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Abstract Frontline medical workers (FMWs) faced high stress levels in frontline clinics during the COVID-19 pandemic. This study aims to alleviate FMWs’ psychological stress by enhancing protection measures, which includes refining the selection process, improving protective measures for their work in frontline clinics, and determining the appropriate time for evacuation. A Chinese-language-version stress questionnaire, covering stress-related issues and sociodemographic characteristics, was administered to FMWs in Shanghai during the COVID-19 pandemic in 2022, yielding 107 valid responses. The questionnaire’s reliability and validity were subsequently confirmed. Exploratory factor analysis (EFA) was applied to the stress-related questions, with the KMO and Bartlett’s test values of 0.886, 877.8522 (df = 120, p 0.80), significant positive linear and negative quadratic time parameters. Based on the trajectory analysis, a stress scorecard with an AUC of 0.856 was developed. This study found that there are four factors for FMWs’ psychological stress (anxiety about infection, exhaustion, lack of cognition for infection, and hesitant feeling), and improving targeted hospital protection could alleviate FMWs’ psychological stress through SOFAIDC, as all the total effect of factors or items from the feeling of being protected by hospital were negative. Meanwhile, although overall FMWs’ stress trend has decreased, 22.43% of FMWs have maintained or experienced a high stress trend, and the stress might increase over weeks in frontline clinics. To perfect hospital protection to alleviate FMWs’ psychological stress, the scorecard could be used to select FMWs with low-stress trends, LGM30 could be used to determine the withdrawal time (such as week 8), and targeted hospital protections could be designed based on the relationships between feeling of being protected by the hospital and factors in SOFAIDC, such as providing adequate protective equipment, improving the rationality of FMWs’ work schedules, recruiting personnel with higher education levels, and paying adequate monetary compensation.

With the development of digital technology， an increasing number of artificial intelligence （AI） technologies are being applied in the field of public health， significantly improving the efficiency of healthcare systems. However， such technological advancement also introduces a series of ethical risks. In this article， we conducted a systematic review by searching nine domestic and international databases and analyzing the ethical issues related to AI in public health， ultimately including 158 articles. Based on the analysis of the included literature， ethical risks were categorized into four aspects： data， algorithms， rights and responsibilities， and social impact. A total of 15 key issues were identified， among which privacy and confidentiality， informed consent， data security， and fairness， justice and inclusion emerged as the most prominent issues. The ethical challenges posed by AI in the field of public health cannot be ignored， and it is necessary to formulate ethical guidelines and practical recommendations for AI in this field， establish sound regulatory and review mechanisms， thereby ensuring the healthy development of AI research in public health.

ObjectiveTo propose targeted prevention and control measures， improve product quality， and ensure food safety， Salmonella contamination in the production and processing of prefabricated crispy pork in a large food processing factory was analyzed.MethodsA total of 103 meat samples were collected during the production and processing of prefabricated crispy pork， and 165 environmental samples were collected before processing. Qualitative and quantitative analyses of Salmonella were performed. Finally， serological typing， multilocus sequence typing （MLST）， and pulsed filed gel electrophoresis （PFGE） typing of the isolated Salmonella strains were evaluated.ResultsThe isolation rate of Salmonella from meat samples was 47.6% （49/103）. The isolation rate of Salmonella from environmental samples was 1.2% （2/165）. Fifty-one Salmonella strains were divided into 9 serotypes and 9 STs. Among them， Salmonella enteritidis ST11 was dominant in the production and processing of prefabricated crispy pork， and was detected in the environment. Twenty-two strains of Salmonella enteritidis were divided into 7 PFGE bands， with a similarity of over 92.9%.ConclusionA high overall contamination rate of Salmonella was found in meat samples. Therefore， controlling Salmonella contamination of raw meat is key to improving product quality. The amount of Salmonella contamination in fried products can be effectively controlled. Furthermore， strict separation of processing and storage areas for poultry and stored products is necessary to prevent cross-contamination of microorganisms in food.

Backgroud Diabetic foot is a common complication of diabetes patients, most of whom are seriously ill with rapid disease progression. A well-performing risk prediction model for the development of diabetic foot can help healthcare professionals to identify high-risk patients and take early interventions. Objective To systematically review the risk prediction models for diabetic foot, and provide reference for the construction and optimization of the model. Methods PubMed, Cochrane Library, Embase, Web of Science, CNKI and Wanfang Data were searched to collect the related studies on risk prediction models for diabetic foot from inception to May 15th, 2022. Two reviewers independently screened the literature, extracted data and evaluated the quality of models using prediction model risk of bias assessment tool (PROBAST). Meta-analysis of the predictors in the model was performed using Stata 17.0 software. Results A total of 13 papers were included, containing 13 models, 12 of which had AUC>0.7. Model calibration was performed on 7 models and 8 models were validated. PROBAST results showed that 1 of the 13 included papers was at low risk of bias and the remaining 12 were at high risk of bias; for model applicability, only 1 was of low applicability. The results of Meta-analysis showed that age (OR=1.13, 95%CI=1.04-1.24), glycated hemoglobin (OR=1.56, 95%CI=1.26-1.94), foot ulcer history (OR=5.93, 95%CI=2.85-12.37), previous amputation (OR=7.79, 95%CI=2.74-22.17), diminished sensitivity of the monofilament test (OR=1.59, 95%CI=1.42-1.78), foot fungal infection (OR=6.14, 95%CI=1.71-22.01), and kidney disease (OR=2.09, 95%CI= 1.65-2.65) were independent influencing factors for diabetic foot (P

ObjectiveTo understand the contamination and drug resistance characteristics of non-typhoidal Salmonella （NTS） during pig slaughter.MethodsA total of 170 carcasses and environmental samples were collected from two large-scale pig slaughterhouses in May and August 2021 in Shandong Province， China. Whole genome sequencing （WGS） combined with bioinformatics was used to analyze the data of the NTS isolates.ResultsNTS was detected in 29 of 170 pork samples， detection rate was 17.1%. Salmonella typhimurium was the predominant serotype （58.6%， 17/29） in the samples. The results of the drug sensitivity analysis showed that 29 NTS strains had 15 drug resistance spectra， with the highest rate of tetracycline resistance （100.0%， 29/29）， followed by ampicillin and chloramphenicol （79.3%， 23/29）. The multidrug resistance rate was 86.2% （25/29 patients）. Multilocus sequence typing （MLST） showed that ST19 was the main type， accounting for 48.3% （14/29） of cases. Analysis of drug resistance genes showed that they carried mainly tet（A）， tet（B）， tet（M）， blaTEM-1B， blaCMY-97， floR， cmlA1， etc.ConclusionThe phenomenon of NTS contamination exists in some pig slaughterhouses in Shandong Province. NTS has shown resistance to multiple antibiotics， suggesting that attention should be paid to the prevention of NTS in pig slaughterhouses.