Predictors of Virologic Failure in HIV/AIDS Patients Treated with Highly Active Antiretroviral Therapy in Brasília, Brazil During 2002–2008

Little data exists concerning the efficacy of the antiretroviral therapy in the Federal District in Brazil, therefore in order to improve HIV/AIDS patients’ therapy and to pinpoint hot spots in the treatment, this research work was conducted. Of 139 HIV/AIDS patients submitted to the highly active antiretroviral therapy, 12.2% failed virologically. The significant associated factors related to unresponsiveness to the lentiviral treatment were: patients’ place of origin (OR = 3.28; IC95% = 1.0–9.73; P = 0.032) and Mycobacterium tuberculosis infection (RR = 2.90; IC95% = 1.19–7.02; P = 0.019). In the logistic regression analysis, the remaining variables in the model were: patients’ birthplace (OR = 3.28; IC95% = 1.10–9.73; P = 0.032) and tuberculosis comorbidity (OR = 3.82; IC95% = 1.19–12.22; P = 0.024). The patients enrolled in this survey had an 88.0% therapeutic success rate for the maximum period of one year of treatment, predicting that T CD4+ low values and elevated viral loads at pretreatment should be particularly considered in tuberculosis coinfection, besides the availability of new antiretroviral drugs displaying optimal activity both in viral suppression and immunological reconstitution

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Childhood food insecurity, mental distress in young adulthood and the supplemental nutrition assistance program.

Food insecurity affects 14% of US homes with children and has been associated with increased mental health problems. Few studies have examined long-term consequences for mental health and the role of social policies. This study examined the association between childhood household food insecurity (HHFI) and young adult psychological distress, and the moderating role of caregiver psychological distress and the Supplemental Nutrition Assistance Program (SNAP) using data from the Panel Study of Income Dynamics (1995-2015). The sample comprised 2782 children ages 0-12 years in 1997. Past-year HHFI was measured using the USDA 18-item questionnaire in 1997, 1999, 2001 and 2003. Young adults' non-specific psychological distress was measured with the Kessler (K6) scale in 2005, 2007, 2009, 2011, 2013 and 2015. Three trajectories of food insecurity were identified: 1) Persistent food security (70.5%); 2) Intermediate/fluctuating food insecurity (24.6%), and; 3) Persistent food insecurity (4.9%). Compared to persistent food security, fluctuating and persistent food insecurity were associated with significantly higher levels of psychological distress. This association was robust to adjusting for socio-demographic factors, caregiver psychological distress, and family access to governmental supports: [Adj. ORs (95% CI's = 1.72 (1.59-1.85) and 2.06 (1.81-2.33)]. Having a caregiver who suffered from psychological distress (1997 and/or 2002) and growing up with persistent food insecurity placed children at greater risk for mental health problems. Access to SNAP attenuated this risk. Early HHFI is associated with psychological distress in young adulthood. Interventions to increase access to SNAP and address caregivers mental health may prevent mental health problems associated with childhood HHFI

Variability and reproducibility of multi-echo T2 relaxometry: Insights from multi-site, multi-session and multi-subject MRI acquisitions

Quantitative magnetic resonance imaging (qMRI) can increase the specificity and sensitivity of conventional weighted MRI to underlying pathology by comparing meaningful physical or chemical parameters, measured in physical units, with normative values acquired in a healthy population. This study focuses on multi-echo T <sub>2</sub> relaxometry, a qMRI technique that probes the complex tissue microstructure by differentiating compartment-specific T <sub>2</sub> relaxation times. However, estimation methods are still limited by their sensitivity to the underlying noise. Moreover, estimating the model's parameters is challenging because the resulting inverse problem is ill-posed, requiring advanced numerical regularization techniques. As a result, the estimates from distinct regularization strategies are different. In this work, we aimed to investigate the variability and reproducibility of different techniques for estimating the transverse relaxation time of the intra- and extra-cellular space ( ) in gray (GM) and white matter (WM) tissue in a clinical setting, using a multi-site, multi-session, and multi-run T <sub>2</sub> relaxometry dataset. To this end, we evaluated three different techniques for estimating the T <sub>2</sub> spectra (two regularized non-negative least squares methods and a machine learning approach). Two independent analyses were performed to study the effect of using raw and denoised data. For both the GM and WM regions, and the raw and denoised data, our results suggest that the principal source of variance is the inter-subject variability, showing a higher coefficient of variation (CoV) than those estimated for the inter-site, inter-session, and inter-run, respectively. For all reconstruction methods studied, the CoV ranged between 0.32 and 1.64%. Interestingly, the inter-session variability was close to the inter-scanner variability with no statistical differences, suggesting that is a robust parameter that could be employed in multi-site neuroimaging studies. Furthermore, the three tested methods showed consistent results and similar intra-class correlation (ICC), with values superior to 0.7 for most regions. Results from raw data were slightly more reproducible than those from denoised data. The regularized non-negative least squares method based on the L-curve technique produced the best results, with ICC values ranging from 0.72 to 0.92