The Use of CBD and Its Synthetic Analog HU308 in HIV-1-Infected Myeloid Cells
Bibliographic record
Abstract
Currently, there is no cure for human immunodeficiency virus type 1 (HIV-1) infection. However, combined antiretroviral therapy (cART) aids in viral latency and prevents the progression of HIV-1 infection into acquired immunodeficiency syndrome (AIDS). cART has extended many lives, but people living with HIV-1 (PLWH) face lifelong ailments such as HIV-associated neurocognitive disorders (HAND) that range from asymptomatic HAND to HIV-1-associated dementia. HAND has been attributed to chronic inflammation and low-level infection within the central nervous system (CNS) caused by proinflammatory cytokines and viral products. These molecules are shuttled into the CNS within extracellular vesicles (EVs), lipid bound nanoparticles, and are released from cells as a form of intercellular communication. This study investigates the impact of cannabidiol (CBD), as a promising and potential therapeutic for HAND patients, and a similar synthetic molecule, HU308, on the EVs released from HIV-1-infected myeloid cells as well as HIV-1-infected 3D neurospheres. The data shows that both CBD and HU308 decrease non-coding and coding viral RNA (TAR and env) as well as proinflammatory cytokines as IL-1β and TNF-α mRNA. This decrease in viral RNA occurs in in vitro differentiated primary macrophages, in EVs released from HIV-1-infected cells monocytes, and infected neurospheres. Furthermore, a 3D neurosphere model shows an overall decrease in proinflammatory mRNA with HU308. Finally, using a humanized mouse model of HIV-1 infection, plasma viral RNA was shown to significantly decrease with HU308 alone and was most effective in combination with cART, even when compared to the typical cART treatment. Overall, CBD or HU308 may be a viable option to decrease EV release and associated cytokines which would dampen the virus spread and may be used in effective treatment of HAND in combination with cART.
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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 | 0.002 |
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".