Complete assessment of whole-body n-3 and n-6 PUFA synthesis-secretion kinetics and DHA turnover in a rodent model
Bibliographic record
Abstract
Previous assessments of the PUFA biosynthesis pathway have focused on DHA and arachidonic acid synthesis. Here, we determined whole-body synthesis-secretion kinetics for all downstream products of PUFA metabolism, including direct measurements of DHA and n-6 docosapentaenoic acid (DPAn-6, 22:5n-6) turnover, and compared n-6 and n-3 homolog kinetics. We infused labeled α-linolenic acid (ALA, 18:3n-3), linoleic acid (LNA, 18:2n-6), DHA, and DPAn-6 as 2 H 5 -ALA, 13 C 18 -LNA, 13 C 22 -DHA, and 13 C 22 -DPAn-6. Eight 11-week-old Long Evans rats fed a 10% fat diet were infused with the labeled PUFAs over 3 h, and plasma enrichment of labeled products was measured every 30 min. The DHA synthesis-secretion rate (94 ± 34 nmol/day) did not differ from other PUFA products (range, 21.8 ± 4.3 nmol/day to 408 ± 116 nmol/day). Synthesis-secretion rates of n-6 and n-3 PUFA homologs were similar, except 22:4n-6 and DPAn-6 had lower synthesis rates. However, daily turnover from newly synthesized DHA (0.067 ± 0.023%) was 56-fold to 556-fold slower than all other PUFA turnover and was 130-fold slower than that determined directly from the total plasma unesterified DHA pool. In conclusion, n-6 and n-3 PUFA synthesis-secretion kinetics suggest that differences in turnover, not in synthesis-secretion rates, primarily determine PUFA plasma levels.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".