Oxidative Stress and Epigenetics: miRNA Involvement in Rare Autoimmune Diseases
Bibliographic record
Abstract
Autoimmune diseases (ADs) such as Sjögren's syndrome, Kawasaki disease, and systemic sclerosis are characterized by chronic inflammation, oxidative stress, and autoantibodies, which cause joint tissue damage, vascular injury, fibrosis, and debilitation. Epigenetics participate in immune cell proliferation and differentiation, which regulates the development and function of the immune system, and ultimately interacts with other tissues. Indeed, overlapping of certain clinical features between ADs indicate that numerous immunologic-related mechanisms may directly participate in the onset and progression of these diseases. Despite the increasing number of studies that have attempted to elucidate the relationship between miRNAs and oxidative stress, autoimmune disorders and oxidative stress, and inflammation and miRNAs, an overall picture of the complex regulation of these three actors in the pathogenesis of ADs has yet to be formed. This review aims to shed light from a critical perspective on the key AD-related mechanisms by explaining the intricate regulatory ROS/miRNA/inflammation axis and the phenotypic features of these rare autoimmune diseases. The inflamma-miRs miR-155 and miR-146, and the redox-sensitive miR miR-223 have relevant roles in the inflammatory response and antioxidant system regulation of these diseases. ADs are characterized by clinical heterogeneity, which impedes early diagnosis and effective personalized treatment. Redox-sensitive miRNAs and inflamma-miRs can help improve personalized medicine in these complex and heterogeneous diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".