Serum n‐3 Tetracosapentaenoic Acid and Tetracosahexaenoic Acid Increase Following Higher Dietary α‐Linolenic Acid but not Docosahexaenoic Acid
Bibliographic record
Abstract
n-3 Tetracosapentaenoic acid (24:5n-3, TPAn-3) and tetracosahexaenoic acid (24:6n-3, THA) are believed to be important intermediates to docosahexaenoic acid (DHA, 22:6n-3) synthesis. The purpose of this study is to report for the first time serum concentrations of TPAn-3 and THA and their response to changing dietary α-linolenic acid (18:3n-3, ALA) and DHA. The responses will then be used in an attempt to predict the location of these fatty acids in relation to DHA in the biosynthetic pathway. Male Long Evans rats (n = 6 per group) were fed either a low (0.1% of total fatty acids), medium (3%) or high (10%) ALA diet with no added DHA, or a low (0%), medium (0.2%) or high (2%) DHA diet with a background of 2% ALA for 8 weeks post-weaning. Serum n-3 and n-6 polyunsaturated fatty acid (PUFA) concentrations (nmol/mL ± SEM) were determined by gas chromatography-mass spectrometry. Serum THA increases from low (0.3 ± 0.1) to medium (5.8 ± 0.7) but not from medium to high (4.6 ± 0.9) dietary ALA, while serum TPAn-3 increases with increasing dietary ALA from 0.09 ± 0.04 to 0.70 ± 0.09 to 1.23 ± 0.14 nmol/mL. Following DHA feeding, neither TPAn-3 or THA change across all dietary DHA intake levels. Serum TPAn-3 demonstrates a similar response to dietary DHA. In conclusion, this is the first study to demonstrate that increases in dietary ALA but not DHA increase serum TPAn-3 and THA in rats, suggesting that both fatty acids are precursors to DHA in the biosynthetic pathway.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".