HIV epidemic among Brazilian women who have sex with women: An ecological study

The influences of public policies fighting HIV among women who have sex with women is scarcely studied. This study aimed to analyse the time series of human immunodeficiency virus (HIV) epidemic, between 2007 and 2020, among Brazilian women who have sex with women, in order to evaluate the eect of Brazilian policies for fighting HIV in this subpopulation compared to women who have sex with men (WSM). This ecological study employed HIV and acquired immunodeficiency syndrome (AIDS) new cases among women who have sex strictly with women (WSW), women who have sex with men and women (WSMW), and WSM reported to the Sistema de Informação de Agravos de Notificação from 2007 to 2020. Crude Brazilian and regional annual age-adjusted HIV/AIDS population-level incidence rates were calculated for WSW, WSMW and WSM. The rates were then analyzed using the Joinpoint regression model. A total of 102,890, 757, and 1,699 notifications of WSW, WSMW, and WSM living with HIV/AIDS were reported during the study period, respectively. South Brazilian region had the greatest HIV/AIDS incidence rates among WSM and bisexual women while the North region had the greatest incidence among WSW. In the WSM population, the temporal trends showed at least one stable or an increasing trend period from 2007 to 2013 or 2014, followed by one decreasing trend in all Brazilian regions. While among the WSMW most of the regions had a stable trend period from 2007 to 2020, in WSW group most of the trends had only one decreasing period. The decreasing trends were faster in WSM than in WSW. These results suggest a low efficiency of Brazilian policies for fighting HIV among WSW and WSMW and show the necessity of implementing new policies specific to this populatio

A global metagenomic map of urban microbiomes and antimicrobial resistance

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.Funding: the Tri-I Program in Computational Biology and Medicine (CBM) funded by NIH grant 1T32GM083937; GitHub; Philip Blood and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1548562 and NSF award number ACI-1445606; NASA (NNX14AH50G, NNX17AB26G), the NIH (R01AI151059, R25EB020393, R21AI129851, R35GM138152, U01DA053941); STARR Foundation (I13- 0052); LLS (MCL7001-18, LLS 9238-16, LLS-MCL7001-18); the NSF (1840275); the Bill and Melinda Gates Foundation (OPP1151054); the Alfred P. Sloan Foundation (G-2015-13964); Swiss National Science Foundation grant number 407540_167331; NIH award number UL1TR000457; the US Department of Energy Joint Genome Institute under contract number DE-AC02-05CH11231; the National Energy Research Scientific Computing Center, supported by the Office of Science of the US Department of Energy; Stockholm Health Authority grant SLL 20160933; the Institut Pasteur Korea; an NRF Korea grant (NRF-2014K1A4A7A01074645, 2017M3A9G6068246); the CONICYT Fondecyt Iniciación grants 11140666 and 11160905; Keio University Funds for Individual Research; funds from the Yamagata prefectural government and the city of Tsuruoka; JSPS KAKENHI grant number 20K10436; the bilateral AT-UA collaboration fund (WTZ:UA 02/2019; Ministry of Education and Science of Ukraine, UA:M/84-2019, M/126-2020); Kyiv Academic Univeristy; Ministry of Education and Science of Ukraine project numbers 0118U100290 and 0120U101734; Centro de Excelencia Severo Ochoa 2013–2017; the CERCA Programme / Generalitat de Catalunya; the CRG-Novartis-Africa mobility program 2016; research funds from National Cheng Kung University and the Ministry of Science and Technology; Taiwan (MOST grant number 106-2321-B-006-016); we thank all the volunteers who made sampling NYC possible, Minciencias (project no. 639677758300), CNPq (EDN - 309973/2015-5), the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, ECNU, the Research Grants Council of Hong Kong through project 11215017, National Key RD Project of China (2018YFE0201603), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01) (L.S.