HIV-protease inhibitors alter retinoic acid synthesis
Bibliographic record
Abstract
BACKGROUND: An increasing rate of highly-active antiretroviral therapy (HAART)-associated metabolic and morphological abnormalities has been reported in HIV-infected persons. Some of them resemble retinoid-related adverse events, indicating alteration(s) of retinol metabolism or of retinoic acid-mediated signalling. OBJECTIVE: To evaluate retinol levels in patients with or without HAART and to assess the effect of antiretroviral agents on retinal dehydrogenase (RALDH), a key enzyme involved in retinoic acid synthesis. DESIGN: Plasma retinol levels, measured in six patients receiving HAART and in five others with no antiretroviral therapy, were correlated with levels of serum retinol-binding proteins. We then studied the effects of seven antiretroviral agents on RALDH activity and gene expression in a kidney-derived cell line (LLCPK). RESULTS: Plasma retinol levels in patients receiving HAART were decreased in comparison with those not receiving antiretroviral drugs (51 +/- 5 versus 66 +/- 11 microg/dl; P = 0.03), whereas retinol-binding protein levels were increased (68 +/- 18 versus 45 +/- 10 mg/l; P = 0.04). RALDH activity was heightened by ritonavir (24%), indinavir (17%), saquinavir (17%), zalcitabine (14%), delavirdine (12%) and nelfinavir (10%) and decreased (22%) by DMP-450. RALDH gene expression was induced only by indinavir. CONCLUSIONS: These data indicate that certain retinoid-like adverse effects in HAART-receiving patients are not due to higher retinol levels. Enhanced RALDH activity or/and gene expression by some protease inhibitors could increase retinoic acid concentrations. Elevated retinoic acid levels might be responsible for retinoid-like or other adverse effects due to alterations in the expression of retinoic acid-responsive genes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".