{"id":"W2166859885","doi":"10.1039/c5nr05157f","title":"Antisense precision polymer micelles require less poly(ethylenimine) for efficient gene knockdown","year":2015,"lang":"en","type":"article","venue":"Nanoscale","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Gene knockdown; Nucleic acid; Polymer; Micelle; Molecule; Conjugate; Gene; Chemistry; Biophysics; Nanotechnology; Combinatorial chemistry; Materials science; Biochemistry; Biology; Organic chemistry","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":[0.000219753,0.0002086363,0.0002196421,0.00006760348,0.0001088396,0.00002564687,0.0001730368,0.0002468757,0.000001253239],"category_scores_gemma":[0.00009283896,0.0001732342,0.0001762247,0.000129005,0.0001070235,0.000003973579,0.0001151668,0.00006012133,0.000004890141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002369946,"about_ca_system_score_gemma":0.00005565704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002291154,"about_ca_topic_score_gemma":0.00002078576,"domain_scores_codex":[0.9986967,0.00003998928,0.0002606417,0.0004955481,0.0001918864,0.0003152365],"domain_scores_gemma":[0.999058,0.00001999353,0.0001129586,0.0004496977,0.0002085486,0.0001507833],"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":[0.0002304231,0.0001081038,0.0001011637,0.000008096419,0.00002700062,0.000002843138,0.00002564815,0.000009044508,0.9805273,0.00001958025,0.003910029,0.01503077],"study_design_scores_gemma":[0.0005863681,0.0003289631,0.00003798918,0.00001848664,0.00003903872,0.00002112464,0.00009069964,0.0003107865,0.9688795,0.00007017395,0.0293663,0.0002506091],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9741717,0.004749383,0.01956177,0.0003609085,0.000231571,0.0003024056,0.000100021,0.00007045586,0.0004517909],"genre_scores_gemma":[0.9813356,0.0001257287,0.01156972,0.0001972661,0.0003389967,0.00001744987,0.0001836459,0.00003368693,0.00619787],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02545627,"threshold_uncertainty_score":0.7064285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02387483310435285,"score_gpt":0.2917629710587947,"score_spread":0.2678881379544418,"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."}}