[Intrinsic "self-consistent" phenomenon based on holistic view of traditional Chinese medicine].

The fundamental principle of traditional Chinese medicine(TCM) is holism, and it is crucial for TCM to address the key issue of the "holistic view" of Chinese herbal medicine. While the overall regulatory effects of Chinese herbal medicine have been widely recognized, the holistic internal logic of individual ingredients of Chinese herbal medicines require further clarification. In order to comprehensively understand the mechanism of action of Chinese herbal medicine, this paper combined the holistic view of Chinese herbal medicine with differentiation thinking to explore the intrinsic logical relationships within Chinese herbal medicine. Starting from the perspective of the coexistence of multiple components in Chinese herbal medicine, this paper systematically examined the "self-consistent" phenomenon within single Chinese herbal medicine. This phenomenon refers to the consistent or opposing actions of various components in terms of their physical and chemical properties, pharmacokinetic effects, biological effects, flavors and properties, and TCM efficacy. The paper summarized various logical relationships of syndrome differentiation exhibited by the same Chinese herbal medicine, analyzed the underlying reasons, and focused on analyzing external factors affecting the "self-consistent" phenomenon in the efficacy of Chinese herbal medicine, aiming to better elucidate the theoretical basis of the pharmacological effects of Chinese herbal medicine, further enrich the scientific connotation of the holistic view of Chinese herbal medicine, and provide theoretical guidance for the preparation process, compatibility patterns, and formulation design of Chinese herbal medicine.

A study on the rapidly non-destructive detection method of Huoxiang Zhengqi oral liquid using near infrared spectroscopy / 药学学报

Based on near infrared spectroscopy and high performance liquid chromatography, this paper established the regression relationship between near infrared spectroscopy and index component content of Huoxiang Zhengqi oral liquid, so as to realize the rapid detection of index component content based on near infrared spectroscopy. Magnolol, honokiol and hesperidin were used as the quality indexes of Huoxiang Zhengqi oral liquid. After using the first derivative and normalization pretreatment method, characteristic variables were screened by CARS, and the correction model was finally established by partial least-squares regression (PLSR) method. The method accuracy was evaluated with the external validation, and the prediction results were tested for significance. The results indicated that when the near infrared spectrum was scanned through the bottle, the model's correlation coefficients of prediction (Rp) were higher than 0.99, the root mean square errors of the prediction models (RMSEP) were all less than 0.008 4, and the relative standard errors of prediction set (RSEP) were all less than 2.83%. There was no significant difference in the predicted results between these two kinds of model. The models established in the non-destructive way have good performance and high prediction accuracy. The rapid and nondestructive way has application value in the quality control of Huoxiang Zhengqi oral liquid.

[Critical quality attribute assessment of big brand traditional Chinese medicine: study on NIR field detection method of chemical properties of moisture in Tongren Niuhuang Qingxin Pills].

For the field detection problems of critical quality attribute(CQA) of moisture content in traditional Chinese medicine(TCM) manufacturing process, big brand TCM Tongren Niuhuang Qingxin Pills were used as the carrier, to establish a moisture content NIR field detection model with or without cellophane in real world production with use of near infrared(NIR) spectroscopy combined with stoichiometry. With the moisture content determined by drying method as reference value, the partial least square method(PLS) was used to analyze the correlation between the spectrum and the moisture reference value. Then the spectral pretreatment methods were screened and optimized to further improve the accuracy and stability of the model. The results showed that the best quantitative model was developed by the spectral data pretreatment of standard normal variate(SNV) with the latent variable factor number of 2 and 7 of Tongren Niuhuang Qingxin Pills with or without cellophane samples. The prediction coefficient of determination(R_(pre)~2) and standard deviation of prediction(RMSEP) of the model with cellophane samples were 0.765 7 and 0.157 2%; R_(pre)~2 and RMSEP of the model without cellophane samples were 0.772 2 and 0.207 8%. The NIR quantitative models of moisture content of Tongren Niuhuang Qingxin Pills with and without cellophane both showed good predictive performance to realize the rapid, accurate and non-destructive quantitative analysis of moisture content in such pills, and provide a method for the field quality control of the critical chemical attributes of moisture in the manufacturing of big brand TCM.

[Critical quality attribute assessment of big brand traditional Chinese medicine: modular identification of Tongren Niuhuang Qingxin Pills based on efficacy with chemical properties].

Identification of critical quality attribute(CQA) is crucial in quality control of Tongren Niuhuang Qingxin Pills(TRNHQXP). In this study, 661 active components in TRNHQXP were selected by liquid chromatography-mass spectrometry(LC-MS) and network pharmacology based on reported data and TCMSP, BATMAN-TCM, and TCMID databases, as well as mass spectrometry data, and 1 413 targets of the active components were obtained through SwissTargetPrediction. The 152 potential targets obtained from the intersection of predicted targets with 456 stroke targets underwent functional enrichment analysis by Metascape. The 27 Chinese medicinals in TRNHQXP were divided into four sets according to efficacies. Thirty-seven key targets in the blood-activating and stasis-resolving set and 41 in the tonifying set were screened out. On the basis of these potential key targets, 137 potential key CQA of TRNHQXP for stroke were reversely predicted. This study revealed the possible mechanism of TRNHQXP in treating stroke and established a modular identification method for the potential CQA of big brand traditional Chinese medicine(TCM) based on efficacies and chemical properties. Consequently, the CQA of TRNHQXP were identified by this method, which has provided a reference for the following experimental studies of CQA.

[Criticalquality attribute assessment of big brand traditional Chinese medicine: visualization method for quality control of Ginkgo Leaves Tablets based on spatial distribution uniformity].

Spatial distribution uniformity is the critical quality attribute(CQA) of Ginkgo Leaves Tablets, a variety of big brand traditional Chinese medicine. The evaluation of the spatial distribution uniformity of active pharmaceutical ingredients(APIs) in Ginkgo Leaves Tablets is important in ensuring their stable and controllable quality. In this study, hyperspectral imaging technology was used to construct the spatial distribution map of API concentration based on three prediction models, further to realize the visualization research on the spatial distribution uniformity of Ginkgo Leaves Tablets. The region of interest(ROI) was selected from each Ginkgo Leaves Tablet, with length and width of 50 pixels, and a total of 2 500 pixels. Each pixel had 288 spectral channels, and the number of content prediction data could reach 1×10~5 for a single sample. The results of the three models showed that the Partial Least Squares(PLS) model had the highest prediction accuracy, with calibration set determination coefficient R_(pre)~2 of 0.987, prediction set determination coefficient R_(pre)~2 of 0.942, root mean square error of calibration(RMSEC) of 0.160%, and root mean square error of prediction(RMSEP) of 0.588%. The classical least-squares(CLS) model had a greater prediction error, with the RMSEP of 0.867%. Multivariate Curve Resolution-Alternating Least Square(MCR-ALS) model showed the worst predictive ability among the three models, and it couldn't realize content prediction. Based on the prediction results of PLS and CLS models, the spatial distribution map of APIs concentration was obtained through three-dimensional data reconstruction. Furthermore, histogram method was used to evaluate the spatial distribution uniformity of API. The data showed that the spatial distribution of APIs in Ginkgo Leaves Tablets was relatively uniform. The study explored the feasibility of visualization of spatial distribution of Ginkgo Leaves Tablets based on three models. The results showed that PLS model had the highest prediction accuracy, and MCR-ALS model had the lowest prediction accuracy. The research results could provide a new strategy for the visualization method of quality control of Ginkgo Leaves Tablets.

[Rapid determination of active components in Ginkgo biloba leaves by near infrared spectroscopy combined with genetic algorithm joint extreme learning machine].

Near-infrared spectroscopy(NIRS) combined with band screening method and modeling algorithm can be used to achieve the rapid and non-destructive detection of the traditional Chinese medicine(TCM) production process. This paper focused on the ginkgo leaf macroporous resin purification process, which is the key technology of Yinshen Tongluo Capsules, in order to achieve the rapid determination of quercetin, kaempferol and isorhamnetin in effluent. The abnormal spectrum was eliminated by Mahalanobis distance algorithm, and the data set was divided by the sample set partitioning method based on joint X-Y distances(SPXY). The key information bands were selected by synergy interval partial least squares(siPLS); based on that, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA) and Monte Carlo uninformative variable(MC-UVE) were used to select wavelengths to obtain less but more critical variable data. With selected key variables as input, the quantitative analysis model was established by genetic algorithm joint extreme learning machine(GA-ELM) algorithm. The performance of the model was compared with that of partial least squares regression(PLSR). The results showed that the combination with siPLS-CARS-GA-ELM could achieve the optimal model performance with the minimum number of variables. The calibration set correlation coefficient R_c and the validation set correlation coefficient R_p of quercetin, kaempferol and isorhamnetin were all above 0.98. The root mean square error of calibration(RMSEC), the root mean square error of prediction(RMSEP) and the relative standard errors of prediction(RSEP) were 0.030 0, 0.029 2 and 8.88%, 0.041 4, 0.034 8 and 8.46%, 0.029 3, 0.027 1 and 10.10%, respectively. Compared with the PLSR me-thod, the performance of the GA-ELM model was greatly improved, which proved that NIRS combined with GA-ELM method has a great potential for rapid determination of effective components of TCM.

Rapid determination of active components in Ginkgo biloba leaves by near infrared spectroscopy combined with genetic algorithm joint extreme learning machine / 中国中药杂志

Near-infrared spectroscopy(NIRS) combined with band screening method and modeling algorithm can be used to achieve the rapid and non-destructive detection of the traditional Chinese medicine(TCM) production process. This paper focused on the ginkgo leaf macroporous resin purification process, which is the key technology of Yinshen Tongluo Capsules, in order to achieve the rapid determination of quercetin, kaempferol and isorhamnetin in effluent. The abnormal spectrum was eliminated by Mahalanobis distance algorithm, and the data set was divided by the sample set partitioning method based on joint X-Y distances(SPXY). The key information bands were selected by synergy interval partial least squares(siPLS); based on that, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA) and Monte Carlo uninformative variable(MC-UVE) were used to select wavelengths to obtain less but more critical variable data. With selected key variables as input, the quantitative analysis model was established by genetic algorithm joint extreme learning machine(GA-ELM) algorithm. The performance of the model was compared with that of partial least squares regression(PLSR). The results showed that the combination with siPLS-CARS-GA-ELM could achieve the optimal model performance with the minimum number of variables. The calibration set correlation coefficient R_c and the validation set correlation coefficient R_p of quercetin, kaempferol and isorhamnetin were all above 0.98. The root mean square error of calibration(RMSEC), the root mean square error of prediction(RMSEP) and the relative standard errors of prediction(RSEP) were 0.030 0, 0.029 2 and 8.88%, 0.041 4, 0.034 8 and 8.46%, 0.029 3, 0.027 1 and 10.10%, respectively. Compared with the PLSR me-thod, the performance of the GA-ELM model was greatly improved, which proved that NIRS combined with GA-ELM method has a great potential for rapid determination of effective components of TCM.

Critical quality attribute assessment of big brand traditional Chinese medicine: study on NIR field detection method of chemical properties of moisture in Tongren Niuhuang Qingxin Pills / 中国中药杂志

For the field detection problems of critical quality attribute(CQA) of moisture content in traditional Chinese medicine(TCM) manufacturing process, big brand TCM Tongren Niuhuang Qingxin Pills were used as the carrier, to establish a moisture content NIR field detection model with or without cellophane in real world production with use of near infrared(NIR) spectroscopy combined with stoichiometry. With the moisture content determined by drying method as reference value, the partial least square method(PLS) was used to analyze the correlation between the spectrum and the moisture reference value. Then the spectral pretreatment methods were screened and optimized to further improve the accuracy and stability of the model. The results showed that the best quantitative model was developed by the spectral data pretreatment of standard normal variate(SNV) with the latent variable factor number of 2 and 7 of Tongren Niuhuang Qingxin Pills with or without cellophane samples. The prediction coefficient of determination(R_(pre)~2) and standard deviation of prediction(RMSEP) of the model with cellophane samples were 0.765 7 and 0.157 2%; R_(pre)~2 and RMSEP of the model without cellophane samples were 0.772 2 and 0.207 8%. The NIR quantitative models of moisture content of Tongren Niuhuang Qingxin Pills with and without cellophane both showed good predictive performance to realize the rapid, accurate and non-destructive quantitative analysis of moisture content in such pills, and provide a method for the field quality control of the critical chemical attributes of moisture in the manufacturing of big brand TCM.

Critical quality attribute assessment of big brand traditional Chinese medicine: modular identification of Tongren Niuhuang Qingxin Pills based on efficacy with chemical properties / 中国中药杂志

Identification of critical quality attribute(CQA) is crucial in quality control of Tongren Niuhuang Qingxin Pills(TRNHQXP). In this study, 661 active components in TRNHQXP were selected by liquid chromatography-mass spectrometry(LC-MS) and network pharmacology based on reported data and TCMSP, BATMAN-TCM, and TCMID databases, as well as mass spectrometry data, and 1 413 targets of the active components were obtained through SwissTargetPrediction. The 152 potential targets obtained from the intersection of predicted targets with 456 stroke targets underwent functional enrichment analysis by Metascape. The 27 Chinese medicinals in TRNHQXP were divided into four sets according to efficacies. Thirty-seven key targets in the blood-activating and stasis-resolving set and 41 in the tonifying set were screened out. On the basis of these potential key targets, 137 potential key CQA of TRNHQXP for stroke were reversely predicted. This study revealed the possible mechanism of TRNHQXP in treating stroke and established a modular identification method for the potential CQA of big brand traditional Chinese medicine(TCM) based on efficacies and chemical properties. Consequently, the CQA of TRNHQXP were identified by this method, which has provided a reference for the following experimental studies of CQA.

Criticalquality attribute assessment of big brand traditional Chinese medicine: visualization method for quality control of Ginkgo Leaves Tablets based on spatial distribution uniformity / 中国中药杂志

Spatial distribution uniformity is the critical quality attribute(CQA) of Ginkgo Leaves Tablets, a variety of big brand traditional Chinese medicine. The evaluation of the spatial distribution uniformity of active pharmaceutical ingredients(APIs) in Ginkgo Leaves Tablets is important in ensuring their stable and controllable quality. In this study, hyperspectral imaging technology was used to construct the spatial distribution map of API concentration based on three prediction models, further to realize the visualization research on the spatial distribution uniformity of Ginkgo Leaves Tablets. The region of interest(ROI) was selected from each Ginkgo Leaves Tablet, with length and width of 50 pixels, and a total of 2 500 pixels. Each pixel had 288 spectral channels, and the number of content prediction data could reach 1×10~5 for a single sample. The results of the three models showed that the Partial Least Squares(PLS) model had the highest prediction accuracy, with calibration set determination coefficient R_(pre)~2 of 0.987, prediction set determination coefficient R_(pre)~2 of 0.942, root mean square error of calibration(RMSEC) of 0.160%, and root mean square error of prediction(RMSEP) of 0.588%. The classical least-squares(CLS) model had a greater prediction error, with the RMSEP of 0.867%. Multivariate Curve Resolution-Alternating Least Square(MCR-ALS) model showed the worst predictive ability among the three models, and it couldn't realize content prediction. Based on the prediction results of PLS and CLS models, the spatial distribution map of APIs concentration was obtained through three-dimensional data reconstruction. Furthermore, histogram method was used to evaluate the spatial distribution uniformity of API. The data showed that the spatial distribution of APIs in Ginkgo Leaves Tablets was relatively uniform. The study explored the feasibility of visualization of spatial distribution of Ginkgo Leaves Tablets based on three models. The results showed that PLS model had the highest prediction accuracy, and MCR-ALS model had the lowest prediction accuracy. The research results could provide a new strategy for the visualization method of quality control of Ginkgo Leaves Tablets.

[Research of hospital preparations based on QbD concept: whole process optimization strategy of gynecological antipruritic lotion].

The stable quality of hospital preparations is the basis for their clinical efficacy. Gynecological antipruritic prescription is widely used in gynecology clinics of Chinese medicine hospitals. Therefore,in this study,the production process of gynecological antipruritic lotion was optimized based on the concept of quality by design( QbD). The production process of the gynecological antipruritic lotion was developed to ensure its process stability and reliable quality,and enhance its clinical applicability. With total amount of matrine and oxymatrine used as the critical quality attribute( CQA) of the production process,parameter levels were designed based on production practice of hospital preparations,and Plackett-Burman and Box-Behnken experiments were used to optimize the water extraction and alcohol precipitation process of antipruritic lotion based on CQA of intermediates and final product. The soaking time,the first extraction time,and the second extraction time were determined as the critical process parameters( CPPs) of the production process. The optimal preparation process was as follows: water volume of 8 times,soaking for 0. 5 h,extraction for 2 times,the first extraction for 30 min,the second extraction for 56 min,alcohol concentration of 50%,and alcohol precipitation for 3 h. Furthermore,the design space was established based on the binomial regress model between CPPs and CQA,so as to set the optimization target and risk range; and the control space was displayed by overlay plot. The results of three repeated experiments in the control space showed that the relative standard deviation( RSD) of CQA was 4. 70%,and the similarity of chromatogram for gynecological antipruritic lotion was 0. 978,0. 974,and 0. 998,respectively. The above results indicated that the operation in the control space can guarantee the quality and stability of gynecological antipruritic lotion,suitable for practical application.

Artemisinin attenuates tubulointerstitial inflammation and fibrosis via the NF-κB/NLRP3 pathway in rats with 5/6 subtotal nephrectomy.

Artemisinin (Art) is isolated from Artemisia annua L. and known as the most effective antimalaria drugs. Previous studies demonstrated that it could exert an immune-regulatory effect on autoimmune diseases. In this study, we first investigated its potential role in tubulointerstitial inflammation and fibrosis in rats with 5/6 nephrectomy. Subtotal nephrectomized (SNx) rats were orally administered Art (100 mg·kg -1 ·d - 1) for 16 weeks. Blood and urine samples were collected for biochemical examination. Kidney tissues were collected for immunohistochemistry and Western blot analyses. Ang II-induced injury of the human kidney 2 (HK-2) cells was used for in vitro study. It was shown that Art could significantly attenuate the renal function decline in SNx rats compared with control. More importantly, Art treatment significantly reduced the tubulointerstitial inflammation and fibrosis, as demonstrated by the evaluation of renal pathology. Furthermore, Art inhibited the activation of NLRP3 inflammasome and NF-κB in the kidneys. In in vitro study, Art pretreatment could significantly prevent the activation of NLRP3 inflammasome and NF-κB in Ang II-treated HK-2 cells, while BAY11-7082 (an inhibitor of NF-κB) significantly inhibited Ang II-induced NLRP3 inflammasome activation. This study suggested that Art could provide renoprotective role by attenuating the tubulointerstitial inflammation and fibrosis in SNx rats by downregulating the NF-κB/NLRP3 signaling pathway.