Impact of short‐term aerobic and resistance training on acute post‐exercise blood glucose in Type 1 diabetic rodents
Bibliographic record
Abstract
Despite the benefits of exercise (EX), physical activity levels are lower in Type 1 diabetics (T1D) due to the frequency of EX‐induced hypoglycemia. The objective of this study was to examine T1D post‐EX blood glucose (BG) levels in response to different EX modalities and to determine if short‐term training can modulate these responses. Forty rats were randomly divided into 4 groups; sedentary T1D (CD), T1D high intensity EX (DH), T1D low intensity EX (DL) and T1D resistance EX (DR). For 6 weeks, DH and DL rats ran on a treadmill at 27m/min and 15m/min, respectively, whereas DR climbed a vertical ladder while weighted. BG was measured for 2hrs post‐EX at week 3 and week 6. At week 3, DH had a significant fall in BG post‐EX, which sustained for 75min (p<0.05). DH demonstrated similar immediate reductions in BG at week 6; however, BG returned to pre‐EX values by 45min (p<0.05). DL had a significant drop in BG post‐ EX for >2hrs (p<0.05) at week 3; while at week 6, no immediate reductions in BG were evident up to 2hrs post‐EX. In contrast to aerobically trained animals, DR exhibited a post‐EX BG time course that was not significantly different from the BG regulation of CD animals throughout the 6 week study. To conclude, different EX modalities lead to differential regulation of BG following EX. In DH and DL, there appears to be a glucoregulatory adaptation to training that was not evident in DR rats. Supported by CIHR Grant #CCT‐ 83029.
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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 | 0.001 |
| 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".