Treatment of ricin A-chain-induced hepatotoxicity with liposome-encapsulated N-acetylcysteine
Bibliographic record
Abstract
BACKGROUND: The toxicity of ricin resides in the ricin A-chain (RTA) and is attributed to the inhibition of protein synthesis but inflammation and oxidative stress have also been implicated. RTA can independently enter cells producing comparable tissue injury and inflammation, although at much higher concentrations than intact ricin. Treatment for exposure to ricin or RTA is supportive. PURPOSE: To examine the effectiveness of conventional or liposome-encapsulated N-acetylcysteine (Lipo-NAC) in ameliorating RTA-induced hepatotoxicity. METHODS: Four hours after RTA administration (90 µg/kg b.wt, iv), rats were treated with conventional NAC or Lipo-NAC (25 mg/kg NAC). The hepatoprotective effects of the antioxidant formulations were assessed by measuring indexes for liver injury (alanine [ALT] and aspartate [AST] aminotransferase activities), inflammation (myeloperoxidase, tumor necrosis factor-α, chloramine levels), and oxidant response (lipid peroxidation, nitrotyrosine, glutathione levels) 24-h post-RTA exposure. RESULTS: Administration of RTA to animals resulted in hepatotoxicity as demonstrated by elevated plasma ALT and AST levels, increases in an inflammatory response, and increases in oxidant response. Treatment of animals with the antioxidant formulations reversed the RTA-induced hepatotoxicity, being most evident following the administration of Lipo-NAC. CONCLUSION: NAC, administered in a liposomal form, may serve as a potentially effective pharmacological agent in the treatment of RTA-induced liver injuries.
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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".