Cannabinoids Reduce Extracellular Vesicle Release from HIV-1 Infected Myeloid Cells and Inhibit Viral Transcription
Bibliographic record
Abstract
Of the 37.9 million individuals infected with human immunodeficiency virus type 1 (HIV-1), approximately 50% exhibit HIV-associated neurocognitive disorders (HAND). We and others previously showed that HIV-1 viral RNAs, such as trans-activating response (TAR) RNA, are incorporated into extracellular vesicles (EVs) and elicit an inflammatory response in recipient naïve cells. Cannabidiol (CBD) and Δ9-tetrahydrocannabinol (THC), the primary cannabinoids present in cannabis, are effective in reducing inflammation. Studies show that cannabis use in people living with HIV-1 is associated with lower viral load, lower circulating CD16+ monocytes and high CD4+ T-cell counts, suggesting a potentially therapeutic application. Here, HIV-1 infected U1 monocytes and primary macrophages were used to assess the effects of CBD. Post-CBD treatment, EV concentrations were analyzed using nanoparticle tracking analysis. Changes in intracellular and EV-associated viral RNA were quantified using RT-qPCR, and changes in viral proteins, EV markers, and autophagy proteins were assessed by Western blot. Our data suggest that CBD significantly reduces the number of EVs released from infected cells and that this may be mediated by reducing viral transcription and autophagy activation. Therefore, CBD may exert a protective effect by alleviating the pathogenic effects of EVs in HIV-1 and CNS-related infections.
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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".