{"id":"W4392201645","doi":"10.1101/2024.02.18.580792","title":"MicroRNA-26b protects against MASH development in mice and can be efficiently targeted with lipid nanoparticles","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Liver Disease Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Infection and Immunity","funders":"China Scholarship Council; Fritz Thyssen Stiftung; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Austrian Science Fund; Deutsche Forschungsgemeinschaft; Bundesministerium für Bildung und Forschung; ZonMw; RWTH Aachen University; Wilhelm Sander-Stiftung","keywords":"Inflammation; Apolipoprotein E; microRNA; Steatohepatitis; Lipid metabolism; Phenotype; Foam cell; Fibrosis; Hepatic fibrosis; Cell biology; Chemistry; Biology; Endocrinology; Internal medicine; Cancer research; Immunology; Cholesterol; Fatty liver; Medicine; Lipoprotein; Biochemistry; Gene","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003668561,0.0008446685,0.0005462531,0.0005236389,0.0002057203,0.000377025,0.0002789265,0.000686455,0.001992963],"category_scores_gemma":[0.000145927,0.0003981763,0.0005659647,0.0001647159,0.0004366211,0.0002997546,0.0002237389,0.001089795,0.001037707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002289297,"about_ca_system_score_gemma":0.0002975638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000275096,"about_ca_topic_score_gemma":0.0004027055,"domain_scores_codex":[0.999708,0.00004338528,0.00003232614,0.00007315874,0.00008461848,0.00005855217],"domain_scores_gemma":[0.9997162,0.00001639051,0.0001230554,0.00003517589,0.00002600742,0.00008305645],"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":[0.0006232088,0.0001019802,0.00007581911,0.00006798929,0.00001204191,0.00004234652,0.00001010777,0.00006945236,0.9974576,0.0001068928,0.0001075973,0.001325055],"study_design_scores_gemma":[0.00009683309,0.001104321,0.000999875,0.00001597358,0.00003107251,0.0001486958,0.00002143172,0.0007313205,0.9939289,0.00006313723,0.002850081,0.00000833947],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9712955,0.003599096,0.01658829,0.0006564787,0.0003921275,0.0002594712,0.001586533,0.001201738,0.004420732],"genre_scores_gemma":[0.9703808,0.002388959,0.01166583,0.0002862298,0.00006790959,0.0004291482,0.001304299,0.0002796393,0.01319725],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001992963,"threshold_uncertainty_score":0.006667137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0107511509765182,"score_gpt":0.2079296650859151,"score_spread":0.1971785141093969,"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."}}