Fueling shivering thermogenesis during passive hypothermic recovery
Bibliographic record
Abstract
In humans, the relative importance of oxidative fuels for sustaining shivering during passive hypothermic recovery or rewarming is still unclear. The main goals of this study were 1) to quantify the respective contributions of lipids and carbohydrates (CHO) during passive rewarming and 2) to determine the effects of precooling exercise on the pattern of fuel utilization. With indirect calorimetry methodologies, changes in fuel metabolism were quantified in nonacclimatized adult men shivering to rewarm from moderate hypothermia (core temperature approximately 34.5 degrees C) not following (Con) or following a precooling exercise at 75% (.)Vo(2max) for 15 min (Pre-CE). As hypothermic individuals shiver to normothermia, results showed that CHO dominate at all shivering intensities above 50% Shiv(peak,) while lipids were preferred at lower intensities. This change in the relative importance of CHO and lipids to total heat production was dictated entirely by modulating CHO oxidation rate, which decreased by as much as 10-fold from the beginning to the end of rewarming (from 1,611 +/- 396 to 141 +/- 361 mg/min for Con and 1,555 +/- 230 to 207 +/- 261 mg/min for Pre-CE). In contrast, lipid oxidation rate remained constant and low (relatively to maximal rates at exercise) throughout rewarming, averaging 183 +/- 141 for Con and 207 +/- 118 mg lipids/min for Pre-CE. In addition, this pattern of fuel selection remained the same between treatments. We concluded that fuel selection is regulated entirely by changes in CHO oxidation rate. Further research should focus on establishing the exact regulatory processes involved in achieving this large upregulation of CHO utilization rate following hypothermia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".