Insight into post-transcriptional gene regulation: stress-responsive microRNAs and their role in the environmental stress survival of tolerant animals
Bibliographic record
Abstract
Living animals are constantly faced with various environmental stresses that challenge normal life, including: oxygen limitation, very low or high temperature, as well as restriction of water and food. It has been well established that in response to these stresses, tolerant organisms regularly respond with a distinct suite of cellular modifications that involve transcriptional, translational and post-translational modification. In recent years, a new mechanism of rapid and reversible transcriptome regulation, via the action of non-coding RNA molecules, has emerged into post-transcriptional regulation and has since been shown to be part of the survival response. However, these RNA-based mechanisms by which tolerant organisms respond to stressed conditions are not well understood. Recent studies have begun to show that non-coding RNAs control gene expression and translation of mRNA to protein, and can also have regulatory influence over major cellular processes. For example, select microRNAs have been shown to have regulatory influence over the cell cycle, apoptosis, signal transduction, muscle atrophy and fatty acid metabolism during periods of environmental stress. As we are on the verge of dissecting the roles of non-coding RNA in environmental stress adaptation, this Commentary summarizes the hallmark alterations in microRNA expression that facilitate stress survival.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".