A real-time monitoring system for physiological signals, developed for patients in coronary care units (CCUs), is described. This system monitors the signals that have the greatest clinical value in a CCU environment (ECG and cardiovascular pressures), taking charge of detecting dangerous situations and of extracting information significant to the correct monitoring of the patient. The information it extracts, mainly from the ECG, is presented to the user in an ergonomic way using written reports and graphs which collect and compile the information, facilitating its interpretation. Some utilities have been developed to allow the user to modify certain monitoring conditions as well as to correct results derived from them, thereby improving the reliability of the monitoring process. The system uses a multimicroprocessor architecture (imposed by the need to perform a large number of tasks in real time) with a block based on the VME bus, charged with acquiring and processing the monitored signals, and an IBM-compatible PC/XT which is used as a system user interface and a massive storage device in which the information resulting from the monitoring of signals is stored.
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Br J Nutr
January 2025
SAMRC/Wits Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Although research on the relationship between lean body mass and blood pressure (BP) has been inconsistent, most studies reported that measures of lean body mass are associated with a higher risk of hypertension. We explored relationships between body composition (fat and skeletal muscle mass) and BP in 1162 young adult African women. DXA-derived measures of whole body, central and arm fat mass were associated with higher systolic and diastolic BP, while leg fat percentage was associated with lower systolic and diastolic BP.
View Article and Find Full Text PDFJ Vasc Access
January 2025
College of Nursing, Xuzhou Medical University, Xuzhou, Jiangsu, China.
Objective: To develop and validate a nomogram model for predicting central venous catheter-related infections (CRI) in patients with maintenance hemodialysis (MHD).
Methods: MHD patients with central venous catheters (CVCs) visiting the outpatient hemodialysis (HD) center of Xuzhou Medical University Affiliated Hospital from January 2020 to December 2023 were retrospectively selected through a HD monitoring system. Patient data were collected, and the patients were divided into training and validation sets in a 7:3 ratio.
Anal Chem
January 2025
Department of Nature Sciences, Mathematics and Education, Federal University of São Carlos, 13600-970 Araras, São Paulo, Brazil.
A few decades ago, the technological boom revolutionized access to information, ushering in a new era of research possibilities. Electrochemical devices have recently emerged as a key scientific advancement utilizing electrochemistry principles to detect various chemical species. These versatile electrodes find applications in diverse fields, such as healthcare diagnostics and environmental monitoring.
View Article and Find Full Text PDFIntroductionAsthma attacks are set off by triggers such as pollutants from the environment, respiratory viruses, physical activity and allergens. The aim of this research is to create a machine learning model using data from mobile health technology to predict and appropriately warn a patient to avoid such triggers.MethodsLightweight machine learning models, XGBoost, Random Forest, and LightGBM were trained and tested on cleaned asthma data with a 70-30 train-test split.
View Article and Find Full Text PDFCurr Med Imaging
January 2025
Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95, Yong An Road, Xicheng District, Beijing 100050, China.
Background: The neuroanatomical basis of white matter fiber tracts in gait impairments in individuals suffering from Parkinson's Disease (PD) is unclear.
Methods: Twenty-four individuals living with PD and 29 Healthy Controls (HCs) were included. For each participant, two-shell High Angular Resolution Diffusion Imaging (HARDI) and high-resolution 3D structural images were acquired using the 3T MRI.
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