Combined insulin treatment and intense exercise training improved basal cardiac function and Ca<sup>2+</sup>-cycling proteins expression in type 1 diabetic rats
Bibliographic record
Abstract
This study investigated the effects of 8 weeks of intense exercise training combined with insulin treatment on the Ca(2+)-cycling protein complex expression and their functional consequences on cardiac function in type 1 diabetic rat hearts. Diabetic Wistar rats were randomly assigned into the following groups: received no treatment, insulin-treated diabetic, trained diabetic, and trained insulin-treated diabetic. A control group was also included. Insulin treatment and (or) treadmill intense exercise training were conducted over 8 weeks. Basal cardiac function was evaluated by Langendorff technique. Cardiac expression of the main Ca(2+)-cycling proteins (RyR2, FKBP 12.6, SERCA2, PLB, NCX1) was assessed by Western blot. Diabetes altered basal cardiac function (±dP/dt) and decrease the expression of the main Ca(2+)-cycling proteins expression: RyR2, SERCA2, and NCX1 (p < 0.05). Whereas combined treatment was not able to normalize -dP/dt, it succeeded to normalize +dP/dt of diabetic rats (p < 0.05). Moreover, both insulin and intense exercise training, applied solely, increased the expression of the Ca(2+)-cycling proteins: RyR2, SERCA2, PLB. and NCX1 (p < 0.05). But this effect was higher when the 2 treatments were combined. These data are the first to show that combined insulin treatment and intense exercise training during diabetes synergistically act on the expression of the main Ca(2+)-cycling proteins, providing insights into mechanisms by which the dual treatment during diabetes improves cardiac function.
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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 | 0.000 | 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 | 0.001 |
| 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".