{"id":"W2318409359","doi":"10.1021/nn502596b","title":"Layer-by-Layer Assembled Antisense DNA Microsponge Particles for Efficient Delivery of Cancer Therapeutics","year":2014,"lang":"en","type":"article","venue":"ACS Nano","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":124,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Science Foundation; National Cancer Institute; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology; Natural Sciences and Engineering Research Council of Canada; U.S. Department of Defense","keywords":"Nucleic acid; Oligonucleotide; Materials science; Biodistribution; DNA; Antisense therapy; Nanotechnology; Polyelectrolyte; Drug delivery; Locked nucleic acid; Chemistry; In vitro; Biochemistry; Polymer","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001828887,0.0001448149,0.0001935588,0.00002918883,0.00006912751,0.00001299808,0.0001243048,0.0001391349,9.906383e-7],"category_scores_gemma":[0.00002547075,0.0001189415,0.0001195435,0.00007888839,0.00006529344,0.000002045983,0.00004758767,0.00003551523,0.000001137352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001122744,"about_ca_system_score_gemma":0.00002795218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001999792,"about_ca_topic_score_gemma":0.00003011244,"domain_scores_codex":[0.9991288,0.00004595012,0.0002172935,0.0002804846,0.00009649988,0.0002310348],"domain_scores_gemma":[0.9993086,0.00003157664,0.0001359376,0.0003050291,0.0001789045,0.00003994608],"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":[0.0001030652,0.00009128042,0.001005427,0.00001457127,0.00008781818,2.243642e-7,0.00001134161,0.00001816019,0.9892451,0.00002019183,0.0009842287,0.008418546],"study_design_scores_gemma":[0.0003950793,0.0002004991,0.0002200073,0.00001608986,0.00009209577,0.0000014031,0.00001376119,0.0003707015,0.9739977,0.00003155298,0.02449608,0.0001650608],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947842,0.0008986622,0.003831483,0.0001590042,0.00005456298,0.0001564793,0.00005576171,0.00002407129,0.00003572374],"genre_scores_gemma":[0.9962291,0.0004627618,0.002562838,0.0004092047,0.00007931457,0.00001453361,0.0000407008,0.0000217905,0.0001797487],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02351185,"threshold_uncertainty_score":0.4850293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01683380022186206,"score_gpt":0.299177665257197,"score_spread":0.282343865035335,"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."}}