Existing atherosclerotic plaque imaging techniques such as intravascular ultrasound, multidetector computed tomography, optical coherence tomography, and high-resolution magnetic resonance imaging (hrMRI) require computerized methods to separate and analyze the plaque morphology. In this work, we perform in vitro balloon angioplasty experiments with 10 human femoral arteries using hrMRI and image processing. The vessel segments contain low-grade to high-grade lesions with very different plaque compositions. The experiments are designed to mimic the in vivo situation. We use a semi-automatic image processing tool to extract the three-dimensional (3D) geometries of the tissue components at four characteristic stages of the angioplasty procedure. The obtained geometries are then used to determine geometrical and mechanical indices in order to characterize, classify, and analyze the atherosclerotic plaques by their specific geometrical changes. During inflation, three vessels ruptured via helical crack propagation. The adventitia, media, and intima did not preserve their area/volume during inflation; the area changes of the lipid pool during inflation were significant. The characterization of changes in individual 3D tissue geometries, together with tissue-specific mechanical properties, may serve as a basis for refined finite element (FE) modeling, which is key to better understand stress evolution in various atherosclerotic plaque configurations.
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http://dx.doi.org/10.1007/s10439-010-9954-0 | DOI Listing |
Biomed Phys Eng Express
January 2025
Applied Sciences, Indian Institute of Information Technology Allahabad, Deoghat, Jhalwa, Allahabad, 211012, INDIA.
Photoacoustic tomography (PAT) is a non-destructive, non-ionizing, and rapidly expanding hybrid biomedical imaging technique, yet it faces challenges in obtaining clear images due to limited data from detectors or angles. As a result, the methodology suffers from significant streak artifacts and low-quality images. The integration of deep learning (DL), specifically convolutional neural networks (CNNs), has recently demonstrated powerful performance in various fields of PAT.
View Article and Find Full Text PDFACS Nano
January 2025
National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei 230026, China.
Metal ions are indispensable to life, as they can serve as essential enzyme cofactors to drive fundamental biochemical reactions, yet paradoxically, excess is highly toxic. Higher-order cells have evolved functionally distinct organelles that separate and coordinate sophisticated biochemical processes to maintain cellular homeostasis upon metal ion stimuli. Here, we uncover the remodeling of subcellular architecture and organellar interactome in yeast initiated by several metal ion stimulations, relying on near-native three-dimensional imaging, cryo-soft X-ray tomography.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Neurology, West China Hospital, Sichuan University, Chengdu, China.
Background: Despite the increasing popularity of electronic devices, the longitudinal effects of daily prolonged electronic device usage on brain health and the aging process remain unclear.
Objective: The aim of this study was to investigate the impact of the daily use of mobile phones/computers on the brain structure and the risk of neurodegenerative diseases.
Methods: We used data from the UK Biobank, a longitudinal population-based cohort study, to analyze the impact of mobile phone use duration, weekly usage time, and playing computer games on the future brain structure and the future risk of various neurodegenerative diseases, including all-cause dementia (ACD), Alzheimer disease (AD), vascular dementia (VD), all-cause parkinsonism (ACP), and Parkinson disease (PD).
JMIR Med Inform
January 2025
Institute of History and Ethics in Medicine, School of Medicine and Health, Technical University of Munich, Munich, Germany.
Background: In data-sparse areas such as health care, computer scientists aim to leverage as much available information as possible to increase the accuracy of their machine learning models' outputs. As a standard, categorical data, such as patients' gender, socioeconomic status, or skin color, are used to train models in fusion with other data types, such as medical images and text-based medical information. However, the effects of including categorical data features for model training in such data-scarce areas are underexamined, particularly regarding models intended to serve individuals equitably in a diverse population.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Geriatric Medicine, the Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, China.
Objective: To develop a predictive model for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) through radiomics analysis, integrating data from both enhanced computed tomography (CT) and magnetic resonance imaging (MRI).
Methods: A retrospective analysis was conducted on 93 HCC patients who underwent partial hepatectomy. The gold standard for MVI was based on the histopathological diagnosis of the tissue.
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