Inhibition of Dexamethasone-induced Fatty Liver Development by Reducing miR-17-5p Levels
Bibliographic record
Abstract
Steatosis is a pivotal event in the initiation and progression of nonalcoholic fatty liver disease (NAFLD) which can be driven by peroxisome proliferator-activated receptor-α (PPAR-α) dysregulation. Through examining the effect of PPAR-α on fatty liver development, we found that PPAR-α is a target of miR-17-5p. Transgenic mice expressing miR-17 developed fatty liver and produced higher levels of triglyceride and cholesterol but lower levels of PPAR-α. Ectopic expression of miR-17 enhanced cellular steatosis. Gain-of-function and loss-of-function experiments confirmed PPAR-α as a target of miR-17-5p. On the other hand, PPAR-α bound to the promoter of miR-17 and promoted its expression. The feed-back loop between miR-17-5p and PPAR-α played a key role in the induction of steatosis and fatty liver development. Mice with high levels of miR-17-5p were sensitive to Dexamethasone-induced fatty liver formation. Inhibition of miR-17-5p suppressed this process and enhanced PPAR-α expression in mice treated with Dexamethasone. Clofibrate, Ciprofibrate, and WY-14643: three agents used for treatment of metabolic disorders, were found to promote PPAR-α expression while decreasing miR-17-5p levels and inhibiting steatosis. Our studies show that miR-17-5p inhibitor and agents used in metabolic disorders may be applied in combination with Dexamethasone in the treatment of anti-inflammation, immunosuppression, and cancer patients.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".