{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002714883,0.0004706355,0.0002980622,0.0001257735,0.00008420238,0.0004293744,0.0001815074,0.0004519644,0.002091591],"category_scores_gemma":[0.0003398437,0.0001962152,0.0001782575,0.0001104099,0.0001941126,0.0005007063,0.0002389038,0.0005393958,0.0009787462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003115818,"about_ca_system_score_gemma":0.0002811178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004190638,"about_ca_topic_score_gemma":0.0009687472,"domain_scores_codex":[0.9998211,0.00002386024,0.00002188249,0.0000425729,0.00007082142,0.00001971492],"domain_scores_gemma":[0.9997906,0.00005590704,0.00006776859,0.00001795477,0.0000474791,0.00002034712],"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":[0.00002010468,0.000009875531,0.00003959483,0.0000361307,0.000003522949,0.00002014099,0.000008879256,0.00009213539,0.9977559,0.0002727025,0.00003844043,0.001702446],"study_design_scores_gemma":[0.000009023215,0.0001177906,0.000382247,0.000004461498,0.000007692401,0.00013474,0.00000430429,0.0006654797,0.9924948,0.00007221426,0.006103245,0.000003981444],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8727264,0.008153149,0.1016786,0.0007792554,0.0001917903,0.0002913774,0.0003601962,0.0008644021,0.01495479],"genre_scores_gemma":[0.9280069,0.003187077,0.04159294,0.0004789672,0.00003820158,0.0002410199,0.0006566755,0.0001602082,0.02563805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002091591,"threshold_uncertainty_score":0.006997049,"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."}}