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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'.

Street vendors are an indispensable part of the urban social ecosystem, but due to a lack of comprehensive understanding, many cities have adopted simple eviction policies, resulting in the gradual marginalization and stigmatization of the street economy. The efficient governance of street vendors requires the comprehensive investigation of their business scale and spatial distribution information. However, traditional methods have limitations in terms of automatically surveying large-scale street vendor information, particularly spatial distribution. This paper proposes a method for the automatic investigation spatial distribution of street vendors based on street-view images and a deep-learning object recognition model. Street-view images were collected at fixed intervals according to the urban road network, and 1,957 images containing one or more vendors were selected through human-machine interaction to establish street vendor label data. To achieve high recognition model accuracy, the category labels were subdivided into four categories: ground stalls, table stalls, tricycle stalls, and small truck stalls, based on the goods carriers used by street vendors. A deep neural-network-based image object detection model based on YOLO v4 was constructed to identify street vendors in the street-view image library, with an average F1 value of 0.77 and an mAP of 0.67. The accuracy of the model was satisfactory for investigating the number and location of street vendors covering the main roads in the city and then applying a kernel density distribution model to evaluate the spatial distribution pattern of street vendors. Using street vendors in Guangzhou as a case study, the proposed automatic investigation model identified 26,119 street vendors from 3,339,062 street-view images. The results showed that the street vendors were distributed in a multicenter aggregation pattern in the central urban area, mainly concentrated in areas with high pedestrian traffic, such as subway stations and urban villages; their numbers increased as road grades decreases. Street vendors were mainly distributed in areas with medium rents. The proposed method is helpful for performing the efficient, low-cost, and city-scale mapping of street vendors; the results obtained provide suggestions for formulating and implementing spatial governance policies for the informal economy and further provide suggestions for improving and implementing spatial governance policies for open and diverse urban street-view images. The results can be used as a reference for the location preference analysis of practitioners, the exploration of NIMBY syndrome, and the determination of the formalization zone. Although street-view images have an insufficient spatiotemporal coverage, using them to perform street vendor investigations is a low-cost and efficient method compared with the use of traditional investigation methods and data sources. In addition, the method proposed in this article can be coupled with multitask deep learning algorithms to investigate additional dimensions of street vendor information, such as the sex, age, and type of business of street vendors. Relevant research needs to be conducted in the future.

Background: Accelerated biological aging has been verified to be a critical risk factor for a number of age-related diseases, but its role in dementia remained unclear. Whether it modified the effects of genetic factors was also unknown. This study evaluated the associations between accelerated biological aging and dementia and the moderating role of accelerated biological aging in the genetic susceptibility to the disease. Methods: We included 200,731 participants in the UK biobank. Nine clinical blood biomarkers and chronological age were used to calculate Phenotypic age acceleration (PhenoAgeAccel), which is a novel indicator for accelerated biological aging. The associations of PhenoAgeAccel with dementia, both young-onset and late-onset dementia, were assessed by Cox proportional hazard models. Apolipoprotein E (APOE) alleles and polygenic risk scores (PRS) were used to evaluate the genetic risk of dementia. The interactions between genetic susceptibility and biological aging were tested on both multiplicative and additive scales. Results: These findings showed individuals who were in the highest quartile of PhenoAgeAccel had a higher risk with incidence of dementia compared to individuals in the lowest quartile of PhenoAgeAccel (HR: 1.145 (95% CI: 1.050, 1.249)). Individuals with biologically older had a higher risk of dementia than individuals with biologically younger (HR: 1.069 (95% CI: 1.004, 1.138)). Furthermore, compared to individuals with biologically younger and low APOE ε4-related genetic risk, individuals with biologically younger and high APOE ε4-related genetic risk (HR:3.048 (95% CI: 2.811, 3.305)) had a higher risk of dementia than individuals with biologically older and high APOE ε4-related genetic risk (HR: 2.765 (95% CI: 2.523, 3.029)). Meanwhile, referring to low dementia PRS and biologically younger, the risk of dementia increased by 72.7% (HR: 1.727 (95% CI: 1.538, 1.939) in the biologically younger and high PRS group and 58.7% (HR: 1.587 (95% CI: 1.404, 1.793) in the biologically older and high PRS group, respectively. The negative interactions between PhenoAgeAccel with APOE ε4 and PRS were also tested on the additive scale. Conclusions: Accelerated biological aging could bring the extra risk of dementia but attenuate the effects of genetic risk on dementia. These findings provide insights for precise prevention and intervention of dementia. [ABSTRACT FROM AUTHOR]

Aim: The relationship between the gut microbiota, metabolites and body fat percentage (BFP) remains unexplored. We systematically assessed the causal relationships between gut microbiota, metabolites and BFP using Mendelian randomization analysis. Materials and Methods: Single nucleotide polymorphisms associated with gut microbiota, blood metabolites and BFP were screened via a genome‐wide association study enrolling individuals of European descent. Summary data from genome‐wide association studies were extracted from the MiBioGen consortium and the UK Biobank. The inverse variance‐weighted model was the primary method used to estimate these causal relationships. Sensitivity analyses were performed using pleiotropy, Mendelian randomization‐Egger regression, heterogeneity tests and leave‐one‐out tests. Results: In the aspect of phyla, classes, orders, families and genera, we observed that o_Bifidobacteriales [β = −0.05; 95% confidence interval (CI): −0.07 to −0.03; false discovery rate (FDR) = 2.76 × 10−3], f_Bifidobacteriaceae (β = −0.05; 95% CI: −0.07 to −0.07; FDR = 2.76 × 10−3), p_Actinobacteria (β = −0.06; 95% CI: −0.09 to −0.03; FDR = 6.36 × 10−3), c_Actinobacteria (β = −0.05; 95% CI: −0.08 to −0.02; FDR = 1.06 × 10−2), g_Bifidobacterium (β = −0.05; 95% CI: −0.07 to −0.02; FDR = 1.85 × 10−2), g_Ruminiclostridium9 (β = −0.03; 95% CI: −0.06 to −0.01; FDR = 4.81 × 10−2) were negatively associated with BFP. G_Olsenella (β = 0.02; 95% CI: 0.01‐0.03; FDR = 2.16 × 10−2) was positively associated with BFP. Among the gut microbiotas, f_Bifidobacteriales, o_Bifidobacteriales, c_Actinobacteria and p_Actinobacteria were shown to be significantly associated with BFP in the validated dataset. In the aspect of metabolites, we only observed that valine (β = 0.77; 95% CI: 0.5‐1.04; FDR = 8.65 × 10−6) was associated with BFP. Conclusions: Multiple gut microbiota and metabolites were strongly associated with an increased BFP. Further studies are required to elucidate the mechanisms underlying this putative causality. In addition, BFP, a key indicator of obesity, suggests that obesity‐related interventions can be developed from gut microbiota and metabolite perspectives. [ABSTRACT FROM AUTHOR]