Here, we report first results on the development of computational health information technology for monitoring chronic non-communicable diseases (NCDs) risks in Russia based on data of the large-scale ongoing population survey in Health Centers (HCs). The technology involve algorithms for automated raw data process and generation of joint database, tools for data standardization and visualization, the assessment of risks, and other components. The data on physical status of Russians, including height, weight, and BMI are provided and compared with Belgian (1835), Swiss (2002), and US (1988-1994) reference datasets. The age-standardized prevalence of obesity in 5-85 years-old Russians according to the conventional WHO criteria was found to be high (18.9% in males and 26.7% in females) and varied significantly across federal subjects of Russia thus suggesting an importance of the Russian NCDs risks monitoring system for planning and evaluation of the effectiveness of preventive and therapeutic measures.
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JMIR Form Res
January 2025
Department of Computer Science, University Hospital of Geneva, Geneva, Switzerland.
Background: Mobile health apps have shown promising results in improving self-management of several chronic diseases in patients. We have developed a mobile health app (Cardiomeds) dedicated to patients with heart failure (HF). This app includes an interactive medication list; daily self-monitoring of symptoms, weight, blood pressure, and heart rate; and educational information on HF delivered through various formats.
View Article and Find Full Text PDFACS Biomater Sci Eng
January 2025
Mechanical Engineering Department, Worcester Polytechnic Institute, Worcester, Massachusetts 01609, United States.
Mechanical properties of engineered connective tissues are critical for their success, yet modern sensors that measure physical qualities of tissues for quality control are invasive and destructive. The goal of this work was to develop a noncontact, nondestructive method to measure mechanical attributes of engineered skin substitutes during production without disturbing the sterile culture packaging. We optimized a digital holographic vibrometry (DHV) system to measure the mechanical behavior of Apligraf living cellular skin substitute through the clear packaging in multiple conditions: resting on solid agar as when the tissue is shipped, on liquid media in which it is grown, and freely suspended in air as occurs when the media is removed for feeding.
View Article and Find Full Text PDFMethods Mol Biol
January 2025
Institute for Biomedicine, Eurac Research, Bolzano, Italy.
Metabolomics data analysis includes, next to the preprocessing, several additional repetitive tasks that can however be heavily dataset dependent or experiment setup specific due to the vast heterogeneity in instrumentation, protocols, or also compounds/samples that are being measured. To address this, various toolboxes and software packages in Python or R have been and are being developed providing researchers and analysts with bioinformatic/chemoinformatic tools to create their own workflows tailored toward their specific needs. This chapter presents tools and example workflows for common tasks focusing on the functionality provided by R packages developed as part of the RforMassSpectrometry initiative.
View Article and Find Full Text PDFEur Radiol Exp
January 2025
Computational Clinical Imaging Group (CCIG), Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
Good practices in artificial intelligence (AI) model validation are key for achieving trustworthy AI. Within the cancer imaging domain, attracting the attention of clinical and technical AI enthusiasts, this work discusses current gaps in AI validation strategies, examining existing practices that are common or variable across technical groups (TGs) and clinical groups (CGs). The work is based on a set of structured questions encompassing several AI validation topics, addressed to professionals working in AI for medical imaging.
View Article and Find Full Text PDFInsights Imaging
January 2025
Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
Objectives: Renal cell carcinoma (RCC) with extrarenal fat (perinephric or renal sinus fat) invasion is the main evidence for the T3a stage. Currently, computed tomography (CT) is still the primary modality for staging RCC. This study aims to determine the diagnostic performance of CT in RCC patients with extrarenal fat invasion.
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