Sub-Saharan Africans infected with HIV-1C make up the largest AIDS patient population in the world and exhibit large heterogeneity in disease progression before initiating antiretroviral therapy. To identify host variants associated with HIV disease progression, we performed genome-wide association studies on a total of 556 treatment-naive HIV-infected individuals in Botswana. We characterized the pattern of HIV disease progression using a novel functional principal component analysis, which can better capture longitudinal CD4 and viral load (VL) trajectories. Two single-nucleotide polymorphisms (SNPs) near HCG22 (chr6, peak variant rs2535307, combined p = 3.72 × 10, minor allele as risky allele) and CCNG1 (chr5, peak variant kgp22385164, combined p = 1.88 × 10, minor allele as risky allele) were significantly associated with CD4 and VL dynamics. Inspection of SNPs in these gene regions in a third Botswana cohort (using GWATCH) also revealed a strong association of HCG22 with HIV-1C acquisition, suggesting that this region is associated with infection as well as disease progression. Our study uncovered two genetic regions that are significant and have specific effects on HIV-1C acquisition or progression in sub-Saharan Africans, and the result suggested new potential targets for AIDS prevention and treatment. In addition, our results also indicate the possibility of using genetic markers as HIV disease progression indicators in sub-Saharan Africans to prioritize fast progressors for antiretroviral treatment.
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http://dx.doi.org/10.1089/AID.2016.0017 | DOI Listing |
Medicine (Baltimore)
January 2025
Department of Hepatobiliary Surgery, The Third Central Hospital of Tianjin, Tianjin, China.
Background: In patients with advanced hepatocellular carcinoma (HCC) following sorafenib failure, regorafenib has been used as an initial second-line drug. It is unclear the real efficacy and safety of sorafenib-regorafenib sequential therapy compared to placebo or other treatment (cabozantinib or nivolumab or placebo) in advanced HCC.
Methods: Four electronic databases (PubMed, Embase, Web of Science, and Ovid) were systematically searched for eligible articles from their inception to July, 2024.
JCO Clin Cancer Inform
January 2025
Machine Learning Department, H. Lee Moffit Cancer Center and Research Institute, Tampa, FL.
Purpose: Adaptive radiotherapy accounts for interfractional anatomic changes. We hypothesize that changes in the gross tumor volumes identified during daily scans could be analyzed using delta-radiomics to predict disease progression events. We evaluated whether an auxiliary data set could improve prediction performance.
View Article and Find Full Text PDFPLoS One
January 2025
School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR.
To investigate the pattern and threshold of physiological growth, defining as axial length (AL) elongation that results in little refraction progression, among Chinese children and teenagers, a total of 916 children aged between 7 and 18 years from a 6-year longitudinal cohort study were included for analysis. Ocular biometry, cycloplegic refraction and demographic data were obtained annually. Physiological growth was calculated based on myopic progression and Gullstrand eye model, respectively.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Medical and Surgical Sciences and Advanced Technologies "G. F. Ingrassia", University of Catania, Catania, Italy.
Background: To date, few data to transcranial Doppler sonography (TCD) are available in patients with mild vascular cognitive impairment (VCI) at risk for vascular or mixed dementia. In a previous study in patients with mild VCI and cerebral small vessels disease, a hemodynamic pattern of cerebral hypoperfusion and enhanced vascular resistance were observed; however, longitudinal data are currently lacking. Here, we perform a clinical, psychopathological, and neurosonological follow-up of patients with VCI in order to monitor any progression and to identify TCD measures to detect it.
View Article and Find Full Text PDFTransl Vis Sci Technol
January 2025
Glaucoma Service, Wills Eye Hospital, Philadelphia, PA, USA.
Purpose: The integration of artificial intelligence (AI), particularly deep learning (DL), with optical coherence tomography (OCT) offers significant opportunities in the diagnosis and management of glaucoma. This article explores the application of various DL models in enhancing OCT capabilities and addresses the challenges associated with their clinical implementation.
Methods: A review of articles utilizing DL models was conducted, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, and large language models (LLMs).
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