Search

We examined relationships between neurocognition and immune activation in Ugandan adolescents with perinatally acquired HIV (PHIV). Eighty-nine adolescents in Kampala, Uganda (32 virally suppressed [<400 copies/mL] PHIV and 57 sociodemographically matched HIV-negative controls), completed a tabletbased neurocognitive test battery. Control-derived z-scores for 12 individual tests and a global/overall z-score were calculated. We measured plasma (soluble CD14 and CD163), monocyte (proportions of monocyte subsets), and T-cell (expression of CD38 and HLA-DR on CD4+ and CD8+) activation and gut markers. Spearman rank correlations and median regressions examined associations between test performance and immune activation. The median [IQR] age was 15 [13-16] years, and 40% were girls. The median time on antiretroviral therapy was 10 years [7-11] for PHIV; 87% had viral load <50 copies/mL. Compared with controls, global z-scores were lower among PHIV (P0.05) and significantly worse on tests of executive functioning and delayed recall (P's ≤ 0.05). Overall, monocyte activation significantly correlated with worse test performance on global z-score (r = 0.21, P=0.04), attention, processing speed, and motor speed (r = 0.2-0.3, P≤0.01). T-cell activation was significantly correlated with worse performance on tests of learning, executive functioning, and working memory (r=0.2-0.4, P≤ 0.04). In PHIV, after adjusting for age, sex, and antiretroviral therapy duration, activated CD4 T cells remained associated with worse memory (B-0.3, 95% CI: -0.55 to 0.07, P 0.01). PHIV with virologic suppression on antiretroviral therapy shows evidence of worse neurocognitive test performance compared with controls. Monocyte and T-cell activation is correlated with worse neurocognition in Ugandan youth with and without HIV, which has not been previously investigated in this setting. [ABSTRACT FROM AUTHOR]

Background: The present study aims to understand the socioeconomic and physical activity impact of the COVID-19 pandemic on children living with perinatally acquired HIV (PHIV) and without HIV (HIV−) in Kampala (Uganda). Methods: The authors included children aged 10–18 years who filled out questionnaires at baseline (2017–2018, prepandemic) and 2 years later (March 2020–January 2021, pandemic) in an observational cohort study at Joint Clinical Research Centre (Kampala). Physical activity energy expenditure was calculated using a youth compendium from the National Collaborative on Childhood Obesity Research. Descriptive and standard test statistics including Kruskal–Wallis were used. Results: One hundred and ninety-eight children from Kampala Uganda were included prepandemic (101 PHIV and 97 HIV−); 131 (71 PHIV and 60 HIV−) had information collected during the pandemic. At baseline, median and interquartile range age was 13 years (11; 15), and 52% were females. During the pandemic, overall weekly physical activity increased by a median of 854 minutes (interquartile range: 270–1890), and energy expenditures increased by 16% in both PHIV and in HIV− (P <.001 for groups overall prepandemic vs pandemic). Conclusions: The authors found in this Ugandan cohort of children that children engaged in more physical activity. Further research is warranted to understand the long-term effects of the pandemic on children's well-being. [ABSTRACT FROM AUTHOR]

The popularity of mobile gaming is increasing globally, fueled by technological advancements, high-quality smartphones with gaming features, and widespread internet access. A large number of young people and also children are mainly involved in different types of mobile gaming, primarily online mobile gaming, which is a matter of concern for society. This addiction leads to various psychological issues, including mental health problems, loneliness, introversion, insomnia, and a lack of self-control. Bangladesh's lack of public mental health facilities exacerbates this challenge, particularly in rural areas. To address this issue, we have developed a machine learning model to predict the level of mental health issues faced by Bangladeshi urban children due to gaming addiction. A total of 1996 data were collected from urban parents about their children's gaming activities and categorized them as 'Serious,' 'Partial,' and 'Normal.' The dataset was then split into a 70:30 ratio for training and testing purposes. K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multinomial Naïve Bayes, & Random Forest were applied as the machine learning approaches for prediction. Here, the Support Vector Machine gives good accuracy (92.75%), and other classifiers have different accuracy. The research highlights the significance of addressing gaming addiction in children and the potential of machine learning technology in predicting mental health issues, particularly in the absence of public mental health services. The study concludes by proposing the expansion of the research to include smartphone and mobile gaming addiction among Bangladeshi rural children and ensure a holistic approach to tackling the broader issue of technology-related addiction. [ABSTRACT FROM AUTHOR]