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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

Single-layer isophorone diisocyanate (IPDI) are one of the most popular self-healing microcapsules but suffers from low shell strength, poor heat resistance, stability and aging properties. In this paper, IPDI microcapsules were encapsulated into double-layer phenolic (PF)/polyurethane (PU) by a two-step process involving interfacial polymerization and in-situ polymerization. The prepared microcapsule composites were comprehensively characterized for their physical and chemical properties using optical microanalysis, scanning electron microscope, Fourier transform infrared spectroscopy, thermal gravimetric analysis and depth-sensing indentation analysis. Compared with the single-layer PU-IPDI microcapsule counterpart, the mechanical performance, thermal resistance, aging property and environmental stability of double-layer PF/PU-IPDI microcapsules were significantly improved. The epoxy coating was enhanced with the incorporation of 10 wt.% PF/PU-IPDI microcapsules, whose self-healing performance was evaluated by scratch corrosion test. The results demonstrated successful repair of coating scratches, along with the absence of corrosion on the coated steel substrate soaked in a 10 wt.% NaCl solution for 7 days. By comparing the tensile strength of epoxy coating before and after crack formation, it could be found that the self-healing efficiency was 57.9% when loaded with 10 wt.% of PF/PU-IPDI microcapsules in coating. This study highlights that the rational design of double-layer microcapsules integrated into the epoxy coating matrix could provide excellent anti-corrosion and self-healing properties. [ABSTRACT FROM AUTHOR]

Aims: This study aimed to investigate the association between diarrhea or constipation and urinary incontinence (UI) in adults. Methods: Data from the National Health and Nutrition Examination Survey for 2009–2010 was used to include 4686 adults aged 20 and over in the analysis. Stress urinary incontinence (SUI) and urgency urinary incontinence (UUI) were used as outcome variables, with diarrhea and constipation as exposure factors. We first compared the baseline characteristics of those with and without SUI, as well as those with and without UUI. The impact of diarrhea or constipation on SUI and UUI was assessed using multivariate logistic regression models. To ensure the stability of the results, subgroup and stratified analyses were conducted. Results: The prevalence rates of UUI and SUI were 22.49% and 23.39%, respectively. Adjusted multivariate logistic regression analysis revealed that the risk of UUI was increased by either diarrhea (OR 1.66, 95% CI 1.36–2.04) or constipation (OR 1.42, 95% CI 1.11–1.83). The risk of SUI was also elevated by either diarrhea (OR 1.36, 95% CI 1.11–1.67) or constipation (OR 1.32, 95% CI 1.06–1.63). Subgroup analysis revealed no significant differences in the interaction tests between constipation or diarrhea and UI. Conclusions: This study found that both constipation and diarrhea increase the risk of UUI and SUI. [ABSTRACT FROM AUTHOR]