Hepatic ratio of phosphatidylcholine to phosphatidylethanolamine predicts survival after partial hepatectomy in mice
Bibliographic record
Abstract
UNLABELLED: A major predictor of failed liver resection and transplantation is nonalcoholic fatty liver disease (NAFLD). NAFLD is linked to a wide spectrum of diseases including obesity and diabetes that are increasingly prevalent in Western populations. Thus, it is important to develop therapies aimed at improving posthepatectomy outcomes in patients with NAFLD, as well as to improve the evaluation of patients slated for hepatic surgery. Decreased hepatic phosphatidylcholine (PC) content and decreased ratio of hepatic PC to phosphatidylethanolamine (PE) have previously been linked to NAFLD. To determine if decreased hepatic PC/PE could predict survival after hepatectomy, we used mouse models lacking key enzymes in PC biosynthesis, namely, phosphatidylethanolamine N-methyltransferase and hepatic-specific CTP:phosphocholine cytidylyltransferase α. These mice were fed a high-fat diet to induce NAFLD. We then performed a 70% partial hepatectomy and monitored postoperative survival. We identified hepatic PC/PE to be inversely correlated with the development of steatosis and inflammation in the progression of NAFLD. Decreased hepatic PC/PE before surgery was also strongly associated with decreased rates of survival after partial hepatectomy. Choline supplementation to the diet increased hepatic PC/PE in Pemt(-/-) mice with NAFLD, decreased inflammation, and increased the survival rate after partial hepatectomy. CONCLUSION: Decreased hepatic PC/PE is a predictor of NAFLD and survival following partial hepatectomy. Choline supplementation may serve as a potential therapy to prevent the progression of NAFLD and to improve postoperative outcome after liver surgery.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".