Influence of alpha-lipoic acid on nicotine-induced lung and liver damage in experimental rats
Bibliographic record
Abstract
Nicotine mediates some of the injurious effects caused by consuming tobacco products. This work aimed at investigating the defensive role of alpha-lipoic acid (ALA) with its known antioxidant and antiinflammatory effect in nicotine-induced lung and liver damage. Rats were arranged into 4 groups: control, nicotine, ALA, and ALA-nicotine groups. Oxidative stress and antioxidant status were determined by assessing thiobarbituric acid reactive substances (TBARS), superoxide dismutase (SOD), and glutathione (GSH) levels in lung and liver. Liver enzymes and lipid profiles were measured and pulmonary and hepatic damage were assessed by histopathological examination. Also, serum levels of transforming growth factor beta 1 (TGF-β1) and vascular cell adhesion molecule 1 (VCAM-1) were determined. The results revealed an increase in TBARS in tissues and a reduction in both SOD and GSH activity in the nicotine-treated rats. Nicotine induced high levels of liver enzymes, TGF-β1, VCAM-1, and dyslipidemia with histopathological changes in the lung and liver. ALA administration along with nicotine attenuated oxidative stress and normalized the SOD and GSH levels, ameliorated dyslipidemia, and improved TGF-β1 and VCAM-1 with better histopathology of the lung and liver. The study data revealed that ALA may be beneficial in alleviating nicotine-induced oxidative stress, dyslipidemia, and both lung and liver damage.
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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 | 0.001 | 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".