Resistance Exercise Training Improves Metabolic and Inflammatory Control in Adipose and Muscle Tissues in Mice Fed a High-Fat Diet
Bibliographic record
Abstract
This study investigates whether ladder climbing (LC), as a model of resistance exercise, can reverse whole-body and skeletal muscle deleterious metabolic and inflammatory effects of high-fat (HF) diet-induced obesity in mice. To accomplish this, Swiss mice were fed for 17 weeks either standard chow (SC) or an HF diet and then randomly assigned to remain sedentary or to undergo 8 weeks of LC training with progressive increases in resistance weight. Prior to beginning the exercise intervention, HF-fed animals displayed a 47% increase in body weight (BW) and impaired ability to clear blood glucose during an insulin tolerance test (ITT) when compared to SC animals. However, 8 weeks of LC significantly reduced BW, adipocyte size, as well as glycemia under fasting and during the ITT in HF-fed rats. LC also increased the phosphorylation of AktSer473 and AMPKThr172 and reduced tumor necrosis factor-alpha (TNF-α) and interleukin 1 beta (IL1-β) contents in the quadriceps muscles of HF-fed mice. Additionally, LC reduced the gene expression of inflammatory markers and attenuated HF-diet-induced NADPH oxidase subunit gp91phox in skeletal muscles. LC training was effective in reducing adiposity and the content of inflammatory mediators in skeletal muscle and improved whole-body glycemic control in mice fed an HF diet.
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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 | 0.000 | 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".