Exercise and adrenaline increase PGC‐1α mRNA expression in rat adipose tissue
Bibliographic record
Abstract
The purpose of the present investigation was to explore the effects of exercise and adrenaline on the mRNA expression of PGC-1alpha, a master regulator of mitochondrial biogenesis, in rat abdominal adipose tissue. We hypothesized that (1) exercise training would increase PGC-1alpha mRNA expression in association with increases in mitochondrial marker enzymes, (2) adrenaline would increase PGC-1alpha mRNA expression and (3) the effect of exercise on PGC-1alpha mRNA expression in white adipose tissue would be attenuated by a beta-blocker. Two hours of daily swim training for 4 weeks led to increases in mitochondrial marker proteins and PGC-1alpha mRNA expression in epididymal and retroperitoneal fat depots. Additionally, a single 2 h bout of exercise led to increases in PGC-1alpha mRNA expression immediately following exercise cessation. Adrenaline treatment of adipose tissue organ cultures led to dose-dependent increases in PGC-1alpha mRNA expression. A supra-physiological concentration of adrenaline increased PGC-1alpha mRNA expression in epididymal but not retroperitoneal adipose tissue. beta-Blockade attenuated the effects of an acute bout of exercise on PGC-1alpha mRNA expression in epididymal but not retroperitoneal fat pads. In summary, this is the first investigation to demonstrate that exercise training, an acute bout of exercise and adrenaline all increase PGC-1alpha mRNA expression in rat white adipose tissue. Furthermore it would appear that increases in circulating catecholamine levels may be one potential mechanism mediating exercise induced increases in PGC-1alpha mRNA expression in rat abdominal adipose tissue.
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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 | 0.000 |
| 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".