Food insecurity is considered to be an important contributor to HIV associated wasting in sub-Saharan Africa. Low body mass index (BMI) is a strong risk factor for early mortality during antiretroviral therapy (ART). Nutritional supplementation has become standard of care in wasted patients starting ART in many countries in the region, but there is no unequivocal evidence base for this intervention. Against this background, we performed a retrospective study to compare food supplementation versus no nutritional intervention in wasted adults starting ART in Blantyre, Malawi. All patients received free nevirapine, lamivudine, and stavudine. Participants in an effectiveness trial of two food supplements received either corn-soy blend (CSB) or ready-to-use food spread (RUFS) during the first 14 weeks of ART. Results were compared with a historical control group receiving no food supplement that was part of an observational cohort study of outcomes of the same ART regimen. Characteristics on initiation of ART were similar in the three groups, except the use of cotrimoxazole prophylaxis which was more frequent in the food-supplemented groups. Linear regression analysis showed that increase in BMI was greatest in the RUFS group and better in the CSB group than in those receiving no food supplementation at 14 weeks. These differences were no longer significant at 26 weeks. Lower BMI, CD4 count and hemoglobin, WHO clinical stage IV, male gender, and not receiving cotrimoxazole prophylaxis were independent risk factors for mortality at 14 and 26 weeks in the logistic regression analysis. Supplementary food use was not directly associated with improved survival.
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http://dx.doi.org/10.1080/09540120903373581 | DOI Listing |
J Imaging Inform Med
January 2025
Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region, China.
Deep learning models have shown promise in diagnosing neurodevelopmental disorders (NDD) like ASD and ADHD. However, many models either use graph neural networks (GNN) to construct single-level brain functional networks (BFNs) or employ spatial convolution filtering for local information extraction from rs-fMRI data, often neglecting high-order features crucial for NDD classification. We introduce a Multi-view High-order Network (MHNet) to capture hierarchical and high-order features from multi-view BFNs derived from rs-fMRI data for NDD prediction.
View Article and Find Full Text PDFJ Int Assoc Provid AIDS Care
January 2025
Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA.
We evaluated a couple-based intervention targeting human immunodeficiency virus (HIV) care needs of women, with the option to support HIV-related needs of male partners. Adult women with HIV adherence difficulties in a monogamous relationship with a male partner for ≥6 months were recruited in KwaZulu-Natal, South Africa. Twenty couples were randomized (1:1) to either START Together, a five-session manualized behavioral intervention, or treatment as usual, adherence counseling referral.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
School of Computer Science and Technology, University of Science and Technology of China, 443 Huangshan Road, Hefei, 230027, China.
Background: Drug-drug interactions (DDIs) especially antagonistic ones present significant risks to patient safety, underscoring the urgent need for reliable prediction methods. Recently, substructure-based DDI prediction has garnered much attention due to the dominant influence of functional groups and substructures on drug properties. However, existing approaches face challenges regarding the insufficient interpretability of identified substructures and the isolation of chemical substructures.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Mechanical Engineering, University of Siegen, Paul-Bonatz-Straße 9-11, 57076 Siegen, Germany.
This work leverages ultrasonic guided waves (UGWs) to detect and localize damage in structures using lightweight Artificial Intelligence (AI) models. It investigates the use of machine learning (ML) to train the effects of the damage on UGWs to the model. To reduce the number of trainable parameters, a physical signal processing approach is applied to the raw data before passing the data to the model.
View Article and Find Full Text PDFBMC Public Health
January 2025
School of Nursing, Southwest Medical University, Luzhou, 646000, China.
Background: Achieving viral suppression through effective treatment adherence is critical for adolescents with HIV; however, the role of treatment adherence self-efficacy-an individual's confidence in their ability to consistently follow antiretroviral therapy (ART) regimens-remains under-explored among Chinese adolescents. This gap is particularly concerning given the United Nations' "95-95-95" targets to end the AIDS epidemic by 2030.
Objective: The aim of this study is to investigate the treatment adherence self-efficacy levels of Yi ethnic adolescents with HIV in a county in Liangshan Prefecture, and to explore the association between self-acceptance, emotion regulation, and treatment adherence self-efficacy.
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