Olprinone protects the liver from ischemia–reperfusion injury through oxidative stress prevention and protein kinase Akt activation
Bibliographic record
Abstract
Liver ischemia-reperfusion (IR) injury is inevitable in surgical procedures such as hepatic resection and liver transplantation. It represents a leading cause of liver graft dysfunction and primary nonfunction after transplantation. Phosphodiesterase (PDE) inhibitors are emerging as effective drugs able to reduce IR damage. The aim of this study was to investigate the effect of selective PDE-3 inhibitor olprinone (Olp) against liver IR injury. Male Wistar rats were subjected to 1 h of partial warm ischemia (70%) followed by 6 h of reperfusion. Before ischemia, rats were treated with saline (IR group), Olp (Olp group), or Olp with Akt inhibitor LY294002 (Olp plus LY group). After reperfusion, hepatic injury (transaminase activities), mitochondrial damage (glutamate dehydrogenase activity), oxidative stress (malondialdehyde and glutathione concentrations and catalase and superoxide dismutase activities), and protein kinase Akt activation were evaluated. Rat treatment with Olp reduced liver injury, prevented mitochondrial damage, decreased lipid peroxidation, and enhanced antioxidant enzymes. Also, Olp induced a significant activation in protein kinase Akt. Inhibition of Akt with LY294002 abolished all of the protective effects of Olp. In conclusion, Olp treatment may be an effective strategy in reducing liver IR injury through oxidative stress prevention and Akt activation.
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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".