Evidence map›Paper›PMID 38363761›Full record

ArticlePloS one2024

Molecular network analysis of 308 newly diagnosed HIV infection and 210 ART failure patients from rural counties in Sichuan.

Xia Zhong, Dan Yuan, Shuang Feng Fan, Yang Liu, Ling Su, Shi Jiao He, Shu Liang, Yi Yang

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Xia ZhongSchool of Management, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Dan YuanInstitute of HIV/AIDS prevention, Sichuan Center for Disease Control and Prevention, Chengdu, China.
Shuang Feng FanDepartment of HIV/AIDS prevention, Chengdu Center for Disease Control and Prevention, Chengdu, China.
Yang LiuDepartment of HIV/AIDS prevention, Chengdu Center for Disease Control and Prevention, Chengdu, China.
Ling SuInstitute of HIV/AIDS prevention, Sichuan Center for Disease Control and Prevention, Chengdu, China.
Shi Jiao HeDepartment of HIV/AIDS prevention, Chengdu Center for Disease Control and Prevention, Chengdu, China.
Shu LiangInstitute of HIV/AIDS prevention, Sichuan Center for Disease Control and Prevention, Chengdu, China.
Yi YangSchool of Management, Chengdu University of Traditional Chinese Medicine, Chengdu, China.ORCID 0000-0003-2825-4144

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFew studies on molecular epidemiology have studied people with newly diagnosed HIV infection and ART Failure Patients at the same time in rural China. With more serious HIV epidemic than in other provinces in China, Sichuan is an area suitable for this study.

objectiveTo analyze the characteristics of HIV-1 molecular networks and factors related to network entry among newly diagnosed HIV infection and ART Failure Patients in three county-level cities (A, B, C) in Sichuan Province, to provide scientific basis for accurate prevention and control.

methodsNested PCR amplification method was used to amplify HIV-1 pol gene region of 530 blood samples, Sequencer 4.9 was used to edit, clean and splice the gene sequence, Bioedit correction, Fastree 2.1.8 and Figtree 1.4.2 to construct evolutionary tree and determine genotype. HyPhy2.2.4 and Cytoscape 3.6.1 software were used to construct molecular network. Logistic regression analysis was applied.

results523(98.68%) pol sequences were obtained, and a total of 518 valid sequences with basic information came into the final analyses. A total of 6 genotypes were detected, namely CRF01_AE (320,61.78%), CRF07_BC (149,28.76%), B (30,5.79%), CRF08_BC (11, 2.12%), CRF55_01B (6, 1.16%) and C (2, 0.39%). 186 of 518(35.91%) sequences entered the network at a genetic distance of 0.8%, forming 42 propagation clusters. "High-risk transmitters"(connected with two and more) accounted for 21.62%. Logistic regression showed that≥50 years old (OR = 2.474) were more risky than 18-49 years old, CRF07_BC sub-type (OR = 0.174) were less risky than CRF01_AE sub-type, B sub-type (OR = 6.698) is higher risky than CRF01_AE sub-type, and District B (OR = 0.077) less risky than that of A city.

conclusionThe sources of HIV infection in rural Sichuan are diversified and complicated. The prevention and control of HIV infection in Sichuan Province should focus on strengthening the long-term dynamic detection of elderly population, B strain sub-type, and in City A.

Indexed as

HIV-1HIV InfectionsHIV SeropositivityAdolescentAdultAgedChinaGenotypeHumansMiddle AgedPhylogenyPolymerase Chain ReactionYoung Adult

Identifiers

PMID38363761
PMCPMC10871515

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.