Vinpocetine protects liver against ischemia–reperfusion injury
Bibliographic record
Abstract
Hepatic ischemia-reperfusion (IR) injury is a clinical problem that leads to cellular damage and organ dysfunction mediated mainly via production of reactive oxygen species and inflammatory cytokines. Vinpocetine has long been used in cerebrovascular disorders. This study aimed to explore the protective effect of vinpocetine in IR injury to the liver. Ischemia was induced in rats by clamping the common hepatic artery and portal vein for 30 min followed by 30 min of reperfusion. Serum transaminases and liver lactate dehydrogenase (LDH) activities, liver inflammatory cytokines, oxidative stress biomarkers, and liver histopathology were assessed. IR resulted in marked histopathology changes in liver tissues coupled with elevations in serum transaminases and liver LDH activities. IR also increased the production of liver lipid peroxides, nitric oxide, and inflammatory cytokines interleukin-1β and interleukin-6, in parallel with a reduction in reduced glutathione and interleukin-10 in the liver. Pretreatment with vinpocetine protected against liver IR-induced injury, in a dose-dependent manner, as evidenced by the attenuation of oxidative stress as well as inflammatory and liver injury biomarkers. The effects of vinpocetine were comparable with that of curcumin, a natural antioxidant, and could be attributed to its antioxidant and anti-inflammatory properties.
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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".