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The occurrence of abnormal phytoplankton blooms is one of the significant changes in coastal ecosystems due to climate change. However, the underlying mechanism of such blooms remains poorly understood due to the complexity of the system. In this study, the data from numerous observations was used to elucidate the unprecedented phytoplankton blooms in the autumn and winter of 2021 in Laizhou Bay, a typical aquaculture bay in the southern Bohai Sea of China. The abundance of phytoplankton cells increased by more than tenfold in the southern waters compared to that in the same period from 2019 to 2020. The phytoplankton bloom was first observed in winter in the Bohai Sea, with the cell abundance in the southern bay exceeding 108 cells L-1 in December 2021. The diversity and evenness of phytoplankton communities decreased in the southern area. Cerataulina pelagica was the dominant algae, comprising 69 % of the total phytoplankton in October and 99 % in December. In autumn 2021, the largest flood of the Yellow River in recent decades occurred. This was attributed to extreme rainfall events within the river basin. The input of substantial riverine nutrients played a significant role in promoting phytoplankton blooms. Correlation analysis indicated the important cumulative impact of the Yellow River on phytoplankton blooms rather than a direct short-term effect. Numerical modeling results indicated that exceptionally high Yellow River discharge in autumn could significantly affect the entire bay from autumn to the following spring. This study may contribute to understanding the abnormal phytoplankton blooms in coastal waters and provide valuable insights for environmental management in river basins and coastal waters.
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Diatomeas , Fitoplancton , Ecosistema , Ríos , ChinaRESUMEN
The crucial pathophysiological and prognostic roles of the right ventricle in various diseases have been well-established. Nonetheless, conventional cardiovascular imaging modalities are frequently associated with intrinsic limitations when evaluating right ventricular (RV) morphology and function. The integration of artificial intelligence (AI) in multimodality imaging presents a promising avenue to circumvent these obstacles, paving the way for future fully automated imaging paradigms. This review aimed to address the current challenges faced by clinicians and researchers in integrating RV imaging and AI technology, to provide a comprehensive overview of the current applications of AI in RV imaging, and to offer insights into future directions, opportunities, and potential challenges in this rapidly advancing field.
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Inteligencia Artificial , Ventrículos Cardíacos , Humanos , Ventrículos Cardíacos/diagnóstico por imagen , Imagen Multimodal , CabezaRESUMEN
Introduction: Amyloid light-chain cardiac amyloidosis is a progressive infiltrative disease characterized by the deposition of amyloid fibrils in the cardiac tissue, which can cause serious atrioventricular block requiring pacemaker implantation. Left bundle branch pacing has emerged as an alternative method for delivering physiological pacing to achieve electrical synchrony of the left ventricle. However, left bundle branch pacing in patients with amyloid light-chain cardiac amyloidosis has not been studied in detail. Therefore, in this study, we present a case of left bundle branch pacing in a patient with amyloid light-chain cardiac amyloidosis. Case summary: A 66-year-old male patient with amyloid light-chain cardiac amyloidosis presented with syncope for 1 month. Holter monitoring revealed intermittent third-degree atrioventricular block. Left bundle branch pacing was performed successfully. During the 1-year follow-up, it was observed that the left bundle branch capture threshold remained stable without any pacemaker-related complications or left ventricle systolic dysfunction, and there was no recurrence of syncope. Conclusion: Left bundle branch pacing appears to be a safe and feasible option for patients with amyloid light-chain cardiac amyloidosis experiencing atrioventricular block.