{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001246582,0.0003364676,0.0001073858,0.0001415791,0.0001002416,0.0001474486,0.0001615757,0.0002590589,0.0006443046],"category_scores_gemma":[0.0001108865,0.0001931647,0.0001586314,0.00006737473,0.0001291195,0.0002199232,0.0001519003,0.000257242,0.0003224448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002664824,"about_ca_system_score_gemma":0.0001941413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004811628,"about_ca_topic_score_gemma":0.001133528,"domain_scores_codex":[0.9999477,0.000007798929,0.00000417135,0.00001209235,0.00002071213,0.00000749912],"domain_scores_gemma":[0.9999497,0.00001051558,0.00001895671,0.000003533534,0.000008311574,0.000008992339],"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":[0.000007191601,0.000005620263,0.00002916056,0.00001609403,0.000001652014,0.00001060818,0.000005213652,0.0001026646,0.9986928,0.00008824398,0.00002529812,0.001015513],"study_design_scores_gemma":[0.00000711742,0.0001021462,0.0003194389,0.000001538934,0.000005711143,0.00004419435,0.000002816099,0.002880439,0.9946833,0.00002976566,0.001919809,0.000003723875],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9061125,0.002747898,0.08704015,0.0002175264,0.00008156904,0.0001585323,0.0002639477,0.0007633716,0.0026146],"genre_scores_gemma":[0.9115502,0.001143878,0.08192309,0.0001010182,0.00002186783,0.00007586779,0.0002203013,0.00005230329,0.00491148],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006443046,"threshold_uncertainty_score":0.002155364,"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."}}