Objective: To explore the feasibility of remotely obtaining complex information on traditional Chinese medicine (TCM) pulse conditions through voice signals. Methods: We used multi-label pulse conditions as the entry point and modeled and analyzed TCM pulse diagnosis by combining voice analysis and machine learning. Audio features were extracted from voice recordings in the TCM pulse condition dataset. The obtained features were combined with information from tongue and facial diagnoses. A multi-label pulse condition voice classification DNN model was built using 10-fold cross-validation, and the modeling methods were validated using publicly available datasets. Results: The analysis showed that the proposed method achieved an accuracy of 92.59% on the public dataset. The accuracies of the three single-label pulse manifestation models in the test set were 94.27%, 96.35%, and 95.39%. The absolute accuracy of the multi-label model was 92.74%. Conclusion Voice data analysis may serve as a remote adjunct to the TCM diagnostic method for pulse condition assessment.

Smart manufacturing still remains critical challenges for pharmaceutical manufacturing. Here, an original data-driven engineering framework was proposed to tackle the challenges. Firstly, from sporadic indicators to five kinds of systematic quality characteristics, nearly 2,000,000 real-world data points were successively characterized from Ginkgo Folium tablet manufacturing. Then, from simplex to the multivariate system, the digital process capability diagnosis strategy was proposed by multivariate C_pk integrated Bootstrap-t. The C_pk of Ginkgo Folium extracts, granules, and tablets were discovered, which was 0.59, 0.42, and 0.78, respectively, indicating a relatively weak process capability, especially in granulating. Furthermore, the quality traceability was discovered from unit to end-to-end analysis, which decreased from 2.17 to 1.73. This further proved that attention should be paid to granulating to improve the quality characteristic. In conclusion, this paper provided a data-driven engineering strategy empowering industrial innovation to face the challenge of smart pharmaceutical manufacturing.

After more than 100 years of development， spray drying technology has become more mature and widely used， and it is of great importance in the field of traditional Chinese medicine （TCM）. TCM powders prepared by spray drying is the raw material of dispensing granules， and the powder properties have an important influence on subsequent molding process and product quality. As a new form of TCM， dispensing granules have been included in the management category of TCM decoction pieces， indicating a broader application market， and a consensus has also been reached on the importance of TCM powder research. Based on this， the author summarized the application progress of spray drying in the study of TCM powders， including the factors affecting spray drying process， such as liquid properties， process parameters and equipment factors， as well as the application of computational fluid dynamics （CFD） and thermodynamic model in spray drying process simulation. Moreover， some commonly used pharmaceutical excipients for the modification of TCM powders were also introduced such as maltodextrin， microcrystalline cellulose and povidone. In addition， spray drying technology can also be used as a preparation technology for new drug delivery systems such as microcapsules and solid dispersions. Through the summary of this paper， the author suggests that the future research direction of spray drying of TCM can be carried out from the aspects of application rule of the coprocessing auxiliary materials based on the "unification of medicines and excipients"， the "structure-property" relationship of spray-dried powders and the application of computer simulation and design， so as to further enrich the application of spray drying in the field of TCM powders.

Objective: This study was devoted to identifying natural thrombin inhibitors from traditional Chinese medicine (TCM) and evaluating its biological activity in vitro and binding characteristics. Methods: A combination strategy containing molecular docking, thrombin inhibition assay, surface plasmon resonance (SPR) and molecular dynamics simulation were applied to verify the study result. Results: Gallic acid was confirmed as a direct thrombin inhibitor with IC

OBJECTIVE： To prepare pogostone transfersomes， and to evaluate its quality. METHODS： Film dispersion method was used to prepare pogostone transfersomes. Using the accumulative penetration volume （Qn） and accumulative penetration ratio （PR） of pogostone as evaluation indexes， the types of surfactant， formulation were screened in respects of the dosage of surfactant and the dosage of pogostone. The pogostone transfersomes were prepared with optimal formulation； the morphology， particle size distribution and Zeta potential were observed and the entrapment efficiency was measured. RESULTS： The optimal formulation was as follows as the sodium cholate was selected as surfactant； the dosage of sodium cholate was 0.25 g； the dosage of pogostone was 15 mg. The optimal pogostone transfersomes were ivory-white suspension； average particle size was （115.6±3.65） nm （RSD＝3.20％，n＝3）； PDI was 0.185±0.008 （RSD＝4.30％， n＝3）； Zeta potential was （－13.76±0.225） mV （RSD＝1.70％，n＝3）； entrapment efficiency of pogostone was （46.01±0.40）％ （RSD＝0.87％，n＝3）； Qn was （378.76±0.61）   μg/cm2 （RSD＝0.20％，n＝3）； PR was （89.02±0.96）％ （RSD＝1.10％，n＝3）. CONCLUSIONS： Prepared pogostone transfersomes are in line with quality requirements， which can provide reference for the further study of new dosage form of pogostone.

New sensor technologies play an important role in quality evaluation of Chinese materia medica (CMM) and include near-infrared spectroscopy, chemical imaging, electronic nose and electronic tongue. This review on quality evaluation of CMM and the application of the new sensors in this assessment is based on studies from 2010 to 2015, with prospects and opportunities for future research.

Abstract: The design space of the high shear wet granulation process was established and validated within the framework of quality by design (QbD). The system of microcrystalline cellulose-de-ioned water was used in this study. The median granule size and bulk density of granules were identified as critical quality attributes. Plackeet-Burmann experimental design was used to screen these factors as follows: dry mixing time, the impeller and chopper speed of dry mixing, water amount, water addition time, wet massing time, the impeller and chopper speed of wet massing and drying time. And the optimization was implemented with the central composite experimental design based on screened critical process parameters. The design space of the high shear wet granulation process was established based on the quadratic polynomial regression model. Since the P-values of both models were less than 0.05 and values of lack of fit were more than 0.1, the relationship between critical quality attributes and critical process parameters could be well described by the two models. The reliability of design space, illustrated by overlay plot, was improved with the addition of 95% confidence interval. For those granules whose process parameters were in the design space, the granule size could be controlled within 250 to 355 μm, and the bulk density could be controlled within a range of 0.4 to 0.6 g x cm(-3). The robustness and flexibility of the high shear wet granulation process have been enhanced via the establishment of the design space based on the QbD concept.

Gastrodia elatais graded as top medication in theShen Nong’s Herbal Classic. It was mainly distributed in southwest China. Its quality varied with geographical location. And the quality difference between wild and cultivated sample was extreme. Identifications using traditional methods were unable to accurately distinguish the quality ofG. elata. Therefore, near-infrared (NIR) spectroscopy combined with pattern recognition method was used to distinguish the quality ofG. elata from different geographical locations as well as cultivated or wild. The results demonstrated that using NIR spectroscopy combined with multiclass classification algorithm, the geographical location ofG. elata can be accurately distinguished. The prediction accuracy can reach as high as 94.3% and 96.4% for both applications. Besides, the classification model was built without preprocessing; hence, it can be extended to be applied on-site.

Objective To construct the competitiveness evaluation index system of listed TCM pharmaceutical companies and provide efficient technology and methods for the evaluation in related field.Methods Index base was founded by the means of the literature research method at first. Then 20 experts were asked to score all these indexes according to the importance of each index. Dimensionality and index base of competitiveness evaluation index system of listed TCM pharmaceutical companies were screened. With two rounds of questionnaires, the evaluation index system was constructed finally.Results The positive coefficients of two rounds of expert consultation were 95% and 100%;the cooperative coefficients were 0.659 and 0.639;the authoritative coefficient was 0.713 2. Evaluation system consisted of 5 first grade indexes and 26 second grade indexes.Conclusion The positive coefficients and the authoritative coefficients are both high enough through Delphi method. Opinions of all the experts in the two round of expert consultation tend to be uniform, which reveals that the evaluation index system of listed TCM pharmaceutical companies is relatively scientific.

ToprovidethemethodologyforrapidqualityevaluationofLonicerajaponica,wehaveestablished the stable quantitative model of near infrared spectroscopy ( NIR) . The performance of Bagging partial least squares (Bagging-PLS) model and Boosting partial least squares (Boosting-PLS) model was compared with that partial least squares ( PLS ) model based on the NIR data of ethanol precipitation process of Lonicera japonica. On this basis, the performance of these two models after variables selection was also studied by the methods of siPLS ( synergy interval partial least squares ) and CARS ( competitive adaptive reweighted sampling) . The experimental results showed that the prediction performance of Bagging-PLS and Boosting-PLS models was superior to PLS model with the latent factor of 10 . The band of 820-1029 . 5 nm and 1030-1239. 5 nm for the first batch was selected by the method of siPLS. In addition, the band of 820-1029. 5 nm and 1030-1239. 5 nm was selected for the second batch sample in the same method. Furthermore, the method of CARS was taken to select variables for the two batches samples with 5-fold cross-validation and 10-fold cross-validation. And the lowest RMSECV( root mean square error of cross-validation) values were used to take subset. Compared to the model performance without the method of CARS, the RMSEP value of the Bagging-PLS model and Boosting-PLS model for the concentration of chlorogenic acid reduced by 0 . 02-0 . 04 g/L and rp(correlation coefficient of prediction)value increased by 4%-5%. Generally, Bagging-PLS and Boosting-PLS could be regarded as rapid prediction methodsfor NIR quantitative models of ethanol precipitation process of Lonicera japonica.