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BackgroundVaried intake of dietary minerals critically affects cardiovascular health. This study examines the associations of nine dietary minerals intake with all-cause mortality in atherosclerotic cardiovascular diseases (ASCVDs).MethodsThis study analyzed 4,125 individuals with ASCVD from the National Health and Nutrition Examination Survey, employing Kaplan–Meier survival analyses, weighted Cox models, and restricted cubic splines to assess linear and nonlinear relationships between dietary minerals intake and all-cause mortality. Associations across different body mass index (BMI) categories were also evaluated separately.ResultsOver 6.25 years of median follow-up, 1,582 deaths were documented. Adjusted for potential covariates, results show a negative linear correlation between dietary magnesium intake and all-cause mortality (p for trend

Background: In the study of atrial fibrillation (AF), a prevalent cardiac arrhythmia, the utilization of artificial intelligence (AI) in diagnostic and therapeutic strategies holds the potential to address existing limitations. This research employs bibliometrics to objectively investigate research hotspots, development trends, and existing issues in the application of AI within the AF field, aiming to provide targeted recommendations for relevant researchers. Methods: Relevant publications on the application of AI in AF field were retrieved from the Web of Science Core Collection (WoSCC) database from 2013 to 2023. The bibliometric analysis was conducted by the R (4.2.2) “bibliometrix” package and VOSviewer(1.6.19). Results: Analysis of 912 publications reveals that the field of AI in AF is currently experiencing rapid development. The United States, China, and the United Kingdom have made outstanding contributions to this field. Acharya UR is a notable contributor and pioneer in the area. The following topics have been elucidated: AI's application in managing the risk of AF complications is a hot mature topic; AI-electrocardiograph for AF diagnosis and AI-assisted catheter ablation surgery are the emerging and booming topics; smart wearables for real-time AF monitoring and AI for individualized AF medication are niche and well-developed topics. Conclusion: This study offers comprehensive analysis of the origin, current status, and future trends of AI applications in AF, aiming to advance the development of the field.

The mitochondrial unfolded protein response (UPRmt) is a stress response pathway that regulates the expression of mitochondrial chaperones, proteases, and other proteins involved in protein folding and degradation, thereby ensuring proper mitochondrial function. In addition to this critical function, the UPRmt also plays a role in other cellular processes such as mitochondrial biogenesis, energy metabolism, and cellular signaling. Moreover, the UPRmt is strongly associated with various diseases. From 2004 to 2022, there has been a lot of interest in UPRmt. The present study aims to utilized bibliometric tools to assess the genesis, current areas of focus, and research trends pertaining to UPRmt, thereby highlighting avenues for future research. There were 442 papers discovered to be related to UPRmt, with the overall number of publications rising yearly. International Journal of Molecular Sciences was the most prominent journal in this field. 2421 authors from 1,402 institutions in 184 nations published studies on UPRmt. The United States was the most productive country (197 documents). The top three authors were Johan Auwerx, Cole M Haynes, and Dongryeol Ryu. The early focus of UPRmt is “protein.” And then the UPRmt research shifted from Caenorhabditis elegans back to mammals, and its close link to aging and various diseases. The top emerging research hotspots are neurodegenerative diseases and metabolic diseases. These findings provide the trends and frontiers in the field of UPRmt, and valuable information for clinicians and scientists to identify new perspectives with potential collaborators and cooperative countries.

The tropical cyclone Idai caused severe floods in Mozambique in March 2019. Sentinel-1 and Sentinel-2 images are processed to monitor the flood disaster in Pungue and Buzi rivers by combining the object-based image analysis approach and the decision tree algorithm. Water is preliminarily extracted from Sentinel-1 image, and shadows of mountain and buildings in the study area are extracted from Sentinel-2 image. Mountain shadow is extracted based on the decision tree classification rules constructed by the digital elevation model and the index model, while building shadow is extracted by constructing the decision tree classification rules using the features of objects. Water extraction results are combined with shadows to eliminate confusing shadows in the preliminary extraction to obtain the accurate flood extent. Sentinel-2 images are used to classify land use types in multiple periods before and after the flood and analyze the disaster by combining with the change information of the water. Land use type changes are analyzed and predicted with the CA-Markov model. The results show that the maximum submerged area was 3.5 times of the normal water area, and that most areas were submerged for more than 10 d. The area between Pungue and Buzi rivers were severely affected, where a large number of crops and villages were submerged. Grassland and cropland were the most seriously submerged. The overall accuracy of extracted water results ranges from 86% to 92%. The results indicated that the combining method can effectively suppress the influence of shadows on water extraction results.