OBJECTIVE To investigate the effects and mechanism of Yigan huayu formula in alleviating liver fibrosis in mice. METHODS Mice were randomly divided into blank group （normal saline）， model group （normal saline）， Yigan huayu formula low- and high-dose groups （28.98， 57.96 g/kg， calculated by crude drug）， with 8 mice in each group. Except for the blank group， the liver fibrosis model was induced by intraperitoneal injection of 15%CCl 4 -olive oil solution. From the third week， the mice received the medicine/normal saline intragastrically， once a day， for 4 consecutive weeks. After the last medication， liver indexes were calculated， the activities of alanine aminotransferase （ALT） and aspartate aminotransferase （AST） in serum， as well as the hydroxyproline （HYP） content in liver tissue， were measured. Liver histopathology was evaluated. Differentially expressed proteins （DEPs） in liver tissue were analyzed based on proteomics， followed by bioinfo rmatics analysis. The expressions of core DEPs were validated using Western blot （WB） and immunohistochemistry （IHC） methods. RESULTS Compared with the blank group， the model group showed significantly elevated liver indexes， serum activities of ALT and AST， and hepatic HYP content （ P ＜0.05）， along with obvious pathological damage and collagen deposition. Compared with the model group， the above indexes of mice in the Yigan huayu formula high-dose group were decreased significantly （ P ＜0.05）， with marked improvement in liver pathological damage and collagen deposition. Proteomics identified 210 DEPs between the model group and Yigan huayu formula high-dose group. DEPs were significantly enriched in extracellular matrix （ECM）-receptor interaction and lipid metabolism pathways. WB and IHC confirmed that Yigan huayu formula could significantly inhibit the abnormally elevated expressions of collagen type Ⅳ alpha1 chain （COL4A1）， secreted protein acidic and rich in cysteine （SPARC）， vitronectin （VTN） and laminin subunit alpha5 （LAMA5） in liver tissue of mice （ P ＜0.05）. CONCLUSIONS Yigan huayu formula may exert anti-hepatic fibrosis effects by inhibiting the expressions of proteins such as COL4A1， LAMA5， SPARC， and VTN， thereby blocking the ECM-receptor interaction signaling pathway， and subsequently suppressing excessive ECM deposition and basement membrane remodeling.

Objective:To explore the feasibility of automatic target volume tracing in the Elekta Unity magnetic resonance imaging (MRI)-guided radiotherapy system and to further enhance the real-time target volume tracing performance of MRI-guided radiotherapy by introducing the deep learning technology based on a large Transformer model.Methods:A total of 4 661 frames of cine MRI binary images from 75 patients with malignant tumors in the chest/abdomen who were treated with MRI-guided radiotherapy were retrospectively collected as a training set. Another 500 frames of cine MRI binary images from 10 patients were collected as an independent test set. A module for medical image format conversion was developed to convert binary images into medical meta-images. The outer contours of tumor target volumes in the cine MRI images of the test set were manually delineated as actual control labels. With the first frame of the cine MRI images of each patient as the reference image and the other frames as motion images, a Transformer-based deep learning model was constructed to describe the deformable vector field (DVF) of motion images relative to the reference image. The Dice similarity coefficient (DSC), the 95% Hausdorff distance (HD 95), the negative Jacobian determinant (NegJ), and the average processing time per frame of cine MRI images were calculated. These values were compared to those of the conventional B-Spline scheme to quantitatively assess the target volume tracing accuracy, DVF physical plausibility, and execution efficiency of the Transformer-based deep learning model. Results:The Transformer-based deep learning model constructed in this study delivered superior target volume tracing performance, with improved DSC [(0.84 ± 0.05) vs. (0.74 ± 0.16), t = 11.44, P < 0.05] and HD 95 [(9.25 ± 2.98) vs. (14.70 ± 8.55) mm, t = -11.83, P < 0.05]. Furthermore, this model reduced the average image processing time from 1.95 s to 30.99 ms, enhancing the efficiency by two orders of magnitude. Besides, this model yielded NegJ similar to that of the B-Spline scheme. This suggests that the DVF extracted using this model had comparable physical plausibility with that obtained using the B-Spline scheme. Conclusions:The Transformer-based deep learning model for automatic target volume tracing fills the functional gap of the Elekta Unity MRI-guided radiotherapy system, facilitating relatively accurate, efficient automatic tracing of moving tumor targets in the chest and abdomen.

Objective:To explore the feasibility of automatic target volume tracing in the Elekta Unity magnetic resonance imaging (MRI)-guided radiotherapy system and to further enhance the real-time target volume tracing performance of MRI-guided radiotherapy by introducing the deep learning technology based on a large Transformer model.Methods:A total of 4 661 frames of cine MRI binary images from 75 patients with malignant tumors in the chest/abdomen who were treated with MRI-guided radiotherapy were retrospectively collected as a training set. Another 500 frames of cine MRI binary images from 10 patients were collected as an independent test set. A module for medical image format conversion was developed to convert binary images into medical meta-images. The outer contours of tumor target volumes in the cine MRI images of the test set were manually delineated as actual control labels. With the first frame of the cine MRI images of each patient as the reference image and the other frames as motion images, a Transformer-based deep learning model was constructed to describe the deformable vector field (DVF) of motion images relative to the reference image. The Dice similarity coefficient (DSC), the 95% Hausdorff distance (HD 95), the negative Jacobian determinant (NegJ), and the average processing time per frame of cine MRI images were calculated. These values were compared to those of the conventional B-Spline scheme to quantitatively assess the target volume tracing accuracy, DVF physical plausibility, and execution efficiency of the Transformer-based deep learning model. Results:The Transformer-based deep learning model constructed in this study delivered superior target volume tracing performance, with improved DSC [(0.84 ± 0.05) vs. (0.74 ± 0.16), t = 11.44, P < 0.05] and HD 95 [(9.25 ± 2.98) vs. (14.70 ± 8.55) mm, t = -11.83, P < 0.05]. Furthermore, this model reduced the average image processing time from 1.95 s to 30.99 ms, enhancing the efficiency by two orders of magnitude. Besides, this model yielded NegJ similar to that of the B-Spline scheme. This suggests that the DVF extracted using this model had comparable physical plausibility with that obtained using the B-Spline scheme. Conclusions:The Transformer-based deep learning model for automatic target volume tracing fills the functional gap of the Elekta Unity MRI-guided radiotherapy system, facilitating relatively accurate, efficient automatic tracing of moving tumor targets in the chest and abdomen.

Heart failure is a fatal stage of end-stage cardiovascular disease,which brings a huge medical burden to the society because of its high mortality and re-hospitalisation rates.Intestinal microecology is the largest and most com-plex microecosystem of human body.It is inhabited by tens of thousands of microorganisms in human gastrointestinal tract.In recent years,with the deepening of the study of intestinal flora,more and more studies have found that the im-balance of intestinal microecology can cause changes of metabolites in heart failure patients,which is one of the key triggers for the development of heart failure,therefore,using the intestinal microbial homeostasis as a new entry point for the treat-ment of heart failure will be a hotspot in medical research.However,the theory of Chinese medicine,"the spleen is the guardian",covers the physiological functions of the spleen,such as the spleen's main function of transporting,spleen's main function of ascending and clearing,and its main function of hiding camping,etc.,and the functions of intestinal flora and the"spleen is the guardian"are similar to a certain extent.Therefore,this paper starts from a holistic viewpoint and takes the theory of"spleen as the guardian"in Chinese medicine as an entry point to elaborate on the pathogenesis of intes-tinal microecological imbalance and heart failure,so as to provide a reference for Chinese medicine treatment or drug re-search.

Objective To investigate the clinical effect of magnesium sulfate combined with nifedipine in the treatment of patients with pregnancy induced hypertension( HDCP) .Methods Retrospective study was used in this study and 116 patients with HDCP from January 2013 to July 2015 in department of obstetrics and gynecology from our hospital were divided into two groups, including routine group of 62 patients who received routine treatment +magnesium sulfate) and combination group of 54 patients who received routine treatment +magnesium sulphate +nifedipine.The clinical effect was analyzed after five days’ continuous treatment.Results The systolic blood pressure, diastolic blood pressure,24h urinary protein, random urine protein /creatinine,serum homocysteine (Hcy) and CRP values in combination group were lower than routine group (P<0.05).There were no statistical difference in maternal uterine inertia, neonatal asphyxia, fetal distress, postpartum hemorrhage rate between the two groups after the treatment.But the rate of cesarean section in the combination group(50.00%)was significantly lower than that in the routine group(68.25%)(P <0.05).Conclusion Magnesium sulfate combined with nifedipine in the treatment of patients with HDCP had better antihypertensive effect, and would not increase fetal adverse birth outcomes incidence and significantly reduce the rate of cesarean section.