Objective To predict the core targets and action pathways of Hedysari Radix based on UPLC-MS/MS and network pharmacology methods,and to verify the results of network pharmacology by molecular docking and molecular dynamics techniques.This article aims to investigate immune regulation mechanism of effective components absorbed into blood from Hedysari Radix.Methods Qualitative quantification of effective components absorbed into blood from Hedysari Radix were operated by using UPLC-MS/MS technique.The corresponding targets of effective components absorbed into blood from Hedysari Radix were screened by TCMSP and HERB databases.Targets of immune-related disease were obtained through DisGeNET,OMIM,TTD,and MalaCards databases.The network of"components absorbed into blood from Hedysari Radix-immune-related diseases"was then constructed.GO and KEGG enrichment analysis and mapped the PPI network were performed.Molecular docking and molecular dynamics techniques were applied for validation.Results A total of 8 prototype components absorbed into blood,synergistically acting on 101 targets,were identified by UPLC-MS/MS.They mediated 538 biological processes including immune response,positive regulation of gene expression,receptor binding,and cytokine activity.Meanuhile,116 signaling pathways,such as HIF-1,Toll-like receptor,JAK-STAT,T cell receptor,PI3K-Akt,and FoxO etc.were involved.The core targets were MAPK14,PTGS2,MMP9,PPARG,CCND1,etc..The results of molecular docking showed that formononetin and calycosin had strong docking binding activity with MAPK14.And molecular dynamics simulations further demonstrated that the binding between MAPK14 and formononetin or calycosin had good structural stability and binding affinity.Conclusion The results of serum pharmacochemistry,network pharmacology and molecular dynamics were verified to reveal the material basis and mechanism of Hedysari Radix in regulating immunity.The aim of this study is to provide scientific basis for its immunomodulatory mechanism.

To explore how the joint-teaching mode of tomographic-radiologic anatomy and topographic anatomy in a way of case-based learning (CBL) promotes the education of medical students.

Objective To investigate the clearance capacity of cardiac resident macrophages in post-sepsis and its underlying mechanism.Methods A mouse model of sepsis was established using cecum perforation ligation.Thirty male C57BL/6 mice(8 weeks old,weighing 20～25 g)were randomly and equally divided into a sham operation group(sham group)and a model group(sepsis group).Immunofluorescence assay was employed to label the cardiomyocytes and macrophages to observe the apoptosis of cardiomyocytes and the phagocytosis by cardiac resident macrophages.Cardiac resident macrophages were extracted for transcriptomic sequencing to determine the functional changes of the cells after sepsis.Cardiac resident macrophage cell lines were established at the cellular level and served as the normal group(RAC group),and the RAC cells treated with LPS were subjected as the sepsis group(RAC+LPS group).Then the differences in the ability to clear apoptotic cardiomyocytes between the 2 groups were observed.Then DQ-BSA-RED lysosomal activity detection probe,Lyso-Sensor yellow/bule dye,ELISA,and Western blotting were applied to detect the lysosomal function of cardiac resident macrophages,activity and expression of important lysosomal hydrolases,changes in contents and related subunits of vacuolar-type adenosine triphosphatases(V-ATPase).Results Compared with the sham group,the sepsis group had larger number of apoptotic cardiomyocytes(P＜0.05)and increased phagocytosis of cardiomyocytes by cardiac macrophages(P＜0.05).The results of transcriptomic sequencing revealed a significant dysfunction of lysosome-associated functions of cardiac-resident macrophages after sepsis.In in vitro experiments,the RAC+LPS group had a reduced fragmentation capacity of apoptotic cardiomyocytes,reduction in the intensity of yellow fluorescence of lysosomes(P＜0.05),and decrease in lysosomal hydrolase activity(P＜0.05)when compared with the RAC group.In addition,LPS treatment significantly decreased the activity and expression of V-ATPase and its major subunit ATP6V1A in cardiac resident macrophages(P＜0.05).Conclusion Cardiac resident macrophages show reduced clearance of apoptotic cardiomyocytes after sepsis,which may be related to a decrease in the activity of ATP6V1A,an important subunit of its lysosomal V-ATPase,and reduced activity of lysosomal hydrolases.

Objective:To investigate the value of preoperative prediction of Ki-67 expression status in breast cancer based on multi-phase enhanced MRI combined with clinical imaging characteristics prediction model.Methods:This study was retrospective. A total of 213 breast cancer patients who underwent surgical treatment at Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College between June 2016 and May 2017 were enrolled. All patients were female, aged 24-78 (51±10) years, and underwent routine breast MRI within 2 weeks prior to surgery. According to the different Ki-67 expression of postoperative pathological results, patients were divided into high expression group (Ki-67≥20%, 153 cases) and low expression group (Ki-67<20%, 60 cases). The radiomic features of breast cancer lesions were extracted from phase 2 (CE-2) and phase 7 (CE-7) images of dynamic contrast enhanced (DCE)-MRI, and all cases were divided into training and test sets according to the ratio of 7∶3. The radiomic features were first selected using ANOVA and Wilcoxon signed-rank test, followed by the least absolute shrinkage and selection operator method regression model. The same method of parameters selection was applied to clinical information and conventional imaging features [including gland classification, degree of background parenchymal enhancement, multifocal/multicentric, lesion location, lesion morphology, lesion long diameter, lesion short diameter, T 2WI signal characteristics, diffusion-weighted imaging (DWI) signal characteristics, apparent diffusion coefficient (ADC) values, time-signal intensity curve type, and axillary lymph nodes larger than 1 cm in short axis]. Support vector machine (SVM) was then used to construct prediction models for Ki-67 high and low expression states. The predictive performance of the models were evaluated using receiver operating characteristic (ROC) curves and area under cueve(AUC). Results:Totally 1 029 radiomic features were extracted from CE-2 and CE-7 images, respectively, and 9 and 7 best features were obtained after selection, respectively. And combining the two sets of features for a total of 16 features constituted the CE-2+CE-7 image best features. Five valuable parameters including lesion location, lesion short diameter, DWI signal characteristics, ADC values, and axillary lymph nodes larger than 1 cm in short axis, were selected from all clinical image features. The SVM prediction models obtained from the radiomic features of CE-2 and CE-7 images had a high AUC in predicting Ki-67 expression status (>0.70) in both the training set and the test set. The models were constructed by combining the CE-2, CE-7, and CE-2+CE-7 radiomic features with clinical imaging features, respectively, and the corresponding model performance in predicting Ki-67 expression status was improved compared with the models obtained by using the CE-2, CE-7, and CE-2+CE-7 radiomic features alone. The SVM prediction model obtained from CE-2+CE-7 radiomic features combined with clinical imaging features had the best prediction performance, with AUC of 0.895, accuracy of 84.6%, sensitivity of 87.9%, and specificity of 76.2% for predicting Ki-67 expression status in the training set and AUC of 0.822, accuracy of 70.3%, sensitivity of 76.1%, and specificity of 55.6% in test sets.Conclusion:The SVM prediction model based on DCE-MRI radiomic features can effectively predict Ki-67 expression status, and the combination of radiomic features and clinical imaging features can further improve the model prediction performance.

Objective:To explore the value of MRI features in predicting the pathological grade of rectal neuroendocrine tumors and to develop a predicting model.Methods:A retrospective analysis was performed on 30 cases of rectal neuroendocrine tumors confirmed by surgery and pathology between 2013 and 2019. All of them underwent plain rectal MRI, DWI and dynamic contrast-enhanced MRI. The clinical features and MRI characteristics (ie. tumor location, maximum tumor diameter, boundary, growth pattern, enhancement of three-staged lesions, and the lymph node metastasis) were analyzed by statistical methods to evaluate the difference between different tumor pathologic grades (G1, G2 and G3). Characteristics with statistical significance were analyzed by collinearity diagnostics, and stepwise regression method was used to select independent predictors. Ordinal logistic regression analysis was then conducted to develop the predicting model.Results:Maximum tumor diameter, tumor boundary, growth pattern, mr-T, mr-N, EMVI, MRF, T2WI signal intensity, tumor enhancement degree in venous phase and distant metastasis were closely correlated with the pathological grade of rectal neuroendocrine tumors ( P<0.001, 0.001, 0.001, <0.001, 0.001, 0.004, 0.024, 0.015, 0.001, and <0.001, respectively). The mr-T and tumor enhancement degree in venous phase were identified as the independent predictors to construct the prediction model. The model got ideal performance in predicting the grades, with the areas under the receiver operating characteristic (ROC) curves (AUCs) of 0.945, 0.624 and 0.896, the sensitivities were 75.0%, 85.7%, and 90.9% and corresponding specificities were 88.9%, 52.6% and 93.3% for G1, G2 and G3 rectal neuroendocrine tumors, respectively. Conclusion:The model based on mr-T and tumor enhancement degree in venous phase can serve as a clinical tool for predicting the pathological grade of rectal neuroendocrine tumors.

Objective:To explore the value of MRI features in predicting the pathological grade of rectal neuroendocrine tumors and to develop a predicting model.Methods:A retrospective analysis was performed on 30 cases of rectal neuroendocrine tumors confirmed by surgery and pathology between 2013 and 2019. All of them underwent plain rectal MRI, DWI and dynamic contrast-enhanced MRI. The clinical features and MRI characteristics (ie. tumor location, maximum tumor diameter, boundary, growth pattern, enhancement of three-staged lesions, and the lymph node metastasis) were analyzed by statistical methods to evaluate the difference between different tumor pathologic grades (G1, G2 and G3). Characteristics with statistical significance were analyzed by collinearity diagnostics, and stepwise regression method was used to select independent predictors. Ordinal logistic regression analysis was then conducted to develop the predicting model.Results:Maximum tumor diameter, tumor boundary, growth pattern, mr-T, mr-N, EMVI, MRF, T2WI signal intensity, tumor enhancement degree in venous phase and distant metastasis were closely correlated with the pathological grade of rectal neuroendocrine tumors ( P<0.001, 0.001, 0.001, <0.001, 0.001, 0.004, 0.024, 0.015, 0.001, and <0.001, respectively). The mr-T and tumor enhancement degree in venous phase were identified as the independent predictors to construct the prediction model. The model got ideal performance in predicting the grades, with the areas under the receiver operating characteristic (ROC) curves (AUCs) of 0.945, 0.624 and 0.896, the sensitivities were 75.0%, 85.7%, and 90.9% and corresponding specificities were 88.9%, 52.6% and 93.3% for G1, G2 and G3 rectal neuroendocrine tumors, respectively. Conclusion:The model based on mr-T and tumor enhancement degree in venous phase can serve as a clinical tool for predicting the pathological grade of rectal neuroendocrine tumors.

Dysregulation of microRNAs (miRNAs) is involved in abnormal development and pathophysiology in the brain. Although miR-20b plays essential roles in various human diseases, its function in cerebral ischemic stroke remains unclear. A cell model of oxygen glucose deprivation/reoxygenation (OGD/R) and A rat model of middle cerebral artery occlusion/reperfusion (MCAO/R) were constructed. qRT-PCR and western blot were used to evaluate the expression of miR-20b and TXNIP. Cell viability was detected by MTT assay, and cell apoptosis was evaluated by flow cytometry. Targetscan and Starbase were used to predict the potential targets of miR-20b. Luciferase reporter assay was applied to determine the interaction between miR-20b and TXNIP. Rescue experiments were conducted to confirm the functions of miR-20b/TXNIP axis in cerebral ischemic stroke. MiR-20b was significantly downregulated after I/R both in vitro and in vivo. Upregulation of miR-20b inhibited OGD/R-induced neurons apoptosis and attenuated ischemic brain injury in rat model. Bioinformatic prediction suggested that TXNIP might be a target of miR-20b, and luciferase reporter assay revealed that miR-20b negatively regulated TXNIP expression by directly binding to the 3’-UTR of TXNIP. Downregulation of TXNIP inhibited OGD/R-induced neurons apoptosis in vitro and ischemic brain injury in vivo. Rescue experiments indicated that downregulation of TXNIP effectively reversed the effect of miR-20b inhibitor in neurons apoptosis after OGD/R-treatment and ischemic brain injury in a mouse model after MCAO/R-treatment. Our study demonstrated that upregulation of miR-20b protected the brain from ischemic brain injury by targeting TXNIP, extending our understanding of miRNAs in cerebral ischemic stroke.

Dysregulation of microRNAs (miRNAs) is involved in abnormal development and pathophysiology in the brain. Although miR-20b plays essential roles in various human diseases, its function in cerebral ischemic stroke remains unclear. A cell model of oxygen glucose deprivation/reoxygenation (OGD/R) and A rat model of middle cerebral artery occlusion/reperfusion (MCAO/R) were constructed. qRT-PCR and western blot were used to evaluate the expression of miR-20b and TXNIP. Cell viability was detected by MTT assay, and cell apoptosis was evaluated by flow cytometry. Targetscan and Starbase were used to predict the potential targets of miR-20b. Luciferase reporter assay was applied to determine the interaction between miR-20b and TXNIP. Rescue experiments were conducted to confirm the functions of miR-20b/TXNIP axis in cerebral ischemic stroke. MiR-20b was significantly downregulated after I/R both in vitro and in vivo. Upregulation of miR-20b inhibited OGD/R-induced neurons apoptosis and attenuated ischemic brain injury in rat model. Bioinformatic prediction suggested that TXNIP might be a target of miR-20b, and luciferase reporter assay revealed that miR-20b negatively regulated TXNIP expression by directly binding to the 3’-UTR of TXNIP. Downregulation of TXNIP inhibited OGD/R-induced neurons apoptosis in vitro and ischemic brain injury in vivo. Rescue experiments indicated that downregulation of TXNIP effectively reversed the effect of miR-20b inhibitor in neurons apoptosis after OGD/R-treatment and ischemic brain injury in a mouse model after MCAO/R-treatment. Our study demonstrated that upregulation of miR-20b protected the brain from ischemic brain injury by targeting TXNIP, extending our understanding of miRNAs in cerebral ischemic stroke.

Primary biliary cholangitis (PBC) is an autoimmune liver disease manifesting as cholestasis and is often observed in the middle-aged and elderly women. About 50% of the patients have fatigue and itching, and 20% have depression or mood changes. In recent years, a number of studies have shown that the non-specific symptoms of patients with primary biliary cholangitis (PBC), such as fatigue, itching, and cognitive changes, are associated with the structural and functional changes of the central nervous system. Early identification of preclinical PBC patients through brain imaging changes may be one of the ways for the early diagnosis of this disease.

Objective:To explore the diagnostic accuracy improved by magnetic resonance imaging (MRI) biomarkers for lymph node metastasis in T1-2 stage rectal cancer before treatment.Methods:Medical records of 327 patients with T1-2 rectal cancer who underwent pretreatment MRI and rectal tumor resection between January 2015 and November 2019 were retrospectively analyzed. Fifty-seven cases were divided into the lymph node metastasis group (N+ group) while other 270 cases in the non-lymph node metastasis group (N-group) according to the pathologic diagnosis. Two radiologist evaluated the tumor characteristics of MRI images. The relationship of the clinical and imaging characteristics of lymph node metastasis was assessed by using univariate analysis and multivariable logistic regression analysis. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic abilities for the differentiation of N- from N+ tumors.Results:Among the 327 patients, MR-N evaluation was positive in 67 cases, which was statistically different from the pathological diagnosis ( P<0.001). The sensitivity, specificity and accuracy of MRI for lymph node metastasis were 45.6%, 84.8% and 78.0%, respectively. Multivariate regression analysis showed that tumor morphology ( P=0.002), including mucus or not ( P<0.001), and MR-N evaluation ( P<0.001) were independent influencing factors for stage T1-2 rectal cancer with lymph node metastasis. The area under the ROC curve of rectal cancer with lymph node metastasis analyzed by the logistic regression model was 0.786 (95% CI: 0.720~0.852). Conclusions:Tumor morphology, including mucus or not, and MR-N evaluation can serve as independent biomarkers for differentiation of N- and N+ tumors. The model combined with these biomarkers facilitates to improve the diagnostic accuracy of lymph node metastasis in T1-2 rectal cancers by using MRI.