Search

Abstract Zoonotic infections (swine-human) caused by influenza A viruses (IAVs) have been reported and linked to close contact between these species. Here, we describe eight human IAV variant infections (6 mild and 2 severe cases, including 1 death) detected in Paraná, Brazil, during 2020–2023. Genomes recovered were closely related to Brazilian swIAVs of three major lineages (1 A.3.3.2/pdm09, 1B/human-like, and H3.1990.5), including three H1N1v, two H1N2v, two H3N2v and one H1v. Five H1v were closely related to pdm09 lineage, one H1v (H1N2v) grouped within 1B.2.3 clade, and the two H3v grouped within a clade composed exclusively of Brazilian H3 swIAV (clade H3.1990.5.1). Internal gene segments were closely related to H1N1pdm09 isolated from pigs. IAV variant rarely result in sustained transmission between people, however the potential to develop such ability is of concern and must not be underestimated. This study brings into focus the need for continuous influenza surveillance and timely risk assessment.

In this study, we hypothesize that HIV geographical clusters (geospatial areas with significantly higher numbers of HIV positive individuals) can behave as the highly connected nodes in the transmission network. Using data come from one of the most comprehensive demographic surveillance systems in Africa, we found that more than 70% of the HIV transmission links identified were directly connected to an HIV geographical cluster located in a peri-urban area. Moreover, we identified a single central large community of highly connected nodes located within the HIV cluster. This module was composed by nodes highly connected among them, forming a central structure of the network that was also connected with the small sparser modules located outside of the HIV geographical cluster. Our study supports the evidence of the high level of connectivity between HIV geographical high-risk populations and the entire community.

Understanding spatial configuration of sexual network structure is critical for effective use of HIV preventative interventions in a community. However, this has never been described at the population level for any setting in sub-Saharan Africa. We constructed the comprehensive geospatial sexual network among new heterosexual partnerships in rural KwaZulu-Natal, South Africa. In the Africa Health Research Institute (AHRI)’s population-based surveillance, we identified stable sexual partnerships among individuals (≥15 years) from 2003 to 2016. Sexual partnerships and residency were recorded via household surveys (every 4–6 months). We geolocated residents and migration events and mapped the geospatial linkages of sexual partners at the start of sexual partnerships. In a grid composed by 108 cells (nodes; 3kmx3km per cell) covering the surveillance area (438km2), we calculated the degree of connectivity and centrality of the nodes and examined their association with HIV prevalence and incidence per cell. Of 2401 new sexual partnerships, 21% (n = 495) had both partners living within the surveillance area at the start of sexual partnerships, and 76% (376/495) were linked to the geographic HIV cluster with high HIV prevalence identified in a peri-urban community. Overall, 57 nodes had at least one connection to another node. The nodes in the peri-urban cluster had higher connectivity (mean = 19, range: 9–32), compared to outside the cluster (6, range: 1–16). The node’s degree of connectivity was positively associated with HIV prevalence of the cell (Pearson correlation coefficient = 0.67; p

In South America, the evolutionary history of influenza A virus (IAV) in swine has been obscured by historically low levels of surveillance, and this has hampered the assessment of the zoonotic risk of emerging viruses. The extensive genetic diversity of IAV in swine observed globally has been attributed mainly to bidirectional transmission between humans and pigs. We conducted surveillance in swine in Brazil during 2011–2020 and characterized 107 H1N1, H1N2, and H3N2 IAVs. Phylogenetic analysis based on HA and NA segments revealed that human seasonal IAVs were introduced at least eight times into swine in Brazil since the mid-late 1980s. Our analyses revealed three genetic clades of H1 within the 1B lineage originated from three distinct spillover events, and an H3 lineage that has diversified into three genetic clades. The N2 segment from human seasonal H1N2 and H3N2 viruses was introduced into swine six times and a single introduction of an N1 segment from the human H1N1 virus was identified. Additional analysis revealed further reassortment with H1N1pdm09 viruses. All these introductions resulted in IAVs that apparently circulate only in Brazilian herds. These results reinforce the significant contributions of human IAVs to the genetic diversity of IAV in swine and reiterate the importance of surveillance of IAV in pigs.

Understanding spatial configuration of sexual network structure is critical for effective use of HIV preventative interventions in a community. However, this has never been described at the population level for any setting in sub-Saharan Africa. We constructed the comprehensive geospatial sexual network among new heterosexual partnerships in rural KwaZulu-Natal, South Africa. In the Africa Health Research Institute (AHRI)'s population-based surveillance, we identified stable sexual partnerships among individuals (≥15 years) from 2003 to 2016. Sexual partnerships and residency were recorded via household surveys (every 4-6 months). We geolocated residents and migration events and mapped the geospatial linkages of sexual partners at the start of sexual partnerships. In a grid composed by 108 cells (nodes; 3kmx3km per cell) covering the surveillance area (438km2), we calculated the degree of connectivity and centrality of the nodes and examined their association with HIV prevalence and incidence per cell. Of 2401 new sexual partnerships, 21% (n = 495) had both partners living within the surveillance area at the start of sexual partnerships, and 76% (376/495) were linked to the geographic HIV cluster with high HIV prevalence identified in a peri-urban community. Overall, 57 nodes had at least one connection to another node. The nodes in the peri-urban cluster had higher connectivity (mean = 19, range: 9-32), compared to outside the cluster (6, range: 1-16). The node's degree of connectivity was positively associated with HIV prevalence of the cell (Pearson correlation coefficient = 0.67; p

In this study, we hypothesize that HIV geographical clusters (geospatial areas with significantly higher numbers of HIV positive individuals) can behave as the highly connected nodes in the transmission network. Using data come from one of the most comprehensive demographic surveillance systems in Africa, we found that more than 70% of the HIV transmission links identified were directly connected to an HIV geographical cluster located in a peri-urban area. Moreover, we identified a single central large community of highly connected nodes located within the HIV cluster. This module was composed by nodes highly connected among them, forming a central structure of the network that was also connected with the small sparser modules located outside of the HIV geographical cluster. Our study supports the evidence of the high level of connectivity between HIV geographical high-risk populations and the entire community.

Cytokines are intrinsically related to disease progression in HIV infection. We evaluated the plasma levels of Th1/Th2/Th17 cytokines in extreme progressors, including slow (SPs) and rapid (RPs) progressors, who were thus classified based on clinical and laboratory follow-up covering a period of time before the initiation of HAART, ranging from 93-136.5 months for SPs and 7.5-16.5 months for RPs. Analyses were also performed based on the different stages of HIV infection (chronic, pre-HAART individuals-subjects sampled before initiating HAART but who initiated therapy from 12 to 24 months-and those receiving HAART). The plasma cytokine levels of 16 HIV-infected rapid progressors and 25 slow progressors were measured using a Human Th1/Th2/Th17 CBA kit. The IL-6 and IL-10 plasma levels differed significantly between the stages of HIV infection. The IL-6 levels were higher in slow progressors pre-HAART than in chronically infected SPs and HIV-seronegative individuals. The IL-10 levels were higher in slow progressors pre-HAART than in slow progressors receiving HAART and HIV-seronegative controls, and in rapid progressors, the IL-10 levels were higher in pre-HAART subjects than in HIV-seronegative controls. The results reflect the changes in the cytokine profile occurring during different clinical stages in HIV+ subjects. Our results suggest an association between increased IL-6 and IL-10 levels and pre-HAART stages independent of the slow or rapid progression status of the subjects. Thus, increased IL-6 and IL-10 levels could indicate a global inflammatory status and could be used as markers of the disease course in HIV-infected individuals.

The Human Immunodeficiency Virus type 1 protease enzyme (HIV-1 PR) is one of the most important targets of antiretroviral therapy used in the treatment of AIDS patients. The success of protease-inhibitors (PIs), however, is often limited by the emergence of protease mutations that can confer resistance to a specific drug, or even to multiple PIs. In the present study, we used bioinformatics tools to evaluate the impact of the unusual mutations D30V and V32E over the dynamics of the PR-Nelfinavir complex, considering that codons involved in these mutations were previously related to major drug resistance to Nelfinavir. Both studied mutations presented structural features that indicate resistance to Nelfinavir, each one with a different impact over the interaction with the drug. The D30V mutation triggered a subtle change in the PR structure, which was also observed for the well-known Nelfinavir resistance mutation D30N, while the V32E exchange presented a much more dramatic impact over the PR flap dynamics. Moreover, our in silico approach was also able to describe different binding modes of the drug when bound to different proteases, identifying specific features of HIV-1 subtype B and subtype C proteases.