Background: Human immunodeficiency virus (HIV)-infected individuals are at high risk for ischemic stroke. To investigate the physiological basis for this risk, we used magnetic resonance imaging (MRI) to measure oxygen extraction fraction (OEF) and cerebral blood flow (CBF) in treatment-naive asymptomatic HIV-infected subjects and controls.
Methods: In treatment-naive asymptomatic HIV-infected subjects and age-, gender-, and race-matched controls, OEF was measured by MRI asymmetric spin-echo echo-planar imaging sequences and CBF was measured by MRI pseudocontinuous arterial spin labeling.
Results: Twenty-six treatment-naive HIV-infected subjects and 27 age-, gender-, race-matched controls participated. Whole-brain, gray matter (GM), and white matter OEF were not different between the groups (all P > .70). Unexpectedly, HIV-infected subjects had significantly higher CBF in cortical GM (72.9 ± 16.2 mL/100 g/min versus 63.9 ± 9.9 mL/100 g/min; P = .01) but not in subcortical GM (P = .25).
Conclusions: The observed increase in cortical GM CBF in treatment-naive HIV-infected subjects is unexpected, contrary to CBF decreases reported in HIV-infected subjects on treatment, and may represent an initial increase in metabolic activity due to an HIV-mediated inflammation.
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http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2016.03.045 | DOI Listing |
Int J Health Sci (Qassim)
January 2025
Department of Medicine, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China.
Objectives: This study aims to assess the correlation between clinical features and mortality in human immunodeficiency virus (HIV)-infected individuals with COVID-19.
Methods: A systematic literature search was conducted for cohort, cross-sectional, and case series that reported co-infection with HIV and COVID-19 published from January to September 2020. Clinical features such as age, comorbidities, CD4T lymphocyte counts, HIV RNA levels, and antiretroviral regimens were evaluated using meta-analyses and systematic reviews.
Front Cell Infect Microbiol
January 2025
Department of Infectious Diseases, Tianjin Second People's Hospital, Tianjin, China.
Background: Although MDSCs are widely recognized for their immunoinhibitory effects in pathological conditions, their function during HIV infection particularly within the mechanisms underlying incomplete immune recovery remains elusive.
Methods: We conducted a cross-sectional study in which 30 healthy controls and 62 HIV-1-infected subjects [31 immunological non-responders (INRs) and 31 immunological responders (IRs)] were selected. The proportion of MDSCs was determined in each category of participants.
Cureus
November 2024
Urology, All India Institute of Medical Sciences, Rishikesh, Rishikesh, IND.
The initial six months following HIV infection have a high viral load. Nonspecific presentations might lead to the missing primary HIV diagnosis. Multiorgan and multisystem diagnosis is a rare presentation of primary HIV.
View Article and Find Full Text PDFCurr HIV/AIDS Rep
November 2024
The Wistar Institute, Philadelphia, PA, USA.
Purpose Of Review: Combination antiretroviral therapy (cART) does not act on latent HIV reservoirs, and no latency-reversing agent (LRA) to date consistently reduces viral reservoirs in humans. In Sub-Saharan Africa and elsewhere, complementary and alternative medicines (CAM) are traditionally used to manage HIV/AIDS, including a subset with LRA properties.
Recent Findings: Several plants from the Euphorbiaceae and Thymelaeaceae families have been recently documented for traditional HIV/AIDS management and contain LRAs that function through protein kinase C activation.
J Med Microbiol
November 2024
Guangxi Key Laboratory of AIDS Prevention and Treatment, School of Public Health, Guangxi Medical University, Nanning, Guangxi, PR China.
() is a widely disseminated betaherpesvirus that typically induces latant infections. In immunocompromised populations, especially transplant and HIV-infected patients, infection increases in-hospital mortality. Although machine learning models have been widely used in clinical diagnosis and prognosis prediction, reports on machine learning model predictions for the in-hospital mortality of HIV/AIDS patients with infection have not been reported.
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