Peroxisome Proliferator-Activated Receptor-gamma agonists exhibit anti-inflammatory and antiviral effects in an EcoHIV mouse model
Bibliographic record
Abstract
The widespread use of combination antiretroviral therapy (cART) has resulted in significantly reduced deaths from HIV-1 associated complications and opportunistic infections. However, it is estimated that up to 50% of HIV-1 infected individuals still develop HIV-1 associated neurocognitive disorders (HAND). With no treatment currently available for patients, there is a critical need to identify therapeutic approaches that can treat this disorder. Evidence suggests that targeting Peroxisome Proliferator-Activated Receptor-gamma (PPARγ) can be anti-inflammatory in neurological disorders. Here we show that treatment with PPARγ agonists (rosiglitazone or pioglitazone) in primary cultures of mouse glial cells reversed EcoHIV-induced inflammatory genes (TNFα, IL-1β, CCL2, CCL3, CXCL10) and indicator of oxidative stress (iNOS). Furthermore, in vivo, mice administered with EcoHIV through intracranial injection resulted in upregulation of inflammatory genes (TNFα, IL-1β, IFNγ, CCL2, CCL3, CXCL10) and oxidative stress marker (iNOS) in the brain which was reversed through intraperitoneal administration of PPARγ agonists (rosiglitazone or pioglitazone). Finally, we demonstrated that treatment with these compounds in vivo reduced EcoHIV p24 protein burden in the brain. Our results suggest that treatment with PPARγ agonists are anti-inflammatory and antiviral in an in vivo model of EcoHIV infection. These drugs hold promise as potential candidates for HAND treatment in the future.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".