DHA Esterified to Phosphatidylserine or Phosphatidylcholine is More Efficient at Targeting the Brain than DHA Esterified to Triacylglycerol
Bibliographic record
Abstract
SCOPE: Docosahexaenoic acid (DHA, 22:6n-3) is crucial for optimal neuronal development and function, but the brain has a poor capacity to synthesize this fatty acid. When consumed acutely esterified to phosphatidylcholine, DHA is more efficient at targeting the brain than when consumed esterified to triacylglycerol. However, the brain DHA bioavailability of other forms of DHA-containing phospholipids, after oral ingestion, is unknown. The objective of this study is to compare brain uptake of DHA after acute gavage with different DHA carriers. METHODS AND RESULTS: H-labeled DHA esterified to phosphatidylcholine (DHA-PtdCho), phosphatidylethanolamine (DHA-PtdEtn), phosphatidylserine (DHA-PtdSer) or triacylglycerol (DHA-TG). Six hours post-gavage, the animals were euthanized and radioactivity was quantified in the cortex and serum lipid classes. Radioactivity recovered in cortex total phospholipids was similar between the DHA-PtdCho and DHA-PtdSer groups and were 5.8 and 6.7 times higher than in the DHA-TG group, respectively. Serum total lipid radioactivity was higher in the DHA-PtdSer group than in the DHA-PtdCho and DHA-PtdEtn groups, but not compared to the DHA-TG group. CONCLUSION: These results suggest that different mechanisms must be present to explain the serum and brain bioavailability differences between DHA-PtdCho and DHA-PtdSer, but these require further investigation.
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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 | 0.000 |
| 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".