Acute lung injury （ALI） is one of the most common and critical diseases in clinical practice， with extremely high morbidity and mortality， seriously threatening human life and health. The pathogenesis of ALI is complex， in which the inflammatory response is a key factor. Studies have shown that NOD-like receptor protein 3 （NLRP3） inflammasomes are involved in ALI through mechanisms such as inflammation induction， increased microvascular permeability， recruitment of neutrophils， oxidative stress， and pyroptosis， playing a key role in the occurrence and progression of ALI. Therefore， regulating NLRP3 inflammasomes and inhibiting the release of inflammatory factors can alleviate the damage in ALI. At present， ALI is mainly treated by mechanical ventilation and oxygen therapy， which have problems such as high costs and poor prognosis. In recent years， studies have shown that traditional Chinese medicine （TCM） can reduce the inflammatory response and the occurrence of oxidative stress and pyroptosis by regulating the NLRP3 inflammasome， thus alleviating the damage and decreasing the mortality of ALI. Based on the relevant literature in recent years， this article reviews the research progress in TCM treatment of ALI by regulating NLRP3 inflammasomes， discusses how NLRP3 inflammasomes participate in ALI， and summarizes the active ingredients， extracts， and compound prescriptions of TCM that regulate NLRP3 inflammasomes， aiming to provide new ideas for the clinical treatment of ALI and the development of relevant drugs.

OBJECTIVE To investigate the effects and potential mechanism of paeoniflorin on oxidative stress of ulcerative colitis （UC） mice based on adenosine monophosphate-activated protein kinase （AMPK）/nuclear factor-erythroid 2-related factor 2 （Nrf2） pathway. METHODS Male BALB/c mice were randomly divided into control group， model group， inhibitor group （AMPK inhibitor Compound C 20 mg/kg）， paeoniflorin low-， medium- and high-dose groups （paeoniflorin 12.5， 25， 50 mg/kg）， high- dose of paeoniflorin+inhibitor group （paeoniflorin 50 mg/kg+Compound C 20 mg/kg）， with 8 mice in each group. Except for the control group， mice in all other groups were given 4% dextran sulfate sodium solution for 5 days to establish the UC model. Subsequently， mice in each drug group were given the corresponding drug solution intragastrically or intraperitoneally， once a day， for 7 consecutive days. The changes in body weight of mice were recorded during the experiment. Twenty-four hours after the last administration， colon length， malondialdehyde （MDA） content， and activities of superoxide dismutase （SOD） and glutathione peroxidase （GSH-Px） in colon tissues were measured； histopathological morphology of colon tissues， tight junctions between intestinal epithelial cells， and histopathological scoring were all observed and evaluated； the mRNA expressions of AMPK and Nrf2， as well as the protein expressions of heme oxygenase-1（HO-1）， occludin and claudin-1， were all determined in colon tissue. RESULTS Compared with model group， paeoniflorin groups exhibited recovery from pathological changes such as inflammatory cell infiltration and crypt damage in the colon tissue， as well as improved tight junction damage between intestinal epithelial cells. Additionally， significant increases or upregulations were observed in body weight， colon length， activities of SOD and GSH-Px， phosphorylation level of AMPK， and protein expression of Nrf2， HO-1， occludin， claudin-1， and mRNA expressions of AMPK and Nrf2； concurrently， MDA content and histopathological scores were significantly reduced （P＜ 0.05 or P＜0.01）. In contrast， the inhibitor group showed comparable （P＞0.05） or worse （P＜0.05 or P＜0.01） indicators compared to the model group. Conversely， the addition of AMPK inhibitor could significantly reverse the improvement of high- dose paconiflorin （P＜0.01）. CONCLUSIONS Paeoniflorin can repair intestinal epithelial cell damage in mice， improve tight junctions between epithelial cells， upregulate the expression of related proteins， and promote the expression and secretion of antioxidant-promoting molecules， thereby ameliorating UC； its mechanism may be associated with activating AMPK/Nrf2 antioxidant pathway.

Objective: To analyze respiratory syncytial virus(RSV) outbreaks surveillance results and the epidemiological characteristics in kindergarten and school in Shenzhen during 2017-2023 , so as to provide a scientific reference for control and prevention of RSV. Methods: Epidemiological data and surveillance results of RSV outbreaks in kindergarten and school from 2017 to 2023 were collected for descriptive analyses. Results: A total of 31 RSV outbreaks were identified in kindergarten and school in 2017-2023 in Shenzhen, 346 cases were reported, the average incidence rate was 22.02%. The most annual RSV outbreaks were reported in 2020 with 14 outbreaks, followed by 8 outbreaks in 2023. A total of 64.52% of RSV outbreaks were identified in kindergarten with rest occurring in primary school or middle school. The greatest monthly count of outbreak was 18 (58.06%) in September, followed by 3 outbreaks (9.68%) in March and October. A total of 244 swab samples were collected, 169 samples were positive for respiratory viruses, the positive rate was 69.26%, 121 samples were positive for RSV,from 31 respiratory syncytical virus outbreaks 57 and samples were positive for other respiratory viruses(9 samples were positive for two respiratory viruses). A toral of 14(45.16%) outbreaks are caused by RSV alone, 17 outbreaks (54.84%) were caused by RSV and other respiratory viruses. Conclusions Most RSV outbreaks in kindergarten and school are reported after 2020 in Shenzhen, most RSV outbreaks occur in kindergarten, peak seasons of RSV outbreaks are autumn and spring.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

Objective: To establish a risk assessment system for infectious disease prevention and control in schools in Shangcheng District, Hangzhou and determine risk levels for each school, and propose corresponding risk management measures, so as to provide a scientific reference for infectious disease prevention and control in primary and secondary schools. Methods: Based on the Failure Mode and Effects Analysis (FMEA) method, potential failure analysis and current situation investigation of infectious disease prevention and control risks were conducted in 110 primary and secondary schools from 2022 to 2024 in Shangcheng District, Hangzhou. Risk levels were classified using K-Means cluster analysis. Results: Through expert panel discussions using FMEA, 6 first level indicators and 28 second level indicators were identified. The top three risk priority numbers were implementation of required prevention and control measures for clustered infectious disease outbreaks in schools in the past three years ( 189.00 ), student morning/afternoon health checks (168.00), and reporting status of clustered infectious disease outbreaks in schools in the past three years (144.00). The comprehensive prevention scores of schools ranged from 61.00 to 98.00 (mean: 87.40 ). There were no statistically significant differences in the average scores(primary school: 88.17±7.39, nine year consistent education: 86.26±7.68, junior high school: 85.55±8.20, and high school: 88.72±4.91) and risk level distribution of schools with different educational stages( F/H=0.95,1.47, P >0.05).K-Means cluster analysis divided the schools into 5 risk levels with cluster centers at 93.25, 85.78, 79.69, 70.29, 61.00 ( F=309.21, P <0.05), with 80% of schools classified as low risk or below. Conclusion The infectious disease prevention and control risk assessment system for primary and secondary schools can be established, and hierarchical management can be conducted according to school risk levels, thereby improving the efficiency and effectiveness of school infectious disease prevention and control, and enhancing the precision and sustainability of prevention efforts.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

Objective: To analyze the monitoring results and characteristics of human metapneumovirus (HMPV) outbreaks in Shenzhen in kindergartens and schools during 2017-2023, so as to provide a evidence for prevention and control of respiratory diseases. Methods: Based on the epidemiological data and laboratory test results of HMPV outbreaks in kindergartens and schools of Shenzhen during 2017 to 2023, collected from the influenza monitoring network platform, Mann Kendall test was applied for trend analysis pf HMPV. Results: A total of 11 HMPV outbreaks were identified in kindergartens and schools from 2017 to 2023 for 128 cases with an average incidence rate of 26.61% in 2018. The most yearly HMPV outbreaks were reported in 2020 being 4 outbreaks, followed by 2 outbreaks in 2018. A total of 72.73% (8 outbreaks) of HMPV outbreaks were identified in kindergartens with 27.27% (3 outbreaks) occurring in primary schools. The monthly greatest count of outbreak was 3(27.27%) in March and December, followed by 2 outbreaks (18.18%) in April and June. A total of 98 swab samples were collected, and 80 samples were positive for respiratory viruses with a positive rate of 81.63%; among them, 67 samples were positive for HMPV alone, 10 samples were positive for other respiratory viruses alone, and 3 samples were positive both for HMPV and other respiratory virus. Among the 11 human metalung virus cases, 6 HMPV outbreaks were caused by human metapneumovirus alone, and 5 outbreaks were caused by HMPV and other respiratory viruses. Except for 2020, the percentage of HMPV outbreaks in influenza like illness outbreaks increased with year ( Z=2.25, P =0.02). Conclusion HMPV outbreaks have been reported every year from 2017 to 2023 in kindergartens and schools in Shenzhen, most of HMPV in kindergartens, and the peak seasons of HMPV outbreaks are spring and winter.

Objective:To prepare rabbit polyclonal antibodies against respiratory syncytial virus (RSV) N protein and use them as the detection antibodies to establish a N-ELISA-based method for rapid detection of neutralizing antibodies.Methods:A plasmid of pET30a-N for the expression of RSV N protein was constructed. After purification, the protein was immunized into New Zealand rabbits to prepare polyclonal antibodies, which were used as the detection antibodies. Positive serum samples were diluted and used to neutralize RSV (100 TCID 50/well). Hep-2 cells were inoculated and cultured, and then the cells were fixed with 80% acetone. ELISA was performed to detect RSV N protein in infected cells. When the absorbance value of a well was below the cut-off value, it was regarded as the positive well in the neutralization test. The highest dilution of a positive well serum was the neutralizing antibody titer. After optimizting the antibody dilution, detection time, cell density and the duration of neutralization, the method for neutralizing antibody detection was established based on N-ELISA. The established method was verified by analyzing the influences of different cell generations and edge effects, and calculating the accuracy, repeatability and precision. The correlation between the established method and microneutralization method was analyzed by detecting human RSV IgG-positive serum. Results:The plasmid pET30a-N was successfully constructed, and the expressed N protein showed high purity and good specificity. After the third immunization, the antibody titer in rabbit serum was 1∶51 200, and the antibodies could specifically bind to RSV. The prepared rabbit anti-RSV N polyclonal antibodies had a titer of 1∶51 200, and showed good specificity. The neutralizing antibodies could be detected on day 4 with the established method, and the duration of neutralization was shortened to 30 min. Cell generations and the position of wells in the 96-well plate (edge well and non-edge well) had no significant effect on the method, and the repeatability, precision and accuracy of the method were good. In the detection of 64 RSV IgG-positive human serum samples by the established method and microneutralization method, the correlation coefficient was 0.929 6, indicating a good positive correlation between the two methods.Conclusions:A N-ELISA-based method for rapid neutralizing antibody detection is successfully established, which can be used to evaluate the serum antibody level after RSV vaccination.