OBJECTIVES: To explore the active components that mediate the therapeutic effect of Centella asiatica on psoriasis and their therapeutic mechanisms. METHODS: TCMSP, TCMIP, PharmMapper, Swiss Target Prediction, GeneCards, OMIM and TTD databases were searched for the compounds in Centella asiatica and their targets and the disease targets of psoriasis. A drug-active component-target network and the protein-protein interaction network were constructed, and DAVID database was used for pathway enrichment analysis. In a RAW264.7 macrophage model of LPS-induced inflammation, the anti-inflammatory effect of 7.5, 15, 30, and 60 μmol/L quercetin, asiaticoside, and asiatic acid, which were identified as the main active components in Centella asiatica, were tested by measuring cellular production of NO, TNF‑α and IL-6 using Griess method and ELISA and by detecting mRNA expressions of IL-23, IL-17A, TNF-α and IL-6 and protein expressions of p-STAT3 (Tyr705) and p-STAT3 (Ser727) with RT-qPCR and Western blotting. RESULTS: A total of 139 targets of Centella asiatica and 4604 targets of psoriasis were obtained, and among them CASP3, EGFR, PTGS2, and ESR1 were identified as the core targets. KEGG analysis suggested that quercetin, asiaticoside, and asiatic acid in Centella asiatica were involved in cancer and IL-17 and MAPK signaling pathways. In the RAW264.7 macrophage model of inflammation, treatment with quercetin significantly reduced cellular production of NO, TNF‑α and IL-6, and lowered mRNA expressions of IL-23, IL-17A, TNF‑α and IL-6 and protein expressions of p-STAT3 (Tyr705) and p-STAT3 (Ser727). CONCLUSIONS Quercetin, asiaticoside and asiatic acid are the main active components in Centella asiatica to mediate the therapeutic effect against psoriasis, and quercetin in particular is capable of suppressing cellular production of NO, TNF‑α and IL-6 and regulating the IL-23/IL-17A inflammatory axis by mediating STAT3 phosphorylation to inhibit inflammatory response.
Quercetin/pharmacology* ; Psoriasis/metabolism* ; STAT3 Transcription Factor/metabolism* ; Mice ; Animals ; Centella/chemistry* ; Triterpenes/pharmacology* ; Phosphorylation ; Interleukin-17/metabolism* ; Interleukin-23/metabolism* ; RAW 264.7 Cells ; Pentacyclic Triterpenes/pharmacology* ; Macrophages/drug effects* ; Signal Transduction ; Plant Extracts

Objective:To construct a risk classification model for adverse events of medical consumables,so as to achieve automatic evaluation for risk level of such events,and enhance the capability for risk management of adverse events of medical consumables,and ensure the safety of medical apparatuses.Methods:The data of adverse events of medical consumables of 370 cases of 148 types that were reported by Shandong Provincial Third Hospital from 2020 to 2023 were selected,and they were divided into high-risk and low-risk types.Eight key factors of them,which included the number of cases,injury level,type of registration certificate,with source and without source,high-value and low-value,domestic and imports,product classification,and risk levels,were counted to form a dataset.K-nearest neighbor(KNN),support vector machine(SVM)and decision tree algorithms in machine learning were used to construct a risk classification model for adverse events of medical consumables.The data of 12 adverse events of medical consumables of 5 types of our hospital,which were newly reported in 2024,were integrated for their parameters.Then,the accuracy rate and prediction performance of the model were further analyzed.Results:By comparing the KNN,SVM and decision tree algorithm models,the effect of SVM algorithm model was better,and its accuracy rate was 90.54%,and its area under curve(AUC)value of the receiver operating characteristic(ROC)curve was 0.944,and its Kolmogorov-Smirnov(KS)test value was 0.808.The model had favorable predictive performance.The results,that invoked SVM algorithm model to conduct verification of actual prediction for 12 adverse events of medical consumables of 5 types,indicated it was same between predictive outcomes and risk levels of manual evaluation.Conclusion:The risk classification model of adverse events of medical consumables has established an operational model for assessing the risk level of such events,which can assist monitoring personnel for adverse event of medical apparatuses to quickly and accurately find risk signals of adverse events of medical consumables,and improve the monitoring capability of them for these adverse events.

Objective:To construct a risk classification model for adverse events of medical consumables,so as to achieve automatic evaluation for risk level of such events,and enhance the capability for risk management of adverse events of medical consumables,and ensure the safety of medical apparatuses.Methods:The data of adverse events of medical consumables of 370 cases of 148 types that were reported by Shandong Provincial Third Hospital from 2020 to 2023 were selected,and they were divided into high-risk and low-risk types.Eight key factors of them,which included the number of cases,injury level,type of registration certificate,with source and without source,high-value and low-value,domestic and imports,product classification,and risk levels,were counted to form a dataset.K-nearest neighbor(KNN),support vector machine(SVM)and decision tree algorithms in machine learning were used to construct a risk classification model for adverse events of medical consumables.The data of 12 adverse events of medical consumables of 5 types of our hospital,which were newly reported in 2024,were integrated for their parameters.Then,the accuracy rate and prediction performance of the model were further analyzed.Results:By comparing the KNN,SVM and decision tree algorithm models,the effect of SVM algorithm model was better,and its accuracy rate was 90.54%,and its area under curve(AUC)value of the receiver operating characteristic(ROC)curve was 0.944,and its Kolmogorov-Smirnov(KS)test value was 0.808.The model had favorable predictive performance.The results,that invoked SVM algorithm model to conduct verification of actual prediction for 12 adverse events of medical consumables of 5 types,indicated it was same between predictive outcomes and risk levels of manual evaluation.Conclusion:The risk classification model of adverse events of medical consumables has established an operational model for assessing the risk level of such events,which can assist monitoring personnel for adverse event of medical apparatuses to quickly and accurately find risk signals of adverse events of medical consumables,and improve the monitoring capability of them for these adverse events.

Objective Based on the assumption of spatial heterogeneity,the distribution pattern and risk characteristics of health poverty in middle-aged and elderly people with chronic diseases are described from the perspective of spatial differentiation.In order to providing a theoretical basis for the optimization of subsequent poverty reduction policies and a model policy for other countries.Methods It used factor detector and interaction detector to capture the role of single-factor and multi-factor interactions on the spatial differentiation of health poverty,and risk detectors were utilized to explore the high-risk factors in risky areas Results The single factor explanation of medical assistance and health education activities is prominent,and the factors such as PM2.5,old-age dependency ratio and urban unemployment rate have strong interaction.Furthermore,it identified high-risk factor characteristics in areas at high risk of health poverty.Conclusion The spatial differentiation pattern of health poverty among the middle-aged and elderly chronic disease population in China is the result of the synergistic driving effect of multidimensional factors,and there is variability in the risk characteristics among regions.The government should establish a contextual optimization strategy and pay attention to the joint effect of multiple factors to establish a synergistic management system.

Objective:To analyze the distribution and epidemiological characteristics of non-bacterial pathogens in hospitalized children with acute respiratory infections at a tertiary pediatric hospital in Shanghai during 2023.Methods:A retrospective study was conducted on 10 591 children with acute respiratory tract infections who were hospitalized in Shanghai Children's Hospital from January to December 2023. A multiplex PCR combined with capillary electrophoresis platform was used to detect 11 common non-bacterial respiratory pathogens（including viruses and atypical pathogens）. Statistical analysis was carried out using SPSS 29.0 software. Qualitative data were presented as numbers and percentages，and the Chi-square test was employed to make comparisons between groups，aiming to analyze the differences in the distribution of different pathogens according to gender，age group，and season. Additionally，based on the severity of the disease，patients were calssified into a severe pneumonia group and a non-severe pneumonia group to further explore the characteristics of the pathogen spectrum of severe pneumonia.Results:The total detection rate of pathogens was 54.39%（5 760/10 591），and the proportion of mixed infections was 12.76%（735/5 760）. The dominant pathogens and their proportions were as follows： Mycoplasma pneumoniae（19.20%，2 034/10 591），human rhinovirus（12.16%，1 288/10 591），influenza A virus（8.31%，880/10 591），and respiratory syncytial virus（8.14%，862/10 591）. Epidemiological characteristics showed that：（1）In terms of age： Mycoplasma pneumoniae was more common in older children（29.55%，901/3 049，in the school-age group，χ 2 = 653.67， P<0.001）. Influenza A virus had a high incidence in the adolescent group（11.34%，45/397，χ 2=48.69， P<0.001）. Respiratory syncytial virus was most susceptible in the infant group（20.94%，280/1 337，χ 2=739.92， P<0.001）. Human rhinovirus showed the characteristic of general susceptibility across all ages.（2）Monthly and seasonal distribution： Mycoplasma pneumoniae had a seasonal epidemic in summer and autumn（it began to rise in May and peaked in October at 34.22%，439/1 283）；influenza A virus had a bimodal distribution in spring and winter（the peak was 37.15% in March，315/848）；respiratory syncytial virus had a dominant epidemic in spring and summer（the detection rate was 21.24% in May，206/970），and human rhinovirus was prevalent throughout the year.（3）Clinical correlation：The detection rate of pathogens in the severe pneumonia group was significantly higher than that in the non-severe group：84.19%（426/506） vs 2.89%（5 334/10 085），χ 2=56.23， P<0.001. Conclusions:In 2023，the pathogen spectrum of hospitalized children with acute respiratory infections in the Shanghai area exhibits an epidemic pattern dominated by Mycoplasma pneumoniae，and its transmission dynamics are significantly age-dependent. This study delineates the pathogen-host-environment tripartite interactions，establishing an evidence-based foundation for formulating precision diagnostic-therapeutic algorithms and seasonal nosocomial infection prevention frameworks.

Objective:To analyze the distribution and epidemiological characteristics of non-bacterial pathogens in hospitalized children with acute respiratory infections at a tertiary pediatric hospital in Shanghai during 2023.Methods:A retrospective study was conducted on 10 591 children with acute respiratory tract infections who were hospitalized in Shanghai Children's Hospital from January to December 2023. A multiplex PCR combined with capillary electrophoresis platform was used to detect 11 common non-bacterial respiratory pathogens（including viruses and atypical pathogens）. Statistical analysis was carried out using SPSS 29.0 software. Qualitative data were presented as numbers and percentages，and the Chi-square test was employed to make comparisons between groups，aiming to analyze the differences in the distribution of different pathogens according to gender，age group，and season. Additionally，based on the severity of the disease，patients were calssified into a severe pneumonia group and a non-severe pneumonia group to further explore the characteristics of the pathogen spectrum of severe pneumonia.Results:The total detection rate of pathogens was 54.39%（5 760/10 591），and the proportion of mixed infections was 12.76%（735/5 760）. The dominant pathogens and their proportions were as follows： Mycoplasma pneumoniae（19.20%，2 034/10 591），human rhinovirus（12.16%，1 288/10 591），influenza A virus（8.31%，880/10 591），and respiratory syncytial virus（8.14%，862/10 591）. Epidemiological characteristics showed that：（1）In terms of age： Mycoplasma pneumoniae was more common in older children（29.55%，901/3 049，in the school-age group，χ 2 = 653.67， P<0.001）. Influenza A virus had a high incidence in the adolescent group（11.34%，45/397，χ 2=48.69， P<0.001）. Respiratory syncytial virus was most susceptible in the infant group（20.94%，280/1 337，χ 2=739.92， P<0.001）. Human rhinovirus showed the characteristic of general susceptibility across all ages.（2）Monthly and seasonal distribution： Mycoplasma pneumoniae had a seasonal epidemic in summer and autumn（it began to rise in May and peaked in October at 34.22%，439/1 283）；influenza A virus had a bimodal distribution in spring and winter（the peak was 37.15% in March，315/848）；respiratory syncytial virus had a dominant epidemic in spring and summer（the detection rate was 21.24% in May，206/970），and human rhinovirus was prevalent throughout the year.（3）Clinical correlation：The detection rate of pathogens in the severe pneumonia group was significantly higher than that in the non-severe group：84.19%（426/506） vs 2.89%（5 334/10 085），χ 2=56.23， P<0.001. Conclusions:In 2023，the pathogen spectrum of hospitalized children with acute respiratory infections in the Shanghai area exhibits an epidemic pattern dominated by Mycoplasma pneumoniae，and its transmission dynamics are significantly age-dependent. This study delineates the pathogen-host-environment tripartite interactions，establishing an evidence-based foundation for formulating precision diagnostic-therapeutic algorithms and seasonal nosocomial infection prevention frameworks.

Objective Based on the assumption of spatial heterogeneity,the distribution pattern and risk characteristics of health poverty in middle-aged and elderly people with chronic diseases are described from the perspective of spatial differentiation.In order to providing a theoretical basis for the optimization of subsequent poverty reduction policies and a model policy for other countries.Methods It used factor detector and interaction detector to capture the role of single-factor and multi-factor interactions on the spatial differentiation of health poverty,and risk detectors were utilized to explore the high-risk factors in risky areas Results The single factor explanation of medical assistance and health education activities is prominent,and the factors such as PM2.5,old-age dependency ratio and urban unemployment rate have strong interaction.Furthermore,it identified high-risk factor characteristics in areas at high risk of health poverty.Conclusion The spatial differentiation pattern of health poverty among the middle-aged and elderly chronic disease population in China is the result of the synergistic driving effect of multidimensional factors,and there is variability in the risk characteristics among regions.The government should establish a contextual optimization strategy and pay attention to the joint effect of multiple factors to establish a synergistic management system.

Background::Breast cancer (BC) risk-stratification tools for Asian women that are highly accurate and can provide improved interpretation ability are lacking. We aimed to develop risk-stratification models to predict long- and short-term BC risk among Chinese women and to simultaneously rank potential non-experimental risk factors.Methods::The Breast Cancer Cohort Study in Chinese Women, a large ongoing prospective dynamic cohort study, includes 122,058 women aged 25-70 years old from the eastern part of China. We developed multiple machine-learning risk prediction models using parametric models (penalized logistic regression, bootstrap, and ensemble learning), which were the short-term ensemble penalized logistic regression (EPLR) risk prediction model and the ensemble penalized long-term (EPLT) risk prediction model to estimate BC risk. The models were assessed based on calibration and discrimination, and following this assessment, they were externally validated in new study participants from 2017 to 2020.Results::The AUC values of the short-term EPLR risk prediction model were 0.800 for the internal validation and 0.751 for the external validation set. For the long-term EPLT risk prediction model, the area under the receiver operating characteristic curve was 0.692 and 0.760 in internal and external validations, respectively. The net reclassification improvement index of the EPLT relative to the Gail and the Han Chinese Breast Cancer Prediction Model (HCBCP) models for external validation was 0.193 and 0.233, respectively, indicating that the EPLT model has higher classification accuracy.Conclusions::We developed the EPLR and EPLT models to screen populations with a high risk of developing BC. These can serve as useful tools to aid in risk-stratified screening and BC prevention.

It is believed that retention due to deficient qi is an important pathogenesis of endometriosis （EMs）. Deficient qi is the root of the disease， mainly manifested as spleen deficiency， while retention is the branch pathogenesis of the disease， mainly with blood stasis， complicated with constraint， phlegm， heat， toxin and other pathological factors. Therefore， it is proposed to follow the treatment principle of supplementing deficiency and unblocking stagnation， and take the methods of replenishing qi and fortifying the spleen， removing stasis and eliminating concretions. Self-made Fuzheng Huayu Formula （扶正化瘀方） is taken as the basic formula， and can be modified with the symptoms in menstrual and non-menstrual periods. Additionally， the methods of moving qi， dispelling phlegm， clearing heat， relieving toxin and others can be combined， and it is recommended to treat the root and the branch simultaneously.

Objective: To understand the coincidence rate of adjustable and non adjustable desks and chairs in primary and secondary schools in Shanghai, so as to provide a reference for the effective management of desks and chairs in schools. Methods: Stratified random cluster sampling was applied to measure 1 091 sets of desks and chairs in 33 classrooms of 7 primary and secondary schools in 4 districts of Shanghai including Huangpu, Hongkou, Yangpu and Jiading districts. The height examination data of seated students in the same semester were also collected to evaluate the matching type of functional size of desks and chairs and students height according to Functional Sizes and Technical Requirements of Chairs and Tables for Educational Institutions. Results: The matching coincidence rate of desk and chair distribution in primary and secondary schools was 22.5%, and the matching coincidence rates of primary, middle and high schools were 11.7%, 27.1% and 40.9% respectively ( χ 2=83.23, P <0.01); the matching coincidence rate of adjustable desks and chairs (28.2%) was significantly higher than that of fixed (18.2%) ( χ 2=14.49, P <0.01). The type of distribution of desks and chairs that do not correspond to national standards, from elementary school to high school, showing a trend of high desks with high chairs, low desks with high chairs to high desks with low chairs. Conclusion The coincidence rate of desk and chair distribution in primary and secondary schools is low, and the adjustable desks and chairs has improved the problems to a certain extent, but has not significantly improved the coincidence rate. It is necessary to further explore the effective management mode in the use of adjustable desks and chairs and improve the current situation of the use of desks and chairs in schools.