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.

Objective To investigate the predictive value of serum vasoactive intestinal peptide(VIP)and 5-hydroxytryptamine(5-HT)levels on the outcome of spinal cord electrical stimulation(SCS)in patients with postherpetic neuralgia(PHN).Methods A total of 96 PHN patients who received SCS treatment in Ziy-ang Central Hospital from January 2022 to December 2023 were selected.According to the disease outcomes of all PHN patients after 6 months of treatment,a good group(n=71)and a poor group(n=25)were set up.The clinical data of the two groups were collected and the serum VIP and 5-HT levels were detected in all pa-tients before treatment.The predictive value of serum VIP and 5-HT on disease outcome after SCS treatment in PHN patients was evaluated by receiver operating characteristic(ROC)curve,and the influencing factors of disease outcome after SCS treatment in PHN patients was explored by multivariate Logistic steppe gression a-nalysis.Results The levels of serum VIP and 5-HT in poor group were higher than those in good group(P＜0.05).The area under the curve(AUC)of serum VIP and 5-HT for predicting the disease outcome of PHN patients after SCS treatment were 0.829(95％CI:0.779-0.874)and 0.743(95％CI:0.693-0.793),respec-tively,and the AUC of combined prediction was 0.941(0.891-0.986).There were no significant differences in age,gender,body moss index,education,location of onset,hypertension and drinking history between the two groups(P＞0.05).The time of initial hospital admission in the poor group was longer than that in the good group,skin rash area in the poor group was larger than that in the good group,and diabetes mellitus and smoking history in the poor group were higher than those in the good group(P＜0.05).The time of admis-sion for initial treatment＞3 d(OR=2.188,95％CI:1.383-3.461),skin rash area＞10 cm2(OR=2.018,95％CI:1.283-3.173),diabetes mellitus(OR=2.264,95％CI:1.379-3.717),serum VIP level ≥41.78 ng/L(OR=3.022,95％CI:1.685-5.420),serum 5-HT level ≥99.27 ng/mL(OR=3.579,95％CI:1.885-6.793)were the influencing factors of disease outcome after SCS treatment in PHN patients(P＜0.05).Con-clusion The elevated levels of serum VIP and 5-HT before treatment are associated with poor outcomes after SCS in patients with PHN,and could be used as potential markers to predict the outcomes of SCS in patients with PHN.

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 investigate the prognostic value of mitochondrial autophagy-related genes(MRGs)in prostate cancer(PCa),and to reveal their regulatory relationship with interstitial epidermal transforming factor(C-Met)based on the Gene Expression Database(GEO).Methods Single-cell RNA sequencing(scRNA-seq)data of three PCa samples were obtained from the GSE153892 dataset of GEO,and MRGs were collected from the Genecards database and previous literature.The scRNA-seq data were processed and analyzed using the Seurat software package,including quality control,gene expression screening,cell type annotation,differentially expressed genes(DEGs)identification,and intersection analysis with MRGs.The transcriptome data of PCa and control samples were downloaded from the Cancer Genome Atlas Database(TCGA)-PRAD cohort,and differential expression analysis and copy number variation analysis were conducted.The non-negative matrix factorization algorithm is adopted to conduct cluster analysis on PCa samples to identify different PCa subtypes.A prognostic risk model based on intersection genes was constructed,and the predictive ability of the model was analyzed through Kaplan-Meier survival curve analysis and time-dependent receiver operating characteristic(ROC)curve analysis.Conduct independent prognostic analysis,construct a nomogram model based on risk scores and clinical characteristics,and evaluate its ability to predict patient survival rates.The possibility of immune infiltration and tumor immune escape in PCa samples was evaluated by using the single-sample Gene Set Enrichment analysis(ssGSEA)algorithm and the TIDE database.The relationship between intersection genes and C-Met expression was analyzed using Pearson correlation analysis.Results scRNA-seq data analysis identified five cell types including B lymphocytes,epithelial cells,monocytes,natural killer cells and T lymphocytes,and discovered the intersection genes that were highly expressed in different cell types.Through differential expression analysis,genes significantly related to the prognosis of PCa patients were screened out,and a prognostic risk model was constructed.Six genes such as ADH5 and CAT were retained through LASSO analysis.A diagnostic model was constructed and grouped.There was a significant difference in survival time between the two groups in the internal test set(P<0.05).ROC curve evaluation showed that the model had a good predictive ability for 1-,3-,and 5-year survival rates.The external test set verified that there was a statistically significant difference in the expression of intersection genes(P<0.05).Independent prognostic analysis identified T stage and risk score as independent prognostic factors.A nomogram model was constructed.Calibration curve and ROC curve analyses showed that the predictive ability of this model was superior to that of the simple risk model.ssGSEA analysis revealed differences in the abundance of immune cell inflammation and immune function scores between the two groups.Most immune cells,immune function,and risk scores were related to the modeling genes.There were significant differences in TIDE scores and multiple immune checkpoints between the high-risk and low-risk groups(P<0.05).BCAT2,DCXR,OGT and FUS were positively correlated with the expression of C-Met,while ADH5 and CAT were negatively correlated with the expression of C-Met(P<0.05).Conclusion The prognostic risk model based on intersection genes can effectively predict the prognosis of patients with PCa,and the risk score and T stage are independent prognostic factors for PCa.The correlation analysis of intersection genes and C-Met expression provides a new idea for the targeted therapy of PCa.

Objective To investigate the prognostic value of mitochondrial autophagy-related genes(MRGs)in prostate cancer(PCa),and to reveal their regulatory relationship with interstitial epidermal transforming factor(C-Met)based on the Gene Expression Database(GEO).Methods Single-cell RNA sequencing(scRNA-seq)data of three PCa samples were obtained from the GSE153892 dataset of GEO,and MRGs were collected from the Genecards database and previous literature.The scRNA-seq data were processed and analyzed using the Seurat software package,including quality control,gene expression screening,cell type annotation,differentially expressed genes(DEGs)identification,and intersection analysis with MRGs.The transcriptome data of PCa and control samples were downloaded from the Cancer Genome Atlas Database(TCGA)-PRAD cohort,and differential expression analysis and copy number variation analysis were conducted.The non-negative matrix factorization algorithm is adopted to conduct cluster analysis on PCa samples to identify different PCa subtypes.A prognostic risk model based on intersection genes was constructed,and the predictive ability of the model was analyzed through Kaplan-Meier survival curve analysis and time-dependent receiver operating characteristic(ROC)curve analysis.Conduct independent prognostic analysis,construct a nomogram model based on risk scores and clinical characteristics,and evaluate its ability to predict patient survival rates.The possibility of immune infiltration and tumor immune escape in PCa samples was evaluated by using the single-sample Gene Set Enrichment analysis(ssGSEA)algorithm and the TIDE database.The relationship between intersection genes and C-Met expression was analyzed using Pearson correlation analysis.Results scRNA-seq data analysis identified five cell types including B lymphocytes,epithelial cells,monocytes,natural killer cells and T lymphocytes,and discovered the intersection genes that were highly expressed in different cell types.Through differential expression analysis,genes significantly related to the prognosis of PCa patients were screened out,and a prognostic risk model was constructed.Six genes such as ADH5 and CAT were retained through LASSO analysis.A diagnostic model was constructed and grouped.There was a significant difference in survival time between the two groups in the internal test set(P<0.05).ROC curve evaluation showed that the model had a good predictive ability for 1-,3-,and 5-year survival rates.The external test set verified that there was a statistically significant difference in the expression of intersection genes(P<0.05).Independent prognostic analysis identified T stage and risk score as independent prognostic factors.A nomogram model was constructed.Calibration curve and ROC curve analyses showed that the predictive ability of this model was superior to that of the simple risk model.ssGSEA analysis revealed differences in the abundance of immune cell inflammation and immune function scores between the two groups.Most immune cells,immune function,and risk scores were related to the modeling genes.There were significant differences in TIDE scores and multiple immune checkpoints between the high-risk and low-risk groups(P<0.05).BCAT2,DCXR,OGT and FUS were positively correlated with the expression of C-Met,while ADH5 and CAT were negatively correlated with the expression of C-Met(P<0.05).Conclusion The prognostic risk model based on intersection genes can effectively predict the prognosis of patients with PCa,and the risk score and T stage are independent prognostic factors for PCa.The correlation analysis of intersection genes and C-Met expression provides a new idea for the targeted therapy of PCa.

Objective:To analyze the robustness of the dose of clinical target volume (CTV) and tolerance dose of normal tissues after applying in-room CT before carbon ion radiotherapy for prostate cancer.Methods:Thirty prostate cancer patients treated with carbon ion in Shanghai Proton and Heavy Ion Center from January 2020 to June 2021 were enrolled in this study. Five in-room CT images of each patient were selected randomly before treatment. Dose distributions were recalculated using the original plan on in-room CT images and dose volume histogram (DVH) parameters were obtained, including V 95% and V 90% of CTV and V 80% of rectum. The values were compared with the dosimetric parameters of the original plan. Statistical analysis was performed by paired or two independent samples t-tests. Results:The dose distribution was recalculated by applying in-room CT. The mean values of V 95% and V 90% of CTV and V 80% of rectum were 98.1%±1.2% ( P<0.001), 99.9%±0.2% ( P=0.001) and (5.8±1.6) ml ( P<0.001), respectively. The differences were statistically significant compared with those of the original plan. The frequency of V 95%≥95%, V 90%≥98% of CTV, and V 80%<10 ml of rectum was 148 (98.7%), 150 (100.0%) and 147 (98.0%), respectively. Conclusion:Based on in-room CT analysis and the patient management and positioning methods of our research center, the uncertainty of target dose and normal tissue dose in the entire process of prostate cancer carbon ion therapy is small, and the robustness is good.