Physical resistance training-induced changes in lipids metabolism pathways and apoptosis in prostate
Bibliographic record
Abstract
BACKGROUND: Altered lipid metabolism is an important characteristic of neoplastic cells, with androgens and growth factors being major regulatory agents of the lipid metabolism process. We investigated the effect of physical resistance training on lipid metabolism and apoptosis in the adult Wistar rat prostate. METHODS: Two experimental groups represented sedentary and physical resistance training. Three days per week for 13 weeks, rats performed jumps in water carrying a weight load strapped to their chests as part of a physical resistance exercise protocol. Two days after the last training session, rats were anesthetized and sacrificed for blood and prostate analysis. RESULTS: Physical exercise improved feeding efficiency, decreased weight gain, regulated the serum-lipid profile, and modulated insulin-like growth factor-1 (IGF-1) and free testosterone concentration. Furthermore, upregulation of cluster of differentiation 36 (CD36), sterol regulatory element binding protein-1 (SREBP-1), sterol regulatory element-binding protein cleavage-activating protein (SCAP), and reduced lysosome membrane protein (LIMPII) expression were also observed in the blood and prostates of trained rats. Consistent with these results, caspase-3 expression was upregulating and the BCL-2/Bax index ratio was decreased in trained rats relative to sedentary animals. CONCLUSIONS: In this work, physical resistance training can alter lipid metabolism and increase markers of apoptosis in the prostate, suggesting physical resistance training as a potential novel therapeutic strategy for treating prostate cancer.
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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 | 0.000 |
| 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".