Forty-two babies with different congenital cardiac conduction defects, and in 12 cases the mothers, were tested for autoantibodies to Ro, La, U1RNP and Sm. Ro-specific antibodies were detected most frequently. They were to be found in 16 sera from infants and in 8 maternal serum samples. The occurrence of anti-Ro was associated preferentially with several atrioventricular conduction blocks. The sex relation of anti-Ro associated congenital heart block did not show a typical preference (6 male/10 female). At the time of giving birth, 5 anti-Ro-positive mothers did not have any clinical symptoms of rheumatic autoimmune diseases. Three of them had a first degree atrioventricular block. Our findings indicate that all pregnant women at risk for anti-Ro like connective tissue disease or cardiac conduction defects should be tested for these autoantibodies because of the suspicion of cardiac conduction abnormalities in the offspring. Anti-Ro-positive infants should be examined for structural heart disease by echocardiography.
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http://dx.doi.org/10.1007/BF00210230 | DOI Listing |
J Cardiol
January 2025
Department of Cardiovascular Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan. Electronic address:
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Centre Hospitalier Intercommunal nord-Ardennes, 45 Avenue de Manchester, 08000 Charleville-Mézières, France. Electronic address:
Introduction: Acute respiratory failure is a leading cause of admission to the intensive care unit (ICU), with mortality rates remaining stagnant despite advances in resuscitation techniques. Comorbidities, notably chronic obstructive pulmonary disease, significantly impact ICU patient outcomes. Pulmonary emphysema, commonly associated with chronic obstructive pulmonary disease, poses a significant risk, yet its influence on ICU mortality remains understudied.
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View Article and Find Full Text PDFNeural Netw
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Tsinghua University, Beijing, China. Electronic address:
Artificial neural networks (ANNs) can help camera-based remote photoplethysmography (rPPG) in measuring cardiac activity and physiological signals from facial videos, such as pulse wave, heart rate and respiration rate with better accuracy. However, most existing ANN-based methods require substantial computing resources, which poses challenges for effective deployment on mobile devices. Spiking neural networks (SNNs), on the other hand, hold immense potential for energy-efficient deep learning owing to their binary and event-driven architecture.
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