Author response: Remodeling of skeletal muscle myosin metabolic states in hibernating mammals
Bibliographic record
Abstract
Many animals use hibernation as a tactic to survive harsh winters. During this dormant, inactive state, animals reduce or limit body processes, such as heart rate and body temperature, to minimise their energy use. To conserve energy during hibernation, animals can use different approaches. For example, garden dormice undergo periodic states of extremely low core temperatures (down to 4–8oC); whereas Eurasian brown bears see milder temperature drops (down to 23–25oC). An important organ that changes during hibernation is skeletal muscle. Skeletal muscle typically uses large amounts of energy, making up around 50% of body mass. To survive, hibernating animals must change how their skeletal muscle uses energy. Traditionally, active myosin – a protein found in muscles that helps muscles to contract – was thought to be responsible for most of the energy use by skeletal muscle. But, more recently, resting myosin has also been found to use energy when muscles are relaxed. Lewis et al. studied myosin and skeletal muscle energy use changes during hibernation and whether they could impact the metabolism of hibernating animals. Lewis et al. assessed myosin changes in muscle samples from squirrels, dormice and bears during hibernation and during activity. Experiments showed changes in resting myosin in squirrels and dormice (whose temperature drops to 4–8oC during hibernation) but not in bears. Further analysis revealed that cooling samples from non-hibernating muscle to 4–8oC increased energy use in resting myosin, thereby generating heat. However, no increase in energy use was found after cooling hibernating muscle samples to 4–8oC. This suggest that resting myosin generates heat at cool temperatures – a mechanism that is switched off in hibernating animals to allow them to cool their body temperature. These findings reveal key insights into how animals conserve energy during hibernation. In addition, the results show that myosin regulates energy use in skeletal muscles, which indicates myosin may be a potential drug target in metabolic diseases, such as obesity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.193 | 0.047 |
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".