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With the sustained growth of economy and significant changes in social demographics, the issue of elderly-related diseases has increasingly drawn attention particularly. Alzheimer's disease (AD),as a representative disease of neurodegenerative diseases has become a major challenge, affecting the health and quality of life among the elderly population severely. In recent years, the incidence, prevalence, and mortality rate of AD increase in China, imposing substantial economic burdens on families, society, and the entire healthcare system. To proactively address this challenge and respond to the national 'Healthy China Action' initiative, leading experts from Renji Hospital, Shanghai Jiao Tong University School of Medicine,and Chinese Center for Disease Control and Prevention Chronic Non-communicable Disease Control Center, Fudan University School of Public Health, Shanghai Mental Health Center, Ruijin Hospital,Shanghai Jiao Tong University School of Medicine, Fujian Medical University, and other authoritative institutions, have jointly authored the 'China Alzheimer Disease Report 2024'. Building upon previous editions of 2021, 2022, and 2023, this report updates epidemiological data on AD in China, thoroughly analyzes the latest economic burdens of the disease, and comprehensively evaluates the current status of AD diagnosis and treatment services, as well as the allocation of public health resources in our country. The release of the 'China Alzheimer Disease Report 2024' not only reflects China's progress and efforts in AD research and prevention, but also underscores the social heightened concern for elderly health issues. It aims to provide scientific and technical guidance and robust data support for the prevention, diagnosis, and treatment of AD, offering a professional basis for the government and relevant departments to formulate targeted health policies and intervention measures. Furthermore, it serves as a platform for promoting academic exchanges and cooperation in this field domestically and internationally. Through the dissemination and application of this report, we anticipate it will not only serve as a reference for professionals but also enhance public awareness of AD, promote active participation across various sectors of society, and jointly advance the development of elderly health care in China, empowering us towards achieving 'healthy aging'.

Apricot trees, serving as critical agricultural resources, hold a significant role within the agricultural domain. Conventional methods for detecting pests and diseases in these trees are notably labor-intensive. Many conditions affecting apricot trees manifest distinct visual symptoms that are ideally suited for precise identification and classification via deep learning techniques. Despite this, the academic realm currently lacks extensive, realistic datasets and deep learning strategies specifically crafted for apricot trees. This study introduces ATZD01, a publicly accessible dataset encompassing 11 categories of apricot tree pests and diseases, meticulously compiled under genuine field conditions. Furthermore, we introduce an innovative detection algorithm founded on convolutional neural networks, specifically devised for the management of apricot tree pests and diseases. To enhance the accuracy of detection, we have developed a novel object detection framework, APNet, alongside a dedicated module, the Adaptive Thresholding Algorithm (ATA), tailored for the detection of apricot tree afflictions. Experimental evaluations reveal that our proposed algorithm attains an accuracy rate of 87.1% on ATZD01, surpassing the performance of all other leading algorithms tested, thereby affirming the effectiveness of our dataset and model. The code and dataset will be made available at https://github.com/meanlang/ATZD01. [ABSTRACT FROM AUTHOR]

BackgroundBurnout has become a prominent issue as the increase of workload in family doctor team members in primary healthcare institutions during the promotion of contracted family doctor services. There is still a lack of research comparing the differences in burnout among family doctor team members in different types of primary healthcare institutions.ObjectiveTo compare burnout prevalence and associated factors between family doctors in community/township health centers, and those in community health stations/village clinics, providing a basis for improving the mental health status and team stability of family doctors, as well as the quality of services provided by them.MethodsFrom August 1 to 21, 2020, a multistage cluster random sampling method was used to select 760 family doctor team members〔201 (26.4%) working at community/township health centers, and 559 (73.6%) working at community health stations/village clinics〕 as the participants from primary healthcare institutions in 6 counties/county-level cities /districts of Taian City, Shandong Province. They were invited to attend a survey to complete Demographic Questionnaire and the Chinese version of Maslach Burnout Inventory-General Survey (MBI-GS) .ResultsOverall, the prevalence of burnout among the participants was 68.9% (524/760) . Overall, the prevalence of burnout among the participants was 68.9% (524/760) , and the prevalence of burnoutof family doctor team members in community/township health centers and community health stations/village clinics was 63.7% (128/201) and 70.8% (396/559) , respectively. The levels of burnout of family doctor team members in community health stations/village clinics was higher than that of those in community/township health centers, with a statistically significant difference (P4 000 yuan is lower than that with monthly income