MicroRNA Cues from Nature: A Roadmap to Decipher and Combat Challenges in Human Health and Disease?
Bibliographic record
Abstract
MicroRNAs are small non-coding RNA (18-24 nt long) that fine-tune gene expression at the post-transcriptional level. With the advent of "multi-omics" analysis and sequencing approaches, they have now been implicated in every facet of basic molecular networks, including metabolism, homeostasis, and cell survival to aid cellular machinery in adapting to changing environmental cues. Many animals must endure harsh environmental conditions in nature, including cold/freezing temperatures, oxygen limitation (anoxia/hypoxia), and food or water scarcity, often requiring them to revamp their metabolic organization, frequently on a seasonal or life stage basis. MicroRNAs are important regulatory molecules in such processes, just as they are now well-known to be involved in many human responses to stress or disease. The present review outlines the role of miRNAs in natural animal models of environmental stress and adaptation including torpor/hibernation, anoxia/hypoxia tolerance, and freeze tolerance. We also discuss putative medical applications of advances in miRNA biology including organ preservation for transplant, inflammation, ageing, metabolic disorders (e.g., obesity), mitochondrial dysfunction (mitoMirs) as well as specialized miRNA subgroups respective to low temperature (CryomiRs) and low oxygen (OxymiRs). The review also covers differential regulation of conserved and novel miRNAs involved at cell, tissue, and stress specific levels across multiple species and their roles in survival. Ultimately, the species-specific comparison and conserved miRNA responses seen in evolutionarily disparate animal species can help us to understand the complex miRNA network involved in regulating and reorganizing metabolism to achieve diverse outcomes, not just in nature, but in human health and disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".