Role of long non‑coding RNAs and related epigenetic mechanisms in liver fibrosis (Review)
Bibliographic record
Abstract
Liver fibrosis is one of the major liver pathologies affecting patients worldwide. It results from an improper tissue repair process following liver injury or inflammation. If left untreated, it ultimately leads to liver cirrhosis and liver failure. Long non‑coding RNAs (lncRNAs) have been implicated in a wide variety of diseases. They can regulate gene expression and modulate signaling. Some of the lncRNAs promote, while others inhibit liver fibrosis. Similarly, other epigenetic processes, such as methylation and acetylation regulate gene transcription and can modulate gene expression. Notably, there are several regulatory associations of lncRNAs with other epigenetic processes. A major mechanism of action of long non‑coding RNAs is to competitively bind to their target microRNAs (miRNAs or miRs), which in turn affects miRNA availability and bioactivity. In the present review, the role of lncRNAs and related epigenetic processes contributing to liver fibrosis is discussed. Finally, various potential therapeutic approaches targeting lncRNAs and related epigenetic processes, which are being considered as possible future treatment targets for liver fibrosis are identified.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".