Fatty acid profiles in blood and tissues of parenteral nutrition–fed neonatal piglets using a novel lipid emulsion containing choline
Bibliographic record
Abstract
BACKGROUND: For parenteral nutrition (PN)-dependent neonates, soybean oil intravenous lipid emulsions (SO-ILEs) and mixed emulsions (SO, medium-chain triglyceride [MCT], olive oil [OO], and fish oil [FO] ILEs) are likely not providing adequate amounts of key fatty acids (FAs) arachidonic acid (AA) and docosahexaenoic acid (DHA) and are devoid of choline. Current FO-containing ILEs provide excessive amounts of eicosapentaenoic acid (EPA). In neonatal piglets, we compared a novel lipid (NOV-C) with the addition of AA, DHA, and choline while sparing EPA with SO-ILE and SO,MCT,OO,FO-ILE. METHODS: We compared FA deposition in serum and tissues among groups of neonatal piglets fed exclusive PN based on SO-ILE (n = 7), SO,MCT,OO,FO-ILE (n = 7), or NOV-C (n = 8). On day 14, serum, liver, lung, brain, retina, and jejunum were collected and FAs in total phospholipid (PL) measured using gas liquid chromatography. RESULTS: In total PL, AA was higher for NOV-C compared with SO,MCT,OO,FO-ILE in serum (P = 0.003) and all tissues except brain (P = 0.08). DHA was higher for NOV-C and SO,MCT,OO,FO-ILE compared with SO-ILE in the liver (P = 0.001), jejunum (P < 0.001), and lung (P < 0.001). In the retina, DHA was higher for NOV-C compared with SO,MCT,OO,FO-ILE and SO-ILE (P = 0.004). EPA was higher for SO,MCT,OO,FO-ILE compared with SO-ILE and NOV-C in serum (P = 0.002) and all tissues except brain (P = 0.48). CONCLUSION: Compared with SO-ILE and SO,MCT,OO,FO-ILE, a novel lipid designed to deliver optimal AA, DHA, EPA, and choline for neonates resulted in higher AA and DHA in blood and tissues. The impact on neonatal immune development and key organ functions needs further exploration.
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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".