Circular RNAs: Promising Biomarkers for Age-related Diseases
Bibliographic record
Abstract
Aging is a complex biological process closely linked with the occurrence and development of age-related diseases. Despite recent advances in lifestyle management and drug therapy, the late diagnosis of these diseases causes severe complications, usually resulting in death and consequently impacting social economies. Therefore, the identification of reliable biomarkers and the creation of effective treatment alternatives for age-related diseases are needed. Circular RNAs (circRNAs) are a novel class of RNA molecules that form covalently closed loops capable of regulating gene expression at multiple levels. Several studies have reported the emerging functional roles of circRNAs in various conditions, providing new perspectives regarding cellular physiology and disease pathology. Notably, accumulating evidence demonstrates the involvement of circRNAs in the regulation of age-related pathologies, including cardio-cerebrovascular disease, neurodegenerative disease, cancer, diabetes, rheumatoid arthritis, and osteoporosis. Therefore, the association of circRNAs with these age-related pathologies highlights their potential as diagnostic biomarkers and therapeutic targets for better disease management. Here, we review the biogenesis and function of circRNAs, with a special focus on their regulatory roles in aging-related pathologies, as well as discuss their potential as biological biomarkers and therapeutic targets for these 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".