Upon confirming an HIV diagnosis, patients need to start life-long antiretroviral therapy (ART) as soon as possible. During HIV treatment, ART drugs can cause intolerable adverse reactions, leading to poor medication compliance, treatment failure, and advancement of the HIV stage. Herein, we report a case of AIDS intolerant to multiple antiviral drugs due to side effects that we finally stabilized with the Albuvirtide (ABT) and Dolutegravir (DTG) combination. A 48 -year-old woman developed intractable nausea, vomiting and abdominal discomfort within one month of starting ART. Over the course of four years, she was switched to four different ART regimens due to her intolerance of severe adverse effects, mainly gastrointestinal symptoms, rash, and lethargy. Over four years, she failed to attain viral suppression due to poor drug compliance. After several ART changes, we started her on the Long-acting antiretroviral therapy (LA ART), Albuvirtide, combined with Dolutegravir, which she tolerated well. The patient's general condition improved significantly and attained marked virologic suppression. The patient's condition has been well controlled for nearly two years with good adherence. This case emphasizes the influence of ART treatment options on medication compliance and the outcome of HIV infection.
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http://dx.doi.org/10.1016/j.heliyon.2024.e27219 | DOI Listing |
Health Inf Sci Syst
December 2025
School of Mathematics and Computing, University of Southern Queensland, 487-535 West Street, Toowoomba, QLD 4350 Australia.
Purpose: This paper aims to develop a three-dimensional (3D) Alzheimer's disease (AD) prediction method, thereby bettering current predictive methods, which struggle to fully harness the potential of structural magnetic resonance imaging (sMRI) data.
Methods: Traditional convolutional neural networks encounter pressing difficulties in accurately focusing on the AD lesion structure. To address this issue, a 3D decoupling, self-attention network for AD prediction is proposed.
MethodsX
June 2025
Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Nigdi, Pune 411044, India.
Recent advancements in artificial intelligence (AI) have increased interest in intelligent transportation systems, particularly autonomous vehicles. Safe navigation in traffic-heavy environments requires accurate road scene segmentation, yet traditional computer vision methods struggle with complex scenarios. This study emphasizes the role of deep learning in improving semantic segmentation using datasets like the Indian Driving Dataset (IDD), which presents unique challenges in chaotic road conditions.
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June 2025
Faculty of Design and Art, University of Wuppertal, 42119 Wuppertal, Germany.
Project-based learning, with its emphasis on 'learning by doing', is the dominant teaching method in industrial design. Learners are supposed to be motivated to tackle complex problems such as those in the dynamic field of sustainability. However, it is still unclear how the process of increasing motivation within projects can be systematically targeted for specific sustainability challenges and directed towards potential later pro-environmental behavior.
View Article and Find Full Text PDFJ Contact Lens Res Sci
July 2024
Illinois College of Optometry, Chicago.
Background And Objective: This study determined whether practitioners specializing in keratoconus (KC) adhere to published guidelines for disease management and to what extent comorbid conditions of dry eye, contact lens tolerance, and psychological consequences of KC are formally assessed as part of long-term management.
Materials And Methods: This cross-sectional study used an IRB-approved, Internet-based, REDCap platform. Descriptive statistics are presented.
Med Image Comput Comput Assist Interv
September 2022
Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus.
Quantitative evaluation of pediatric craniofacial anomalies relies on the accurate identification of anatomical landmarks and structures. While segmentation and landmark detection methods in standard clinical images are available in the literature, image-based methods are not directly applicable to 3D photogrammetry because of its unstructured nature consisting in variable numbers of vertices and polygons. In this work, we propose a graph-based convolutional neural network based on Chebyshev polynomials that exploits vertex coordinates, polygonal connectivity, and surface normal vectors to extract multi-resolution spatial features from the 3D photographs.
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