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Development of an Automatic Dispensing System for Traditional Chinese Herbs.

ABSTRACT: The gathering of ingredients for decoctions of traditional Chinese herbs still relies on manual dispensation, due to the irregular shape of many items and inconsistencies in weights. In this study, we developed an automatic dispensing system for Chinese herbal decoctions with the aim of reducing manpower costs and the risk of mistakes. We employed machine vision in conjunction with a robot manipulator to facilitate the grasping of ingredients. The name and formulation of the decoction are input via a human-computer interface, and the dispensing of multiple medicine packets is performed automatically. An off-line least-squared curve fitting method was used to calculate the amount of material grasped by the claws and thereby improve system efficiency as well as the accuracy of individual dosages. Experiments on the dispensing of actual ingredients demonstrate the feasibility of the proposed system.

SUBMITTER: Lin CY

PROVIDER: S-EPMC5610888 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Development of an Automatic Dispensing System for Traditional Chinese Herbs.

Lin Chi-Ying CY Hsieh Ping-Jung PJ

Journal of healthcare engineering 20170907

The gathering of ingredients for decoctions of traditional Chinese herbs still relies on manual dispensation, due to the irregular shape of many items and inconsistencies in weights. In this study, we developed an automatic dispensing system for Chinese herbal decoctions with the aim of reducing manpower costs and the risk of mistakes. We employed machine vision in conjunction with a robot manipulator to facilitate the grasping of ingredients. The name and formulation of the decoction are input ...[more]

PMID: 29081939

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Project description:BackgroundAction ("gongxiao" in Chinese) of traditional Chinese medicine (TCM) is the high recapitulation for therapeutic and health-preserving effects under the guidance of TCM theory. TCM-defined herbal properties ("yaoxing" in Chinese) had been used in this research. TCM herbal property (TCM-HP) is the high generalization and summary for actions, both of which come from long-term effective clinical practice in two thousands of years in China. However, the specific relationship between TCM-HP and action of TCM is complex and unclear from a scientific perspective. The research about this is conducive to expound the connotation of TCM-HP theory and is of important significance for the development of the TCM-HP theory.MethodsOne hundred and thirty-three herbs including 88 heat-clearing herbs (HCHs) and 45 blood-activating stasis-resolving herbs (BAHRHs) were collected from reputable TCM literatures, and their corresponding TCM-HPs/actions information were collected from Chinese pharmacopoeia (2015 edition). The Kennard-Stone (K-S) algorithm was used to split 133 herbs into 100 calibration samples and 33 validation samples. Then, machine learning methods including supported vector machine (SVM), k-nearest neighbor (kNN) and deep learning methods including deep belief network (DBN), convolutional neutral network (CNN) were adopted to develop action classification models based on TCM-HP theory, respectively. In order to ensure robustness, these four classification methods were evaluated by using the method of tenfold cross validation and 20 external validation samples for prediction.ResultsAs results, 72.7-100% of 33 validation samples including 17 HCHs and 16 BASRHs were correctly predicted by these four types of methods. Both of the DBN and CNN methods gave out the best results and their sensitivity, specificity, precision, accuracy were all 100.00%. Especially, the predicted results of external validation set showed that the performance of deep learning methods (DBN, CNN) were better than traditional machine learning methods (kNN, SVM) in terms of their sensitivity, specificity, precision, accuracy. Moreover, the distribution patterns of TCM-HPs of HCHs and BASRHs were also analyzed to detect the featured TCM-HPs of these two types of herbs. The result showed that the featured TCM-HPs of HCHs were cold, bitter, liver and stomach meridians entered, while those of BASRHs were warm, bitter and pungent, liver meridian entered.ConclusionsThe performance on validation set and external validation set of deep learning methods (DBN, CNN) were better than machine learning models (kNN, SVM) in sensitivity, specificity, precision, accuracy when predicting the actions of heat-clearing and blood-activating stasis-resolving based on TCM-HP theory. The deep learning classification methods owned better generalization ability and accuracy when predicting the actions of heat-clearing and blood-activating stasis-resolving based on TCM-HP theory. Besides, the methods of deep learning would help us to improve our understanding about the relationship between herbal property and action, as well as to enrich and develop the theory of TCM-HP scientifically.

Project description:BackgroundThe eosinophil cationic protein (ECP) is cytotoxic to bacteria, viruses, parasites and mammalian cells. Cells are damaged via processes of pore formation, permeability alteration and membrane leaking. Some clinical studies indicate that ECP gathers in the bronchial tract of asthma sufferers, damages bronchial and airway epithelial cells, and leads to in breathing tract inflammation; therefore, prevention of the cytotoxicity caused by ECP may serve as an approach to treat airway inflammation. To achieve the purpose, reduction of the ECP-cell interactions is rational. In this work, the Chinese herbal combinative network was generated to predict and identify the functional herbs from the pools of prescriptions. It was useful to select the node herbs and to demonstrate the relative binding ability between ECP and Beas-2B cells with or withour herb treatments.ResultsEighty three Chinese herbs and prescriptions were tested and five effective herbs and six prescription candidates were selected. On the basis of effective single-herbal drugs and prescriptions, a combinative network was generated. We found that a single herb, Gan-cao, served as a node connecting five prescriptions. In addition, Sheng-di-huang, Dang-guei and Mu-tong also appeared in five, four and three kinds of prescriptions, respectively. The extracts of these three herbs indeed effectively inhibited the interactions between ECP and Beas-2B cells. According to the Chinese herbal combinative network, eight of the effective herbal extracts showed inhibitory effects for ECP internalizing into Beas-2B cells. The major components of Gang-cao and Sheng-di-huang, glycyrrhizic acid and verbascose, respectively, reduced the binding affinity between ECP and cells effectively.ConclusionsSince these Chinese herbs reduced the binding affinity between ECP and cells and inhibited subsequent ECP entrance into cells, they were potential for mitigating the airway inflammation symptoms. Additionally, we mentioned a new concept to study the Chinese herbs using combinative network in the field of systems biology. The functional single herbs could be identified from the set of prescriptions.

Project description:Coronavirus disease 2019 (COVID-19) has caused huge deaths and economic losses worldwide in the current pandemic. The main protease (Mpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is thought to be an ideal drug target for treating COVID-19. Leupeptin, a broad-spectrum covalent inhibitor of serine, cysteine, and threonine proteases, showed inhibitory activity against Mpro, with a 50% inhibitory concentration (IC50) value of 127.2 μM in vitro in our study here. In addition, leupeptin can also inhibit SARS-CoV-2 in Vero cells, with 50% effective concentration (EC50) values of 42.34 μM. More importantly, various strains of streptomyces that have a broad symbiotic relationship with medicinal plants can produce leupeptin and leupeptin analogs to regulate autogenous proteases. Fingerprinting and structure elucidation using high-performance liquid chromatography (HPLC) and high-resolution mass spectrometry (HRMS), respectively, further proved that the Qing-Fei-Pai-Du (QFPD) decoction, a traditional Chinese medicine (TCM) formula for the effective treatment of COVID-19 during the period of the Wuhan outbreak, contains leupeptin. All these results indicate that leupeptin at least contributes to the antiviral activity of the QFPD decoction against SARS-CoV-2. This also reminds us to pay attention to the microbiomes in TCM herbs as streptomyces in the soil might produce leupeptin that will later infiltrate the medicinal plant. We propose that plants, microbiome, and microbial metabolites form an ecosystem for the effective components of TCM herbs. IMPORTANCE A TCM formula has played an important role in the treatment of COVID-19 in China. However, the mechanism of TCM action is still unclear. In this study, we identified leupeptin, a metabolite produced by plant-symbiotic actinomyces (PSA), which showed antiviral activity in both cell culture and enzyme assays. Moreover, leupeptin found in the QFPD decoction was confirmed by both HPLC fingerprinting and HRMS. These results suggest that leupeptin likely contributes to the antiviral activity of the QFPD decoction against SARS-CoV-2. This result gives us important insight into further studies of the PSA metabolite and medicinal plant ecosystem for future TCM modernization research.

Dataset Information

Development of an Automatic Dispensing System for Traditional Chinese Herbs.

Publications

Development of an Automatic Dispensing System for Traditional Chinese Herbs.

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