{"id":"W2909531635","doi":"10.1002/jbm.b.34307","title":"Synthetic fluorinated polyamides as efficient gene vectors","year":2019,"lang":"en","type":"article","venue":"Journal of Biomedical Materials Research Part B Applied Biomaterials","topic":"RNA Interference and Gene Delivery","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hudbay Minerals (Canada)","funders":"Xinxiang Medical University; Foundation of Henan Educational Committee; National Natural Science Foundation of China","keywords":"Polyethylenimine; Polyamide; Transfection; Cationic polymerization; Cytotoxicity; Materials science; Polymer; HEK 293 cells; Gene delivery; Cell culture; Biophysics; Combinatorial chemistry; Gene; Polymer chemistry; Chemical engineering; Biology; Chemistry; Biochemistry; Genetics; In vitro; Composite material","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000386767,0.0005549909,0.0002075678,0.0002637518,0.0001257451,0.000299602,0.0002644543,0.0004202256,0.0009271398],"category_scores_gemma":[0.0003945149,0.0002226444,0.0002212858,0.0002562228,0.0002511418,0.0003423391,0.0002050497,0.000487892,0.0006073884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002471659,"about_ca_system_score_gemma":0.000196649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001096404,"about_ca_topic_score_gemma":0.0001868444,"domain_scores_codex":[0.9996988,0.00007744465,0.00003892156,0.00006083646,0.0000810268,0.00004299814],"domain_scores_gemma":[0.9998155,0.0000419044,0.0000551982,0.00002462202,0.00003686459,0.00002598152],"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":[0.00005561663,0.00001575714,0.00003691591,0.000096817,0.000003334742,0.00005774033,0.00001491678,0.0001653858,0.996977,0.0003493978,0.00004345072,0.002183729],"study_design_scores_gemma":[0.00001287846,0.000127544,0.0001272656,0.000006158885,0.000006736909,0.0001349733,0.000004359626,0.0002614575,0.9949945,0.0000611202,0.004257347,0.000005449465],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8791319,0.008464383,0.102213,0.000204841,0.0002632609,0.000619884,0.001283906,0.000585055,0.007233909],"genre_scores_gemma":[0.9137279,0.004637229,0.07389224,0.0001119475,0.00004123894,0.0004570692,0.001201989,0.0001150996,0.005815243],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009271398,"threshold_uncertainty_score":0.003101647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02166261449521482,"score_gpt":0.3182887164846245,"score_spread":0.2966261019894096,"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."}}