Fuel selection in Wistar rats exposed to cold: shivering thermogenesis diverts fatty acids from re‐esterification to oxidation
Bibliographic record
Abstract
This study characterizes the effects of shivering thermogenesis on metabolic fuel selection in Wistar rats. Because lipids account for most of the heat produced, we have investigated: (1) whether the rate of appearance of non-esterified fatty acids (R(a) NEFAs) is stimulated by shivering, (2) whether mono-unsaturated (oleate) and saturated fatty acids (palmitate) are affected similarly, and (3) whether the partitioning between fatty acid oxidation and re-esterification is altered by cold exposure. Fuel oxidation was measured by indirect calorimetry and fatty acid mobilization by continuous infusion of 9,10-[(3)H]oleate and 1-[(14)C]palmitate. During steady-state cold exposure, results show that total heat production is unequally shared by the oxidation of lipids (52% of metabolic rate), carbohydrates (35%) and proteins (13%), and that the same fuel selection pattern is observed at all shivering intensities. All previous research shows that mammals stimulate R(a) NEFA to support exercise or shivering. In contrast, results reveal that the R(a) NEFA of the rat remains constant during cold exposure (55 micromol kg(1) min(1)). No preferential use of mono-unsaturated over saturated fatty acids could be demonstrated. The rat decreases its rate of fatty acid re-esterification from 48.4 +/- 6.4 to 19.6 +/- 6.3 micromol kg(1) min(1) to provide energy to shivering muscles. This study is the first to show that mammals do not only increase fatty acid availability for oxidation by stimulating R(a) NEFA. Reallocation of fatty acids from re-esterification to oxidation is a novel, alternative strategy used by the rat to support shivering.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".