AIM　To determine the effects of glycoconjugates and their glycans from Lycium barbarum L. on inhibiting low density lipoprotein (LDL) peroxidation. METHODS　Using Cu2+-induced oxidation as a model, the oxidative production of thiobarbituric acid-reactive substances (TBARS) and the LDL electrophoresis migration on agarose gel were measured. RESULTS　The effects of glycoconjugates and their glycans from Lycium barbarum L. on inhibiting LDL peroxidation were different, among them, glycoconjugate LbGp5 showed the best effect on inhibiting LDLperoxidation. CONCLUSION　The glycoconjugates can inhibit LDL peroxidatin while their glycans showed no effects on inhibiting LDL peroxidation.

Uncovering the risk factors of pulmonary hypertension and its mechanisms is crucial for the prevention and treatment of the disease.In the current study,we showed that experimental periodontitis,which was established by ligation of molars followed by orally smearing subgingival plaques from patients with periodontitis,exacerbated hypoxia-induced pulmonary hypertension in mice.Mechanistically,periodontitis dysregulated the pulmonary microbiota by promoting ectopic colonization and enrichment of oral bacteria in the lungs,contributing to pulmonary infiltration of interferon gamma positive(IFNγ+)T cells and aggravating the progression of pulmonary hypertension.In addition,we identified Prevotella zoogleoformans as the critical periodontitis-associated bacterium driving the exacerbation of pulmonary hypertension by periodontitis,and the exacerbation was potently ameliorated by both cervical lymph node excision and IFNγ neutralizing antibodies.Our study suggests a proof of concept that the combined prevention and treatment of periodontitis and pulmonary hypertension are necessary.

Objective To assess the accuracy of pN staging prediction in papillary thyroid carcinoma (PTC) using ultrasound radiomics and deep neural networks (DNN). Methods A retrospective analysis was conducted on 375 patients with pathologically confirmed PTC, comprising 261 cases in the training set and 114 in the test set. Staging was categorized as pN0 (no cervical lymph node metastasis), pN1a (central neck lymph node metastasis), and pN1b (lateral neck lymph node metastasis). An ultrasound physician manually segmented the regions of interest (ROIs) for PTC, extracting 1899 radiomic features. Dimensionality reduction was performed using the least absolute shrinkage and selection operator (LASSO) regression. A DNN model for predicting PTC pN staging was developed using the H2O deep learning platform, trained on the training set, and validated on the test set to assess the accuracy of the optimal model. Results A total of 153 patients were in the pN0 stage, 131 patients in the pN1a stage, and 91 patients in the pN1b stage. LASSO regression selected 15 radiomic features for each PTC. The optimal DNN model, constructed using these 15 features, achieved accuracies of 85.82% on the training set and 81.57% on the test set. Conclusion Ultrasound radiomics of PTC demonstrates high accuracy in predicting pN staging and shows potential for automating N staging in patients.

ObjectiveTo explore the exposure-lag-response relationship between temperature and risk of varicella incidence，and to provide a scientific evidence for early warning and precise prevention and control of varicella epidemic. MethodsDaily varicella cases and daily meteorological data were collected in Minhang District， Shanghai from 2010 to 2022. A distributed lag nonlinear model was used to determine the exposure-lag-response relationship between temperature and risk of varicella incidence. Furthermore， effect of temperature on the incidence risk was determined across different age groups. ResultsIn 2010‒2022， the total number of notified varicella cases was 26 207 in Minhang District， with the highest incidence in the group aged 3‒14 years （50.35%）. The seasonal pattern of daily varicella cases showed a double peak. The large peak was found in November and December， followed by a smaller peak in May and June. Moreover， the distributed lag nonlinear model showed a unimodal curve in the relationship between temperature and varicella incidence. The RR value reached its maximum peak of 1.90 （95%CI： 1.25‒2.87） at 7 ℃. A reverse U-shape was found in the lag-response curves between temperature and varicella incidence. Furthermore， the effect of temperature on the varicella incidence showed a unimodal pattern in the varicella cases aged 3‒14 years. The RR value reached its peak at 11 ℃ （RR=2.89， 95%CI： 1.33‒6.24）. In contrast， the effect of temperature on the varicella incidence in the cases aged 15 years and above showed a unimodal pattern， with RR value reaching the peak at 5 ℃ （RR=2.14， 95%CI： 1.33‒3.44）. ConclusionThe unimodal curve is found in the relationship between temperature and varicella incidence. Low temperature is associated with increased risk of varicella incidence. Children aged 3‒14 years are more susceptible to the effect of temperature on the varicella incidence.

ObjectiveTo explore the establishment of performance assessment indicators for the classification and grading of public health staff in district-level Centers for Disease Control and Prevention (CDCs), and to provide a basis for such evaluations. MethodsThrough literature review and group interviews, performance evaluation indicators were developed based on competency evaluation. Experts were invited to evaluate the weight of performance evaluation indicators for public health staff from different categories, with the average value used to represent the weight of each indicator. ResultsTwenty-nine experts from universities in Shanghai, municipal CDCs, and district CDCs participated, yielding an expert authority coefficient of 0.86. The performance evaluation indicators for department managers were categorized into three levels, with 4 indicators at the primary level, 16 indicators at the secondary level, and 42 indicators at the tertiary level, while those for general staff included 4 primary indicators, 15 secondary indicators, and 36 tertiary indicators. Significant differences were observed in the weight coefficients of the primary indicators (internal operations, professional work, and learning and growth) between department managers and general staff. The top three secondary indicators for department managers were department management, monitoring and prevention, and level of expertise. For mid-level and senior staff, the top three secondary indicators were monitoring and prevention, level of expertise, and research work. The top three secondary indicators for junior staff were monitoring and prevention, professional expertise, and professional attitude. No significant statistical differences were found among tertiary indicators. ConclusionThe developed performance evaluation indicators are reliable. Staff at different levels and classifications should be evaluated using different performance evaluation standards to accurately reflect individual performance and contributions.