Effect of diazepam treatment on metabolic indices in trained and untrained rats
Bibliographic record
Abstract
Exercise training, like diazepam, is commonly employed as a means of reducing anxiety. Both diazepam and exercise training have been shown to modify carbohydrate and lipid metabolism as well as influence calcium metabolism in skeletal muscle. As receptor binding and thereby efficacy of diazepam has been demonstrated to be modulated by the lipid environment of the receptor, and changes in calcium levels can affect a number of intracellular signalling pathways, we sought to determine if the interaction of both chronic diazepam and exercise training would modify selected metabolic indices in an animal model. For this purpose, muscle and liver glycogen, blood glucose and plasma free fatty acids (FFA) were measured in sedentary, exercise trained and exercise trained, acutely exhausted animals. Alterations in lipid and carbohydrate metabolism were observed in all experimental groups. Diazepam treatment alone exerts metabolic consequences, such as elevated muscle glycogen and plasma FFA and depressed blood glucose levels, which are similar to those observed with exercise training. When animals are acutely exercised to exhaustion, however, differences appear, including a reduced rise in plasma FFA, which suggests that long-term diazepam treatment does influence exercise metabolism, possibly as a result of effects on the sympatho-adrenal system.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".