OBJECTIVE To establish a quality control method for Jinbei oral liquid based on multi-wavelength switching high performance liquid chromatography （HPLC） fingerprint， chemical pattern recognition and multi-index content determination. METHODS A total of 15 batches of Jinbei oral liquid were used as test samples. The Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine （ Version 2012 ） was adopted to establish multi-wavelength switching HPLC fingerprints， followed by chromatographic peak identification and similarity evaluation. Cluster analysis， principal component analysis and orthogonal partial least squares-discriminant analysis were applied to conduct chemical pattern recognition on the 15 batches of samples. The multi-wavelength switching HPLC method was used to simultaneously determine the contents of neochlorogenic acid， chlorogenic acid， cryptochlorogenic acid， forsythoside A， baicalin， salvianoli c acid B and wogonoside in samples. RESULTS A total of 25 common peaks were calibrated in the fingerprints of 15 batches of Jinbei oral liquid， among which 7 common peaks were unambiguously identified. The similarity of all samples was higher than 0.960. Chemical pattern recognition results showed that samples S1-S8 were clustered into group 1， and samples S9-S15 were clustered into group 2. Baicalin， wogonoside， neochlorogenic acid， chlorogenic acid and salvianolic acid B were identified as the differential quality markers. The average contents of neochlorogenic acid， chlorogenic acid， cryptochlorogenic acid， forsythoside A， baicalin， salvianolic acid B and wogonoside in 15 batches of Jinbei oral liquid were 0.213 7， 0.085 3， 0.104 9， 0.287 3， 0.420 1， 0.062 1， 0.176 0 mg/mL， respectively. CONCLUSIONS The established quality control method for Jinbei oral liquid combining multi-wavelength switching HPLC fingerprint， chemical pattern recognition and multi-index content determination is stable and reliable， which can provide a reference for the formulation of quality standards of this preparation.

OBJECTIVE To establish a quality control method for Jinbei oral liquid based on multi-wavelength switching high performance liquid chromatography （HPLC） fingerprint， chemical pattern recognition and multi-index content determination. METHODS A total of 15 batches of Jinbei oral liquid were used as test samples. The Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine （ Version 2012 ） was adopted to establish multi-wavelength switching HPLC fingerprints， followed by chromatographic peak identification and similarity evaluation. Cluster analysis， principal component analysis and orthogonal partial least squares-discriminant analysis were applied to conduct chemical pattern recognition on the 15 batches of samples. The multi-wavelength switching HPLC method was used to simultaneously determine the contents of neochlorogenic acid， chlorogenic acid， cryptochlorogenic acid， forsythoside A， baicalin， salvianoli c acid B and wogonoside in samples. RESULTS A total of 25 common peaks were calibrated in the fingerprints of 15 batches of Jinbei oral liquid， among which 7 common peaks were unambiguously identified. The similarity of all samples was higher than 0.960. Chemical pattern recognition results showed that samples S1-S8 were clustered into group 1， and samples S9-S15 were clustered into group 2. Baicalin， wogonoside， neochlorogenic acid， chlorogenic acid and salvianolic acid B were identified as the differential quality markers. The average contents of neochlorogenic acid， chlorogenic acid， cryptochlorogenic acid， forsythoside A， baicalin， salvianolic acid B and wogonoside in 15 batches of Jinbei oral liquid were 0.213 7， 0.085 3， 0.104 9， 0.287 3， 0.420 1， 0.062 1， 0.176 0 mg/mL， respectively. CONCLUSIONS The established quality control method for Jinbei oral liquid combining multi-wavelength switching HPLC fingerprint， chemical pattern recognition and multi-index content determination is stable and reliable， which can provide a reference for the formulation of quality standards of this preparation.

Digital technology has revolutionized the traditional model of quality evaluation for traditional Chinese medicine(TCM).This article systematically reviews the research hotspots and practical applications of digital-driv-en quality evaluation of TCM.Simultaneously,this paper analyzes and assesses the challenges faced by the digitiza-tion of TCM quality evaluation from multiple perspectives,including data acquisition,model construction,applica-tion promotion and market acceptance,and puts forward targeted strategies.Addressing the existing issues in the field of TCM quality,this paper proposes a series of innovative concepts,including the mining and application of TCM property data,the construction of a large database of TCM components,the integration of digital technology and emerging biotechnology for biological effect evaluation of TCM and clinical intelligent evaluation based on real-world data.Based on these,it further proposes a multi-dimensional quality analysis model for grading TCM,which encompasses TCM property data,chemical composition analysis,biological effect assessment and clinical evalua-tion.It aims to provide a useful reference for the modernization,scientification,and standardization of TCM quality evaluation.

Objective:To establish a rapid quantitative model for the determination of moisture,extractives,and content in Pollen Typhae.Methods:Near-infrared spectra of 91 batches of Pollen Typhae samples were collected.Spectral preprocessing was performed using S-G,MSC,SNV,and CWT methods.Variable selection was conducted using CARS,SPA,and VIP methods,and compared with full-spectrum modeling.Partial least squares(PLS)mod-els were established for the quantitative determination of moisture,total ash,extractives,and content.The model performance was evaluated by calculating the coefficient of determination for the calibration set and validation set(R2 c,R2v),root mean square error of calibration and validation(RMSEc,RMSEv),and residual prediction devia-tion(RPD).Results:The PLS models for moisture,extractives,and content in Pollen Typhae showed R2c and R2v values greater than 0.9,RMSEc and RMSEv values approaching 0,and RPD values greater than 3.Conclusion:In this study,near-infrared spectroscopy was used to construct quantitative prediction models for moisture,extractives,typhaneoside,and isorhamnetin-3-O-neohesperidoside content in Pollen Typhae.This method enables rapid detection of the main quality control indicators of Pollen Typhae,providing strong technical support for its quality supervision.

Digital technology has revolutionized the traditional model of quality evaluation for traditional Chinese medicine(TCM).This article systematically reviews the research hotspots and practical applications of digital-driv-en quality evaluation of TCM.Simultaneously,this paper analyzes and assesses the challenges faced by the digitiza-tion of TCM quality evaluation from multiple perspectives,including data acquisition,model construction,applica-tion promotion and market acceptance,and puts forward targeted strategies.Addressing the existing issues in the field of TCM quality,this paper proposes a series of innovative concepts,including the mining and application of TCM property data,the construction of a large database of TCM components,the integration of digital technology and emerging biotechnology for biological effect evaluation of TCM and clinical intelligent evaluation based on real-world data.Based on these,it further proposes a multi-dimensional quality analysis model for grading TCM,which encompasses TCM property data,chemical composition analysis,biological effect assessment and clinical evalua-tion.It aims to provide a useful reference for the modernization,scientification,and standardization of TCM quality evaluation.

Objective:To establish a rapid quantitative model for the determination of moisture,extractives,and content in Pollen Typhae.Methods:Near-infrared spectra of 91 batches of Pollen Typhae samples were collected.Spectral preprocessing was performed using S-G,MSC,SNV,and CWT methods.Variable selection was conducted using CARS,SPA,and VIP methods,and compared with full-spectrum modeling.Partial least squares(PLS)mod-els were established for the quantitative determination of moisture,total ash,extractives,and content.The model performance was evaluated by calculating the coefficient of determination for the calibration set and validation set(R2 c,R2v),root mean square error of calibration and validation(RMSEc,RMSEv),and residual prediction devia-tion(RPD).Results:The PLS models for moisture,extractives,and content in Pollen Typhae showed R2c and R2v values greater than 0.9,RMSEc and RMSEv values approaching 0,and RPD values greater than 3.Conclusion:In this study,near-infrared spectroscopy was used to construct quantitative prediction models for moisture,extractives,typhaneoside,and isorhamnetin-3-O-neohesperidoside content in Pollen Typhae.This method enables rapid detection of the main quality control indicators of Pollen Typhae,providing strong technical support for its quality supervision.

Objective:To construct a machine learning classification prediction model using planning-omics (planomics) features to predict the γ pass rate of intensity-modulated radiotherapy (IMRT) plan dose verification in fixed-field thoracic tumors, and evaluate the application of planomics in radiotherapy quality assurance.Methods:The fixed-field IMRT plans of 240 patients with chest tumors admitted to Department of Radiotherapy, Henan Cancer Hospital from August 2022 to March 2023 were retrospectively analyzed. All plans underwent dose verification using the electronic portal imaging system detector on the Varian accelerator to collect field dose data. The dose verification results were analyzed through Portal Dosimetry in the treatment planning system of Eclipse. The γ pass rate standard was set at 2%/2 mm with a 10% dose threshold. From the planning documents, 48 conventional planning features, 2476 planomics features, and the combination of the previous two feature sets were extracted. Subsequently, an auto-encoder classification model was constructed. To evaluate the classification efficacy of various feature sets, 20 random train-test divisions were conducted by calculating the area under the receiver operating characteristic curve (AUC) values along with the accuracy rates.Results:After the feature selection, 2 conventional features and 16 planomics features were finally selected. In the testing set, the AUC values for the model using combined features, planomics features, and conventional planned features were 0.802±0.030, 0.740±0.069, and 0.673±0.083, respectively. In contrast, in the training set, these AUC values were 0.844±0.074, 0.816±0.047, and 0.687±0.036, respectively. The accuracy rates were 0.752±0.083, 0.703±0.110, and 0.648±0.081 in the testing set, and 0.753±0.098, 0.751±0.075, and 0.624±0.054 in the training set for the combined, planomics, and conventional planning feature sets, respectively.Conclusions:For thoracic fixed-field adjusted radiotherapy planning, the machine learning method based on planomics features can be utilized to build a classification model for predicting the γ pass rate. Combining planomics features with conventional planned features can enhance the predictive performance of the classification models.

Objective:To summarize the early clinical outcomes of aortic valve replacement(AVR) using the Perceval sutureless aortic valve.Methods:This retrospective study included 50 patients who underwent AVR with the Perceval sutureless prostheses at Beijing Anzhen Hospital between June 2023 and January 2025. Surgical parameters, early clinical outcomes, valve function, and hemodynamic performance were evaluated to summarize clinical effectiveness.Results:The mean age of patients was(63.5±9.1) years, predominantly female(36/50). Severe aortic stenosis was present in 43 cases(86.0%). A preoperative aortic annulus dimension of 20.0(19.0, 21.0) mm measured in both anteroposterior and transverse diameters. Preoperative peak transvalvular gradient was(92.7±31.0)mmHg(1 mmHg=0.133 kPa), with a mean gradient of (58.0±21.2) mmHg. All procedures were successfully completed using the Perceval sutureless prostheses. Isolated AVR was performed in 20 patients(40.0%), with cardiopulmonary bypass and aortic cross-clamp times of 75.0(50.5, 99.5) min and 50.5(29.5, 71.5) min, respectively. Postoperative transesophageal echocardiography revealed an immediate reduction in the peak transvalvular gradient to 11.0(8.0, 18.0) mmHg, significantly lower compared to preoperative measurements( P<0.001). Two cases of paravalvular leakage and one case requiring permanent pacemaker implantation were reported postoperatively. All patients completed the 3-month follow-up, with one death during the follow-up period; the remaining patients exhibited normal prosthetic valve function without major adverse cardiovascular events. Significant postoperative reductions were observed in left ventricular end-diastolic diameter(45.8 mm vs. 43.2 mm, P=0.003) and left atrial diameter(53.9 mm vs. 44.6 mm, P<0.001) compared with baseline. Conclusion:AVR using the Perceval sutureless prostheses demonstrated safe and effective early clinical outcomes with excellent hemodynamic performance and low incidence of postoperative paravalvular leakage and permanent pacemaker implantation. The sutureless technique represents a viable alternative strategy, particularly advantageous for patients with small aortic annuli or complex surgical conditions, warranting broader clinical adoption.

Objective:To summarize the early clinical outcomes of aortic valve replacement(AVR) using the Perceval sutureless aortic valve.Methods:This retrospective study included 50 patients who underwent AVR with the Perceval sutureless prostheses at Beijing Anzhen Hospital between June 2023 and January 2025. Surgical parameters, early clinical outcomes, valve function, and hemodynamic performance were evaluated to summarize clinical effectiveness.Results:The mean age of patients was(63.5±9.1) years, predominantly female(36/50). Severe aortic stenosis was present in 43 cases(86.0%). A preoperative aortic annulus dimension of 20.0(19.0, 21.0) mm measured in both anteroposterior and transverse diameters. Preoperative peak transvalvular gradient was(92.7±31.0)mmHg(1 mmHg=0.133 kPa), with a mean gradient of (58.0±21.2) mmHg. All procedures were successfully completed using the Perceval sutureless prostheses. Isolated AVR was performed in 20 patients(40.0%), with cardiopulmonary bypass and aortic cross-clamp times of 75.0(50.5, 99.5) min and 50.5(29.5, 71.5) min, respectively. Postoperative transesophageal echocardiography revealed an immediate reduction in the peak transvalvular gradient to 11.0(8.0, 18.0) mmHg, significantly lower compared to preoperative measurements( P<0.001). Two cases of paravalvular leakage and one case requiring permanent pacemaker implantation were reported postoperatively. All patients completed the 3-month follow-up, with one death during the follow-up period; the remaining patients exhibited normal prosthetic valve function without major adverse cardiovascular events. Significant postoperative reductions were observed in left ventricular end-diastolic diameter(45.8 mm vs. 43.2 mm, P=0.003) and left atrial diameter(53.9 mm vs. 44.6 mm, P<0.001) compared with baseline. Conclusion:AVR using the Perceval sutureless prostheses demonstrated safe and effective early clinical outcomes with excellent hemodynamic performance and low incidence of postoperative paravalvular leakage and permanent pacemaker implantation. The sutureless technique represents a viable alternative strategy, particularly advantageous for patients with small aortic annuli or complex surgical conditions, warranting broader clinical adoption.

Objective:To construct a machine learning classification prediction model using planning-omics (planomics) features to predict the γ pass rate of intensity-modulated radiotherapy (IMRT) plan dose verification in fixed-field thoracic tumors, and evaluate the application of planomics in radiotherapy quality assurance.Methods:The fixed-field IMRT plans of 240 patients with chest tumors admitted to Department of Radiotherapy, Henan Cancer Hospital from August 2022 to March 2023 were retrospectively analyzed. All plans underwent dose verification using the electronic portal imaging system detector on the Varian accelerator to collect field dose data. The dose verification results were analyzed through Portal Dosimetry in the treatment planning system of Eclipse. The γ pass rate standard was set at 2%/2 mm with a 10% dose threshold. From the planning documents, 48 conventional planning features, 2476 planomics features, and the combination of the previous two feature sets were extracted. Subsequently, an auto-encoder classification model was constructed. To evaluate the classification efficacy of various feature sets, 20 random train-test divisions were conducted by calculating the area under the receiver operating characteristic curve (AUC) values along with the accuracy rates.Results:After the feature selection, 2 conventional features and 16 planomics features were finally selected. In the testing set, the AUC values for the model using combined features, planomics features, and conventional planned features were 0.802±0.030, 0.740±0.069, and 0.673±0.083, respectively. In contrast, in the training set, these AUC values were 0.844±0.074, 0.816±0.047, and 0.687±0.036, respectively. The accuracy rates were 0.752±0.083, 0.703±0.110, and 0.648±0.081 in the testing set, and 0.753±0.098, 0.751±0.075, and 0.624±0.054 in the training set for the combined, planomics, and conventional planning feature sets, respectively.Conclusions:For thoracic fixed-field adjusted radiotherapy planning, the machine learning method based on planomics features can be utilized to build a classification model for predicting the γ pass rate. Combining planomics features with conventional planned features can enhance the predictive performance of the classification models.