Novel 13C enrichment technique reveals early turnover of DHA in peripheral tissues
Bibliographic record
Abstract
The brain is rich in DHA, which plays important roles in regulating neuronal function. Recently, using compound-specific isotope analysis that takes advantage of natural differences in carbon-13 content ( 13 C/ 12 C ratio or δ 13 C) of the food supply, we determined the brain DHA half-life. However, because of methodological limitations, we were unable to capture DHA turnover rates in peripheral tissues. In the current study, we applied compound-specific isotope analysis via high-precision GC combustion isotope ratio mass spectrometry to determine half-lives of brain, liver, and plasma DHA in mice following a dietary switch experiment. To model DHA tissue turnover rates in peripheral tissues, we added earlier time points within the diet switch study and took advantage of natural variations in the δ 13 C-DHA of algal and fish DHA sources to maintain DHA pool sizes and used an enriched (uniformly labeled 13 C) DHA treatment. Mice were fed a fish-DHA diet (control) for 3 months, then switched to an algal-DHA treatment diet, the 13 C enriched-DHA treatment diet, or they stayed on the control diet for the remainder of the study time course. In mice fed the algal and 13 C enriched-DHA diets, the brain DHA half-life was 47 and 46 days, the liver half-life was 5.6 and 7.2 days, and the plasma half-life was 4.7 and 6.4 days, respectively. By using improved methodologies, we calculated DHA turnover rates in the liver and plasma, and our study for the first time, by using an enriched DHA source (very high δ 13 C), validated its utility in diet switch studies.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".