Differential Expression of Mature MicroRNAs Involved in Muscle Maintenance of Hibernating Little Brown Bats, <i>Myotis Lucifugus</i>: A Model of Muscle Atrophy Resistance
Bibliographic record
Abstract
Muscle wasting is common in mammals during extended periods of immobility. However, many small hibernating mammals manage to avoid muscle atrophy despite remaining stationary for long periods during hibernation. Recent research has highlighted roles for short non-coding microRNAs (miRNAs) in the regulation of stress tolerance. We proposed that they could also play an important role in muscle maintenance during hibernation. To explore this possibility, a group of 10 miRNAs known to be normally expressed in skeletal muscle of non-hibernating mammals were analyzed by RT-PCR in hibernating little brown bats, Myotis lucifugus. We then compared the expression of these miRNAs in euthermic control bats and bats in torpor. Our results showed that compared to euthermic controls, significant, albeit modest (1.2-1.6 fold), increases in transcript expression were observed for eight mature miRNAs, including miR-1a-1, miR-29b, miR-181b, miR-15a, miR-20a, miR-206 and miR-128-1, in the pectoral muscle of torpid bats. Conversely, expression of miR-21 decreased by 80% during torpor, while expression of miR-107 remained unaffected. Interestingly, these miRNAs have been either validated or predicted to affect multiple muscle-specific factors, including myostatin, FoxO3a, HDAC4 and SMAD7, and are likely involved in the preservation of pectoral muscle mass and functionality during bat hibernation.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".