{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008775844,0.0009640245,0.0004082813,0.0005347168,0.000398816,0.0006404943,0.000646135,0.0008539944,0.00127359],"category_scores_gemma":[0.001185554,0.0004398471,0.000469621,0.0004727338,0.00069378,0.00055603,0.0003336304,0.00154969,0.0008980421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006398342,"about_ca_system_score_gemma":0.0008814915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005822559,"about_ca_topic_score_gemma":0.001066023,"domain_scores_codex":[0.9988629,0.0001741292,0.00008713149,0.0004419795,0.0003466241,0.00008724997],"domain_scores_gemma":[0.9992425,0.0002368508,0.0002305142,0.00009363105,0.0001507332,0.0000458255],"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":[0.00001547559,0.0000103695,0.00003439722,0.0000283223,0.000001528416,0.0000131346,0.0000121888,0.00006079632,0.9978316,0.0001871093,0.00005485857,0.001750132],"study_design_scores_gemma":[0.000002383362,0.00005667383,0.00009153608,0.000001985241,0.000002993291,0.00004874657,0.000003754711,0.0008224745,0.9978416,0.0000231059,0.001099124,0.000005605692],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.232035,0.001528338,0.7530085,0.0006176373,0.0003864662,0.0007474346,0.002080052,0.004421278,0.005175277],"genre_scores_gemma":[0.4626143,0.001821516,0.5202129,0.000623308,0.00008540374,0.001528229,0.002952322,0.0005068396,0.00965517],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00127359,"threshold_uncertainty_score":0.004642367,"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."}}