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Abstract Background Ureteral cancer is a rare cancer. This study aimed to provide an up-to-date and comprehensive analysis on the global trends of ureteral cancer incidence and its association with lifestyle and metabolic risk factors. Methods The incidence of ureteral cancer was estimated from the Cancer Incidence in Five Continents Plus and Global Cancer Observatory databases. We analyzed the (1) global incidence of ureteral cancer by region, country, sex, and age group by age-standardized rates (ASR); (2) associated risk factors on a population level by univariable linear regression with logarithm transformation; and (3) incidence trend of ureteral cancer by sex and age group in different countries by Average Annual Percentage Change (AAPC). Results The global age-standardized rate of ureteral cancer incidence in 2022 was 22.3 per 10,000,000 people. Regions with higher human development index (HDI), such as Europe, Northern America, and East Asia, were found to have a higher incidence of ureteral cancer. Higher HDI and gross domestic product (GDP) and a higher prevalence of smoking, alcohol drinking, physical inactivity, unhealthy dietary, obesity, hypertension, diabetes, and lipid disorder were associated with higher incidence of ureteral cancer. An overall increasing trend of ureteral cancer incidence was observed for the past decade, especially among the female population. Conclusions Although ureteral cancer was relatively rare, the number of cases reported was rising over the world. The rising trends among females were more evident compared with the other subgroups, especially in European countries. Further studies could be conducted to examine the reasons behind these epidemiological changes and confirm the relationship with the risk factors identified.

Summary: Background: Efficient identification of individuals at high risk of skin cancer is crucial for implementing personalized screening strategies and subsequent care. While Artificial Intelligence holds promising potential for predictive analysis using image data, its application for skin cancer risk prediction utilizing facial images remains unexplored. We present a neural network-based explainable artificial intelligence (XAI) approach for skin cancer risk prediction based on 2D facial images and compare its efficacy to 18 established skin cancer risk factors using data from the Rotterdam Study. Methods: The study employed data from the Rotterdam population-based study in which both skin cancer risk factors and 2D facial images and the occurrence of skin cancer were collected from 2010 to 2018. We conducted a deep-learning survival analysis based on 2D facial images using our developed XAI approach. We subsequently compared these results with survival analysis based on skin cancer risk factors using cox proportional hazard regression. Findings: Among the 2810 participants (mean Age = 68.5 ± 9.3 years, average Follow-up = 5.0 years), 228 participants were diagnosed with skin cancer after photo acquisition. Our XAI approach achieved superior predictive accuracy based on 2D facial images (c-index = 0.72, 95% CI: 0.70–0.74), outperforming that of the known risk factors (c-index = 0.59, 95% CI 0.57–0.61). Interpretation: This proof-of-concept study underscores the high potential of harnessing facial images and a tailored XAI approach as an easily accessible alternative over known risk factors for identifying individuals at high risk of skin cancer. Funding: The Rotterdam Study is funded through unrestricted research grants from Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. G.V. Roshchupkin is supported by the ZonMw Veni grant (Veni, 549 1936320).

Background In 2010, China began to implement a free training program for rural order-oriented medical students (the "program") , which has played an important role in improving the construction of grass-roots health personnel and upgrading the level of grass-roots medical and health services. As the program has progressed, despite certain successes, there are many problems that remain to be solved, and it is not yet known which are the urgent and important core issues. Objective Sorting out the problems with the program to provide lessons learned for moving the program forward. Methods In May 2023, Chinese databases were searched using the literature analysis method to sort out the problems of the project documented in the literature; the problems resulting from the sorting out were analyzed using the social network analysis method, and the results calculated based on the social network analysis method were comprehensively evaluated using the TOPSIS method. Results The relevant problems in the literature were organized and counted. The top-ranked problems in order of frequency were low salary and treatment at the grassroots level for oriented medical students (14 times) , limited space for career development at the grassroots level for oriented medical students (10 times) , imperfect relevant supporting policies (10 times) , poor working conditions at the grassroots level (7 times) , and lack of motivation and initiative in learning for oriented medical students (7 times) ; and the results of the social network analysis were synthesized and evaluated by the TOPSIS method. The results of the social network analysis method were comprehensively evaluated by the TOPSIS method, and the top four rankings were imperfect relevant supporting policies (Ci=0.867) , limited space for grassroots career development of oriented medical students (Ci=0.800) , low grassroots salary and treatment of oriented medical students (Ci=0.786) , and poor grassroots working conditions (Ci=0.621) in the order of ranking. Conclusion Guangxi's free training program for rural order-oriented medical students still has major problems in the process of promoting the program. It is recommended that the program be optimized in the following ways to ensure its high-quality development: improve relevant supporting policies and strengthen supervision and evaluation; smooth career promotion channels and establish a long-term incentive mechanism; improve the remuneration system and raise the level of treatment; and improve the grass-roots working environment to enhance the quality of grass-roots work and increase efficiency.