Impact of cigarette smoke and aerobic physical training on histological and molecular markers of prostate health in rats
Bibliographic record
Abstract
Recent evidence suggests that aerobic physical training may attenuate the deleterious effects of cancer risk factors, including smoking. We investigated the effects of cigarette smoke inhalation and aerobic physical training on the expression of steroid receptors and inflammatory and apoptotic proteins in the prostate. Forty male Wistar rats were distributed in four groups: control (CO), exercise (EXE), cigarette smoke exposure (CS), and cigarette smoke exposure with exercise (CS+EXE). For eight weeks, animals were repeatedly exposed to cigarette smoke for 30 min or performed aerobic physical training either with or without the cigarette smoke inhalation protocol. Following these experiments, we analyzed prostate epithelial morphology and prostatic expression of androgen (AR) and glucocorticoid receptors (GR), insulin-like growth factor (IGF-1), B-cell lymphoma-2 (BCL-2), BCL-2-associated X protein (BAX), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and nuclear factor-kappa B (NF-κB) via immunohistochemistry. Cigarette smoke exposure stimulated the expression of AR, IGF-1, BCL-2, and NF-κB while downregulating BAX, IL-6, and TNF-α labeling in the prostate. In contrast, aerobic physical training attenuated cigarette smoke-induced changes in AR, GR, IGF-1, BCL-2, IL-6, TNF-α, and NF-κB. This suggests that cigarette smoke stimulates inflammation and reduces apoptosis, culminating in increased prostatic epithelial and extracellular matrices, whereas physical training promoted beneficial effects towards maintaining normal prostate morphology and protein levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".