Protease Inhibitor Effects on Triglyceride Synthesis and Adipokine Secretion in Human Omental and Subcutaneous Adipose Tissue
Bibliographic record
Abstract
OBJECTIVE: Significant advances in the treatment of the morbidity and mortality associated with AIDS are also associated with undesirable side-effects in fat redistribution (lipodystrophy), insulin resistance and cardiovascular risk, which is directly linked to protease inhibitor (PI) treatment. METHODS: The effects of four different PIs on triglyceride (TG) storage and adipokine production (leptin, adiponectin, and acylation stimulating protein [ASP]) in omental (OM) and subcutaneous (SC) adipose tissues were examined. RESULTS: Initial results demonstrated that saquinivir (SQV) and ritonivir (RTV) had little observed effect on de novo TG synthesis ([3H]glucose incorporation into TG) or fatty acid re-esterification ([14C]oleate incorporation into TG), whereas amprenivir (APV) and indinivir (IDV) reduced TG synthesis, especially in SC tissue up to 30+/-5.8% P<0.05 and 46+/-7.8% P<0.001, at 20 microM, respectively. There was no observed effect on phospholipid synthesis, tissue protein or toxicity. Only APV and IDV decreased leptin and adiponectin secretion in SC tissue, in a time- and concentration-dependent manner: at 18 h, leptin was inhibited by 54+/-3.1% (P<0.001) and 44+/-6.4% (P<0.001) by APV and IDV (40 microM), respectively, and adiponectin was inhibited by 35+/-5.6%(P<0.001) and 25+/-12.3% (P<0.05) by APV and IDV (40 IuM), respectively. By contrast, both IDV and APV decreased ASP secretion in OM tissues by a maximum of 53 +/-7.8% (P<0.001) and 59+/-5.9% (P<0.001), respectively, and by a maximum of 86+/-1.6% (P<0.001) and 72 +/-4% (P<0.001), respectively, in SC tissues. CONCLUSION: PI have a direct effect on human adipose tissue which are site, PI and adipokine specific; these effects may contribute to the overall adipose imbalance and development of lipodystrophy, and metabolic syndrome in HIV-positive individuals.
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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".