Objectives: We investigated the effect of cartilage degeneration on ultrasound speed in human articular cartilage in vitro.
Methods: Ultrasound speed was calculated by the time-of-flight method for 22 femoral condyle osteochondral blocks obtained from osteoarthritis patients. In parallel, histological evaluation of specimens was performed using the modified Mankin and OARSI scores.
Results: The mean ultrasound speed was 1757 ± 109 m/s. Ultrasound speed showed significant negative correlation with OARSI score, and a decreasing tendency with high Mankin scores. Good correlation was found between the optically measured and the calculated cartilage thickness.
Conclusion: Our results show that articular cartilage degeneration has relatively little influence on ultrasound speed. In addition, morphological evaluation of articular cartilage using a preset value of ultrasound speed seems to offer relatively accurate results.
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http://dx.doi.org/10.3109/14397595.2015.1097012 | DOI Listing |
Sci Rep
January 2025
Melbourne Dental School, University of Melbourne, Level 5, 720 Swanston Street, Carlton, Melbourne, VIC, 3053, Australia.
Oral cancer detection is based on biopsy histopathology, however with digital microscopy imaging technology there is real potential for rapid multi-site imaging and simultaneous diagnostic analysis. Fifty-nine patients with oral mucosal abnormalities were imaged in vivo with a confocal laser endomicroscope using the contrast agents acriflavine and fluorescein for the detection of oral epithelial dysplasia and oral cancer. To analyse the 9168 images frames obtained, three tandem applied pre-trained Inception-V3 convolutional neural network (CNN) models were developed using transfer learning in the PyTorch framework.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Medical Life Sciences, College of Medicine, The Catholic University of Korea, Seoul, 06591, Korea.
Human cerebral organoids serve as a quintessential model for deciphering the complexities of brain development in a three-dimensional milieu. However, imaging these organoids, particularly when they exceed several millimeters in size, has been curtailed by the technical impediments such as phototoxicity, slow imaging speeds, and inadequate resolution and imaging depth. Addressing these pivotal challenges, our study has pioneered a high-speed scanning microscope, synergistically coupled with advanced computational image processing.
View Article and Find Full Text PDFAging Ment Health
January 2025
Internal Medicine, Geriatric Medicine section, Amsterdam UMC, location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Objectives: To explore interrelations between cognitive, physical, affective, and daily functioning, quality of life and white matter hyperintensities (WMH) in a geriatric memory clinic sample.
Method: Participants received brain imaging, comprehensive geriatric assessment and neuropsychological evaluation including measurements of cognitive, physical, affective, and daily functioning and health-related quality of life. Data was analyzed using multiple linear regressions and network analysis using (moderated) mixed graphical models.
Brain Behav
January 2025
Department of Kinesiology, University of Maryland, College Park, Maryland, USA.
Background: Higher cardiorespiratory fitness and cardiovascular endurance (CE) have been shown to be neuroprotective in older adulthood, but the mechanisms underlying this neuroprotection across the adult lifespan are poorly understood. The current study sought to examine the neuroprotective effects of CRF on gray matter (GM) and white matter (WM) volumes, and mean cortical thickness (MCT), using a large sample across the adult lifespan. We also examined sex differences in these relationships.
View Article and Find Full Text PDFSupracondylar humerus fractures in children are among the most common elbow fractures in pediatrics. However, their diagnosis can be particularly challenging due to the anatomical characteristics and imaging features of the pediatric skeleton. In recent years, convolutional neural networks (CNNs) have achieved notable success in medical image analysis, though their performance typically relies on large-scale, high-quality labeled datasets.
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