Background: Salvia miltiorrhiza Bunge, commonly known as “Danshen” in China due to the distinctive red color of its roots, is one of the most widely used traditional Chinese medicines. It is cultivated in various regions across China, and environmental differences among these regions can affect the secondary metabolites of plants, thereby influencing the quality of S. miltiorrhiza. In recent years, increasing demand for S. miltiorrhiza has exacerbated the problem of origin fraud. Therefore, ensuring the authenticity of its geographical origin is crucial for the sustainable development of the industry. Objective: The red coloration of S. miltiorrhiza is closely associated with the content of its primary active compounds, particularly tanshinones. Therefore, both its internal chemical composition and external color characteristics serve as key indicators for quality assessment. This study utilized hyperspectral imaging technology to evaluate its potential in classifying the geographical origin of S. miltiorrhiza. Methods: Spectral data reflecting the internal chemical properties of S. miltiorrhiza were integrated with color information representing its external features through 3 levels of data fusion. These fused datasets were then combined with deep learning algorithms to achieve accurate origin classification. Results: The results demonstrated that the Transformer model combined with soft-voting decision-level fusion achieved the highest classification accuracy of 98.72% by integrating image color and short-wave infrared spectral data. Conclusion: This study demonstrates that integrating hyperspectral imaging spectral data with color information provides a reliable and innovative approach for verifying the authenticity and traceability of S. miltiorrhiza.

Background:Salvia miltiorrhiza Bunge,commonly known as"Danshen"in China due to the distinctive red color of its roots,is one of the most widely used traditional Chinese medicines.It is cultivated in various regions across China,and environmental differences among these regions can affect the secondary metabolites of plants,thereby influencing the quality of S.miltiorrhiza.In recent years,increasing demand for S.miltiorrhiza has exacerbated the problem of origin fraud.Therefore,ensuring the authenticity of its geo-graphical origin is crucial for the sustainable development of the industry.Objective:The red coloration of S.miltiorrhiza is closely associated with the content of its primary active compounds,particularly tanshinones.Therefore,both its internal chemical composition and external color characteristics serve as key indicators for quality assessment.This study utilized hyperspectral imaging technology to evaluate its potential in classifying the geographical origin of S.miltiorrhiza.Methods:Spectral data reflecting the internal chemical properties of S.miltiorrhiza were integrated with color information represent-ing its external features through 3 levels of data fusion.These fused datasets were then combined with deep learning algorithms to achieve accurate origin classification.Results:The results demonstrated that the Transformer model combined with soft-voting decision-level fusion achieved the highest classification accuracy of 98.72％by integrating image color and short-wave infrared spectral data.Conclusion:This study demonstrates that integrating hyperspectral imaging spectral data with color information provides a reliable and innovative approach for verifying the authenticity and traceability of S.miltiorrhiza.

Objective To analyze the risk factors of ultrasound-guided percutaneous transluminal angioplasty(PTA)for treating thrombotic occlusion of autogenous arteriovenous fistula(AVF).Methods A total of 144 patients with thrombotic occlusion of autologous AVF were retrospectively enrolled and divided into success group(n=114)and failure group(n=30)according to the success of treatment or not.Clinical data and ultrasonic parameters of AVF were compared between groups.A multivariate logistic regression model was constructed to analyze the risk factors of PTA for treating thrombotic occlusion of autologous AVF,and the results were visualized by nomogram.Then receiver operating characteristic curve was drawn,and the area under the curve(AUC)was calculated to evaluate the predictive efficacy of this model.Results Patients'age,use years of AVF,degree of vascular calcification,the mean Young modulus(Emean),the maximum Young modulus(Emax)and the minimum Young modulus(Emin)were all higher,while the number of venous outflow tracts of AVF was less in failure group than those in success group(all P＜0.05).Moderate to severe calcification of vascular,high Emean of thrombus and 1 venous outflow tract of in AVF were all independent risk factors of ultrasound-guided PTA for treating thrombotic occlusion of autologous AVF(all P＜0.05),and AUC of the obtained model for predicting failure of treatment was 0.969.Conclusion Moderate to severe calcification of vascular,high Emean of thrombus and 1 venous outflow tract of AVF were all independent risk factors of ultrasound-guided PTA for treating thrombotic occlusion of autologous AVF.

Background:Salvia miltiorrhiza Bunge,commonly known as"Danshen"in China due to the distinctive red color of its roots,is one of the most widely used traditional Chinese medicines.It is cultivated in various regions across China,and environmental differences among these regions can affect the secondary metabolites of plants,thereby influencing the quality of S.miltiorrhiza.In recent years,increasing demand for S.miltiorrhiza has exacerbated the problem of origin fraud.Therefore,ensuring the authenticity of its geo-graphical origin is crucial for the sustainable development of the industry.Objective:The red coloration of S.miltiorrhiza is closely associated with the content of its primary active compounds,particularly tanshinones.Therefore,both its internal chemical composition and external color characteristics serve as key indicators for quality assessment.This study utilized hyperspectral imaging technology to evaluate its potential in classifying the geographical origin of S.miltiorrhiza.Methods:Spectral data reflecting the internal chemical properties of S.miltiorrhiza were integrated with color information represent-ing its external features through 3 levels of data fusion.These fused datasets were then combined with deep learning algorithms to achieve accurate origin classification.Results:The results demonstrated that the Transformer model combined with soft-voting decision-level fusion achieved the highest classification accuracy of 98.72％by integrating image color and short-wave infrared spectral data.Conclusion:This study demonstrates that integrating hyperspectral imaging spectral data with color information provides a reliable and innovative approach for verifying the authenticity and traceability of S.miltiorrhiza.

Objective To analyze the risk factors of ultrasound-guided percutaneous transluminal angioplasty(PTA)for treating thrombotic occlusion of autogenous arteriovenous fistula(AVF).Methods A total of 144 patients with thrombotic occlusion of autologous AVF were retrospectively enrolled and divided into success group(n=114)and failure group(n=30)according to the success of treatment or not.Clinical data and ultrasonic parameters of AVF were compared between groups.A multivariate logistic regression model was constructed to analyze the risk factors of PTA for treating thrombotic occlusion of autologous AVF,and the results were visualized by nomogram.Then receiver operating characteristic curve was drawn,and the area under the curve(AUC)was calculated to evaluate the predictive efficacy of this model.Results Patients'age,use years of AVF,degree of vascular calcification,the mean Young modulus(Emean),the maximum Young modulus(Emax)and the minimum Young modulus(Emin)were all higher,while the number of venous outflow tracts of AVF was less in failure group than those in success group(all P＜0.05).Moderate to severe calcification of vascular,high Emean of thrombus and 1 venous outflow tract of in AVF were all independent risk factors of ultrasound-guided PTA for treating thrombotic occlusion of autologous AVF(all P＜0.05),and AUC of the obtained model for predicting failure of treatment was 0.969.Conclusion Moderate to severe calcification of vascular,high Emean of thrombus and 1 venous outflow tract of AVF were all independent risk factors of ultrasound-guided PTA for treating thrombotic occlusion of autologous AVF.

Compound Yizhihao, consists of Radix isatidis, Folium isatidis, Artemisia rupestris, has a significant therapeutic effect on the treatment of influenza and fever. However, the mechanism of its action is still unclear. In this investigation, we collected the key target molecule of influenza disease and the chemical constituents of Compound Yizhihao, and developed Naïve Bayesian classification models based on the input molecular fingerprints and molecule descriptors. The built models were further applied to construct classifiers for predicting the effective constituents. We used the professional network-building software to build the constituent-target network and target-pathway network, which revealed the network pharmacology of the effective constituents in Compound Yizhihao. It will contribute to the further research of mechanism of Compound Yizhihao.