From the human perspective, sleep is vital for the normal functioning of many advanced functions, such as learning and memory. Therefore, the importance of high-quality sleep is apparent. Although we already have certain research about the functions and mechanisms of sleep, there is still a long way to go to master sleep regulation and its complete application in treatment. In the context of biological evolution, researchers should compare the characteristics and mechanisms of sleep in different animal species to further understand the evolutionary origin of sleep and relationships between sleep and other advanced functions, which will help humans manage and utilize sleep beneficially. This review discusses the sleep characteristics of different animals that have critical significance in sleep research, including mammals, birds, and non-avian reptiles, as well as the current research progress of associated regulatory mechanisms. Finally, we outline the potential applications and challenges of research in the sleep mechanism.

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The COVID-19 pandemic led to physical distancing, thus increasing the use of digital health programs such as artificial intelligence (AI) platforms. Throughout this paper, we will be using AI to describe a system that performs actions that typically require human thinking skills. AI has the potential to transform into a healthcare organization using machine learning, deep learning, and natural language processing. This review will include AI-based diagnostic and prediction tools, and their potential in changing the detection and management of disease in various fields of medicine. AI algorithms are needed to analyze complex medical data, such as X-ray images and heart signals, and often exceed the accuracy of early human detection. Additionally, there are now AI-based wearable devices and supported systems available for real-time detection and personal management, which could also advance a person’s capability toward earlier detection and prevention of disease. Of course, data privacy challenges, constant data access issues, and algorithmic inequity are still present. Collaboration in addressing data collection, algorithm design, and constant monitoring or evaluation will be needed across disciplines. As institutions of health care, it is critical to ensure that the data collected, and the algorithm designed are transparent for AI to be applied in the real-world healthcare field.

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Shenqi Fuzheng Injection (SFI) is a typical single compound from traditional Chinese medicine with a huge potential of SFI is mostly polysaccharide extracts of the substances of astragalus and Codonopsis, and the major three active components of SFI are astragalus polysaccharides, astragalosides and flavonoids. Molecular mechanisms of SFI have been extensively elucidated in gastric cancer, liver cancer, and other digestive system tumors based on recent basic and clinical studies. SFI interacts with multiple molecular pathways and molecular targets to achieve its therapeutic effects. The most prominent one is immune regulation, which is evidenced by the increased production of cytokines and chemokines and the alleviation of chemotherapy-induced immunosuppression. In the adjuvant treatment of digestive system cancers, SFI shows remarkable immunoregulatory and antitumor activities. Combining the SFI with immune checkpoint inhibitors and targeted therapies can be further explored to achieve synergistic antitumor effects based on the potential therapy effects of SFI. This review article summarizes the antitumor mechanisms of Shenqi Fuzheng Injection in digestive system cancers that are currently known, intending to help clinical use and future research directions.

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Obesity remains a major public health challenge globally. Sleep length has been identified as a behaviorally adjustable factor that may influence the risk of obesity. Recent research reveals that the effects of sleeping longer hours remain unclear, but it has been shown that poor sleep is linked to an increase in the prevalence of obesity. This study used data from a nationally representative sample for the period between 2021 and 2023 to examines the relationship between nighttime sleep duration and obesity in the adult population of the United States. The sample size of the dataset is 8,000 participants. Sleep duration is classified into three groups: <6 hours, 6-8 hours, >8 hours. Participants reported their usual hours of nighttime sleep using item SLD012 of the sleep questionnaire. Obesity status was determined by calculating body mass index (BMI) from height and weight data collected in the survey; individuals with a BMI of 30 kg/m² or higher were classified as obese. After controlling for age, sex, and race/ethnicity, we used logistic regression, chi-square tests, and descriptive statistics. Based on the analysis, this study confirmed that participants sleeping <6 hours had the highest obesity prevalence (approximately 48%), compared with those sleeping 6–8 hours (42%) and >8 hours (39%). Sleep duration and obesity were significantly correlated (p<0.001), according to chi-square tests. Short sleepers had considerably higher risks of obesity than regular sleepers, according to logistic regression result. There is currently no clear evidence showing a direct connection between long sleep duration and obesity. In contrast, studies have found that getting too little sleep may raise the chance of getting obesity among U.S. adults. These findings emphasize how crucial it is to get the right amount of sleep to prevent becoming overweight.

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