Introduction: Coronary atherosclerosis serves as the primary pathological etiology underlying coronary artery disease (CAD). Thyroid hormones show potential as risk factors, aside from the main standard modifiable cardiovascular risk factors (SMuRFs). This research seeks to elucidate the link between thyroid activity and coronary atherosclerosis.
View Article and Find Full Text PDF(Illiciaceae), an ecologically significant endemic plant, predominantly grows in Guangxi, China, which is the primary region for its cultivation. This area accounts for more than 80% of the total cultivation and yield in China. Despite its importance, comprehensive studies on the chloroplast (cp) genome of are limited.
View Article and Find Full Text PDFBackground: Loneliness is a common emotional experience among international students that can affect their mental health, cultural adaptation, and academic development. Therefore, the present study aimed to explore the relationship between a sense of power and loneliness among international students, and to examine the mediating role of loneliness stigma and perceived discrimination.
Methods: The study used the generalized sense of power scale, experiences with discrimination scale, stigma loneliness scale (SLS), and UCLA loneliness scale-6 (ULS -6) for 529 international students in China.
Although many studies have reported the relationship between female hormone intake and cardiovascular disease (CVD) development, their association has not been fully elucidated and defined, based on data from the Third National Health and Nutrition Examination Survey intending to assess the health and nutritional status of non-institutionalized children and adults in the United States. This study examined the relationship between female hormone intake and coronary artery disease (CVD) development in 38,745 women, averaging 38.10 ± 12.
View Article and Find Full Text PDFTransformer neural networks based on multi-head self-attention are effective in several fields. To capture brain activity on electroencephalographic (EEG) signals and construct an effective pattern recognition model, this paper explores the multi-channel deep feature decoding method utilizing the self-attention mechanism. By integrating inter-channel features with intra-channel features, the self-attention mechanism generates a deep feature vector that encompasses information from all brain activities.
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