Objective The study aimed to investigate whether montelukast sodium could alleviate airway inflammatory responses in asthmatic mice by affecting the PHD2/HIF-1α pathway.Methods An allergic asthma model was established by ovalbumin(OVA)induction,and 18 female BALB/c mice were randomly divided into a control group(Con group),an asthma group(OVA group),and an asthma group with montelukast sodium intervention(30 mg/kg montelukast sodium by oral administration 1 h before OVA challenge,Mon group).HE staining was used to analyze the pathological changes in the lungs of mice.Blood cell analyzer and kits were used to determine the number of inflammatory cells and the levels of cytokines,the content of lactic acid and pyruvic acid in the lungs,respectively.RT-PCR and Western blot were used to detect the mRNA and protein expression of HIF-1α,PHD2,E-cad and p120 in the lungs of mice.Results Compared with the Con group,there was a significant increase in the number of eosinophils,lymphocytes,neutrophils and monocytes,the levels of IL-5,IL-13,complement factor D(CFD)and contents of lactate and pyruvate in the lungs of mice in the OVA group.Lung HIF-1α,PHD2,p120 and E-cad mRNA levels were reduced,meanwhile HIF-1α and PHD2 protein expression were upregulated but E-cad and p120 protein expression were downregulated(all with P<0.05).After montelukast sodium intervention,the number of eosinophils and monocytes and CFD expression were significantly decreased in the lungs of Mon group,the contents of lactate and pyruvate were basically restored to normal,and the mRNA and protein expression of HIF-1α,PHD2,p120 and E-cad were effectively improved.Conclusion Montelukast sodium could alleviate the airway inflammatory responses in the lungs of asthmatic mice by regulating the PHD2/HIF-1α signaling pathway.

Objective The study aimed to investigate whether montelukast sodium could alleviate airway inflammatory responses in asthmatic mice by affecting the PHD2/HIF-1α pathway.Methods An allergic asthma model was established by ovalbumin(OVA)induction,and 18 female BALB/c mice were randomly divided into a control group(Con group),an asthma group(OVA group),and an asthma group with montelukast sodium intervention(30 mg/kg montelukast sodium by oral administration 1 h before OVA challenge,Mon group).HE staining was used to analyze the pathological changes in the lungs of mice.Blood cell analyzer and kits were used to determine the number of inflammatory cells and the levels of cytokines,the content of lactic acid and pyruvic acid in the lungs,respectively.RT-PCR and Western blot were used to detect the mRNA and protein expression of HIF-1α,PHD2,E-cad and p120 in the lungs of mice.Results Compared with the Con group,there was a significant increase in the number of eosinophils,lymphocytes,neutrophils and monocytes,the levels of IL-5,IL-13,complement factor D(CFD)and contents of lactate and pyruvate in the lungs of mice in the OVA group.Lung HIF-1α,PHD2,p120 and E-cad mRNA levels were reduced,meanwhile HIF-1α and PHD2 protein expression were upregulated but E-cad and p120 protein expression were downregulated(all with P<0.05).After montelukast sodium intervention,the number of eosinophils and monocytes and CFD expression were significantly decreased in the lungs of Mon group,the contents of lactate and pyruvate were basically restored to normal,and the mRNA and protein expression of HIF-1α,PHD2,p120 and E-cad were effectively improved.Conclusion Montelukast sodium could alleviate the airway inflammatory responses in the lungs of asthmatic mice by regulating the PHD2/HIF-1α signaling pathway.

Objective:To establish and validate a multimodal model based on radiomics and deep learning for predicting acute pancreatitis (AP) complicated with acute respiratory distress syndrome (ARDS).Methods:Patients diagnosed with AP from The First Affiliated Hospital of Soochow University, Donghai County People's Hospital and Jintan Affiliated Hospital of Jiangsu University between January 2017 and December 2023 were enrolled. Based on the diagnosis of ARDS within 1 week after admission, the patients were classified into the ARDS group and the non-ARDS group. Patients in the First Affiliated Hospital of Soochow University ( n=406) was used as the training set (non-ARDS group n=212 vs ARDS group n=194), while Donghai and Jintan hospitals served as the test set ( n=175; non-ARDS group n=104 vs ARDS group n=71). Clinical data, laboratory tests and the occurrence of systemic inflammatory response syndrome (SIRS) within 24 hours after admission were collected. Scoring systems such as bedside index for severity in acute pancreatitis (BISAP), Ranson score and modified CT severity index (MCTSI) were calculated. Radiomics features were extracted from three-dimensional CT images to develop a radiomics model based on XGBoost algorithm. At the same time, a deep learning model was constructed using deep convolutional networks to extract deep features. Finally, clinical features and the predictions from the aforementioned models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. The receiver operating characteristic (ROC) curves of the three models and the three scores including BISAP, Ranson and MCTSI were plotted and the area under the curves (AUCs) were calculated to evaluate the prediction performance for ARDS in AP patients, as well as sensitivity and specificity. Results:In the multimodal model for predicting ARDS in AP patients, predictions of the deep learning model and the radiomics model were the most important variables, followed by SIRS, C-reactive protein, procalcitonin, albumin, glucose, creatinine, neutrophil, and Ca 2+. In the training set, the multimodal model achieved an AUC of 0.933 for predicting ARDS in AP patients, higher than the radiomics model (0.727), the deep learning model (0.877), MCTSI (0.870), Ranson (0.620) and BISAP (0.898). In the test set, the model's AUC was 0.916 for predicting ARDS in AP patients, higher than the radiomics model (0.660), the deep learning model (0.864), MCTSI (0.851), Ranson (0.609), and BISAP (0.860). Conclusions:Based on clinical structured data, radiomics and deep learning features, the multimodal model could predict the risk of ARDS in AP patients at an early stage, whose performance is better than the single-modal models and the traditional scoring systems.

Objective:To establish and validate a multimodal model based on radiomics and deep learning for predicting acute pancreatitis (AP) complicated with acute respiratory distress syndrome (ARDS).Methods:Patients diagnosed with AP from The First Affiliated Hospital of Soochow University, Donghai County People's Hospital and Jintan Affiliated Hospital of Jiangsu University between January 2017 and December 2023 were enrolled. Based on the diagnosis of ARDS within 1 week after admission, the patients were classified into the ARDS group and the non-ARDS group. Patients in the First Affiliated Hospital of Soochow University ( n=406) was used as the training set (non-ARDS group n=212 vs ARDS group n=194), while Donghai and Jintan hospitals served as the test set ( n=175; non-ARDS group n=104 vs ARDS group n=71). Clinical data, laboratory tests and the occurrence of systemic inflammatory response syndrome (SIRS) within 24 hours after admission were collected. Scoring systems such as bedside index for severity in acute pancreatitis (BISAP), Ranson score and modified CT severity index (MCTSI) were calculated. Radiomics features were extracted from three-dimensional CT images to develop a radiomics model based on XGBoost algorithm. At the same time, a deep learning model was constructed using deep convolutional networks to extract deep features. Finally, clinical features and the predictions from the aforementioned models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. The receiver operating characteristic (ROC) curves of the three models and the three scores including BISAP, Ranson and MCTSI were plotted and the area under the curves (AUCs) were calculated to evaluate the prediction performance for ARDS in AP patients, as well as sensitivity and specificity. Results:In the multimodal model for predicting ARDS in AP patients, predictions of the deep learning model and the radiomics model were the most important variables, followed by SIRS, C-reactive protein, procalcitonin, albumin, glucose, creatinine, neutrophil, and Ca 2+. In the training set, the multimodal model achieved an AUC of 0.933 for predicting ARDS in AP patients, higher than the radiomics model (0.727), the deep learning model (0.877), MCTSI (0.870), Ranson (0.620) and BISAP (0.898). In the test set, the model's AUC was 0.916 for predicting ARDS in AP patients, higher than the radiomics model (0.660), the deep learning model (0.864), MCTSI (0.851), Ranson (0.609), and BISAP (0.860). Conclusions:Based on clinical structured data, radiomics and deep learning features, the multimodal model could predict the risk of ARDS in AP patients at an early stage, whose performance is better than the single-modal models and the traditional scoring systems.

Objective:To investigate the inhibitory effect of Huqizhengxiao decoction (HQZXD) on subcutaneous tumor in H22 hepatoma-bearing mice and its potential mechanism.Methods:Twenty-five healthy male BALB/c inbred mice aged 4 to 6 weeks and weighing (20±2) g were taken. One of them was used for the amplification of H22 hepatoma cells. The amplified H22 hepatoma cells were inoculated subcutaneously at the left posterior axillary line of the remaining mice for modeling. After subcutaneous tumor formation, the mice were randomly divided into four groups: model group, HQZXD group, sorafenib group and combined (HQZXD+ sorafenib) group, with 6 mice in each group. Tumor inhibition rates, and serum levels of aspartate transaminase (AST) and alanine transaminase (ALT) were observed. The expression of interleukin (IL)-6, signal transducer and activator of transcription 3 (STAT3), phosphorylated STAT3 (p-STAT3), and nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) in tumor tissues was detected using immunohistochemistry and Western blotting. Enzyme-linked immunosorbent assay was used to quantify the levels of IL-6, tumor necrosis factor-alpha (TNF-α), and IL-1β in tumor tissues. Quantitative real-time polymerase chain reaction (qRT-PCR) was employed to assess the mRNA levels of IL-6, STAT3, and C-X-C motif chemokine ligand 1 (CXCL1) in tumor tissues.Results:The general condition of mice in all treatment groups improved compared to the model group. Notably, the tumor weight (0.50±0.22) g and tumor volume (0.37±0.18) cm 3 in the combined group were significantly lower than those in the model group [tumor weight: (1.63±0.26) g, tumor volume: (0.98±0.83) cm 3] with statistical significance (both P<0.05). The tumor inhibition rates for the sorafenib, HQZXD, and combination groups were 35.4%, 48.6%, and 69.7%, respectively. Compared to the model group, serum levels of AST and ALT were reduced in all treatment groups, with the combined group showing the most significant decrease [AST: (48.81±2.82) U/L vs. (188.12±6.51) U/L; ALT: (34.14±1.25) U/L vs. (116.62±4.72) U/L], and the differences were statistically significant (both P<0.05). The protein expression levels of IL-6, STAT3, p-STAT3, IL-1β, TNF-α, and NLRP3 in tumor tissues were reduced in all treatment groups compared to the model group, with the combined group showing the most marked reduction, and the differences were statistically significant (all P<0.05). Similarly, the mRNA levels of IL-6, STAT3, and CXCL1 in tumor tissues were lower in all treatment groups compared to the model group, with the combined group showing lower levels than the single treatment groups, and these differences were statistically significant (all P<0.05). Conclusion:HQZXD can inhibit the activation of IL-6/STAT3 pathway, reduce inflammation in tumors, and consequently play a certain inhibitory effect on tumor.

Objective:To investigate the inhibitory effect of Huqizhengxiao decoction (HQZXD) on subcutaneous tumor in H22 hepatoma-bearing mice and its potential mechanism.Methods:Twenty-five healthy male BALB/c inbred mice aged 4 to 6 weeks and weighing (20±2) g were taken. One of them was used for the amplification of H22 hepatoma cells. The amplified H22 hepatoma cells were inoculated subcutaneously at the left posterior axillary line of the remaining mice for modeling. After subcutaneous tumor formation, the mice were randomly divided into four groups: model group, HQZXD group, sorafenib group and combined (HQZXD+ sorafenib) group, with 6 mice in each group. Tumor inhibition rates, and serum levels of aspartate transaminase (AST) and alanine transaminase (ALT) were observed. The expression of interleukin (IL)-6, signal transducer and activator of transcription 3 (STAT3), phosphorylated STAT3 (p-STAT3), and nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) in tumor tissues was detected using immunohistochemistry and Western blotting. Enzyme-linked immunosorbent assay was used to quantify the levels of IL-6, tumor necrosis factor-alpha (TNF-α), and IL-1β in tumor tissues. Quantitative real-time polymerase chain reaction (qRT-PCR) was employed to assess the mRNA levels of IL-6, STAT3, and C-X-C motif chemokine ligand 1 (CXCL1) in tumor tissues.Results:The general condition of mice in all treatment groups improved compared to the model group. Notably, the tumor weight (0.50±0.22) g and tumor volume (0.37±0.18) cm 3 in the combined group were significantly lower than those in the model group [tumor weight: (1.63±0.26) g, tumor volume: (0.98±0.83) cm 3] with statistical significance (both P<0.05). The tumor inhibition rates for the sorafenib, HQZXD, and combination groups were 35.4%, 48.6%, and 69.7%, respectively. Compared to the model group, serum levels of AST and ALT were reduced in all treatment groups, with the combined group showing the most significant decrease [AST: (48.81±2.82) U/L vs. (188.12±6.51) U/L; ALT: (34.14±1.25) U/L vs. (116.62±4.72) U/L], and the differences were statistically significant (both P<0.05). The protein expression levels of IL-6, STAT3, p-STAT3, IL-1β, TNF-α, and NLRP3 in tumor tissues were reduced in all treatment groups compared to the model group, with the combined group showing the most marked reduction, and the differences were statistically significant (all P<0.05). Similarly, the mRNA levels of IL-6, STAT3, and CXCL1 in tumor tissues were lower in all treatment groups compared to the model group, with the combined group showing lower levels than the single treatment groups, and these differences were statistically significant (all P<0.05). Conclusion:HQZXD can inhibit the activation of IL-6/STAT3 pathway, reduce inflammation in tumors, and consequently play a certain inhibitory effect on tumor.

Objective:To investigate the effect of clinical practice of evidence-based medicine (EBM) in facilitating the essential competent capability of ultrasound medicine residents.Methods:A total of 39 residents undergoing standardized residency training in the Department of Ultrasound Medicine at the Second Affiliated Hospital of Zhejiang University School of Medicine from October 2021 to October 2024 were randomly assigned into two groups: control group (19 residents) and an evidence-based traceability group (20 residents). The two groups received same EBM theoretical teaching materials, however, the evidence-based traceability group was additionally required to complete a clinical practice component in ultrasound medicine as part of their EBM curriculum. A comparison was made between the two groups at the conclusion of the teaching cycle with respect to self-assessment (EBM attitude, skills, knowledge), objective test (case analysis, theoretical knowledge), and teaching satisfaction.Results:After the teaching period, the evidence-based traceability group exhibited significantly elevated self-assessment scores in both EBM theoretical knowledge and practice skills when compared to the control group with scores of (26.70±1.17)score vs (21.37±4.15)score and (22.40±1.39)score vs (17.79±3.15)score, respectively (both P<0.001). In the objective test (case analysis, theoretical knowledge), the evidence-based traceability group scored higher in case analysis relevant to clinical scenarios compared to the control group[(59.55±4.56) score vs (52.11±6.58) score, P<0.001], while no statistically significant difference was observed in theoretical knowledge[(29.00±3.08) score vs (27.89±4.19) score, P=0.357]. Both groups reported high teaching satisfaction, with no significant difference between groups ( P>0.05). Conclusions:The incorporation of clinical practice in EBM education for ultrasound medicine residents enhances their clinical practice abilities and improves their analytical and problem-solving skills in real clinical scenarios, contributing to the development of general competent capability among residents.

Objective:To investigate the effect of clinical practice of evidence-based medicine (EBM) in facilitating the essential competent capability of ultrasound medicine residents.Methods:A total of 39 residents undergoing standardized residency training in the Department of Ultrasound Medicine at the Second Affiliated Hospital of Zhejiang University School of Medicine from October 2021 to October 2024 were randomly assigned into two groups: control group (19 residents) and an evidence-based traceability group (20 residents). The two groups received same EBM theoretical teaching materials, however, the evidence-based traceability group was additionally required to complete a clinical practice component in ultrasound medicine as part of their EBM curriculum. A comparison was made between the two groups at the conclusion of the teaching cycle with respect to self-assessment (EBM attitude, skills, knowledge), objective test (case analysis, theoretical knowledge), and teaching satisfaction.Results:After the teaching period, the evidence-based traceability group exhibited significantly elevated self-assessment scores in both EBM theoretical knowledge and practice skills when compared to the control group with scores of (26.70±1.17)score vs (21.37±4.15)score and (22.40±1.39)score vs (17.79±3.15)score, respectively (both P<0.001). In the objective test (case analysis, theoretical knowledge), the evidence-based traceability group scored higher in case analysis relevant to clinical scenarios compared to the control group[(59.55±4.56) score vs (52.11±6.58) score, P<0.001], while no statistically significant difference was observed in theoretical knowledge[(29.00±3.08) score vs (27.89±4.19) score, P=0.357]. Both groups reported high teaching satisfaction, with no significant difference between groups ( P>0.05). Conclusions:The incorporation of clinical practice in EBM education for ultrasound medicine residents enhances their clinical practice abilities and improves their analytical and problem-solving skills in real clinical scenarios, contributing to the development of general competent capability among residents.

Objective To investigate the clinicopathological features of recurrent and de novo focal segmental glomerulosclerosis (FSGS) after kidney transplantation. Methods Thirty-four recipients pathologically diagnosed with FSGS by renal allograft biopsy were enrolled in this clinical trial. According to the detection of primary diseases of renal allografts and circulating permeability factors, 34 recipients were divided into the recurrent FSGS group (n=12) and de novo FSGS group (n=22). The differences of clinical indexes and the degree of pathological injury of renal allografts were compared between two groups. Results There was no significant difference in the mesangial hyperplasia score, glomerulosclerosis rate, renal tubular atrophy score, interstitial fibrosis score and podocyte proliferation rate between two groups (all P > 0.05). In the recurrent FSGS group, segmental glomerulosclerosis rate of the recipients was 0.10 (0.08, 0.27), lower than 0.19 (0.13, 0.33) in the de novo FSGS group (P < 0.05). No significant difference was found in the incidence of antibody-mediated rejection, drug-induced renal tubular injury and BK virus infection between two groups (all P > 0.05). The incidence of T cell-mediated rejection in the recurrent FSGS group was 17%, lower than 55% in the de novo FSGS group (P < 0.05). Immunohistochemical staining showed that the infiltrating inflammatory cells in the renal allografts were mainly T lymphocytes. The positive rates of C4d deposition in peripheral capillaries between the recurrent and de novo FSGS groups were 33% (4/12) and 32% (7/22), with no significant difference (P > 0.05). Immunofluorescence results revealed IgM deposition in the segmental glomerulosclerosis area of renal allografts in most cases. Electron microscopy showed extensive fusion or segmental distribution of podocytes in the glomerulus of renal allografts. Conclusions The degree of renal functional injury and the incidence of T cell-mediated rejection in the recurrent FSGS group are lower than those in the de novo FSGS group. Comprehensive analysis of preoperative and postoperative clinical manifestations, laboratory testing and pathological examination of kidney transplant recipients contribute to early diagnosis and treatment of recurrent and de novo FSGS.

OBJECTIVE: To establish a machine learning model based on extreme gradient boosting (XGBoost) algorithm for early prediction of severe acute pancreatitis (SAP), and explore its predictive efficiency. METHODS: A retrospective cohort study was conducted. The patients with acute pancreatitis (AP) who admitted to the First Affiliated Hospital of Soochow University, the Second Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University from January 1, 2020 to December 31, 2021 were enrolled. Demography information, etiology, past history, and clinical indicators and imaging data within 48 hours of admission were collected according to the medical record system and image system, and the modified CT severity index (MCTSI), Ranson score, bedside index for severity in acute pancreatitis (BISAP) and acute pancreatitis risk score (SABP) were calculated. The data sets of the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University were randomly divided into training set and validation set according to 8 : 2. Based on XGBoost algorithm, the SAP prediction model was constructed on the basis of hyperparameter adjustment by 5-fold cross validation and loss function. The data set of the Second Affiliated Hospital of Soochow University was served as independent test set. The predictive efficacy of the XGBoost model was evaluated by drawing the receiver operator characteristic curve (ROC curve), and compared it with the traditional AP related severity score; variable importance ranking diagram and Shapley additive explanation (SHAP) diagram were drawn to visually explain the model. RESULTS: A total of 1 183 AP patients were enrolled finally, of which 129 (10.9%) developed SAP. Among the patients from the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University, there were 786 patients in the training set and 197 in the validation set; 200 patients from the Second Affiliated Hospital of Soochow University were used as the test set. Analysis of all three datasets showed that patients who advanced to SAP exhibited pathological manifestation such as abnormal respiratory function, coagulation function, liver and kidney function, and lipid metabolism. Based on the XGBoost algorithm, an SAP prediction model was constructed, and ROC curve analysis showed that the accuracy for prediction of SAP reached 0.830, the area under the ROC curve (AUC) was 0.927, which was significantly improved compared with the traditional scoring systems including MCTSI, Ranson, BISAP and SABP, the accuracy was 0.610, 0.690, 0.763, 0.625, and the AUC was 0.689, 0.631, 0.875, and 0.770, respectively. The feature importance analysis based on the XGBoost model showed that the top ten items ranked by the importance of model features were admission pleural effusion (0.119), albumin (Alb, 0.049), triglycerides (TG, 0.036), Ca²⁺ (0.034), prothrombin time (PT, 0.031), systemic inflammatory response syndrome (SIRS, 0.031), C-reactive protein (CRP, 0.031), platelet count (PLT, 0.030), lactate dehydrogenase (LDH, 0.029), and alkaline phosphatase (ALP, 0.028). The above indicators were of great significance for the XGBoost model to predict SAP. The SHAP contribution analysis based on the XGBoost model showed that the risk of SAP increased significantly when patients had pleural effusion and decreased Alb. CONCLUSIONS A SAP prediction scoring system was established based on the machine automatic learning XGBoost algorithm, which can predict the SAP risk of patients within 48 hours of admission with good accuracy.
Humans ; Pancreatitis ; Acute Disease ; Retrospective Studies ; Hospitalization ; Algorithms