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Computed tomography derived predictors of left ventricular obstruction after TAVR.
Int J Cardiol
January 2025
Interventional Cardiology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita Salute San Raffaele University, Milan, Italy.
Background: Left ventricular obstruction (LVO) is an infrequent complication following transcatheter aortic valve replacement (TAVR) that can lead to severe hemodynamic decompensation. Previous studies have analyzed the pathophysiology of this clinical entity; however, little is known about the anatomical characteristics as assessed by computational tomography (CT) of patients at risk.
Methods: Data from 349 patients were retrospectively analyzed from a single center registry of patients undergoing TAVR at San Raffaele Hospital, Milan, Italy, between January 2020 and December 2021.
PreTKcat: A pre-trained representation learning and machine learning framework for predicting enzyme turnover number.
Comput Biol Chem
January 2025
College of Artificial Intelligence, Tianjin University of Science and Technology, No. 9, 13th Street, Tianjin Economic-Technological Development Area, Tianjin, 300457, China. Electronic address:
The enzyme turnover number (k) is crucial for understanding enzyme kinetics and optimizing biotechnological processes. However, experimentally measured k values are limited due to the high cost and labor intensity of wet-lab measurements, necessitating robust computational methods. To address this issue, we propose PreTKcat, a framework that integrates pre-trained representation learning and machine learning to predict k values.
View Article and Find Full Text PDFMorphing the left atrium geometry: The role of the pulmonary veins on flow patterns and thrombus formation.
Comput Biol Med
January 2025
Departamento de Ingeniería Energética, Universidad Politécnica de Madrid, Avda. de Ramiro de Maeztu 7, Madrid, 28040, Spain. Electronic address:
Background: Despite the significant advances made in the field of computational fluid dynamics (CFD) to simulate the left atrium (LA) in atrial fibrillation (AF) conditions, the connection between atrial structure, flow dynamics, and blood stagnation in the left atrial appendage (LAA) remains unclear. Deepening our understanding of this relationship would have important clinical implications, as the thrombi formed within the LAA are one of the main causes of stroke.
Aim: To highlight and better understand the fundamental role of the PV orientation in forming atrial flow patterns and systematically quantifying its effect on blood stasis within the LAA.
Semantic prioritization in visual counterfactual explanations with weighted segmentation and auto-adaptive region selection.
Neural Netw
December 2024
Department of Artificial Intelligence, Korea University, 02841, Seoul, Republic of Korea. Electronic address:
In the domain of non-generative visual counterfactual explanations (CE), traditional techniques frequently involve the substitution of sections within a query image with corresponding sections from distractor images. Such methods have historically overlooked the semantic relevance of the replacement regions to the target object, thereby impairing the model's interpretability and hindering the editing workflow. Addressing these challenges, the present study introduces an innovative methodology named as Weighted Semantic Map with Auto-adaptive Candidate Editing Network (WSAE-Net).
View Article and Find Full Text PDFThe miRNA-mRNA Regulatory Network in Human Hepatocellular Carcinoma by Transcriptomic Analysis From GEO.
Cancer Rep (Hoboken)
January 2025
Department of Medical Biotechnology, School of Advanced Technologies, Shahrekord University of Medical Sciences, Shahrekord, Iran.
Background: Bioinformatics analysis of hepatocellular carcinoma (HCC) expression profiles can aid in understanding its molecular mechanisms and identifying new targets for diagnosis and treatment.
Aim: In this study, we analyzed expression profile datasets and miRNA expression profiles related to HCC from the GEO using R software to detect differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRs).
Methods And Results: Common DEGs were identified, and a PPI network was constructed using the STRING database and Cytoscape software to identify hub genes.
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