Effects of bioactive salmon peptides and fatty acids on cellular transport and secretion of adiponectin
Bibliographic record
Abstract
Plasma adiponectin levels are inversely related to several components of the metabolic syndrome and therapeutics that target adiponectin secretion are emerging. Consumption of fish oil is associated with improved metabolic profile, however, there is additional evidence that the protein component of fish may also have metabolic benefits. The aim of this study was to examine the effects of fish fatty acids and peptides on adiponectin assembly and secretion in differentiated 3T3‐L1 adipocytes. Treatment with polyunsaturated fatty acids (PUFAs) increased total cellular adiponectin levels but did not increase adiponectin secretion. In addition, two fractions of molecular weight fractionated (<1000 dalton) salmon peptides, but not cod peptides, stimulated adiponectin synthesis and secretion. Subcellular fractionation on sucrose density gradients revealed that the majority of cellular adiponectin is located in the endoplasmic reticulum (ER). Treatment of the cells with Brefeldin A prevented adiponectin secretion and caused cellular accumulation of the high molecular weight adiponectin oligomers. These data suggest that the ER is an important site of cellular adiponectin accumulation and that treatment with PUFAs or salmon peptides can increase adiponectin in this adipocyte model. Grant Funding Source : Heart and Stroke Foundation of Canada and Natural Sciences and Engineering Research Council of Canada
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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".