The low levels of eicosapentaenoic acid in rat brain phospholipids are maintained via multiple redundant mechanisms
Bibliographic record
Abstract
Brain eicosapentaenoic acid (EPA) levels are 250- to 300-fold lower than docosahexaenoic acid (DHA), at least partly, because EPA is rapidly β-oxidized and lost from brain phospholipids. Therefore, we examined if β-oxidation was necessary for maintaining low EPA levels by inhibiting β-oxidation with methyl palmoxirate (MEP). Furthermore, because other metabolic differences between DHA and EPA may also contribute to their vastly different levels, this study aimed to quantify the incorporation and turnover of DHA and EPA into brain phospholipids. Fifteen-week-old rats were subjected to vehicle or MEP prior to a 5 min intravenous infusion of (14)C-palmitate, (14)C-DHA, or (14)C-EPA. MEP reduced the radioactivity of brain aqueous fractions for (14)C-palmitate-, (14)C-EPA-, and (14)C-DHA-infused rats by 74, 54, and 23%, respectively; while it increased the net rate of incorporation of plasma unesterified palmitate into choline glycerophospholipids and phosphatidylinositol and EPA into ethanolamine glycerophospholipids and phosphatidylserine. MEP also increased the synthesis of n-3 docosapentaenoic acid (n-3 DPA) from EPA. Moreover, the recycling of EPA into brain phospholipids was 154-fold lower than DHA. Therefore, the low levels of EPA in the brain are maintained by multiple redundant pathways including β-oxidation, decreased incorporation from plasma unesterified FA pool, elongation/desaturation to n-3 DPA, and lower recycling within brain phospholipids.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".