MicroRNAs influence and longevity
Bibliographic record
Abstract
Abstract Background MiRNAs play critical roles in the regulation of cellular function, life span, and the aging process. They can affect longevity positively and negatively through different aging pathways. Main text MiRNAs are a group of short non-coding RNAs that regulate gene expressions at post-transcriptional levels. The different types of alterations in miRNAs biogenesis, mRNA expressions, and activities of miRNA-protein complexes can affect the regulation of normal post-transcriptional gene process, which may lead to aging, age-related diseases, and an earlier death. It seems that the influence of deregulation of miRNAs on senescence and age-related diseases occurring by targeting aging molecular pathways can be used for diagnosis and prognosis of them. Therefore, the expression and function of miRNAs should be studied more accurately with new applicable and validated experimental tools. However, the current review wishes to highlight simply a connection among miRNAs, senescence and some age-related diseases. Conclusion Despite several research indicating the key roles of miRNAs in aging and longevity, further investigations are still needed to elucidate the essential roles of miRNAs in controlling mRNA regulation, cell proliferation, death and/or protection during stress and health problems. Besides, more research on miRNAs will help to identify new targets for alternative strategies regarding effectively screen, treat, and prevent diseases as well as make slow the aging process.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".