The effects of exercise training versus intensive insulin treatment on skeletal muscle fibre content in type 1 diabetes mellitus rodents
Bibliographic record
Abstract
BACKGROUND: Intensive-insulin treatment (IIT) strategy for patients with type 1 diabetes mellitus (T1DM) has been associated with sedentary behaviour and the development of insulin resistance. Exercising patients with T1DM often utilize a conventional insulin treatment (CIT) strategy leading to increased insulin sensitivity through improved intramyocellular lipid (IMCL) content. It is unclear how these exercise-related metabolic adaptations in response to exercise training relate to individual fibre-type transitions, and whether these alterations are evident between different insulin strategies (CIT vs. IIT). PURPOSE: This study examined glycogen and fat content in skeletal muscle fibres of diabetic rats following exercise-training. METHODS: Male Sprague-Dawley rats were divided into four groups: Control-Sedentary, CIT- and IIT-treated diabetic sedentary, and CIT-exercised trained (aerobic/resistance; DARE). After 12 weeks, muscle-fibre lipids and glycogen were compared through immunohistochemical analysis. RESULTS: The primary findings were that both IIT and DARE led to significant increases in type I fibres when compared to CIT, while DARE led to significantly increased lipid content in type I fibres compared to IIT. CONCLUSIONS: These findings indicate that alterations in lipid content with insulin treatment and DARE are primarily evident in type I fibres, suggesting that muscle lipotoxicity in type 1 diabetes is muscle fibre-type dependant.
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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 | 0.001 |
| 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".