The effects of protease inhibitors on basal and insulin-stimulated lipid metabolism, insulin binding, and signaling
Bibliographic record
Abstract
The objective of our research was to investigate the effects of the protease inhibitors ritonavir, saquinavir, and indinavir on triglyceride synthesis, lipolysis, insulin binding, and signaling in differentiating 3T3 L1 pre-adipocytes. Saquinavir, ritonavir, and indinavir all stimulated triglyceride (TG) synthesis. Additionally, all concentrations of protease inhibitors employed (i.e., 0.1 micro M to 10 micro M) significantly decreased insulin-stimulated TG synthesis. No effects of any of the protease inhibitors were observed either on basal lipolysis or after stimulation of lipolysis with 100 nM noradrenaline. Specific (125)I-insulin binding was observed to be decreased by exposure to all the protease inhibitors throughout the period of adipocyte phenotype development. This was mediated by indinavir through a receptor decrease and had no effect on receptor affinity. During differentiation with ritonavir (i.e., 1-11 days post addition of differentiating cocktail), insulin receptor substrate-1 (IRS-1) tyrosine phosphorylation was ascertained (day 11) and found to be decreased in the ritonavir exposed cells when compared with control cells. The results reported herein demonstrate protease inhibitor effects on basal TG synthesis while exhibiting decreased insulin-stimulated TG synthesis at physiological concentrations of protease inhibitors. These effects may be subsequent to decreased insulin binding and/or IRS-1 tyrosine phosphorylation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".