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Differences in Pulmonary Artery Stiffness Measured by CMR in Preterm-Born Young Adults With and Without Bronchopulmonary Dysplasia.
Circ Cardiovasc Imaging
March 2025
Department of Cardiology, Cardiovascular Institute, Thorax Center, Erasmus MC, Rotterdam, The Netherlands (J.J.S., N.v.d.V., D.M., A.H.).
Background: Very preterm-born infants are at risk for developing bronchopulmonary dysplasia (BPD), a chronic lung disease. Nowadays, the majority of these infants reach adulthood. Very preterm-born young adults are at risk for developing pulmonary arterial (PA) hypertension later in life.
View Article and Find Full Text PDFThe sentinels of coronary artery disease: heterogeneous monocytes.
Front Immunol
March 2025
Jiangxi Cardiovascular Research Institute, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China.
Monocytes are heterogeneous immune cells that play a crucial role in the inflammatory response during atherosclerosis, influencing the progression and outcome of the disease. In the pathogenesis of atherosclerotic diseases, such as coronary artery disease (CAD), monocytes not only serve as the initial sensors of endogenous and exogenous pathogenic factors, but also function as intermediators that bridge the circulatory system and localized lesions. In the bloodstream, heterogeneous monocytes, acting as sentinels, are rapidly recruited to atherosclerotic lesions, where they exhibit a heightened capacity to respond to various pathological stimuli upon detecting signals from damaged vascular endothelial cells.
View Article and Find Full Text PDFHead CT Scan Motion Artifact Correction via Diffusion-Based Generative Models.
Appl Med Artif Intell (2024)
February 2025
Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.
Head motion is a major source of image artifacts in head computed tomography (CT), degrading the image quality and impacting diagnosis. Image-domain-based motion correction is practical for routine use since it doesn't rely on hard-to-obtain CT projection data. However, existing convolutional neural network (CNN)-based methods tend to over-smooth images, particularly in cases of moderate to severe 3D motion artifacts.
View Article and Find Full Text PDFAssessment of unrecognized myocardial infarction using cardiac magnetic resonance imaging in patients with endstage renal disease.
Radiol Bras
February 2025
Department of Radiology, Medical Faculty, Ataturk University, Erzurum, Turkey.
Objective: To assess the frequency of unrecognized myocardial infarction and identify additional ischemic conditions, as well as to evaluate the efficacy of cardiac magnetic resonance imaging (CMRI) in risk groups, comparing the imaging findings with electro-cardiographic (ECG) and laboratory data in patients with stage 5 chronic kidney disease, also known as end-stage renal disease.
Materials And Methods: This was a prospective single-center study involving 20 patients who were referred to our radiology department to undergo CMRI between June 2010 and December 2011. Resting left ventricular functions and (early and late) myocardial contrast enhancement were assessed in all patients.
Deep learning-based multi-task prediction of response to neoadjuvant chemotherapy using multiscale whole slide images in breast cancer: A multicenter study.
Chin J Cancer Res
January 2025
Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital of Qingdao University, Yantai 264000, China.
Objective: Early predicting response before neoadjuvant chemotherapy (NAC) is crucial for personalized treatment plans for locally advanced breast cancer patients. We aim to develop a multi-task model using multiscale whole slide images (WSIs) features to predict the response to breast cancer NAC more finely.
Methods: This work collected 1,670 whole slide images for training and validation sets, internal testing sets, external testing sets, and prospective testing sets of the weakly-supervised deep learning-based multi-task model (DLMM) in predicting treatment response and pCR to NAC.
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