<i>In vivo</i> measurement of energy substrate contribution to cold‐induced brown adipose tissue thermogenesis
Bibliographic record
Abstract
The present study was designed to investigate the effects of cold on brown adipose tissue (BAT) energy substrate utilization in vivo using the positron emission tomography tracers [(18)F]fluorodeoxyglucose (glucose uptake), 14(R,S)-[(18)F]fluoro-6-thia-heptadecanoic acid [nonesterified fatty acid (NEFA) uptake], and [(11)C]acetate (oxidative activity). The measurements were performed in rats adapted to 27°C, which were acutely subjected to cold (10°C) for 2 and 6 hours, and in rats chronically adapted to 10°C for 21 days, which were returned to 27°C for 2 and 6 hours. Cold exposure (acutely and chronically) led to increases in BAT oxidative activity, which was accompanied by concomitant increases in glucose and NEFA uptake. The increases were particularly high in cold-adapted rats and largely readily reduced by the return to a warm environment. The cold-induced increase in oxidative activity was meaningfully blunted by nicotinic acid, a lipolysis inhibitor, which emphasizes in vivo the key role of intracellular lipid in BAT thermogenesis. The changes in BAT oxidative activity and glucose and NEFA uptakes were paralleled by inductions of genes involved in not only oxidative metabolism but also in energy substrate replenishment (triglyceride and glycogen synthesis). The capacity of BAT for energy substrate replenishment is remarkable.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".