Shifts in metabolic fuel use coincide with maximal rates of ventilation and body surface rewarming in an arousing hibernator
Bibliographic record
Abstract
It is well established that hibernating mammals rely predominantly on lipid stores to fuel metabolism throughout the hibernation season. However, it is unclear if other endogenous fuels contribute to the rapid, ~400-fold increase in metabolic rate during the early phase of arousal from torpor. To investigate this issue, we used cavity ring-down spectroscopy, a technique that provides a real-time indication of fuel use by measuring the ratio of13C to12C in the exhaled CO2of arousing 13-lined ground squirrels ( Ictidomys tridecemlineatus). We used infrared thermography to simultaneously measure ventilation and surface temperature change in various body regions, and we interpreted these data in light of changing plasma metabolite abundances at multiple stages of arousal from torpor. We found that hibernating squirrels use a combination of lipids and, likely, carbohydrates to fuel the initial ~60 min of arousal before switching to predominantly lipid oxidation. This fuel switch coincided with times of maximal rates of ventilation and rewarming of different body surface regions, including brown adipose tissue. Infrared thermography revealed zonal rewarming, whereby the brown adipose tissue region was the first to warm, followed by the thoracic and head regions and, finally, the posterior half of the body. Consistent with the results from cavity ring-down spectroscopy, plasma metabolite dynamics during early arousal suggested a large reliance on fatty acids, with a contribution from carbohydrates and glycerol. Because of their high oxidative flux rates and efficient O2use, carbohydrates might be an advantageous metabolic fuel during the early phase of arousal, when metabolic demands are high but ventilation rates and, thus, O2supply are relatively low.
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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 | 0.001 |
| 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".