Evidence map›Paper›PMID 41635717›Full record

ArticleFrontiers in cellular and infection microbiology2025

Combining degree centrality and betweenness centrality of molecular networks can effectively pinpoint individuals at high risk of HIV transmission within the network.

Wen Gan, Bin Zhao, Wei Song, Mingming Kang, Xue Dong, Xin Li, Lu Wang, Jianmin Liu, Haibo Ding, Zhenxing Chu and 4 more

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In one paragraph

Article in Frontiers in cellular and infection microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

14 authors.

Wen Gan *State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China.
Bin Zhao *State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China.
Wei SongDepartment of Food Safety and Nutrition, Shenyang Center for Health Service and Administrative Law Enforcement (Shenyang Center for Disease Control and Prevention), Shenyang, China.
Mingming KangState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China.
Xue DongDepartment of Food Safety and Nutrition, Shenyang Center for Health Service and Administrative Law Enforcement (Shenyang Center for Disease Control and Prevention), Shenyang, China.
Xin LiDepartment of Food Safety and Nutrition, Shenyang Center for Health Service and Administrative Law Enforcement (Shenyang Center for Disease Control and Prevention), Shenyang, China.
Lu WangDepartment of Food Safety and Nutrition, Shenyang Center for Health Service and Administrative Law Enforcement (Shenyang Center for Disease Control and Prevention), Shenyang, China.
Jianmin LiuDepartment of Food Safety and Nutrition, Shenyang Center for Health Service and Administrative Law Enforcement (Shenyang Center for Disease Control and Prevention), Shenyang, China.
Haibo DingState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China.
Zhenxing ChuState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China.
Lin WangState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China.
Wen TianState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China.
Hong ShangState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China.
Xiaoxu HanState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: HIV molecular network technology can identify HIV transmission hotspots and individuals at risk of HIV transmission, facilitating precise and targeted interventions. This study explored the molecular network parameters, namely degree centrality (DC) and betweenness centrality (BC), to effectively pinpoint individuals at high risk of HIV transmission within the network. Methods: A previous whole-population sampling cohort comprising all newly diagnosed people living with HIV (PLWH) in Shenyang, from 2016 to 2019, was analyzed. Molecular networks based pol gene were constructed, the DC and BC of each node were calculated, and six groups of nodes were identified based on DC, BC, and DC+BC: high DC group, low DC group, high BC group, low BC group, high DC+BC group, and non-high DC+BC group. The average risk of HIV transmission in each group was calculated by dividing the total probability of recent HIV infection (identified by HIV-1 LAg-Avidity EIA) by the number of cases in each group. A multivariate logistic regression analysis was conducted to identify the characteristics of the high-risk group. Results: Of the 2882 PLWH, 1162 were included in the molecular network. The mean DC and the mean BC of all nodes were 2.6 (range: 1-29) and 0.09 (range: 0-1), respectively. The top three groups with the highest average risk of HIV transmission were the high DC+BC group at 0.62, followed by the high BC group at 0.56, and the high DC group at 0.53. The characteristics of the high DC+BC group were low education levels, Housekeeping, housework, and unemployment, and high baseline viral load (≥10 Discussion: The combined utilization of DC and BC can effectively identify individuals at high risk of HIV transmission, enabling precisely targeted interventions using molecular network technology.

Indexed as

Gene Regulatory NetworksHIV-1HIV InfectionsAdultChinaFemaleHumansMaleRisk Factorsbetweenness centralitydegree centralityHIVmen who have sex with men (MSM)molecular network

Identifiers

PMID41635717
PMCPMC12861916

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