Acylation stimulating protein: a female lipogenic factor?
Bibliographic record
Abstract
Acylation stimulating protein (ASP) is a potent lipogenic factor produced from adipocytes. Plasma ASP levels were shown to increase in obesity, diabetes mellitus type II and dyslipidemia, and decrease after weight loss and fasting. Growing evidence suggests that ASP may significantly contribute to subcutaneous fat storage in females. In vitro, ASP stimulated triglyceride synthesis to a larger extent in subcutaneous compared with omental adipocytes. The ASP receptor binding affinity to plasma membranes prepared from adipose tissue showed higher binding affinity to plasma membranes from female adipose tissue compared with male adipose tissue, and was more pronounced to subcutaneous compared with omental plasma membranes. Human studies demonstrated that postprandial triglyceride clearance predicted by ASP levels was more efficient in women than in men. In mice, postprandial triglyceride clearance, with intraperitoneal ASP administration, was faster in females compared with males. The ASP deficient mice were resistant to weight gain and had reduced fat mass that was more pronounced in females. Recent findings in humans and mice point to a significant association between progesterone and ASP variations in females. In this review, we highlight findings, to date, linking ASP to physiological and hormonal alterations that may contribute to subcutaneous fat distribution typical to females.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".