HIV-related stigma within communities of gay men: A literature review

While stigma associated with HIV infection is well recognised, there is limited information on the impact of HIV-related stigma between men who have sex with men and within communities of gay men. The consequences of HIV-related stigma can be personal and community-wide, including impacts on mood and emotional well-being, prevention, testing behaviour, and mental and general health. This review of the literature reports a growing division between HIV-positive and HIV-negative gay men, and a fragmentation of gay communities based along lines of perceived or actual HIV status. The literature includes multiple references to HIV stigma and discrimination between gay men, men who have sex with men, and among and between many gay communities. This HIV stigma takes diverse forms and can incorporate aspects of social exclusion, ageism, discrimination based on physical appearance and health status, rejection and violence. By compiling the available information on this understudied form of HIV-related discrimination, we hope to better understand and target research and countermeasures aimed at reducing its impact at multiple levels

Deep Learning Segmentation of Satellite Imagery Identifies Aquatic Vegetation Associated with Snail Intermediate Hosts of Schistosomiasis in Senegal, Africa

Schistosomiasis is a debilitating parasitic disease of poverty that affects more than 200 million people worldwide, mostly in sub-Saharan Africa, and is clearly associated with the construction of dams and water resource management infrastructure in tropical and subtropical areas. Changes to hydrology and salinity linked to water infrastructure development may create conditions favorable to the aquatic vegetation that is suitable habitat for the intermediate snail hosts of schistosome parasites. With thousands of small and large water reservoirs, irrigation canals, and dams developed or under construction in Africa, it is crucial to accurately assess the spatial distribution of high-risk environments that are habitat for freshwater snail intermediate hosts of schistosomiasis in rapidly changing ecosystems. Yet, standard techniques for monitoring snails are labor-intensive, time-consuming, and provide information limited to the small areas that can be manually sampled. Consequently, in low-income countries where schistosomiasis control is most needed, there are formidable challenges to identifying potential transmission hotspots for targeted medical and environmental interventions. In this study, we developed a new framework to map the spatial distribution of suitable snail habitat across large spatial scales in the Senegal River Basin by integrating satellite data, high-definition, low-cost drone imagery, and an artificial intelligence (AI)-powered computer vision technique called semantic segmentation. A deep learning model (U-Net) was built to automatically analyze high-resolution satellite imagery to produce segmentation maps of aquatic vegetation, with a fast and robust generalized prediction that proved more accurate than a more commonly used random forest approach. Accurate and up-to-date knowledge of areas at highest risk for disease transmission can increase the effectiveness of control interventions by targeting habitat of disease-carrying snails. With the deployment of this new framework, local governments or health actors might better target environmental interventions to where and when they are most needed in an integrated effort to reach the goal of schistosomiasis elimination