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Deep Learning-Based Identification of Echocardiographic Abnormalities From Electrocardiograms.
JACC Asia
January 2025
Department of Frontier Cardiovascular Science, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Background: Heart failure should be diagnosed as early as possible. Although deep learning models can predict one or more echocardiographic findings from electrocardiograms (ECGs), such analyses are not comprehensive.
Objectives: This study aimed to develop a deep learning model for comprehensive prediction of echocardiographic findings from ECGs.
Insights into the specific capacitance of CNT-based supercapacitor electrodes using artificial intelligence.
RSC Adv
January 2025
Department of Chemistry, College of Science, King Saud University P.O. Box 2455 Riyadh 11451 Saudi Arabia.
In this study, the specific capacitance characteristics of a carbon nanotube (CNT) supercapacitor was predicted using different machine learning algorithms, such as artificial neural network (ANN), random forest regression (RFR), -nearest neighbors regression (KNN), and decision tree regression (DTR), based on experimental studies. The results of the simulation verified the accuracy of the ANN algorithm with respect to the data derived from the specific capacitance of the supercapacitor module. It was observed that there was a strong correlation between the experimental results and the predictions made by the ANN algorithm.
View Article and Find Full Text PDFVersatile methodology for the synthesis of stable magnetic SERS-encoded clusters for sensing applications.
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Medcom Advance, Carrer de Marcel·lí Domingo 2-4, Edifici N5, 43007 Tarragona, Spain.
Surface-enhanced Raman scattering (SERS) substrates are garnering increasing interest for ultrasensitive high-throughput sensing. Notably, SERS-encoded nanostructures stand out due to their potential for nearly unlimited codification with excellent optical properties. In this paper we report a simple, versatile and cost-effective method for preparing SERS-encoded clusters.
View Article and Find Full Text PDFWhat is the pathophysiology of inflammation-induced cortical injury in the perinatal brain?
Neural Regen Res
January 2025
The Ritchie Centre, Hudson Institute of Medical Research, Melbourne, VIC, Australia.
Perinatal exposure to infection/inflammation is highly associated with neural injury, and subsequent impaired cortical growth, disturbances in neuronal connectivity, and impaired neurodevelopment. However, our understanding of the pathophysiological substrate underpinning these changes in brain structure and function is limited. The objective of this review is to summarize the growing evidence from animal trials and human cohort studies that suggest exposure to infection/ inflammation during the perinatal period promotes regional impairments in neuronal maturation and function, including loss of high-frequency electroencephalographic activity, and reduced growth and arborization of cortical dendrites and dendritic spines resulting in reduced cortical volume.
View Article and Find Full Text PDFAdeno-associated viral vectors for modeling Parkinson's disease in non-human primates.
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CNS Gene Therapy Department, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.
The development of clinical candidates that modify the natural progression of sporadic Parkinson's disease and related synucleinopathies is a praiseworthy endeavor, but extremely challenging. Therapeutic candidates that were successful in preclinical Parkinson's disease animal models have repeatedly failed when tested in clinical trials. While these failures have many possible explanations, it is perhaps time to recognize that the problem lies with the animal models rather than the putative candidate.
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