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Using molecular network analysis to understand current HIV-1 transmission characteristics in an inland area of Yunnan, China

HIV-1 molecular surveillance provides a new approach to explore transmission risks and targeted interventions. From January to June 2021, 663 newly reported HIV-1 cases were recruited in Zhaotong City, Yunnan Province, China. The distribution characteristics of HIV-1 subtypes and HIV-1 molecular net...

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Autores principales: Cao, Rui, Lei, Shouxiong, Chen, Huichao, Ma, Yanling, Dai, Jie, Dong, Lijuan, Jin, Xiaomei, Yang, Min, Sun, Pengyan, Wang, Yawen, Zhang, Yuying, Jia, Manhong, Chen, Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10540185/
https://www.ncbi.nlm.nih.gov/pubmed/37462024
http://dx.doi.org/10.1017/S0950268823001140
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author Cao, Rui
Lei, Shouxiong
Chen, Huichao
Ma, Yanling
Dai, Jie
Dong, Lijuan
Jin, Xiaomei
Yang, Min
Sun, Pengyan
Wang, Yawen
Zhang, Yuying
Jia, Manhong
Chen, Min
author_facet Cao, Rui
Lei, Shouxiong
Chen, Huichao
Ma, Yanling
Dai, Jie
Dong, Lijuan
Jin, Xiaomei
Yang, Min
Sun, Pengyan
Wang, Yawen
Zhang, Yuying
Jia, Manhong
Chen, Min
author_sort Cao, Rui
collection PubMed
description HIV-1 molecular surveillance provides a new approach to explore transmission risks and targeted interventions. From January to June 2021, 663 newly reported HIV-1 cases were recruited in Zhaotong City, Yunnan Province, China. The distribution characteristics of HIV-1 subtypes and HIV-1 molecular network were analysed. Of 542 successfully subtyped samples, 12 HIV-1 strains were identified. The main strains were CRF08_BC (47.0%, 255/542), CRF01_AE (17.0%, 92/542), CRF07_BC (17.0%, 92/542), URFs (8.7%, 47/542), and CRF85_BC (6.5%, 35/542). CRF08_BC was commonly detected among Zhaotong natives, illiterates, and non-farmers and was mostly detected in Zhaoyang County. CRF01_AE was frequently detected among married and homosexual individuals and mostly detected in Weixin and Zhenxiong counties. Among the 516 pol sequences, 187 (36.2%) were clustered. Zhaotong natives, individuals aged ≥60 years, and illiterate individuals were more likely to be found in the network. Assortativity analysis showed that individuals were more likely to be genetically associated when stratified by age, education level, occupation, and reporting area. The genetic diversity of HIV-1 reflects the complexity of local HIV epidemics. Molecular network analyses revealed the subpopulations to focus on and the characteristics of the risk networks. The results will help optimise local prevention and control strategies.
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spelling pubmed-105401852023-09-30 Using molecular network analysis to understand current HIV-1 transmission characteristics in an inland area of Yunnan, China Cao, Rui Lei, Shouxiong Chen, Huichao Ma, Yanling Dai, Jie Dong, Lijuan Jin, Xiaomei Yang, Min Sun, Pengyan Wang, Yawen Zhang, Yuying Jia, Manhong Chen, Min Epidemiol Infect Original Paper HIV-1 molecular surveillance provides a new approach to explore transmission risks and targeted interventions. From January to June 2021, 663 newly reported HIV-1 cases were recruited in Zhaotong City, Yunnan Province, China. The distribution characteristics of HIV-1 subtypes and HIV-1 molecular network were analysed. Of 542 successfully subtyped samples, 12 HIV-1 strains were identified. The main strains were CRF08_BC (47.0%, 255/542), CRF01_AE (17.0%, 92/542), CRF07_BC (17.0%, 92/542), URFs (8.7%, 47/542), and CRF85_BC (6.5%, 35/542). CRF08_BC was commonly detected among Zhaotong natives, illiterates, and non-farmers and was mostly detected in Zhaoyang County. CRF01_AE was frequently detected among married and homosexual individuals and mostly detected in Weixin and Zhenxiong counties. Among the 516 pol sequences, 187 (36.2%) were clustered. Zhaotong natives, individuals aged ≥60 years, and illiterate individuals were more likely to be found in the network. Assortativity analysis showed that individuals were more likely to be genetically associated when stratified by age, education level, occupation, and reporting area. The genetic diversity of HIV-1 reflects the complexity of local HIV epidemics. Molecular network analyses revealed the subpopulations to focus on and the characteristics of the risk networks. The results will help optimise local prevention and control strategies. Cambridge University Press 2023-07-18 /pmc/articles/PMC10540185/ /pubmed/37462024 http://dx.doi.org/10.1017/S0950268823001140 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
spellingShingle Original Paper
Cao, Rui
Lei, Shouxiong
Chen, Huichao
Ma, Yanling
Dai, Jie
Dong, Lijuan
Jin, Xiaomei
Yang, Min
Sun, Pengyan
Wang, Yawen
Zhang, Yuying
Jia, Manhong
Chen, Min
Using molecular network analysis to understand current HIV-1 transmission characteristics in an inland area of Yunnan, China
title Using molecular network analysis to understand current HIV-1 transmission characteristics in an inland area of Yunnan, China
title_full Using molecular network analysis to understand current HIV-1 transmission characteristics in an inland area of Yunnan, China
title_fullStr Using molecular network analysis to understand current HIV-1 transmission characteristics in an inland area of Yunnan, China
title_full_unstemmed Using molecular network analysis to understand current HIV-1 transmission characteristics in an inland area of Yunnan, China
title_short Using molecular network analysis to understand current HIV-1 transmission characteristics in an inland area of Yunnan, China
title_sort using molecular network analysis to understand current hiv-1 transmission characteristics in an inland area of yunnan, china
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10540185/
https://www.ncbi.nlm.nih.gov/pubmed/37462024
http://dx.doi.org/10.1017/S0950268823001140
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