Liver Disease, Systemic Inflammation, and Growth Using a Mixed Parenteral Lipid Emulsion, Containing Soybean Oil, Fish Oil, and Medium Chain Triglycerides, Compared With Soybean Oil in Parenteral Nutrition–Fed Neonatal Piglets
Bibliographic record
Abstract
BACKGROUND: The optimal parenteral lipid emulsion for neonates should reduce the risk of intestinal failure-associated liver disease and inflammation, while supporting growth and development. This could be best achieved by balanced content of ω-6 and ω-3 polyunsaturated fatty acids (PUFAs). Using a neonatal piglet model of parenteral nutrition (PN), we compared a 100% soy oil-based emulsion (ω-6:ω-3 PUFA: 7:1) with a mixed lipid emulsion comprising 30% soy oil, 30% medium-chain triglycerides, 25% olive oil, and 15% fish oil (ω-6:ω-3 PUFA: approximately 2.5:1) with regard to liver disease, inflammation, and fatty acid content in plasma and brain. METHOD: Neonatal piglets, 3-6 days old, underwent jugular catheter insertion for isonitrogenous, isocaloric PN delivery over 14 days. The IL group (n = 8) was treated with Intralipid; the ML group (n = 10) was treated with the mixed lipid (SMOFlipid). Bile flow, liver chemistry, C-reactive protein (CRP), and PUFA content in plasma phospholipids and brain were compared. RESULTS: Compared with the IL group, ML-treated piglets had increased bile flow (P = .008) and lower total bilirubin (P = .001) and CRP (P = .023) concentrations. The ω-6 long-chain PUFA content was lower in plasma and brain for the ML group. The key ω-3 long-chain PUFA for neonatal development, docosahexaenoic acid (DHA), was not different between groups. CONCLUSION: The mixed lipid, having less ω-6 PUFA and more ω-3 PUFA, was able to prevent liver disease and reduce systemic inflammation in PN-fed neonatal piglets. However, this lipid did not increase plasma or brain DHA status, which would be desirable for neonatal developmental outcomes.
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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 | 0.001 | 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) | 0.001 | 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".