Compound-specific isotope analysis resolves the dietary origin of docosahexaenoic acid in the mouse brain
Bibliographic record
Abstract
DHA (22:6n-3) may be derived from two dietary sources, preformed dietary DHA or through synthesis from α-linolenic acid (ALA; 18:3n-3). However, conventional methods cannot distinguish between DHA derived from either source without the use of costly labeled tracers. In the present study, we demonstrate the proof-of-concept that compound-specific isotope analysis (CSIA) by GC-isotope ratio mass spectrometry (IRMS) can differentiate between sources of brain DHA based on differences in natural 13 C enrichment. Mice were fed diets containing either purified ALA or DHA as the sole n-3 PUFA. Extracted lipids were analyzed by CSIA for natural abundance 13 C enrichment. Brain DHA from DHA-fed mice was significantly more enriched (−23.32‰ to −21.92‰) compared with mice on the ALA diet (−28.25‰ to −27.49‰). The measured 13 C enrichment of brain DHA closely resembled the dietary n-3 PUFA source, −21.86‰ and −28.22‰ for DHA and ALA, respectively. The dietary effect on DHA 13 C enrichment was similar in liver and blood fractions. Our results demonstrate the effectiveness of CSIA, at natural 13 C enrichment, to differentiate between the incorporation of preformed or synthesized DHA into the brain and other tissues without the need for tracers.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".