Exercise may offset nicotine-induced injury in lung tissue: A preliminary histological study based on a rat model
Bibliographic record
Abstract
Nicotine appears to be the primary pharmacologic agent that causes smoking-related pulmonary diseases. An understanding of the effect of nicotine on lungs is essential to develop interventions that can be used to counter smoking-related diseases. Further, it is shown that physical exercise may partially reverse smoking-induced pathological changes in experimental animals. Hence, this study focuses on the pathological changes in rat lung following nicotine administration and the role of exercise in reversing the nicotine-induced lung injury. This is a randomized controlled trial with 3 groups of rats. Control (CG), nicotine-exposed (NG), and nicotine-exposed and exercise group (NEG). Control group received no intervention. Both NG and NEG were given 1.5 mg/kg nicotine base, daily, subcutaneously, but NEG were also subjected to an intensive daily swimming protocol. The rats were sacrificed and the lung tissue was processed for light and transmission electron microscopic and immunohistochemical studies. Compared with the control group, the nicotine group showed enlargement and destruction of the alveolar septum, cellular hyperplasia and interstitial fibrosis, and interstitial mononuclear cell infiltration with increased intraluminal macrophages. There was only modest morphological change between the nicotine administered and nicotine and exercise groups. Expression of superoxide dismutase (SOD) and catalase showed a mild increase in the NEG, whereas glutathione peroxidase (GPX) showed mild and moderate increase in the expression in the NG and NEG, respectively. This study shows that nicotine induces substantial pathological changes in the lung and prolonged exercise may have some beneficial effects in partially reversing the nicotine-induced lung injury by inducing the expression of antioxidants.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".