{"id":"W4409638613","doi":"10.1021/acs.biomac.4c01110","title":"Glycerol-Based Polymer to Improve the Cellular Uptake of Liposomes","year":2025,"lang":"en","type":"article","venue":"Biomacromolecules","topic":"RNA Interference and Gene Delivery","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; Université Laval","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec; Centre Hospitalier Universitaire de Québec; Canada Foundation for Innovation; Government of Canada; Fonds de recherche du Québec – Nature et technologies; Université Laval","keywords":"Glycerol; Liposome; Chemistry; Polymer; Chemical engineering; Nanotechnology; Polymer science; Biochemistry; Organic chemistry; Materials science; Engineering","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":[0.0001012865,0.0003803339,0.0001082303,0.0002031905,0.00008483701,0.0002013089,0.0001391296,0.0002506851,0.0007674079],"category_scores_gemma":[0.0001245907,0.0001069297,0.0001615876,0.000107669,0.0001608047,0.0002867693,0.00023641,0.0004465143,0.0003509423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003263284,"about_ca_system_score_gemma":0.0002658016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000951839,"about_ca_topic_score_gemma":0.001038986,"domain_scores_codex":[0.9999197,0.00000966761,0.000006964295,0.00001875758,0.00002095461,0.00002406685],"domain_scores_gemma":[0.9999319,0.0000152888,0.00002080223,0.000007553817,0.00001439453,0.000009943948],"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":[0.00001802699,0.00000938539,0.00005266105,0.00002946078,0.000002089579,0.00002470599,0.000009741389,0.0001388877,0.9976592,0.0001491559,0.00002498854,0.001881597],"study_design_scores_gemma":[0.000003113292,0.00003310483,0.0001396182,0.000002149369,0.000004927998,0.00002985047,0.000002800185,0.0004595194,0.9981104,0.00001499377,0.001197444,0.000002061998],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9596041,0.002467767,0.03338849,0.0004098915,0.00006490442,0.000173088,0.0002055806,0.0004706497,0.003215471],"genre_scores_gemma":[0.9741691,0.001261428,0.02094309,0.00015719,0.00001150347,0.00005903226,0.0001564847,0.00006933705,0.003172926],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000951839,"threshold_uncertainty_score":0.002567232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005573584850362432,"score_gpt":0.2409709957928378,"score_spread":0.2353974109424754,"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."}}