The Role of microRNAs in Organismal and Skin Aging
Bibliographic record
Abstract
The aging process starts directly after birth and lasts for the entire lifespan; it manifests itself with a decline in an organism’s ability to adapt and is linked to the development of age-related diseases that eventually lead to premature death. This review aims to explore how microRNAs (miRNAs) are involved in skin functioning and aging. Recent evidence has suggested that miRNAs regulate all aspects of cutaneous biogenesis, functionality, and aging. It has been noted that some miRNAs were down-regulated in long-lived individuals, such as let-7, miR-17, and miR-34 (known as longevity-related miRNAs). They are conserved in humans and presumably promote lifespan prolongation; conversely, they are up-regulated in age-related diseases, like cancers. The analysis of the age-associated cutaneous miRNAs revealed the increased expression of miR-130, miR-138, and miR-181a/b in keratinocytes during replicative senescence. These miRNAs affected cell proliferation pathways via targeting the p63 and Sirtuin 1 mRNAs. Notably, miR-181a was also implicated in skin immunosenescence, represented by the Langerhans cells. Dermal fibroblasts also expressed increased the levels of the biomarkers of aging that affect telomere maintenance and all phases of the cellular life cycle, such as let-7, miR-23a-3p, 34a-5p, miR-125a, miR-181a-5p, and miR-221/222-3p. Among them, the miR-34 family, stimulated by ultraviolet B irradiation, deteriorates collagen in the extracellular matrix due to the activation of the matrix metalloproteinases and thereby potentiates wrinkle formation. In addition to the pro-aging effects of miRNAs, the plausible antiaging activity of miR-146a that antagonized the UVA-induced inhibition of proliferation and suppressed aging-related genes (e.g., p21WAF-1, p16, and p53) through targeting Smad4 has also been noticed. Nevertheless, the role of miRNAs in skin aging is still not fully elucidated and needs to be further discovered and explained.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".