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      Effects of HIV-1 genotype on baseline CD4+ cell count and mortality before and after antiretroviral therapy

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          Abstract

          To assess whether human immunodeficiency virus type 1 (HIV-1) genotype influences baseline CD4+ T lymphocyte (CD4+) cell count and mortality of patients. The study was conducted from 2014 to 2019 in Guangxi, China, and included 2845 newly diagnosed HIV patients. We used a median regression model to compare CD4+ cell counts in patients newly diagnosed with different HIV-1 genotypes, and a Cox regression model to analyze the associations between HIV-1 genotypes and mortality before and after antiretroviral treatment (ART). In newly diagnosed HIV patients, the baseline CD4+ cell counts of patients with CRF01_AE were significantly lower than those of patients with CRF07_BC, CRF08_BC, and other genotypes. Compared with CRF01_AE, patients infected with CRF07_BC (hazard ratio, 0.55; 95% CI 0.36–0.85), CRF08_BC (hazard ratio, 0.67; 95% CI 0.52–0.85), or other genotypes (hazard ratio, 0.52; 95% CI 0.29–0.94) had significantly lower mortality rates before ART. There were no significant associations between different HIV-1 genotypes and mortality after ART. HIV-1 genotype significantly influences baseline CD4+ cell count and mortality before ART in newly diagnosed HIV patients. We find no significant difference in the outcome of death after ART in patients with different HIV-1 genotypes.

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          Regression Quantiles

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            HIV-1 nomenclature proposal.

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              The HIV coreceptors CXCR4 and CCR5 are differentially expressed and regulated on human T lymphocytes.

              The chemokine receptors CXCR4 and CCR5 function as coreceptors for HIV-1 entry into CD4+ cells. During the early stages of HIV infection, viral isolates tend to use CCR5 for viral entry, while later isolates tend to use CXCR4. The pattern of expression of these chemokine receptors on T cell subsets and their regulation has important implications for AIDS pathogenesis and lymphocyte recirculation. A mAb to CXCR4, 12G5, showed partial inhibition of chemotaxis and calcium influx induced by SDF-1, the natural ligand of CXCR4. 12G5 stained predominantly the naive, unactivated CD26(low) CD45RA+ CD45R0- T lymphocyte subset of peripheral blood lymphocytes. In contrast, a mAb specific for CCR5, 5C7, stained CD26(high) CD45RA(low) CD45R0+ T lymphocytes, a subset thought to represent previously activated/memory cells. CXCR4 expression was rapidly up-regulated on peripheral blood mononuclear cells during phytohemagglutinin stimulation and interleukin 2 priming, and responsiveness to SDF-1 increased simultaneously. CCR5 expression, however, showed only a gradual increase over 12 days of culture with interleukin 2, while T cell activation with phytohemagglutinin was ineffective. Taken together, the data suggest distinct functions for the two receptors and their ligands in the migration of lymphocyte subsets through lymphoid and nonlymphoid tissues. Furthermore, the largely reciprocal expression of CXCR4 and CCR5 among peripheral blood T cells implies distinct susceptibility of T cell subsets to viral entry by T cell line-tropic versus macrophage-tropic strains during the course of HIV infection.
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                Author and article information

                Contributors
                ruanyuhua92@163.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                28 September 2020
                28 September 2020
                2020
                : 10
                : 15875
                Affiliations
                [1 ]GRID grid.198530.6, ISNI 0000 0000 8803 2373, State Key Laboratory of Infectious Disease Prevention and Control (SKLID), National Center for AIDS/STD Control and Prevention (NCAIDS), Chinese Center for Disease Control and Prevention (China CDC), , Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, ; Beijing, China
                [2 ]GRID grid.198530.6, ISNI 0000 0000 8803 2373, Guangxi Zhuang Autonomous Region Center for Disease Prevention and Control, ; Nanning, China
                Article
                72701
                10.1038/s41598-020-72701-4
                7522205
                32985559
                aa673833-ef7d-490b-a5f6-b1b080c6e014
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 16 June 2020
                : 4 September 2020
                Funding
                Funded by: Ministry of Science and Technology of China
                Award ID: 2018ZX10721102-006
                Funded by: Guangxi Bagui Honor Scholars
                Funded by: Guangxi Natural Science Foundation Project
                Award ID: 2020GXNSFAA159020
                Funded by: National Natural Science Foundation of China
                Award ID: 11971479
                Funded by: Chinese State Key Laboratory for Infectious Disease Develop Grant
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                computational biology and bioinformatics,medical research,risk factors
                Uncategorized
                computational biology and bioinformatics, medical research, risk factors

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