Whole-body synthesis-secretion rates of long-chain n-3 PUFAs from circulating unesterified α-linolenic acid in unanesthetized rats
Post-publication record
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Bibliographic record
Abstract
Docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA), long-chain n-3 PUFAs important for brain and heart function, can be obtained from dietary fish products or by liver synthesis from alpha-linolenic acid (alpha-LNA). Their daily human dietary requirements are not clear, and their liver synthesis rates in humans and nonhumans are unknown. We estimated whole-body (presumably liver) synthesis rates in unanesthetized rats by infusing [U-(13)C]alpha-LNA intravenously for 2 h and measuring labeled and unlabeled n-3 PUFA in arterial plasma using negative chemical ionization GC-MS. Newly synthesized esterified [(13)C]DHA, [(13)C]EPA, and [(13)C]docosapentaenoic acid (DPA) appeared in arterial plasma after 60 min of infusion, then their concentrations rose in an S-shaped manner. Esterified concentration x plasma volume data were fit with a sigmoidal equation, whose peak first derivatives provided synthesis rates of unlabeled EPA, DPA, and DHA equal to 8.40, 6.27, and 9.84 mumol/day, respectively. The DHA synthesis rate exceeded the published daily rat brain DHA consumption rate by 30-fold, suggesting that liver synthesis from alpha-LNA could maintain brain DHA homeostasis were DHA absent from the diet. This stable isotope infusion method could be used to quantify whole-body DHA synthesis rates in human subjects.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".