Regulation of fatty acid transport and storage: influence of acylation-stimulating protein
Bibliographic record
Abstract
Postprandial lipemia and fatty acid fluxes occur several times daily, resulting in very efficient absorption of dietary fat and redistribution to various tissues. Absorbed dietary lipids are incorporated into chylomicrons to distribute triglycerides either for storage in adipose tissue or for immediate use in muscle. Commonly, the dietary sources of fat exceed the actual needs and the tissues are faced with dealing with the excess. Under these circumstances, the removal process of dietary triglycerides and fatty acids becomes overloaded, resulting in excessive postprandial lipemia and accumulation of chylomicrons, remnant particles and non-esterified fatty acids. These particles are associated with disruptions in lipoprotein metabolism and changes in inflammatory factors, thus their association with cardiovascular disease, metabolic syndrome and diabetes is not surprising. Dietary factors, not just fat, influence postprandial fluxes. This leads to the question: do we need a standardized fat tolerance test? The recognition of the factors influencing postprandial lipemia and fatty acid uptake and clearance is constantly increasing. Numerous proteins, transporters, enzymes and hormones have been shown to affect fatty acid flux at the level of absorption, peripheral uptake and hepatic remnant clearance. This summary targets fatty acid fluxes, with a focus on acylation-stimulating protein. Keywords: C3adesArg; lipoprotein lipase; postprandial; triglyceride
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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".