Effect of the Aerobic and Resistance Training on Follistatin-Like 1 and Leukemia Inhibitory Factor Muscle Gene Expression in Rats Fed With a High-Fat Diet
Bibliographic record
Abstract
Background: Follistatin-like 1 (FSTL-1) and leukemia inhibitory factor (LIF) are two myokines that are affected by overweight and have inflammatory and damaging effects. Considering that exercise reduces excess weight, this study aimed to evaluate the effect of aerobic and resistance training on FSTL-1 and LIF muscle gene expression in rats fed with a high-fat diet. Materials and Methods: In this experimental study, 32 rats were randomly divided into healthy control, obese control, obese+aerobic exercise, and obese+resistance exercise groups. The training was performed for 4 weeks at aerobic moderate intensity (50-65% VO2max). For resistance training, rats were also trained to climb the ladder (height 110 cm, slope 80%, and the distance between the bars of the ladder 2 cm), which is based on the determination of one repetition maximum. A high-fat diet was prepared with 40% fat, 13% protein, and 47% carbohydrates and continued until the rats reached the obesity range. The tissue sample was taken from the gluteus muscle. Results: The expression of FSTL-1 and LIF in the obese control group increased significantly compared to the healthy control group (P=0.044 and P=0.039, respectively). The expression of FSTL-1 and LIF in the resistance training group significantly decreased in comparison to the obese control group (P=0.049 and P=0.046, respectively). There was no significant difference between the aerobic exercise group and the obese control group (P=0.053 and P=0.059, respectively). However, a significant difference was observed between aerobic and resistance training groups in terms of FSTL-1 (P=0.042). Conclusion: Resistance exercise seems to have a greater and better effect on FSTL-1 and LIF in the muscles of obese samples compared to aerobic exercise.
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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".