0007828487,0.00008985987,0.0002495562,0.00001884679,0.00002620049,0.0000181186,0.0000807598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002448975,"threshold_uncertainty_score":0.5421503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01027229457640361,"score_gpt":0.2462846697118613,"score_spread":0.2360123751354577,"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."}}