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

The property theory of Chinese materia medica is one of the foundations of traditional Chinese medicine.The property of Chinese materia medica (PCMM) is a multi-dimensional expression of the effect of Chinese materia medica (CMM),and it is related to the clinical prescription that fully reflects the clinical effect evaluation of CMM in a holistic,systematic,and scientific way.This paper discusses the source,development,and application of the PCMM by considering not only the five dimensions that constitute the PCMM but also the recognition of the human body and disease as given in traditional Chinese medicine.This paper aims to provide theoretical guidance for the rational use and development of CMM.

Objective:The application of Chinese materia medica(CMM)in clinical diseases is the embodiment and continuation of the property theory of CMM(PTCMM).However,due to a lack of precise quantitative description methods,it is difficult to systematically analyze the property of CMM(PCMM)and clinical effect features at the micro molecular level.Methods:The therapeutic drugs and targets were obtained from the Drugbank database.The molecular descriptors of these drugs were calculated based on Dragon software.Drug-effect relationships that integrated the molecular descriptors and effect descriptors were plotted as grayscale images.These images were used to train the LeNet-5 model and the AlexNet model.The best-performing model was used to predict the effect features of the CMM compounds.Finally,the effect features of the PCMM combinations were calculated based on the support vector machine recursive feature elimination algorithm.Results:The AlexNet model showed a superior prediction performance.The results showed that its accuracy,precision,sensitivity,F-measure,and Matthews correlation coefficient on the training set were 0.940,0.936,0.945,0.940,and 0.880,respectively,and those of the test set were 0.909,0.901,0.920,0.910,and 0.819,respectively.A total of 399 compounds in the 42 CMMs for promoting blood circulation and removing blood stasis were predicted by this model.The key effect features of the Han-Ku-Gan combination were anti-inflammatory,anti-tumor,anti-atherosclerosis,anti-Parkinson,hypoglycemic,and anti-coagulant properties,as well as excitation of uterine smooth muscle.The key effect features of the Wen-Xin-Gan combination were anti-inflammatory,anti-atherosclerosis,anti-hypertensive,anti-coagulant,anti-tumor,and anti-cardiac insufficiency effects,as well as enhanced immunity,sedation and hypnosis,and analgesia.Conclusion:This study provides a new method for the further exploration of the relationship between the PCMM and clinical effect features.

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.

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.

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.