Sulforaphane protects against sodium valproate–induced acute liver injury
Bibliographic record
Abstract
Drug-induced hepatotoxicity is one of the most commonly encountered obstacles in the field of medical practice. Sodium valproate (VPA) is among many drugs with reported hepatotoxic effects. Sulforaphane (SFN) is a thiol compound found in wide abundance in cruciferous plants that has numerous reported therapeutic efficacies. The current investigation sheds light on the potential hepatoprotective effect of SFN against VPA-induced liver injury in rats. Twice daily VPA (700 mg/kg, i.p.) for 7 days induced significant biochemical alterations and hepatic histopathological damage. SFN (0.5 mg/kg, orally) for 7 days significantly boosted liver function biomarkers; it reduced serum alanine transaminase, aspartate aminotransferase, and alkaline phosphatase, and restored serum albumin concentration in a significant manner. Meanwhile, SFN significantly mitigated VPA-induced histopathological alterations. To highlight the mechanisms implicated in the observed hepatoprotective action, hepatic malondialdehyde and tumour necrosis factor α content significantly declined with concomitant increase in hepatic heme oxygenase-1 content and glutathione concentration with SFN treatment. In conclusion, SFN can significantly ameliorate VPA-induced hepatotoxicity and liver injury primarily by direct association between antioxidant and anti-inflammatory properties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".