{"id":"W4407163134","doi":"10.1021/acsbiomedchemau.4c00081","title":"A Reverse Transcription Nucleic-Acid-Based Barcoding System for <i>In Vivo</i> Measurement of Lipid Nanoparticle mRNA Delivery","year":2025,"lang":"en","type":"article","venue":"ACS Bio & Med Chem Au","topic":"RNA Interference and Gene Delivery","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; University Health Network; University of Toronto; Connaught Fund; Canada Foundation for Innovation; J.P. Bickell Foundation; Canada Research Chairs","keywords":"Nucleic acid; In vivo; Messenger RNA; Reverse transcriptase; Computational biology; Transcription (linguistics); Nanoparticle; microRNA; Chemistry; Cell biology; Biology; RNA; Biochemistry; Nanotechnology; Gene; Biotechnology; Materials science","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":[0.0003828036,0.0001528489,0.0002099164,0.00007402873,0.00005390708,0.00001281819,0.0001920058,0.0001368517,0.000005773844],"category_scores_gemma":[0.00005561072,0.0001534411,0.0001385893,0.0001399327,0.00004733889,0.000007394493,0.00002840592,0.0000493004,0.000002633134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001231733,"about_ca_system_score_gemma":0.000204044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006142403,"about_ca_topic_score_gemma":0.0001444925,"domain_scores_codex":[0.9988688,0.00004170115,0.0003451816,0.0003144329,0.0001567307,0.0002731289],"domain_scores_gemma":[0.999367,0.00001023653,0.00008665976,0.0002860844,0.000199127,0.00005092204],"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":[0.0003940099,0.00008654313,0.0008407604,0.0002806855,0.00005399282,0.00000123171,0.00007066705,0.00004539454,0.9959021,0.00002865749,0.001581997,0.0007139742],"study_design_scores_gemma":[0.001372603,0.0002026697,0.0001043236,0.0003065289,0.0000493485,8.933845e-7,0.0003306058,0.0002412262,0.9930746,0.000008027075,0.004165635,0.0001435508],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967837,0.0004608059,0.001476144,0.0002334862,0.0002834422,0.0003184295,0.00003151089,0.00001509173,0.0003973344],"genre_scores_gemma":[0.999153,0.00004830255,0.000225739,0.0002714653,0.00007601909,0.00007842344,0.00002072581,0.0000134799,0.0001128327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002827498,"threshold_uncertainty_score":0.6257145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01848724554332237,"score_gpt":0.2355586720195037,"score_spread":0.2170714264761813,"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."}}