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Dynamic chest radiography (DCR) can estimate haemodynamic parameters in heart failure (HF). However, no studies have evaluated its ability to determine cardiac systolic function in HF. This experimental study investigates the correlation between left ventricular (LV) ejection fraction (LVEF) and DCR image parameters in HF.

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Background: Myocardial ischemia-reperfusion (I/R) injury refers to cell damage that occurs as a consequence of the restoration of blood circulation following reperfusion therapy for cardiovascular diseases, and it is a primary cause of myocardial infarction. The search for nove therapeutic targets in the context of I/R injury is currently a highly active area of research. p70 ribosomal S6 kinase (S6K1) plays an important role in I/R induced necrosis, although the specific mechanisms remain unclear.

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Artificial intelligence (AI) in echocardiography represents a transformative advancement in cardiology, addressing longstanding challenges in cardiac diagnostics. Echocardiography has traditionally been limited by operator-dependent variability and subjective interpretation, which impact diagnostic reliability. This study evaluates the role of AI, particularly machine learning (ML), in enhancing the accuracy and consistency of echocardiographic image analysis and its potential to complement clinical expertise.

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Myocardial work (MW) is a new echocardiographic parameter used in the assessment of cardiac energy expenditure. The aim of the current study was to evaluate changes in left ventricular MW parameters in patients with severe aortic stenosis undergoing transcatheter aortic valve implantation (TAVI). One hundred and thirty five consecutive patients who underwent TAVI at one center were evaluated before and after the procedure using transthoracic echocardiography (TTE) to assess the following MW indices: global constructive work (GCW), global wasted work (GWW), global work index (GWI) and global work efficiency (GWE).

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Diagnostic Algorithm Using Multimodal Imaging for the Differential Diagnosis of Intra-Cardiac Masses.

J Clin Med

January 2025

Department of Cardio-Thoracic-Vascular Diseases, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy.

Cardiac masses are complex clinical conditions that frequently pose diagnostic challenges in cardiology practice. These masses can form within heart chambers or near the pericardium and are generally categorized as either non-neoplastic or neoplastic. These latter are further classified into benign and malignant (primary and secondary or metastatic).

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