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 (13C/12C ratio or δ13C) 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 δ13C-DHA of algal and fish DHA sources to maintain DHA pool sizes and used an enriched (uniformly labeled 13C) DHA treatment. Mice were fed a fish-DHA diet (control) for 3 months, then switched to an algal-DHA treatment diet, the 13C 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 13C 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 δ13C), validated its utility in diet switch studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".