{"id":"W4408369060","doi":"10.1080/07366205.2025.2473842","title":"Impact of commercial RNA extraction methods on the recovery of human RNA sequence data from archival fixed tissues","year":2025,"lang":"en","type":"article","venue":"BioTechniques","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; McMaster University Medical Centre","funders":"National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"RNA; RNA extraction; Sequence (biology); Extraction (chemistry); Biology; Computational biology; Molecular biology; Genetics; Gene; Chemistry; Chromatography","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":[0.007994107,0.0007702741,0.0005814321,0.001021679,0.001085604,0.00192484,0.0007270905,0.0008612186,0.001312145],"category_scores_gemma":[0.01434235,0.0005285703,0.0007203021,0.0008788233,0.001488952,0.00060844,0.00122645,0.0009319494,0.000902878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006831507,"about_ca_system_score_gemma":0.0006666124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002145298,"about_ca_topic_score_gemma":0.007501313,"domain_scores_codex":[0.9886819,0.003517815,0.001328887,0.002633016,0.003359641,0.0004787545],"domain_scores_gemma":[0.9914621,0.004612741,0.0009454197,0.00115749,0.001692758,0.0001295063],"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":[0.0006013748,0.00009772211,0.007929689,0.0005088393,0.0002370912,0.0001167528,0.0004694438,0.001733564,0.9655914,0.0002870942,0.0004484273,0.02197871],"study_design_scores_gemma":[0.00001905835,0.000971758,0.03350226,0.0001242784,0.0002624123,0.0004774745,0.0003324503,0.006140438,0.948155,0.000621455,0.009323074,0.00007027204],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8469721,0.007210135,0.1346054,0.0008603595,0.0005902696,0.0007054005,0.003073553,0.000888285,0.005094616],"genre_scores_gemma":[0.7886619,0.003435933,0.1895397,0.001433027,0.0001738253,0.00129693,0.009342486,0.001160803,0.004955423],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007994107,"threshold_uncertainty_score":0.04227734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08335601527567685,"score_gpt":0.4639963052355881,"score_spread":0.3806402899599113,"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."}}