Surprisingly Low Levels of Measles Immunity in Persons With HIV: A Seroprevalence Survey in a United States HIV Clinic

Background: Measles outbreaks have become increasingly common due to deteriorating vaccination rates, fluctuating herd immunity, and varying antibody decline. Limited knowledge exists regarding prevalence and risk factors associated with measles seronegativity among persons with HIV (PWH). Methods: This was a cross-sectional study conducted at an academic HIV clinic in Omaha, Nebraska. Participants were screened for the presence of measles IgG antibody. Demographic and clinical information was obtained through electronic medical record review. Simple and multivariable logistic regressions were performed to identify risk factors for measles seronegativity. Results: Three hundred fifty-one participants were enrolled, with a measles seroprevalence rate of 70.3%. The mean age (range) was 48 (20-74) years, 77% were male, and 53% were Caucasian. The mean CD4 nadir (range) was 334 (1-1675) cells/mm Conclusions: Our study demonstrates a measles seroprevalence rate that is remarkably lower than previously reported in PWH (92%), and, more importantly, is considerably lower than the rate needed to maintain herd immunity (95%). With higher than expected seronegativity and absence of notable risk factors aside from age, our findings support expanded measles immunity screening for PWH who are at risk of measles exposure

Mitochondrial redox environments predict sensorimotor brain-behavior dynamics in adults with HIV.

Despite virologic suppression, people living with HIV (PLWH) remain at risk for developing cognitive impairment, with aberrations in motor control being a predominant symptom leading to functional dependencies in later life. While the neuroanatomical bases of motor dysfunction have recently been illuminated, the underlying molecular processes remain poorly understood. Herein, we evaluate the predictive capacity of the mitochondrial redox environment on sensorimotor brain-behavior dynamics in 40 virally-suppressed PLWH and 40 demographically-matched controls using structural equation modeling. We used state-of-the-art approaches, including Seahorse Analyzer of mitochondrial function, electron paramagnetic resonance spectroscopy to measure superoxide levels, antioxidant activity assays and dynamic magnetoencephalographic imaging to quantify sensorimotor oscillatory dynamics. We observed differential modulation of sensorimotor brain-behavior relationships by superoxide and hydrogen peroxide-sensitive features of the redox environment in PLWH, while only superoxide-sensitive features were related to optimal oscillatory response profiles and better motor performance in controls. Moreover, these divergent pathways may be attributable to immediate, separable mechanisms of action within the redox environment seen in PLWH, as evidenced by mediation analyses. These findings suggest that mitochondrial redox parameters are important modulators of healthy and pathological oscillations in motor systems and behavior, serving as potential targets for remedying HIV-related cognitive-motor dysfunction in the future

Neuropsychological test selection for cognitive impairment classification: A machine learning approach

INTRODUCTION: Reducing the amount of testing required to accurately detect cognitive impairment is clinically relevant. The aim of this research was to determine the fewest number of clinical measures required to accurately classify participants as healthy older adult, mild cognitive impairment (MCI) or dementia using a suite of classification techniques. METHODS: Two variable selection machine learning models (i.e., naive Bayes, decision tree), a logistic regression, and two participant datasets (i.e., clinical diagnosis, clinical dementia rating; CDR) were explored. Participants classified using clinical diagnosis criteria included 52 individuals with dementia, 97 with MCI, and 161 cognitively healthy older adults. Participants classified using CDR included 154 individuals CDR = 0, 93 individuals with CDR = 0.5, and 25 individuals with CDR = 1.0+. Twenty-seven demographic, psychological, and neuropsychological variables were available for variable selection. RESULTS: No significant difference was observed between naive Bayes, decision tree, and logistic regression models for classification of both clinical diagnosis and CDR datasets. Participant classification (70.0 – 99.1%), geometric mean (60.9 – 98.1%), sensitivity (44.2 – 100%), and specificity (52.7 – 100%) were generally satisfactory. Unsurprisingly, the MCI/CDR = 0.5 participant group was the most challenging to classify. Through variable selection only 2 – 9 variables were required for classification and varied between datasets in a clinically meaningful way. CONCLUSIONS: The current study results reveal that machine learning techniques can accurately classifying cognitive impairment and reduce the number of measures required for diagnosis