{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009134138,0.0001526108,0.0001357487,0.00007194485,0.00006629493,0.00002458903,0.000392946,0.0001061714,0.00002030117],"category_scores_gemma":[0.00003860006,0.000111807,0.0001463821,0.0001779791,0.0001001564,0.000001658592,0.0001349913,0.0000406602,0.00002059036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000662916,"about_ca_system_score_gemma":0.0001139478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007961859,"about_ca_topic_score_gemma":0.00003397723,"domain_scores_codex":[0.9991192,0.00003688196,0.0002315817,0.0002992559,0.0001037815,0.0002093369],"domain_scores_gemma":[0.9992686,0.00001131558,0.00005928161,0.0005194164,0.00009343991,0.00004794503],"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":[0.0001314454,0.00005020786,0.0001518368,0.00001759485,0.00007792517,0.000002802309,0.00001790026,0.000006528582,0.9899916,0.0002328816,0.003080225,0.006239024],"study_design_scores_gemma":[0.0002815811,0.0002991746,0.0008033104,0.00002403513,0.00002751708,7.822009e-7,0.00005393838,0.00002443665,0.9839665,0.00004178238,0.01436103,0.0001158608],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9908958,0.002718685,0.003393193,0.0008657937,0.0002693912,0.000199559,0.00004941865,0.00001088365,0.001597243],"genre_scores_gemma":[0.9965708,0.00002490132,0.0002401039,0.001437896,0.00007071978,0.00003153947,0.00003741124,0.00001269676,0.001573935],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0112808,"threshold_uncertainty_score":0.4559359,"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."}}