Sini Decoction (SNT) is a traditional formula recognized for its efficacy in warming the spleen and stomach and dispersing cold. However, elucidating the mechanism of action of SNT remains challenging due to its complex multiple components. This study utilized a synergistic approach combining two-dimensional fluorescence difference in gel electrophoresis (2D-DIGE)-based drug affinity responsive target stability (DARTS) with label-free quantitative proteomics techniques to identify the direct and indirect protein targets of SNT in myocardial infarction. The analysis identified 590 proteins, with 30 proteins showing significant upregulation and 51 proteins showing downregulation when comparing the SNT group with the model group. Through the integration of 2D-DIGE DARTS with proteomics data and pharmacological assessments, the findings indicate that protein disulfide-isomerase A3 (PDIA3) may serve as a potential protein target through which SNT provides protective effects on myocardial cells during myocardial infarction.
Myocardial Infarction/genetics* ; Proteomics/methods* ; Drugs, Chinese Herbal/chemistry* ; Animals ; Protein Disulfide-Isomerases/genetics* ; Male ; Two-Dimensional Difference Gel Electrophoresis/methods* ; Humans ; Rats ; Rats, Sprague-Dawley ; Electrophoresis, Gel, Two-Dimensional

Objective To discuss the application of enhanced MRI-based radiomics nomogram in predicting the efficacy of initial transcatheter arterial chemoembolization(TACE)in patients with intermediate to advanced hepatocellular carcinoma(HCC).Methods A total of 195 patients with advanced HCC(CNLC Ⅱ b-Ⅲb),who received initial TACE at the Affiliated Fifth Hospital of Wenzhou Medical University(Center 1)from January 2019 to March 2024,at the Lishui Municipal People's Hospital(Center 2)from July 2021 to June 2023,and at the Rui'an Municipal People's Hospital(Center 3)from January 2022 to January 2024,were enrolled in this study.A total of 134 patients from Center 1 were randomly divided into a training set(n=94)and an internal validation set(n=40)at a 7∶3 ratio;and other 61 patients from Center 2 and Center 3 were selected as the external validation set.Based on the modified Response Evaluation Criteria in Solid Tumors(mRECIST)criteria,the early efficacy of the initial TACE procedure was evaluated.The patients were divided into an effective group and an ineffective group.The tumor contours were delineated on the arterial,portal,and equilibrium phase images of enhanced MRI,and the corresponding radiomics features were extracted.Based on reduced-dimensional features,the Logistic regression,support vector machine,lightweight gradient boosting machine,and multi-layer perceptron models were established.Univariate analysis and multivariate logistic regression analysis were used to screen independent predictive factors,and a nomogram was established in conjunction with the optimal radiomics score.The area under the receiver operating characteristic curve(AUC)was used to evaluate the performance of the model,and decision curve analysis was adopted to calculate the net benefits.Results After screening,9 key radiomics features were obtained.The lightweight gradient boosting machine model showed good prediction performance.The AUCs of the training set,internal validation set,and external validation set were 0.909,0.836 and 0.783 respectively,which was selected as the optimal radiomics model.The nomogram constructed based on AFP level,peritumoral enhancement,and optimal radiomics score could further improve its performance,with AUC values of 0.962,0.890 and 0.821 in the training set,internal validation set,and external validation set respectively.Decision curve analysis showed that this model could bring higher net benefits to patients.Conclusion The nomogram constructed based on the enhanced MRI-based radiomics combined with AFP level and peritumoral enhancement can effectively predict the efficacy of the initial TACE in patients with intermediate to advanced HCC.

Objective:To investigate the application value of dual-energy computer tomo-graphy (CT) multi-parameter imaging in predicting the pathological grade of pancreatic ductal adeno-carcinoma (PDAC).Methods:The retrospective cohort study was conducted. The clinicopatholo-gical data of 147 patients with PDAC who were admitted to The Fifth Affiliated Hospital of Wenzhou Medical University from January 2017 to August 2023 were collected. There were 102 males and 45 females, aged (59±10)years. All patients underwent preoperative dual-energy CT examination and postoperative histopathological examination. The 147 patients were divided into a training set of 103 cases and a test set of 44 cases by stratified random sampling at a ratio of 7∶3. The training set was used to construct the prediction model, and the test set was used to verify the effectiveness of prediction model. Observation indicators: (1) analysis of factors affecting the pathological grade of PDAC patients in the training set; (2) construction and evaluation of the fusion prediction model for pathological grade of PDAC. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Univariate and multivariate analyses were conducted using the Logistic regression model. The performance of the model was evaluated by receiver operating characteristic (ROC) curve, and the area under the curve (AUC), accuracy, sensitivity and specificity were calculated. The Delong test was used to analyze the effec-tiveness of model. The calibration curve and decision curve of Hosmer-Lemeshow test were used to evaluate the consistency and clinical application value of the nomogram, respectively. Results:(1) Analysis of factors affecting the pathological grade of PDAC patients in the training set. Results of multivariate analysis showed that tumor cystic necrosis, vascular invasion, standardized iodine concentration (NIC) in venous phase, effective atomic number (Zeff) in venous phase, and energy spectrum curve slope (λ HU) in venous phase were all independent factors affecting the pathological grade of PDAC patients in the training set ( odds ratio=4.326, 3.887, 4.155, 5.389, 3.164, 95% confidence interval as 1.167-16.033, 1.111-13.592, 1.707-10.113, 1.284-22.613, 1.247-8.028, P<0.05). (2) Construction and evaluation of the fusion prediction model for pathological grade of PDAC. Accor-ding to the results of multivariate analysis, tumor cystic necrosis, vascular invasion, NIC in venous phase, Zeff in venous phase and λ HU in venous phase were all included to construct the clinical-imaging fusion prediction nomogram model. The AUC, accuracy, sensitivity and specificity of the fusion prediction model in the training set were 0.938 (95% confidence interval as 0.896-0.981), 87.38%, 89.74% and 85.94%, respectively. The above indicators of the fusion prediction model in the test set were 0.893 (95% confidence interval as 0.802-0.985), 84.09%, 82.35% and 85.19%, respectively. Results of Delong test showed that there was no significant difference in AUC between the training set and the test set ( Z=0.343, P>0.05). Results of Hosmer-Lemeshow test showed that the fusion prediction model had a good fit in the training set and the test set ( χ2=3.042, 7.545, P>0.05). Results of calibration curve showed that the predictive ability of the fusion prediction model was good. Conclusions:Multiple parameters in venous phase of the dual-energy CT can be used as imaging markers for preoperative evaluation of the pathological grade of patients with PDAC. Establishing a clinical-imaging fusion prediction model can effectively predict the pathological grade of PDAC.

Cardiovascular disease is the leading cause of death worldwide,with atherosclerosis(AS)-its core pathological manifestation-representing a multifactorial-driven chronic inflammatory disorder.The pathogenesis of AS involves intricate pathological mechanisms including dyslipidemia,inflammatory cascades,and plaque vulnerability,whose complexity necessitates animal models capable of accurately recapitulating specific pathological features.Genetically engineered murine models have emerged as pivotal tools for deciphering AS mechanisms,owing to their genetic manipulability,phenotypic traceability,and molecular conservation with human pathophysiology.This review provides a systematic overview of current methodologies for establishing AS mouse models,with particular emphasis on evaluating the pathological fidelity of dietary induction approaches,genetic modification strategies[notably apolipoprotein E(ApoE)-/-and low density lipoproteins receptor(LDLr)-/-models],and physical injury paradigms.

Cardiovascular disease is the leading cause of death worldwide,with atherosclerosis(AS)-its core pathological manifestation-representing a multifactorial-driven chronic inflammatory disorder.The pathogenesis of AS involves intricate pathological mechanisms including dyslipidemia,inflammatory cascades,and plaque vulnerability,whose complexity necessitates animal models capable of accurately recapitulating specific pathological features.Genetically engineered murine models have emerged as pivotal tools for deciphering AS mechanisms,owing to their genetic manipulability,phenotypic traceability,and molecular conservation with human pathophysiology.This review provides a systematic overview of current methodologies for establishing AS mouse models,with particular emphasis on evaluating the pathological fidelity of dietary induction approaches,genetic modification strategies[notably apolipoprotein E(ApoE)-/-and low density lipoproteins receptor(LDLr)-/-models],and physical injury paradigms.

Objective:To investigate the application value of dual-energy computer tomo-graphy (CT) multi-parameter imaging in predicting the pathological grade of pancreatic ductal adeno-carcinoma (PDAC).Methods:The retrospective cohort study was conducted. The clinicopatholo-gical data of 147 patients with PDAC who were admitted to The Fifth Affiliated Hospital of Wenzhou Medical University from January 2017 to August 2023 were collected. There were 102 males and 45 females, aged (59±10)years. All patients underwent preoperative dual-energy CT examination and postoperative histopathological examination. The 147 patients were divided into a training set of 103 cases and a test set of 44 cases by stratified random sampling at a ratio of 7∶3. The training set was used to construct the prediction model, and the test set was used to verify the effectiveness of prediction model. Observation indicators: (1) analysis of factors affecting the pathological grade of PDAC patients in the training set; (2) construction and evaluation of the fusion prediction model for pathological grade of PDAC. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Univariate and multivariate analyses were conducted using the Logistic regression model. The performance of the model was evaluated by receiver operating characteristic (ROC) curve, and the area under the curve (AUC), accuracy, sensitivity and specificity were calculated. The Delong test was used to analyze the effec-tiveness of model. The calibration curve and decision curve of Hosmer-Lemeshow test were used to evaluate the consistency and clinical application value of the nomogram, respectively. Results:(1) Analysis of factors affecting the pathological grade of PDAC patients in the training set. Results of multivariate analysis showed that tumor cystic necrosis, vascular invasion, standardized iodine concentration (NIC) in venous phase, effective atomic number (Zeff) in venous phase, and energy spectrum curve slope (λ HU) in venous phase were all independent factors affecting the pathological grade of PDAC patients in the training set ( odds ratio=4.326, 3.887, 4.155, 5.389, 3.164, 95% confidence interval as 1.167-16.033, 1.111-13.592, 1.707-10.113, 1.284-22.613, 1.247-8.028, P<0.05). (2) Construction and evaluation of the fusion prediction model for pathological grade of PDAC. Accor-ding to the results of multivariate analysis, tumor cystic necrosis, vascular invasion, NIC in venous phase, Zeff in venous phase and λ HU in venous phase were all included to construct the clinical-imaging fusion prediction nomogram model. The AUC, accuracy, sensitivity and specificity of the fusion prediction model in the training set were 0.938 (95% confidence interval as 0.896-0.981), 87.38%, 89.74% and 85.94%, respectively. The above indicators of the fusion prediction model in the test set were 0.893 (95% confidence interval as 0.802-0.985), 84.09%, 82.35% and 85.19%, respectively. Results of Delong test showed that there was no significant difference in AUC between the training set and the test set ( Z=0.343, P>0.05). Results of Hosmer-Lemeshow test showed that the fusion prediction model had a good fit in the training set and the test set ( χ2=3.042, 7.545, P>0.05). Results of calibration curve showed that the predictive ability of the fusion prediction model was good. Conclusions:Multiple parameters in venous phase of the dual-energy CT can be used as imaging markers for preoperative evaluation of the pathological grade of patients with PDAC. Establishing a clinical-imaging fusion prediction model can effectively predict the pathological grade of PDAC.

Objective To explore the application value of the nomogram based on dual-energy CT in preoperative evaluation of human epidermal growth factor receptor 2(HER-2)status in patient with breast cancer.Methods A total of 269 patients with pathologically confirmed breast cancer were retrospectively collected and randomly divided into a training cohort(n=189)and a validation cohort(n=80)at a ratio of 7︰3.The dual-energy CT parameters and clinical features of all patients were measured and collected.Varia-bles with significant difference in univariate analysis were included in the multivariate logistic analysis to obtain independent risk fac-tors related to HER-2 status,with establishing a nomogram model.Receiver operating characteristic(ROC)curves were plotted to evaluate the predictive performance of the nomogram.Results There was a significant difference in axillary lymph node enlargement between the two groups(P<0.05).The venous phase iodine concentration(IC)and normalized iodine concentration(NIC)in the HER-2 positive group were significantly higher than those in the HER-2 negative group(P<0.05).Axillary lymph node enlargement,venous phase IC,and venous phase NIC were the independent risk factors for predicting HER-2 status in breast cancer.The nomogram con-structed from the above features exhibited good predictive performance,with area under the curve(AUC)of 0.856 and 0.834 in the training and validation cohorts,respectively.Conclusion The nomogram based on dual-energy CT has a high predictive value for HER-2 status in breast cancer patients.

Objective:To investigate the influencing factors of textbook outcomes in liver surgery (TOLS) after radical resection of gallbladder carcinoma.Methods:The retrospective case-control study was conducted. The clinicopathological data of 530 patients who underwent radical resection of gallbladder carcinoma in 15 medical centers, including the First Affiliated Hospital of Army Medical University et al, from January 2014 to January 2020 were collected. There were 209 males and 321 females, aged (61±10)years. Patients underwent radical resection of gallbladder carcinoma, including cholecystectomy, hepatectomy, invasive bile duct resection, and lymph node dissection. Observation indicators: (1) situations of TOLS; (2) influencing factors of TOLS. Measure-ment data with normal distribution were represented as Mean± SD, and comparison between groups was conducted using the independent sample t test. Measurement data with skewed distribution were represented as M( Q1, Q3), and comparison between groups was conducted using the Mann-Whitney U test. Count data were described as absolute numbers or percentages, and comparison between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the Mann-Whitney U test. The univariate analysis was conducted using the corresponding statistical methods based on data type, and variables with P<0.10 were included in multivariate analysis. Multivariate analysis was conducted using the Logistic stepwise regression model. Results:(1) Situations of TOLS. All 530 patients underwent radical resection of gallbladder carcinoma, and there were 498 cases achieving R 0 resection, 508 cases without ≥grade 2 intra-operative adverse events, 456 cases without postoperative grade B and grade C biliary leakage, 513 cases without postoperative grade B and grade C liver failure, 395 cases without severe com-plications within postoperative 90 days, 501 cases did not being re-admission caused by severe com-plications within postoperative 90 days. Of the 530 patients, 54.53%(289/530) of patients achieved postoperative TOLS, while 45.47%(241/530) of patients did not achieve postoperative TOLS. (2) Influencing factors of TOLS. Results of multivariate analysis showed that American Society of Anesthesiologists classification >grade Ⅱ, preoperative jaundice, T staging as T3?T4 stage, N staging as N2 stage, liver resection as right hemi-hepatectomy, and neoadjuvant therapy were independent factors influencing TOLS in patients undergoing radical resection of gallbladder carcinoma ( odds ratio=2.65, 1.87, 5.67, 5.65, 2.55, 3.34, 95% confidence interval as 1.22?5.72, 1.18?2.95, 2.51?12.82, 2.83?11.27, 1.41?4.63, 1.88?5.92, P<0.05). Conclusion:American Society of Anesthesiologists classification >grade Ⅱ, preoperative jaundice, T staging as T3?T4 stage, N staging as N2 stage, liver resection as right hemi-hepatectomy, and neoadjuvant therapy are independent factors influencing TOLS in patients undergoing radical resection of gallbladder carcinoma.

Objective:To calculate the relative biological effectiveness (RBE) value of the released low-energy electrons in gadolinium neutron capture therapy ( 157GdNCT) based on microdosimetry. Methods:The Monte Carlo (MC) code Geant4-DNA package was used to simulate the energy deposition distribution and microdosimetry parameters of low-energy electrons released during gadolinium neutron capture treatment in different sensitive target volumes and physical models on track structures. On this basis, RBE value was obtained based on the microdosimetry kinetic model (MKM).Results:The low-energy electron RBE value was highly variable in different sensitive target volumes and decreases with increasing sensitive target volumes. With 6-nm-diameter sensitive target as reference, RBE value was 1.77 for 6-nm diameter, 1.53 for 10 nm diameter with percentage difference 13%, and 1.40 for 15-nm diameter with percentage difference of 21%, respectively. The effect of different Geant4-DNA physical models on the RBE of low-energy electrons was small. Using the RBE value of 1.53 for physical model option2 as reference, the RBE values of option6 and option7 were 1.49 and 1.52, respectively, with the percentage differences of 2.6% and 0.6%, respectively.Conclusions:The RBE values of low energy electrons released by 157GdNCT in different sensitive target volumes and physical models were calculated by MKM to be 1.40-1.77.

Objective:To investigate the application value of dual-energy CT in the differential diagnosis of lung metastases and benign nodules in breast cancer.Methods:The data of 96 patients with pathology-confirmed breast cancer at the Fifth Affiliated Hospital of Wenzhou Medical University from March 2017 to June 2021 were analyzed retrospectively. All patients received dual-energy chest CT scans within 2 weeks before surgery. All 96 patients were female, aged 31-84 (56±12) years. A total of 207 pulmonary nodules from 96 patients were classified into 81 lung metastases and 126 benign nodules according to pathological findings. Conventional CT features [longest diameter, boundary, location and CT value difference between arterial and venous phases (ΔCT) of nodules] and dual-energy CT parameters [standardized iodine concentration (NIC), slope of energy spectrum (λ HU) and normalized effective atomic number (nZ eff) in arterial and venous phases] were analyzed and measured. The χ 2 test, independent samples t test and Kruskal-Wallis rank-sum test were used to analyze the differences of conventional CT features and dual-energy CT parameters between lung metastases and benign nodules. First, the least shrinkage and selection operator (LASSO) regression method was used to screen conventional CT features and dual-energy CT parameters, and then logistic regression analysis was performed to screen out independent risk factors for lung metastases. Receiver operating characteristic (ROC) curves were used to evaluate the efficacy of CT parameters alone and logistic model in differentiating lung metastases from benign lung nodules. Results:There were statistically significant differences between lung metastases and benign nodules in longest diameter, ?CT, NIC, λ HU and nZ eff in arterial and venous phases (all P<0.05). LASSO regression and binary logistic regression analysis showed that the venous phase λ HU (OR=59.413, 95%CI 14.233-248.002, P<0.001) and the venous phase nZ eff (OR=4.508, 95%CI 2.787-7.290, P<0.001) were independent risk factors for predicting lung metastases. Among them, the venous phase λ HU had the highest diagnostic efficiency, with an area under curve (AUC) of 0.794 and an accuracy of 74.88%. The AUC of the logistic model constructed by combining the venous phase λ HU and the venous phase nZ eff could reach 0.958, and the accuracy was improved to 92.27%, which was significantly higher than the efficacy of the two alone ( Z=6.02, 9.54, all P<0.001). Conclusion:Dual-energy CT has great application value in the identification of lung metastases and benign nodules in patients with breast cancer, especially when combined with venous phase λ HU and venous phase nZ eff, the diagnostic efficiency is further improved.