Impact of Clinical Use of Parenteral Lipid Emulsions on Bile Acid Metabolism and Composition in Neonatal Piglets
Bibliographic record
Abstract
BACKGROUND: Neonates with intestinal failure dependent on parenteral nutrition (PN) are at risk of intestinal failure-associated liver disease (IFALD). PN lipid composition relates to the risk of IFALD, but the mechanisms are poorly understood. We investigated the effects of soybean oil (SO), a mixed-lipid (ML) emulsion containing fish oil (FO), and a pure FO. We hypothesized FO-containing PN lipids would result in increased gene expression of canalicular bile acid transporters and a larger, more hydrophilic bile acid pool, predictive of increased bile flow. METHODS: Neonatal piglets were allocated to receive 1 of SO, ML, or FO throughout 14 days of PN feeding. Relative expression of genes involved in bile acid synthesis and transport were determined through quantitative polymerase chain reaction. Bile secreted from the liver was collected and measured. Bile acid composition was determined using tandem mass spectrometry. Regression analysis was used to determine predictors of bile flow. RESULTS: PN reduced bile acid secretion (P < .001). FO-containing PN lipids were associated with greater expression of bile acid and organic solute transport genes (P < .05) and greater secretion of hydrophobic bile acids (P < .001). Farnesoid X receptor (P = .01), bile salt export pump (P < .01), multidrug resistant protein 2 (P < .01), and unconjugated hyocholic acid (P < .001) independently predicted bile flow. CONCLUSIONS: PN lipid modulation altered bile acid metabolism and composition. These alterations may explain the hepatoprotective effects of FO-containing PN lipids and support their use in the prevention and treatment of IFALD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".