Objective: To investigate the epidemiological characteristics of foodborne disease outbreaks in Shaoxing City, Zhejiang Province, from 2012 to 2022, so as to provide the evidence for improving the foodborne disease control strategy. Methods: Foodborne disease outbreaks in Shaoxing City from 2012 to 2022 were collected from National Foodborne Disease Outbreak Monitoring System in China, including populations, places of outbreak, pathogenic factors and suspected foods. The temporal distribution, regional distribution, distribution of outbreak places and pathogenic factors of foodborne disease outbreaks were descriptively analyzed. Results: A total of 89 foodborne disease outbreaks were reported in Shaoxing City from 2012 to 2022, covering totally 699 patients, with an average annual attack rate of 6.35%. The outbreak peaked during the period between June and October (73 outbreaks, 82.02%), and family was the predominant place of outbreak (41 outbreaks, 46.07%). There were 83 outbreaks with known pathogenic factors, including 51 outbreaks caused by microbial factors, with Vibrio parahaemolyticus, Salmonella and norovirus as predominant pathogens, and 29 outbreaks caused by fungi and their toxins, which were all poisonous mushrooms poisoning, resulting in 2 deaths. In addition, there were 3 outbreaks caused by chemical factors. Conclusions The outbreak of foodborne diseases predominantly occurred in summer and autumn in Shaoxing City from 2012 to 2022. Family was the predominant place of outbreak, and toxic mushroom poisoning was the most lethal pathogenic factor.

Prostate cancer is one of the most common malignant tumors in the world.Without typical early manifestations in the early stage, it is often too late when found. Therefore, early diagnosis, treatment, and prognosis are critical to improving the survival rate of patients with prostate cancer. Over the past few years, artificial intelligence(AI) has developed rapidly in the field of prostate cancer. In terms of diagnosis, AI is used as a tool to screen the images to reduce the error caused by the professionalism and subjectivity of the technician and to improve the repeatability of the results; In the prediction of prognosis, the algorithm calculates and evaluates disease-related parameters such as recurrence rate, lymph node metastasis rate and mortality rate, so as to assistant clinicians in decision-making and treatment improvements. This reviews aims to introduce the application of artificial intelligence in the diagnosis, treatment and prognosis of prostate cancer in recent years, as well as the prospect and challenges faced by artificial intelligence in the medical field.