Background: Segmental and global mitral valve prolapse (MVP) comprise 2 representative phenotypes in this syndrome. While mitral regurgitation (MR) severity is a major factor causing left atrial (LA) remodeling in MVP, prominent mitral valve (MV) annulus dilatation in global MVP may specifically cause inferiorly predominant LA remodeling. We compared MV annulus and LA geometry in patients with segmental and global MVP.Methods and Results:LA volume as well as inferior, middle, and superior LA cross-sectional areas (CSA) were measured on 3-D echocardiography in 20 controls, in 40 patients with segmental MVP, and in 18 with global MVP. On multivariate analysis, MR severity was primarily associated with LA dilatation in segmental MVP (P<0.001), while MV annular dilatation was primarily associated with LA dilatation in global MVP (P<0.001). Although there was no regional predominance in LA dilatation in segmental MVP, inferior predominance of LA dilatation was significant in global MVP (increase in inferior, middle, and superior LA-CSA relative to mean of the controls: +220±70% vs. +171±55% vs. +137±37%, P<0.001).
Conclusions: LA remodeling in segmental and global MVP is considerably different regarding its association with MR volume or MV annular dilatation and its regional predominance. While MR volume may mainly contribute to LA remodeling in segmental MVP, MV annular dilatation seems to have an important role in LA remodeling in global MVP. (Circ J 2016; 80: 2533-2540).
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Sci Rep
December 2024
Centro de Investigação em Saúde de Manhiça (CISM), Maputo, Mozambique.
Post rotavirus vaccine introduction in Mozambique (September 2015), we documented a decline in rotavirus-associated diarrhoea and genotypes changes in our diarrhoeal surveillance spanning 2008-2021. This study aimed to perform whole-genome sequencing of rotavirus strains from 2009 to 2012 (pre-vaccine) and 2017-2018 (post-vaccine). Rotavirus strains previously detected by conventional PCR as G2P[4], G2P[6], G3P[4], G8P[4], G8P[6], and G9P[6] from children with moderate-to-severe and less-severe diarrhoea and without diarrhoea (healthy community controls) were sequenced using Illumina MiSeq platform and analysed using bioinformatics tools.
View Article and Find Full Text PDFEur J Radiol
December 2024
Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, Berlin, Germany Berlin Institute of Health, Berlin, Germany. Electronic address:
Background: The Prostate Imaging-Reporting and Data System (PI-RADS) calls for reporting the prostate index lesion and the location within the transition (TZ) or peripheral zone (PZ) and location on a corresponding sector map. The aim of this study was to train a deep learning DL-based algorithm for automatic prostate sector mapping and to validate its' performance.
Methods: An automatic 24-sector grid-map (ASG) of the prostate was developed, based on an automatic zone-specific deep learning segmentation of the prostate.
Med Phys
December 2024
School of Physics and Optoelectronic Engineering, Foshan University, Foshan, China.
Background: In clinical practices, doctors usually need to synthesize several single-modality medical images for diagnosis, which is a time-consuming and costly process. With this background, multimodal medical image fusion (MMIF) techniques have emerged to synthesize medical images of different modalities, providing a comprehensive and objective interpretation of the lesion.
Purpose: Although existing MMIF approaches have shown promising results, they often overlook the importance of multiscale feature diversity and attention interaction, which are essential for superior visual outcomes.
In unsupervised transfer learning for medical image segmentation, where existing algorithms face the challenge of error propagation due to inaccessible source domain data. In response to this scenario, source-free domain transfer algorithm with reduced style sensitivity (SFDT-RSS) is designed. SFDT-RSS initially pre-trains the source domain model by using the generalization strategy and subsequently adapts the pre-trained model to target domain without accessing source data.
View Article and Find Full Text PDFJ Imaging
December 2024
Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
Radiation therapy (RT) is widely used to treat thoracic cancers but carries a risk of radiation-induced heart disease (RIHD). This study aimed to detect early markers of RIHD using machine learning (ML) techniques and cardiac MRI in a rat model. SS.
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