Whole-body DHA synthesis-secretion kinetics from plasma eicosapentaenoic acid and alpha-linolenic acid in the free-living rat
Bibliographic record
Abstract
Whole body docosahexaenoic acid (DHA, 22:6n-3) synthesis from α-linolenic acid (ALA, 18:3n-3) is considered to be very low, however, the daily synthesis-secretion of DHA may be sufficient to supply the adult brain. The current study aims to assess whether whole body DHA synthesis-secretion kinetics are different when comparing plasma ALA versus eicosapentaenoic acid (EPA, 20:5n-3) as the precursor. Male Long Evans rats (n=6) were fed a 2% ALA in total fat diet for eight weeks, followed by surgery to implant a catheter into each of the jugular vein and carotid artery and 3h of steady-state infusion with a known amount of (2)H-ALA and (13)C-eicosapentaenoic acid (EPA, 20:5n3). Blood samples were collected at thirty-minute intervals and plasma enrichment of (2)H- and (13)C EPA, n-3 docosapentaenoic acid (DPAn-3, 22:5n-3) and DHA were determined for assessment of synthesis-secretion kinetic parameters. Results indicate a 13-fold higher synthesis-secretion coefficient for DHA from EPA as compared to ALA. However, after correcting for the 6.6 fold higher endogenous plasma ALA concentration, no significant differences in daily synthesis-secretion (nmol/day) of DHA (97.6±28.2 and 172±62), DPAn-3 (853±279 and 1139±484) or EPA (1587±592 and 1628±366) were observed from plasma unesterified ALA and EPA sources, respectively. These results suggest that typical diets which are significantly higher in ALA compared to EPA yield similar daily DHA synthesis-secretion despite a significantly higher synthesis-secretion coefficient from EPA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".