Effect of different liver resection methods on liver damage and regeneration factors VEGF and FGF-2 in mice
Bibliographic record
Abstract
BACKGROUND: Different approaches to study liver regeneration in murine models have been proposed. We investigated the effect of different liver resection models on liver damage and regeneration parameters in mice. METHODS: We compared the technical aspect of the 2 most commonly used techniques of 50% and 70% liver resection. Liver damage, as determined by the change in serum alanine aminotransferase and aspartate aminotransferase, as well as the regeneration parameters VEGF and FGF-2 were analyzed at 6 time points. A postoperative vitality score was introduced. RESULTS: Cholestasis was not observed for either technique. Both resection techniques resulted in full weight recovery of the liver after 240 hours, with no significant difference between sham and resection groups. Postoperative animal morbidity and total protein levels did not differ significantly for either method, indicating early and full functional recovery. However, comparing the mitogenic growth factors FGF-2 and VEGF, a significant increase in serum levels and, therefore, increased growth stimulus, was shown in the extended resection group. CONCLUSION: Extended resection led to a greater response in growth factor expression. This finding is important since it shows that growth factor response differs acdording to the extent of resection. We have demonstrated the need to standardize murine hepatic resection models to adequately compare the resulting liver damage.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".