Effect of dietary docosahexaenoic acid (DHA) in phospholipids or triglycerides on brain DHA uptake and accretion
Bibliographic record
Abstract
Tracer studies suggest that phospholipid DHA (PL-DHA) more effectively targets the brain than triglyceride DHA (TAG-DHA), although the mechanism and whether this translates into higher brain DHA concentrations are not clear. Rats were gavaged with [U-(3)H]PL-DHA and [U-(3)H]TAG-DHA and blood sampled over 6h prior to collection of brain regions and other tissues. In another experiment, rats were supplemented for 4weeks with TAG-DHA (fish oil), PL-DHA (roe PL) or a mixture of both for comparison to a low-omega-3 diet. Brain regions and other tissues were collected, and blood was sampled weekly. DHA accretion rates were estimated using the balance method. [U-(3)H]PL-DHA rats had higher radioactivity in cerebellum, hippocampus and remainder of brain, with no differences in other tissues despite higher serum lipid radioactivity in [U-(3)H]TAG-DHA rats. TAG-DHA, PL-DHA or a mixture were equally effective at increasing brain DHA. There were no differences between DHA-supplemented groups in brain region, whole-body, or tissue DHA accretion rates except heart and serum TAG where the PL-DHA/TAG-DHA blend was higher than TAG-DHA. Apparent DHA β-oxidation was not different between DHA-supplemented groups. This indicates that more labeled DHA enters the brain when consumed as PL; however, this may not translate into higher brain DHA concentrations.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".