Voluntary wheel running ameliorates vascular smooth muscle hyper-contractility in type 2 diabetic db/db mice
Bibliographic record
Abstract
Physical activity reduces cardiovascular disease related mortality in diabetic patients. However, it is unknown if the diabetic state reduces voluntary physical activity and, if so, if the voluntary physical activity at the reduced level is sufficient to improve cardiovascular risk factors. To address these two specific questions, we investigated voluntary wheel running performance in an obese and type 2 diabetic mouse model, the db/db mice. In addition, we determined the effects of running on body mass, blood glucose, insulin, plasma free fatty acids, cholesterol, and vascular smooth muscle hyper-contractility. Our results showed that daily running distance, time, and speed were significantly reduced in the db/db mice to about 23%, 32%, and 71%, respectively, of that in non-diabetic control mice. However, this low level of running was sufficient to induce a reduction in the vascular smooth muscle hyper-contractility, cholesterol, and some plasma free fatty acids, as well as to delay the decrease in blood insulin. These changes occurred in the absence of weight loss and a detectable decrease in blood glucose. Thus, the results of this study demonstrated that voluntary wheel running activity was dramatically reduced in db/db mice. However, the low levels of running were beneficial, in the absence of effects on obesity or blood glucose, with significant reductions in cardiovascular risk factors and potential delays in beta-cell dysfunction.
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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 | 0.001 | 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 | 0.001 |
| 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".