The HIV epidemic in Peru is concentrated among men who have sex with men (MSM). Given that MSM have been documented as early adopters of emerging technology, we examined communication technology access and utilization, and mobile health (mHealth) acceptance among Peruvian MSM and transgender women (TGW) in order to gauge opportunities for mHealth-enabled HIV interventions. A convenience sample of 359 HIV-infected MSM and TGW recruited from three sites in Lima, Peru completed standardized assessments of alcohol use disorders (AUDs), risky sexual behavior, and antiretroviral therapy (ART) adherence along with self-constructed measures of communication technology access and utilization, and mHealth acceptance. Most participants (86%) had daily access to any cell phone, including smartphones (30%). The most frequent communication activities were receiving and making calls, and receiving and sending text messages using cell phones. On a 5-point Likert scale, participants expressed interest in using mHealth for medication reminders (M = 3.21, SD = 1.32) and engaging in anonymous online interactions with health professionals to discuss HIV-related issues (M = 3.56, SD = 1.33). Importantly, no significant differences were found in communication technology use and mHealth acceptance among participants with AUDs, depression, and suboptimal ART adherence, all of which are associated with poor HIV treatment outcomes. Findings show support for implementing mHealth-based intervention strategies using cell phones to assess and reduce HIV-risk behaviors among HIV-infected MSM and TGW.
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http://dx.doi.org/10.1080/09540121.2014.963014 | DOI Listing |
Comput Biol Med
January 2025
Emerging Technologies Research Lab (ETRL), College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia; Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia. Electronic address:
- Brain tumors (BT), both benign and malignant, pose a substantial impact on human health and need precise and early detection for successful treatment. Analysing magnetic resonance imaging (MRI) image is a common method for BT diagnosis and segmentation, yet misdiagnoses yield effective medical responses, impacting patient survival rates. Recent technological advancements have popularized deep learning-based medical image analysis, leveraging transfer learning to reuse pre-trained models for various applications.
View Article and Find Full Text PDFGenomics Proteomics Bioinformatics
January 2025
Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Research Unit of Hematologic Malignancies Genomics and Translational Research of Chinese Academy of Medical Sciences, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) techniques hold great value in evaluating the heterogeneity and spatial characteristics of hematopoietic cells within tissues. These two techniques are highly complementary, with scRNA-seq offering single-cell resolution and ST retaining spatial information. However, there is an urgent demand for well-organized and user-friendly toolkits capable of handling single-cell and spatial information.
View Article and Find Full Text PDFSTAR Protoc
January 2025
Heinz-Nixdorf-Chair of Biomedical Electronics, TranslaTUM, School of Computation, Information and Technology, TUM, Germany; Munich Institute of Biomedical Engineering, TUM, Germany. Electronic address:
Blood cell aggregates are clinically useful biomarkers in a number of medical disorders. This protocol provides accurate and quantitative analysis of cell aggregates using a small volume of whole blood and imaging flow cytometry. We describe steps for sample collection, staining, and measurement.
View Article and Find Full Text PDFClin Oral Investig
January 2025
State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, China.
Objectives: To develop a platform including a deep convolutional neural network (DCNN) for automatic segmentation of the maxillary sinus (MS) and adjacent structures, and automatic algorithms for measuring 3-dimensional (3D) clinical parameters.
Materials And Methods: 175 CBCTs containing 242 MS were used as the training, validating and testing datasets at the ratio of 7:1:2. The datasets contained healthy MS and MS with mild (2-4 mm), moderate (4-10 mm) and severe (10- mm) mucosal thickening.
J Gerontol B Psychol Sci Soc Sci
January 2025
Department of Geriatric Medicine, Radboud university medical center, Nijmegen, The Netherlands.
Objective: Maintaining a strong social network in later life can be challenging due to limited resources, life events, and changes in health. Social internet use provides an accessible way for communication that is less susceptible to age-related challenges. Although social internet use is increasingly used by older adults, we do not know how social internet use shapes older adults' offline networks.
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