Bibliographic record
Abstract
: Thanks to the success of modern antiretroviral therapy (ART), people living with HIV (PLWH) have life expectancies which approach that of persons in the general population. However, despite the ability of ART to suppress viral replication, PLWH have high levels of chronic systemic inflammation which drives the development of comorbidities such as cardiovascular disease, diabetes and non-AIDS associated malignancies. Historically, cannabis has played an important role in alleviating many symptoms experienced by persons with advanced HIV infection in the pre-ART era and continues to be used by many PLWH in the ART era, though for different reasons. Δ-Tetrahydrocannabinol (Δ-THC) and cannabidiol (CBD) are the phytocannabinoids, which have received most attention for their medicinal properties. Due to their ability to suppress lymphocyte proliferation and inflammatory cytokine production, there is interest in examining their therapeutic potential as immunomodulators. CB2 receptor activation has been shown in vitro to reduce CD4 T-cell infection by CXCR4-tropic HIV and to reduce HIV replication. Studies involving SIV-infected macaques have shown that Δ-THC can reduce morbidity and mortality and has favourable effects on gut mucosal immunity. Furthermore, ΔTHC administration was associated with reduced lymph node fibrosis and diminished levels of SIV proviral DNA in spleens of rhesus macaques compared with placebo-treated macaques. In humans, cannabis use does not induce a reduction in peripheral CD4 T-cell count or loss of HIV virological control in cross-sectional studies. Rather, cannabis use in ART-treated PLWH was associated with decreased levels of T-cell activation, inflammatory monocytes and pro-inflammatory cytokine secretion, all of which are related to HIV disease progression and comorbidities. Randomized clinical trials should provide further insights into the ability of cannabis and cannabinoid-based medicines to attenuate HIV-associated inflammation. In turn, these findings may provide a novel means to reduce morbidity and mortality in PLWH as adjunctive agents to ART.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".