Objective To analyze chemical constituents of compound Maxing Shigan decoction by ultra-high perfor-mance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS). Methods The separation was performed on a UPLC BEH C₁₈ column (2.1 mm×100 mm, 2.5 µm)，with a gradient elution applying 0.1% aqueous formic acid solution and 0.1% formic acid acetonitrile as a mobile phase. The column temperature was 40 °C. The flow rate was 0.4 ml/min and the analysis time was 15 min. Mass spectrometry (MS) data were collected in both positive and negative ESI ion modes. Results Through UPLC-QTOF/MS analysis and reference validation, a total of 59 chemical components in Maxing Shigan decoction were identified. Conclusion An ultra-high performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) method was established to identify the chemical components of Maxing Shigan decoction. This method is simple, efficient, sensitive and accurate, and provides a basis for the elucidation of the pharmacodynamic material basis and mechanism of Maxing Shigan decoction. It can provide data reference for the optimization of the compatibility of traditional Chinese medicine in the treatment of COVID-19.

Objective:To develop a deep learning-based system for real-time recognition and classification of portal hypertensive gastropathy (PHG) and evaluate its ability to assist junior endoscopists.Methods:A total of 2 848 gastroscopy images from 832 patients with liver cirrhosis were selected from Digestive Endoscopy Center databases of Renmin Hospital of Wuhan University, Wuhan Hospital of Traditional Chinese and Western Medicine, and the Second Hospital of Jingzhou from January 2015 to October 2023. This system referred to 3 endoscopic features of Baveno Ⅱ scoring system. Three models were developed respectively for gastric antral vascular ectasia (GAVE), mosaic-like pattern (MLP), and red marks (RM). The specific classification references were as follows: (1) GAVE model: 0 no, 1 yes; (2) MLP model: 0 no, 1 mild, 2 severe; (3) RM model: 0 no, 1 isolated, 2 fused. The classification results for endoscopic characteristics of PHG of 3 endoscopy experts were taken as the gold standard. The yolov8-m model was used for training. The training dataset, validation dataset, and test dataset were allocated at a ratio of 8∶1∶1. The test dataset was used to evaluate the performance of models and their auxiliary effects on endoscopists. The accuracy, recall, precision, specificity and Kappa coefficient were calculated. Results:The accuracy, recall, specificity of GAVE model were 96.0% (48/50), 87.5% (7/8) and 97.6% (41/42). There was no significant difference between its accuracy and the gold standard ( χ2=316.226, P=1.000). The precision of GAVE1 and GAVE0 were 87.5% (7/8) and 97.6% (41/42) respectively. The accuracy of MLP model was 84.1% (132/157), and there was no significant difference compared with the gold standard ( χ2=3.286, P=0.193). The precision and recall of MLP2 were 88.2% (15/17) and 75.0% (15/20). The precision and recall of MLP1 were 77.9% (60/77) and 88.2% (60/68). The precision and recall of MLP0 were 90.5% (57/63) and 82.6% (57/69). The accuracy of RM model was 87.9% (123/140), and there was no significant difference compared with the gold standard ( χ2=2.891, P=0.409). The precision and recall of RM2 were 94.7% (18/19) and 78.3% (18/23). The precision and recall of RM1 were 72.2% (26/36) and 81.3% (26/32). The precision and recall of RM0 were 92.9% (79/85) and 92.9% (79/85). The mean accuracy of the three junior endoscopists, with and without the assistance of the GAVE model, MLP model, and RM model, respectively increased from 95.3% to 99.3%, from 83.9% to 91.9%, and from 81.9% to 83.1%. The overall consistency analysis of the 3 junior endoscopists with the gold standard indicated that the consistency of the GAVE model before and after assistance was extremely strong (both an overall Kappa of 1.000); the consistency before assistance of the MLP model was moderate (with an overall Kappa of 0.601), which increased to extremely strong after assistance (with an overall Kappa of 0.964); and the consistency of the RM model before and after assistance was also relatively strong (with an overall Kappa of 0.792 before and 0.798 after). Conclusion:The deep learning system accurately identifies and classifies PHG features and significantly enhances diagnostic performance of junior endoscopists.

Objective:To develop a deep learning-based system for real-time recognition and classification of portal hypertensive gastropathy (PHG) and evaluate its ability to assist junior endoscopists.Methods:A total of 2 848 gastroscopy images from 832 patients with liver cirrhosis were selected from Digestive Endoscopy Center databases of Renmin Hospital of Wuhan University, Wuhan Hospital of Traditional Chinese and Western Medicine, and the Second Hospital of Jingzhou from January 2015 to October 2023. This system referred to 3 endoscopic features of Baveno Ⅱ scoring system. Three models were developed respectively for gastric antral vascular ectasia (GAVE), mosaic-like pattern (MLP), and red marks (RM). The specific classification references were as follows: (1) GAVE model: 0 no, 1 yes; (2) MLP model: 0 no, 1 mild, 2 severe; (3) RM model: 0 no, 1 isolated, 2 fused. The classification results for endoscopic characteristics of PHG of 3 endoscopy experts were taken as the gold standard. The yolov8-m model was used for training. The training dataset, validation dataset, and test dataset were allocated at a ratio of 8∶1∶1. The test dataset was used to evaluate the performance of models and their auxiliary effects on endoscopists. The accuracy, recall, precision, specificity and Kappa coefficient were calculated. Results:The accuracy, recall, specificity of GAVE model were 96.0% (48/50), 87.5% (7/8) and 97.6% (41/42). There was no significant difference between its accuracy and the gold standard ( χ2=316.226, P=1.000). The precision of GAVE1 and GAVE0 were 87.5% (7/8) and 97.6% (41/42) respectively. The accuracy of MLP model was 84.1% (132/157), and there was no significant difference compared with the gold standard ( χ2=3.286, P=0.193). The precision and recall of MLP2 were 88.2% (15/17) and 75.0% (15/20). The precision and recall of MLP1 were 77.9% (60/77) and 88.2% (60/68). The precision and recall of MLP0 were 90.5% (57/63) and 82.6% (57/69). The accuracy of RM model was 87.9% (123/140), and there was no significant difference compared with the gold standard ( χ2=2.891, P=0.409). The precision and recall of RM2 were 94.7% (18/19) and 78.3% (18/23). The precision and recall of RM1 were 72.2% (26/36) and 81.3% (26/32). The precision and recall of RM0 were 92.9% (79/85) and 92.9% (79/85). The mean accuracy of the three junior endoscopists, with and without the assistance of the GAVE model, MLP model, and RM model, respectively increased from 95.3% to 99.3%, from 83.9% to 91.9%, and from 81.9% to 83.1%. The overall consistency analysis of the 3 junior endoscopists with the gold standard indicated that the consistency of the GAVE model before and after assistance was extremely strong (both an overall Kappa of 1.000); the consistency before assistance of the MLP model was moderate (with an overall Kappa of 0.601), which increased to extremely strong after assistance (with an overall Kappa of 0.964); and the consistency of the RM model before and after assistance was also relatively strong (with an overall Kappa of 0.792 before and 0.798 after). Conclusion:The deep learning system accurately identifies and classifies PHG features and significantly enhances diagnostic performance of junior endoscopists.

Objective To present our single-center experience in treating traumatic direct carotidcavemous fistulas (TDCCFs)by using detachable balloon,coil and Willis covered stent via arterial route.Methods During the last five years,transarterial endovascular treatment by using detachable balloon,coil and Willis covered stent was performed in fifty-one consecutive patients of traumatic direct carotid-cavernous fistulas.with a total of 54 TDCCFs.The detachable balloon was the material of first choice,while Willis covered stents and coils were regarded as the back-up materials.A clinical and angiographic follow-up for 348 months (mean 20.8 months) was conducted to evaluate the arterial patency and the stability of embolization.The clinical data were retrospectively analyzed.Results By using the detachable balloon alone via transarterial route.85%TDCCFs were successfully treated with good preservation of ICA.A total of 98% TDCCFs in this study were successfully treated by using detachable balloon,coil and/or Willis coveted stent,the fistulas became occluded,and ICAs were preserved except one patient.Forty TDCCFs were treated with detachable balloons alone,two TDCCFs with the Willis covered stent alone,and one DCCF with coils alone.Eight TDCCFs were treated with detachable balloons together with Willis covered stent.Of these eight TDCCFs,two were treated with a single session,three were treated with detachable halloons in combination with coils,and one had to receive Willis covered stent.Second and third times of endovascular treatment were needed in 12 TDCCFs.The TDCCF-rel(at)ed symptoms were gradually relived or improved within 1 day to 6 months after treatment,except for five patients who suffered from ipsilateral moderate visual loss or cranial nerve deficit.No perioperative complications.such as vessel rupture,distal embolization or new neurologic deficits,occurred.During the follow-up period lasting for six months,neither delayed neurologic or vascular complications nor recurrence of the lesions developed.Conclusion Via the transarterial route,using detachable balloon to occlude the fistula and at the same time to preserve ICA remains the optimal treatment for TDCCFs.When the standard treatment fails.various coils and the Willis covered stents can be used as an effective alternative or remedial tool in the treatment of TDCCFs,which can preserve ICA.Willis covered stent deployment seems to be an effective,safe,feasible and economical endovasculal treatment for TDCCF,but more clinical studies are needed before we can further clarify its specifications and indications.