Fucoxanthin Enhances Chain Elongation and Desaturation of Alpha‐Linolenic Acid in HepG2 Cells
Bibliographic record
Abstract
Dietary fucoxanthin (FX), a carotenoid compound from brown algae, was found to increase docosahexaenoic acid (DHA, 22:6n-3) and arachidonic acid (ARA, 20:4n-6) in the liver of mice. DHA and ARA are known to be biosynthesized from the respective precursor α-linolenic acid (ALA, 18:3n-3) and linoleic acid (LNA, 18:2n-6), through desaturation and chain elongation. We examined the effect of FX on the fatty acid metabolism in HepG2 cells (Hepatocellular carcinoma, human). In the first experiment, cells were co-treated with ALA (100 μM) and FX (0-100 μM) or vehicle for 48 h. FX increased eicosapentaenoic acid (EPA, 20:5n-3), docosapentaenoic acid (DPA, 22:5n-3), DHA at concentrations of ≥ 50 μM. To clarify the change in the metabolism of polyunsaturated fatty acid (PUFA), in the second experiment, cells were co-treated with universally-[(13)C]-labeled (U-[(13)C]-) ALA (100 μM) and FX (100 μM) for 0.5, 3, 6, 24 and 48 h. [(13)C] labeled-EPA, DPA and DHA content in HepG2 cells were all increased by FX after 48 h treatment. Furthermore, estimated delta-5 desaturase (D5D) but not delta-6 desaturase (D6D) activity index was increased at 48 h. These results suggested that FX may enhance the conversion of ALA to longer chain n-3 PUFA through increasing D5D activity in the liver.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".