Nitric Oxide and Prostaglandins Potentiate the Liver Regeneration Cascade
Bibliographic record
Abstract
The liver has the remarkable ability to regenerate following damage or surgical resection. Although this feature of the liver has been studied for over 100 years, the trigger of the liver regeneration cascade remains controversial. Recent experimental evidence supports the hypothesis that nitric oxide (NO) and prostaglandins (PGs), released secondary to an increase in the blood flow-to-liver mass ratio following two-thirds partial hepatectomy (PHx), work synergistically to trigger liver regeneration. To extend this research, the hypothesis that NO and PGs are potential therapeutic targets to potentiate the liver regeneration cascade is tested. The NO donor s-nitroso-n-acetylpenicillamine, the phosphodiesterase V antagonist zaprinast (ZAP) and PGI2 each potentiated c-fos messenger RNA expression, an index of initiation of the liver regeneration cascade, following PHx. Also, the triple combination of s-nitroso-n-acetylpenicillamine, ZAP and PGI2 potentiated c-fos messenger RNA expression. These results support the hypothesis that NO and PGs can potentiate initiation of the regeneration cascade. An additional index of liver weight restoration 48 h after PHx was also used to test the hypothesis, because this index encompasses the entire liver regeneration cascade. ZAP and 6-keto-PGF1alpha, a stable metabolite of PGI2, and the combination of ZAP and 6-keto-PGF1alpha, each potentiated liver weight restoration 48 h after PHx. These results also provide support for the hypothesis that NO and PGs are possible therapeutic targets to potentiate liver regeneration following surgical resection.
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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 | 0.001 |
| 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".