Protective Effects of Liposomal N-Acetylcysteine against Paraquat-Induced Cytotoxicity and Gene Expression
Bibliographic record
Abstract
Paraquat (PQ) is a herbicide that preferentially accumulates in the lung and exerts its cytotoxicity via the generation of reactive oxygen species (ROS). There is no specific treatment for paraquat poisoning. Attempts have been made to increase the antioxidant status in the lung using antioxidants (e.g., superoxide dismutase, vitamin E, N-acetylcysteine) but the outcome from such treatments is limited. Encapsulation of antioxidants in liposomes improves their therapeutic potential against oxidant-induced lung damage because liposomes facilitate intracellular delivery and prolong the retention of entrapped agents inside the cell. In the present study, we compared the effectiveness of conventional N-acetylcysteine (NAC) and liposomal-NAC (L-NAC) against PQ-induced cytotoxicity and examined the mechanism(s) by which these antioxidant formulations conferred cytoprotection. The effects of NAC or L-NAC against PQ-induced cytotoxicity in A549 cells were assessed by measuring cellular PQ uptake, intracellular glutathione content, ROS levels, mitochondrial membrane potential, cellular gene expression, inflammatory cytokine release and cell viability. Pretreatment of cells with L-NAC was significantly more effective than pretreatment with the conventional drug in reducing PQ-induced cytotoxicity, as indicated by the biomarkers used in this study. Our results suggested that the delivery of NAC as a liposomal formulation improves its effectiveness in counteracting PQ-induced cytotoxicity.
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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) | 0.001 | 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".