Liver microRNAs are differentially expressed in human simple steatosis and non alcoholic steatohepatitis with potential repercussions on lipid metabolism and inflammatory status
Bibliographic record
Abstract
Non‐alcoholic fatty liver disease is associated with fat deposition in liver. It can present as simple hepatic steatosis (SS) or as non alcoholic steatohepatitis (NASH), which is characterized by inflammation leading to fibrosis. It is unclear which molecular mechanisms differentiate SS and NASH. Progression of SS to NASH is associated with altered hepatic microRNA (miRNA) expression in animal models, but human data are scarce. We analyzed the miRNA expression signature in histology‐proven healthy (H; n= 24), SS (n= 24) or NASH (n= 23) human adult livers by NanoString technology. We found that samples from the 3 groups cluster by disease state based on 142 differentially expressed miRNAs, suggesting that a specific liver miRNA signature distinguishes H, SS and NASH. Putative gene targets of these miRNAs were identified with 12 algorithms and used for gene ontology enrichment analysis. This showed that processes including gene transcription, apoptosis and the TGF‐β signaling pathway are commonly altered between H, SS and NASH. In addition, NASH was associated with altered regulation of lipid storage, IL‐6 and adipocytokine signaling pathways as compared to H and SS and regulation of cholesterol storage as compared to SS. These differences may have therapeutic potential. Funding sources: American College of Gastroenterology, Canadian Institutes of Health Research
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".