Effect of a Seaweed Extract on Fatty Acid Accumulation and Glycerol‐3‐Phosphate Dehydrogenase Activity in 3T3‐L1 Adipocytes
Bibliographic record
Abstract
This study was to determine the effect of a seaweed Ascophyllum nodosum extract (SE) containing 220 mg g(-1) phlorotannins on differentiation and fatty acid accumulation in differentiating 3T3-L1 adipocytes. 3T3-L1 cells (2 x 10(4) mL(-1)) were seeded to 24-well plates and proliferated to reach confluence and then were treated with media containing 0, 12.5, 25, 50, 75 and 100 mug mL(-1) SE for 8 days. Dexamethasone, methyl-isobutylxanthine and insulin (DMI) were added to the media in the first 2 days to induce cell differentiation. On day 8 the adipocytes were harvested for measuring cellular fatty acid concentration and the activity of glycerol-3-phosphate dehydrogenase (GPDH). It was found that treatment with SE increased (P < 0.01, n = 6) cellular myristoleic acid (C14:1), palmitoleic acid (C16:1) and oleic acid (C18:1) and total monounsaturated fatty acids (MUFA) without significantly affecting the cell number and saturated fatty acid (SFA). Ratios of MUFA/SFA, C14:1/C14:0, C16:1/C16:0 and C18:1/C18:0 in cellular lipids increased (P < 0.05, n = 6) with the SE treatment in a dose dependent manner (P < 0.001). Treatment with 75 microg mL(-1) SE depressed (P < 0.05) cellular GPDH activity. The results indicate that the biological factors in the SE may be involved in differentiation and MUFA accumulation in adipocytes.
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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 | 0.001 |
| 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".