Impact of Cannabis Use on Immune Cell Populations and the Viral Reservoir in People With HIV on Suppressive Antiretroviral Therapy.

BACKGROUND: Human immunodeficiency virus (HIV) infection remains incurable due to the persistence of a viral reservoir despite antiretroviral therapy (ART). Cannabis (CB) use is prevalent amongst people with HIV (PWH), but the impact of CB on the latent HIV reservoir has not been investigated. METHODS: Peripheral blood cells from a cohort of PWH who use CB and a matched cohort of PWH who do not use CB on ART were evaluated for expression of maturation/activation markers, HIV-specific T-cell responses, and intact proviral DNA. RESULTS: CB use was associated with increased abundance of naive T cells, reduced effector T cells, and reduced expression of activation markers. CB use was also associated with reduced levels of exhausted and senescent T cells compared to nonusing controls. HIV-specific T-cell responses were unaffected by CB use. CB use was not associated with intact or total HIV DNA frequency in CD4 T cells. CONCLUSIONS: This analysis is consistent with the hypothesis that CB use reduces activation, exhaustion, and senescence in the T cells of PWH, and does not impair HIV-specific CD8 T-cell responses. Longitudinal and interventional studies with evaluation of CB exposure are needed to fully evaluate the impact of CB use on the HIV reservoir.

A Path to Simpler Models Starts With Noise.

The Rashomon set is the set of models that perform approximately equally well on a given dataset, and the Rashomon ratio is the fraction of all models in a given hypothesis space that are in the Rashomon set. Rashomon ratios are often large for tabular datasets in criminal justice, healthcare, lending, education, and in other areas, which has practical implications about whether simpler models can attain the same level of accuracy as more complex models. An open question is why Rashomon ratios often tend to be large. In this work, we propose and study a mechanism of the data generation process, coupled with choices usually made by the analyst during the learning process, that determines the size of the Rashomon ratio. Specifically, we demonstrate that noisier datasets lead to larger Rashomon ratios through the way that practitioners train models. Additionally, we introduce a measure called pattern diversity, which captures the average difference in predictions between distinct classification patterns in the Rashomon set, and motivate why it tends to increase with label noise. Our results explain a key aspect of why simpler models often tend to perform as well as black box models on complex, noisier datasets.

Machine learning approaches identify immunologic signatures of total and intact HIV DNA during long-term antiretroviral therapy.

Antiretroviral therapy (ART) halts HIV replication; however, cellular / immue cell viral reservoirs persist despite ART. Understanding the interplay between the HIV reservoir, immune perturbations, and HIV-specific immune responses on ART may yield insights into HIV persistence. A cross-sectional study of peripheral blood samples from 115 people with HIV (PWH) on long-term ART was conducted. High-dimensional immunophenotyping, quantification of HIV-specific T cell responses, and the intact proviral DNA assay (IPDA) were performed. Total and intact HIV DNA was positively correlated with T cell activation and exhaustion. Years of ART and select bifunctional HIV-specific CD4 T cell responses were negatively correlated with the percentage of intact proviruses. A Leave-One-Covariate-Out (LOCO) inference approach identified specific HIV reservoir and clinical-demographic parameters that were particularly important in predicting select immunophenotypes. Dimension reduction revealed two main clusters of PWH with distinct reservoirs. Additionally, machine learning approaches identified specific combinations of immune and clinical-demographic parameters that predicted with approximately 70% accuracy whether a given participant had qualitatively high or low levels of total or intact HIV DNA. The techniques described here may be useful for assessing global patterns within the increasingly high-dimensional data used in HIV reservoir and other studies of complex biology.