Effects of Oral Cannabinoids on Systemic Inflammation and Viral Reservoir Markers in People with HIV on Antiretroviral Therapy: Results of the CTN PT028 Pilot Clinical Trial
Bibliographic record
Abstract
Chronic HIV infection is characterized by persistent inflammation despite antiretroviral therapy (ART). Cannabinoids may help reduce systemic inflammation in people with HIV (PWH). To assess the effects of oral cannabinoids during HIV, ten PWH on ART were randomized (n = 5/group) to increasing doses of oral Δ9-tetrahydrocannabinol (THC): cannabidiol (CBD) combination (2.5:2.5–15:15 mg/day) capsules or CBD-only (200–800 mg/day) capsules for 12 weeks. Blood specimens were collected prospectively 7–21 days prior to treatment initiation and at weeks 0 to 14. Plasma cytokine levels were determined via Luminex and ELISA. Immune cell subsets were characterized by flow cytometry. HIV DNA/RNA were measured in circulating CD4 T-cells and sperm by ultra-sensitive qPCR. Results from both arms were combined for statistical analysis. Plasma levels of IFN-γ, IL-1β, sTNFRII, and REG-3α were significantly reduced at the end of treatment (p ˂ 0.05). A significant decrease in frequencies of PD1+ memory CD4 T-cells, CD73+ regulatory CD4 T-cells, and M-DC8+ intermediate monocytes was also observed (p ˂ 0.05), along with a transient decrease in CD28–CD57+ senescent CD4 and CD8 T-cells. Ki-67+ CD4 T-cells, CCR2+ non-classical monocytes, and myeloid dendritic cells increased over time (p ˂ 0.05). There were no significant changes in other inflammatory markers or HIV DNA/RNA levels. These findings can guide future large clinical trials investigating cannabinoid anti-inflammatory properties.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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".