Pre-clinical drug research of vascular diseases requires in vitro models of vasculature that are amendable to high-throughput screening. However, current in vitro screening models that have sufficient throughput only have limited physiological relevance, which hinders the translation of findings from in vitro to in vivo. On the other hand, microfluidic cell culture platforms have shown unparalleled physiological relevancy in vitro, but often lack the required throughput, scalability and standardization. We demonstrate a robust platform to study angiogenesis of endothelial cells derived from human induced pluripotent stem cells (iPSC-ECs) in a physiological relevant cellular microenvironment, including perfusion and gradients. The iPSC-ECs are cultured as 40 perfused 3D microvessels against a patterned collagen-1 scaffold. Upon the application of a gradient of angiogenic factors, important hallmarks of angiogenesis can be studied, including the differentiation into tip- and stalk cell and the formation of perfusable lumen. Perfusion with fluorescent tracer dyes enables the study of permeability during and after anastomosis of the angiogenic sprouts. In conclusion, this method shows the feasibility of iPSC-derived ECs in a standardized and scalable 3D angiogenic assay that combines physiological relevant culture conditions in a platform that has the required robustness and scalability to be integrated within the drug screening infrastructure.
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Front Neurosci
January 2025
Neurology Associate P.C., Lincoln, NE, United States.
Introduction: As a hallmark feature of amyotrophic lateral sclerosis (ALS), bulbar involvement significantly impacts psychosocial, emotional, and physical health. A validated objective marker is however lacking to characterize and phenotype bulbar involvement, positing a major barrier to early detection, progress monitoring, and tailored care. This study aimed to bridge this gap by constructing a multiplex functional mandibular muscle network to provide a novel objective measurement tool of bulbar involvement.
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January 2025
CAPA Strategies, Portland, 97242, OR, USA.
This study introduces two refined rainfall anomaly indices-the Modified Rainfall Anomaly Index (MRAI) and the Standardized Rainfall Anomaly Index (SRAI)-to address limitations in the traditional Rainfall Anomaly Index (RAI). The existing RAI struggles to effectively capture extreme wet and dry rainfall conditions and relies on a simplistic formulation. To evaluate these indices on a continental scale, data from the Integrated Multi-Satellite Retrievals for GPM (IMERG) was used for the Conterminous United States (CONUS), enabling scalability to ungaged locations and beyond.
View Article and Find Full Text PDFIISE Trans Occup Ergon Hum Factors
January 2025
The Bradley Department of Electrical and Computer Engineering, College of Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
OCCUPATIONAL APPLICATIONSInnovative tools that align with modern learners' preferences are essential for training in safety-critical professions like Air Traffic Control/Management. This study evaluated a Virtual Reality Visual Flight Rules 3D Map Visualization Tool designed to meet the Federal Aviation Administration's (FAA) modernization goals. The tool immerses trainee in contextually accurate environments, enhancing engagement and self-paced learning.
View Article and Find Full Text PDFSensors (Basel)
January 2025
German Center for Vertigo and Balance Disorders (DSGZ), LMU University Hospital, LMU Munich, 81377 Munich, Germany.
Instrumented gait analysis is widely used in clinical settings for the early detection of neurological disorders, monitoring disease progression, and evaluating fall risk. However, the gold-standard marker-based 3D motion analysis is limited by high time and personnel demands. Advances in computer vision now enable markerless whole-body tracking with high accuracy.
View Article and Find Full Text PDFLife (Basel)
January 2025
Department of Nutrition and Dietetics, School of Physical Education, Sports and Dietetics, University of Thessaly, 42132 Trikala, Greece.
Cardiovascular diseases (CVDs) remain a leading cause of global mortality and morbidity. Traditional risk prediction models, while foundational, often fail to capture the multifaceted nature of risk factors or leverage the expanding pool of healthcare data. Machine learning (ML) and artificial intelligence (AI) approaches represent a paradigm shift in risk prediction, offering dynamic, scalable solutions that integrate diverse data types.
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