Higher efficacy of dietary DHA provided as a phospholipid than as a triglyceride for brain DHA accretion in neonatal piglets
Bibliographic record
Abstract
Long-chain PUFAs (LCPUFAs) occur in foods primarily in the natural lipid classes, triacylglycerols (TAGs) or phospholipids (PLs). We studied the relative efficacy of the neural omega-3 DHA provided in formula to growing piglets as a dose of (13)C-DHA bound to either TAG or phosphatidylcholine (PC). Piglets were assigned to identical formula-based diets from early life and provided with TAG-(13)C-DHA or PC-(13)C-DHA orally at 16 days. Days later, piglet organs were analyzed for (13)C-DHA and other FA metabolites. PC-(13)C-DHA was 1.9-fold more efficacious for brain gray matter DHA accretion than TAG-(13)C-DHA, and was similarly more efficacious in gray matter synaptosomes, retina, liver, and red blood cells (RBCs). Liver labeling was greatest, implying initial processing in that organ followed by export to other organs, and suggesting that transfer from gut to bloodstream to liver in part drove the difference in relative efficacy for tissue accretion. Apparent retroconversion to 22:5n-3 was more than double for PC-(13)C-DHA and was more prominent in neural tissue than in liver or RBCs. These data directly support greater efficacy for PC as a carrier for LCPUFAs compared with TAG, consistent with previous studies of arachidonic acid and DHA measured in other species.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".