Objective To improve the accuracy of intelligent detection and evaluation of traditional Chinese medicine(TCM)pieces and solve the problems of leakage,misdetection,inaccurate localization and low confidence in the study of TCM pieces identification,YOLOv3 algorithm which has good detection effect for high overlap and small targets was improved.Methods An RGB image database containing 148 commonly used TCM pieces was established.Based on the YOLOv3 algorithm model,the anchor box size was improved by K-means clustering algorithm.The CIoU loss function was introduced for bounding box regression to improve the localization accuracy and confidence of bounding boxes.The traditional non-maximum suppression was improved to DIoUNMS to reduce the problems of missed detection and false detection of dense targets with high overlap by YOLOv3 algorithm.Results 148 kinds of TCM pieces were tested with the improved algorithm,and the average detection accuracy of 98.47%was achieved,which is 1.83%better than the original YOLOv3 algorithm.It realizes better detection effect for TCM pieces in complex situations such as dense,high overlapping,etc.Problems such as leakage,misdetection,imprecise positioning and low confidence level have been alleviated to a certain extent.Conclusion The improved algorithm effectively improves the recognition accuracy and generalization ability of TCM pieces,providing a new reference for the realization of automated intelligent detection of TCM pieces.

Objective:To develop a clinical predictive model for dampness constitution based on laboratory testing indicators.Methods:A retrospective cohort study was conducted on 1 355 healthy individuals who underwent physical examinations at the Health Examination Center of Guangdong Provincial Hospital of Traditional Chinese Medicine from October 1 st, 2022 to October 31 st, 2023. Basic information and blood routine, blood glucose, liver function, lipid metabolism, and kidney function test results of 1 355 apparently healthy individuals were collected. According to the diagnostic criteria for dampness constitution in traditional Chinese medicine, they were divided into a dampness constitution group (683 cases, including 394 with phlegm-dampness constitution and 289 with damp-heat constitution) and a non-dampness constitution group (672 cases). Among them, there were 547 males and 136 females in the dampness constitution group, with an age of 38.0 (32.0, 45.0) years; and there were 355 males and 317 females in the non-dampness constitution group, with an age of 33.0 (27.0, 41.0) years. A total of 1 355 apparently healthy individuals were randomly divided into a training set ( n=948) and a validation set ( n=407) using computer-generated random numbers in a 7∶3 ratio. Logistic regression analysis was employed to identify risk factors associated with dampness constitution. Utilizing these identified risk factors, a predictive model was constructed and subsequently visualized. The model′s predictive accuracy, consistency, and clinical utility were assessed using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA), respectively. Results:Among 1 355 subjects, there were statistically significant differences ( P<0.05) in gender, age, body mass index (BMI), blood glucose, some indicators of renal function, some indicators of blood routine, liver function, and four indicators of lipid metabolism between the dampness constitution group and the non-dampness constitution group. Gender ( OR=0.434,95 %CI 0.253-0.738), Cr ( OR=0.981,95 %CI 0.967-0.996), BMI ( OR=1.366,95 %CI 1.290-1.450), and LDL-C ( OR=1.388,95 %CI 1.014-1.897) were independent risk factors for dampness constitution ( P<0.05). A nomogram was subsequently developed based on these identified risk factors. The areas under the ROC curves (AUC) of the training set and validation set were 0.810 (95 %CI 0.783-0.837) and 0.804 (95 %CI 0.762-0.846), respectively. Conclusion:Gender,BMI,Cr and LDL-C were risk factors for the development of dampness constitution, and the clinical predictive model has clinical application value in predicting the risk of dampness constitution.

Objective To explore the risk factors associated with the occurrence of dampness syndrome based on peripheral blood multiple laboratory indicators,construct predictive model and validate it.Methods A retrospective analysis was conducted on 180 patients who visited the Preventive Treatment Center of Guangdong Provincial Hospital of Chinese Medicine from May 2022 to December 2023.They were divided into two groups according to the diagnostic criteria:the damp syndrome of TCM group(n=118)and the balanced yin-yang constitution group(n=62),with the latter serving as the"non-syndrome"control group for dampness syndrome.Serum biochemical indicators were detected by electrochemiluminescence(ECL),immune cell subsets were analyzed through flow cytometer,and routine blood parameters were assessed using an automatic hematology analyzed.Logistic regression analysis was employed to screen risk factors and develop a predictive model.The Bootstrap method was used for data resampling to draw the receiver operating characteristic(ROC)curve,calibration curve,and clinical decision curve analysis(DCA)to evaluate the predictive value,consistency,and clinical efficacy of the model.Results Compared with the balanced yin-yang constitution group,the damp syndrome of TCM group showed increased levels of insulin(INS),non-high-density lipoprotein cholesterol(non HDL-C),red blood cells(RBC)and the proportion of CD4+T cells,the proportion of triglyceride(TG)>1.70 mmol/L,total cholesterol(TC)>5.20 mmol/L,low-density lipoprotein cholesterol(LDL-C)>3.37 mmol/L,and high-density lipoprotein cholesterol(HDL-C)≤1.15 mmol/L were also significantly higher,with statistical significance(U/t/χ2=-2.900～4 626,all P＜0.05).Logistic regression analysis showed that INS,TC>5.20 mmol/L,HDL-C≤1.15 mmol/L,and the proportion of CD4+T cells were independent risk factors for the occurrence of damp syndrome of TCM(all P＜0.05).Based on the screened risk factors,a forecasting model was established and a nomogram was plotted.The model had an area under the ROC curve area under curve(AUC)of 0.747(95%CI=0.672～0.822),a Brier score of 0.184 for the calibration curve,and demonstrated clinical net benefit at threshold probabilities ranging from 0.30 to 1.00.Conclusion The forecasting model constructed based on INS,TC>5.20 mmol/L,HDL-C≤1.15 mmol/L,and CD4+T cells ratio has a high predictive value for damp syndrome of TCM.

Objective To improve the accuracy of intelligent detection and evaluation of traditional Chinese medicine(TCM)pieces and solve the problems of leakage,misdetection,inaccurate localization and low confidence in the study of TCM pieces identification,YOLOv3 algorithm which has good detection effect for high overlap and small targets was improved.Methods An RGB image database containing 148 commonly used TCM pieces was established.Based on the YOLOv3 algorithm model,the anchor box size was improved by K-means clustering algorithm.The CIoU loss function was introduced for bounding box regression to improve the localization accuracy and confidence of bounding boxes.The traditional non-maximum suppression was improved to DIoUNMS to reduce the problems of missed detection and false detection of dense targets with high overlap by YOLOv3 algorithm.Results 148 kinds of TCM pieces were tested with the improved algorithm,and the average detection accuracy of 98.47%was achieved,which is 1.83%better than the original YOLOv3 algorithm.It realizes better detection effect for TCM pieces in complex situations such as dense,high overlapping,etc.Problems such as leakage,misdetection,imprecise positioning and low confidence level have been alleviated to a certain extent.Conclusion The improved algorithm effectively improves the recognition accuracy and generalization ability of TCM pieces,providing a new reference for the realization of automated intelligent detection of TCM pieces.

Objective To explore the risk factors associated with the occurrence of dampness syndrome based on peripheral blood multiple laboratory indicators,construct predictive model and validate it.Methods A retrospective analysis was conducted on 180 patients who visited the Preventive Treatment Center of Guangdong Provincial Hospital of Chinese Medicine from May 2022 to December 2023.They were divided into two groups according to the diagnostic criteria:the damp syndrome of TCM group(n=118)and the balanced yin-yang constitution group(n=62),with the latter serving as the"non-syndrome"control group for dampness syndrome.Serum biochemical indicators were detected by electrochemiluminescence(ECL),immune cell subsets were analyzed through flow cytometer,and routine blood parameters were assessed using an automatic hematology analyzed.Logistic regression analysis was employed to screen risk factors and develop a predictive model.The Bootstrap method was used for data resampling to draw the receiver operating characteristic(ROC)curve,calibration curve,and clinical decision curve analysis(DCA)to evaluate the predictive value,consistency,and clinical efficacy of the model.Results Compared with the balanced yin-yang constitution group,the damp syndrome of TCM group showed increased levels of insulin(INS),non-high-density lipoprotein cholesterol(non HDL-C),red blood cells(RBC)and the proportion of CD4+T cells,the proportion of triglyceride(TG)>1.70 mmol/L,total cholesterol(TC)>5.20 mmol/L,low-density lipoprotein cholesterol(LDL-C)>3.37 mmol/L,and high-density lipoprotein cholesterol(HDL-C)≤1.15 mmol/L were also significantly higher,with statistical significance(U/t/χ2=-2.900～4 626,all P＜0.05).Logistic regression analysis showed that INS,TC>5.20 mmol/L,HDL-C≤1.15 mmol/L,and the proportion of CD4+T cells were independent risk factors for the occurrence of damp syndrome of TCM(all P＜0.05).Based on the screened risk factors,a forecasting model was established and a nomogram was plotted.The model had an area under the ROC curve area under curve(AUC)of 0.747(95%CI=0.672～0.822),a Brier score of 0.184 for the calibration curve,and demonstrated clinical net benefit at threshold probabilities ranging from 0.30 to 1.00.Conclusion The forecasting model constructed based on INS,TC>5.20 mmol/L,HDL-C≤1.15 mmol/L,and CD4+T cells ratio has a high predictive value for damp syndrome of TCM.

Objective:To develop a clinical predictive model for dampness constitution based on laboratory testing indicators.Methods:A retrospective cohort study was conducted on 1 355 healthy individuals who underwent physical examinations at the Health Examination Center of Guangdong Provincial Hospital of Traditional Chinese Medicine from October 1 st, 2022 to October 31 st, 2023. Basic information and blood routine, blood glucose, liver function, lipid metabolism, and kidney function test results of 1 355 apparently healthy individuals were collected. According to the diagnostic criteria for dampness constitution in traditional Chinese medicine, they were divided into a dampness constitution group (683 cases, including 394 with phlegm-dampness constitution and 289 with damp-heat constitution) and a non-dampness constitution group (672 cases). Among them, there were 547 males and 136 females in the dampness constitution group, with an age of 38.0 (32.0, 45.0) years; and there were 355 males and 317 females in the non-dampness constitution group, with an age of 33.0 (27.0, 41.0) years. A total of 1 355 apparently healthy individuals were randomly divided into a training set ( n=948) and a validation set ( n=407) using computer-generated random numbers in a 7∶3 ratio. Logistic regression analysis was employed to identify risk factors associated with dampness constitution. Utilizing these identified risk factors, a predictive model was constructed and subsequently visualized. The model′s predictive accuracy, consistency, and clinical utility were assessed using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA), respectively. Results:Among 1 355 subjects, there were statistically significant differences ( P<0.05) in gender, age, body mass index (BMI), blood glucose, some indicators of renal function, some indicators of blood routine, liver function, and four indicators of lipid metabolism between the dampness constitution group and the non-dampness constitution group. Gender ( OR=0.434,95 %CI 0.253-0.738), Cr ( OR=0.981,95 %CI 0.967-0.996), BMI ( OR=1.366,95 %CI 1.290-1.450), and LDL-C ( OR=1.388,95 %CI 1.014-1.897) were independent risk factors for dampness constitution ( P<0.05). A nomogram was subsequently developed based on these identified risk factors. The areas under the ROC curves (AUC) of the training set and validation set were 0.810 (95 %CI 0.783-0.837) and 0.804 (95 %CI 0.762-0.846), respectively. Conclusion:Gender,BMI,Cr and LDL-C were risk factors for the development of dampness constitution, and the clinical predictive model has clinical application value in predicting the risk of dampness constitution.

Objective To explore the effect of endophytes on Nanyang Mugwort by analyzing the diversity of endophytic flora and its correlation with the content of key secondary metabolites in three compartments of Nanyang Mugwort,and the effect of medicinal material quality.Methods In this study,high-throughput sequencing and high-performance liquid chromatography(HPLC)were carried out to determine the distribution endophytes and key secondary metabolites in three compartments of Nanyang Mugwort respectively.The endophytic communities that were significantly correlated with key secondary metabolites were screened by Pearson correlation analysis.Results We found endophytic diversity and composition showed compartment specificity,and bacterial and fungal alpha diversity values(Chao and Ace)in the root compartment were significantly higher than that of leaf and stem.Linear discriminant analysis efect size(LEfSe)analyses identified some potential microbial biomarkers,such as Rhizobium,Streptomyce,Xanthomonadaceae,and Sordariomycetes.Pearson correlation analysis showed that endophytes(such as Rhizobium,Pseudomonas,Flavobacterium,Streptomyces Aspergillus,and Olpidium)were significantly correlated with contents of some phenylpropanoid and flavonoid metabolites in our study(P<0.05).Conclusion In this study,some endophytic communities with significant correlation between key secondary metabolites of Nanyang Mugwort were identified,which provided valuable information to guide the isolation of endophytic strains related to key secondary metabolites and improve the quality of Nanyang Mugwort.

Objective To characterize the temporal dynamics of endophytic community and key metabolites during the growth developmental stages of Artemisia indica and to explore the effects of endophytes on the medicinal material quality of A.indica from the perspective of microorganism combined with the correlation analysis.Methods In this study,high-throughput sequencing was performed to obtain the temporal dynamics of diversity and composition of endophytic community during the growth stages of A.indica.Key metabolites quantitative analysis were performed using high-performance liquid chromatography and ultraviolet spectrophotometry.Spearman's correlation analysis was further conducted to identify the endophytic communities that were significantly correlated with metabolites.Results There were significant differences in the diversity and composition of endophytic communities during the growth stages of A.indica.Linear discriminant analysis efect size(LEfSe)analyses identified some potential microbial biomarkers,such as Firmicutes,Massilia,Pantoea,Alternaria,Didymella,and Trichomerium.The abundances of some genera were significantly and positively correlated with the content of total polyphenols and total flavonoids(P<0.05),such as Curtobacterium,Pantoea,Tilletiopsis,and Dissoconium.The abundances of Pseudozyma,Ralstonia,and Pleospora were significantly and positively correlated with Chlorogenic acid,Isochlorogenic acid B,Isochlorogenic acid A,and Isochlorogenic acid C(P<0.05).Conclusion This work described the distribution of endophytes and key metabolites during the development stages of A.indica.Some endophytic communities with significant correlation of key metabolites were identified,which provided valuable information to guide the isolation of endophytic strains related to key metabolites and improve the quality of A.indica.

Objective:To develop a national secondary reference material of Urea and Creatinine in frozen human serum as a standard for metrological traceability.Methods:According to JJF1343-2012 "General and Statistical Principles for Characterization of Reference Materials" and JJF 1006-1994 " Technical Norm of Primary Reference Material ", the homogeneity, stability, and commutability were evaluated;Using the JCTLM recommended methods, the value of the reference materials was assigned through collaboration with 6 accredited reference laboratories from Guangdong Provincial Hospital of Chinese Medicine, Beijing Aerospace General Hospital, Shenzhen Mindray Bio-Medical Electronics, Maccura Biotechnology, Beijing Leadman Biochemistry, and Zhejiang MedicalSystem Biotechnology. Uncertainty components including inhomogeneity, stability and value assignment were evaluated.Results:The results of one-way analysis of variance of homogeneity for the reference materials showed P>0.05, and the stability evaluation was less than the critical value of the t-test. The measured values were in the 95% confidence interval in the four conventional detection systems for commutability, and the certified values and expanded uncertainties were urea:(14.7±0.3) mmol/L ( k=2),Cr:(313.9±14.5) μmol/L ( k=2). Conclusion:The prepared secondary reference materials of urea and creatinine had promising homogeneity, stability, and commutable, the values of urea and creatinine concentration in reference materials were accurate and reliable.

The concentration and accumulation rate of advanced glycation end products (AGEs) in the body are highly correlated with glycometabolic disorders. Therefore, the clinical detection of AGEs is of great value for the early diagnosis and prognostic evaluation of these diseases. However, due to the complexity of its structure, the diversity of glycosylation sites, and the limitations of existing detection methods, there is still a lack of widely available detection methods in clinical practice. Starting from the structure and classification of AGEs and the value of clinical testing, this article summarizes current status of various laboratory detection methods of AGEs, and the deficiencies and challenges of these testing methods, future directions are further prospected.