Usefulness of traditionally defined herbal properties for distinguishing prescriptions of traditional Chinese medicine from non-prescription recipes

Surface-enhanced Raman spectroscopy as a powerful method for the analysis of Chinese herbal medicines

Chinese herbal medicines (CHMs) derived from nature have received increasing attention and become more popular. Due to their diverse production processes, complex ingredients, and different storage conditions, it is highly desirable to develop simple, rapid, efficient and trace detection methods to ensure the drug quality. Surface-enhanced Raman spectroscopy has the advantages of being time-saving, non-destructive, usable in aqueous environments, and highly compatible with various biomolecular samples, providing a promising analytical method for CHM. In this review, we outline the major advances in the application of SERS to the identification of raw materials, detection of bioactive constituents, characterization of adulterants, and detection of contaminants. This clearly shows that SERS has strong potential in the quality control of CHM, which greatly promotes the modernization of CHM.

Multiple prescription pattern recognition model based on Siamese network

Prescription data is an important focus and breakthrough in the study of clinical treatment rules, and the complex multidimensional relationships between Traditional Chinese medicine (TCM) prescription data increase the difficulty of extracting knowledge from clinical data. This paper proposes a complex prescription recognition algorithm (MTCMC) based on the classification and matching of TCM prescriptions with classical prescriptions to identify the classical prescriptions contained in the prescriptions and provide a reference for mining TCM knowledge. The MTCMC algorithm first calculates the importance level of each drug in the complex prescriptions and determines the core prescription combinations of patients through the Analytic Hierarchy Process (AHP) combined with drug dosage. Secondly, a drug attribute tagging strategy was used to quantify the functional features of each drug in the core prescriptions; finally, a Bidirectional Long Short-Term Memory Network (BiLSTM) was used to extract the relational features of the core prescriptions, and a vector representation similarity matrix was constructed in combination with the Siamese network framework to calculate the similarity between the core prescriptions and the classical prescriptions. The experimental results show that the accuracy and F1 score of the prescription matching dataset constructed based on this paper reach 94.45% and 94.34% respectively, which is a significant improvement compared with the models of existing methods.

Identification of hepatoprotective traditional Chinese medicines based on the structure–activity relationship, molecular network, and machine learning techniques

The efforts focused on discovering potential hepatoprotective drugs are critical for relieving the burdens caused by liver diseases. Traditional Chinese medicine (TCM) is an important resource for discovering hepatoprotective agents. Currently, there are hundreds of hepatoprotective products derived from TCM available in the literature, providing crucial clues to discover novel potential hepatoprotectants from TCMs based on predictive research. In the current study, a large-scale dataset focused on TCM-induced hepatoprotection was established, including 676 hepatoprotective ingredients and 205 hepatoprotective TCMs. Then, a comprehensive analysis based on the structure–activity relationship, molecular network, and machine learning techniques was performed at molecular and holistic TCM levels, respectively. As a result, we developed an in silico model for predicting the hepatoprotective activity of ingredients derived from TCMs, in which the accuracy exceeded 85%. In addition, we originally proposed a material basis and a drug property-based approach to identify potential hepatoprotective TCMs. Consequently, a total of 12 TCMs were predicted to hold potential hepatoprotective activity, nine of which have been proven to be beneficial to the liver in previous publications. The high rate of consistency between our predictive results and the literature reports demonstrated that our methods were technically sound and reliable. In summary, systematical predictive research focused on the hepatoprotection of TCM was conducted in this work, which would not only assist screening of potential hepatoprotectants from TCMs but also provide a novel research mode for discovering the potential activities of TCMs.

Traditional Chinese medicine promotes bone regeneration in bone tissue engineering

Bone tissue engineering (BTE) is a promising method for the repair of difficult-to-heal bone tissue damage by providing three-dimensional structures for cell attachment, proliferation, and differentiation. Traditional Chinese medicine (TCM) has been introduced as an effective global medical program by the World Health Organization, comprising intricate components, and promoting bone regeneration by regulating multiple mechanisms and targets. This study outlines the potential therapeutic capabilities of TCM combined with BTE in bone regeneration. The effective active components promoting bone regeneration can be generally divided into flavonoids, alkaloids, glycosides, terpenoids, and polyphenols, among others. The chemical structures of the monomers, their sources, efficacy, and mechanisms are described. We summarize the use of compounds and medicinal parts of TCM to stimulate bone regeneration. Finally, the limitations and prospects of applying TCM in BTE are introduced, providing a direction for further development of novel and potential TCM.

HPE-GCN: predicting efficacy of tonic formulae via graph convolutional networks integrating traditionally defined herbal properties

Chinese herbal formulae are the heritage of traditional Chinese medicine (TCM) in treating diseases through thousands of years. The formula function is not just a simple herbal efficacy addition, but produces complex and nonlinear relationships between different herbs and their overall efficacy, which brings challenges to the formula efficacy analysis. In our study, we proposed a model called HPE-GCN that combines graph convolutional networks (GCN) with TCM-defined herbal properties (TCM-HPs) to predict formulae efficacy. In addition, to process the unstructured natural language in the formula text, we proposed a weighting calculation method related to herb frequency and the number of herbs in a formula called Formula-Herb dependence degree (FHDD), to assess the dependency degree of a formula with its herbs. In our research, 214 classic tonic formulae from ancient TCM books such as Synopsis of the Golden Chamber, Jingyue's Complete Works and the Golden Mirror of Medicin were collected as datasets. The performance of HPE-GCN on multi-classification of tonic formulae reached the best result compared with classic machine learning models, such as support vector machine, naive Bayes, logistic regression, gradient boosting decision tree, and K-nearest neighbors. The evaluated index Macro-Precision, Macro-Recall, Macro-F1 of HPE-GCN on the test set were 87.70%, 84.08% and 83.51% respectively, increased by 7.27%, 7.41% and 7.30% respectively from second best compared models. GCN has the advantage of low-dimensional feature expression for herbs and formulae, and is an effective analysis tool for TCM research. HPE-GCN integrates TCM-HPs and fits the complex nonlinear mapping relationship between TCM-HPs and formulae efficacy, which provides new ideas for related research.

The Use of Artificial Intelligence in Complementary and Alternative Medicine: A Systematic Scoping Review

Background: The development of artificial intelligence (AI) in the medical field has been growing rapidly. As AI models have been introduced in complementary and alternative medicine (CAM), a systematized review must be performed to understand its current status.

Objective: To categorize and seek the current usage of AI in CAM.

Method: A systematic scoping review was conducted based on the method proposed by the Joanna Briggs Institute. The three databases, PubMed, Embase, and Cochrane Library, were used to find studies regarding AI and CAM. Only English studies from 2000 were included. Studies without mentioning either AI techniques or CAM modalities were excluded along with the non-peer-reviewed studies. A broad-range search strategy was applied to locate all relevant studies.

Results: A total of 32 studies were identified, and three main categories were revealed: 1) acupuncture treatment, 2) tongue and lip diagnoses, and 3) herbal medicine. Other CAM modalities were music therapy, meditation, pulse diagnosis, and TCM syndromes. The majority of the studies utilized AI models to predict certain patterns and find reliable computerized models to assist physicians.

Conclusion: Although the results from this review have shown the potential use of AI models in CAM, future research ought to focus on verifying and validating the models by performing a large-scale clinical trial to better promote AI in CAM in the era of digital health.

The Impact of Artificial Intelligence on Traditional Chinese Medicine

Traditional Chinese Medicine (TCM) is a well-established medical system with a long history. Currently, artificial intelligence (AI) is rapidly expanding in many fields including TCM. AI will significantly improve the reliability and accuracy of diagnostics, thus increasing the use of effective therapeutic methods for patients. This systematic review provides an updated overview on the major breakthroughs in the field of AI-assisted TCM four diagnostic methods, syndrome differentiation, and treatment. AI-assisted TCM diagnosis is mainly based on digital data collected by modern electronic instruments, which makes TCM diagnosis more quantitative, objective, and standardized. As a result, the diagnosis decisions made by different TCM doctors exhibit more consistency, accuracy, and reliability. Meanwhile, the therapeutic efficacy of TCM can be evaluated objectively. Therefore, AI is promoting TCM from experience to evidence-based medicine, a genuine scientific revolution. Furthermore, huge and non-uniform knowledge on formula-syndrome relationships and the combination rules of herbal TCM formulae could be better standardized with the help of AI analysis, which is necessary for the clinical efficacy evaluation and further optimization on the standardized TCM formulae. AI bridges the gap between TCM and modern science and technology. AI may bring clinical TCM diagnostics closer to western medicine. With the help of AI, more scientific evidence about TCM will be discovered. It can be expected that more unified guidelines for specific TCM syndromes will be issued with the development of AI-assisted TCM therapies in the future.

Polysaccharides from Pinus massoniana pollen improve intestinal mucosal immunity in chickens

Intestinal mucosa is the largest immune organ in animals, and its immune function is directly related to the resistance against various diseases. Taishan Pinus massoniana pollen polysaccharides (TPPPS) have been recognized as an effective vaccine adjuvant and potential immune enhancer against viral infections. However, little is known about their direct immune-enhancing activity on intestinal mucosa. In this study, we extracted the polysaccharides from Taishan masson pine pollen to investigate its promotive effect on intestinal mucosal immunity. A total of 120 1-day-old chickens were divided into 4 groups and inoculated with PBS or 3 different doses of TPPPS (10 mg/mL, 20 mg/mL, and 40 mg/mL), respectively. Feces, intestinal specimens, and serum samples were collected from the chickens at 7, 14, and 21 d after inoculation. The antibodies in serum, mucosal secretion of IgA, structure of intestinal villi, and expressions of cytokine genes and mucosal immune-related genes in the chickens were all significantly improved by TPPPS treatments. At 21 d after inoculation following the challenge of Newcastle disease virus, the chickens inoculated with 20 and 40 mg/mL TPPPS exhibited decreased weight loss and reduced intestinal pathologic damage and viral loads in the intestine. In summary, our results demonstrate that TPPPS can enhance mucosal immunity and promote intestinal villi development. This study has established the foundation for the development of novel immune-enhancing agent with immune-regulatory effects on intestinal mucosa.

Cross-Species Annotation of Expressed Genes and Detection of Different Functional Gene Modules Between 10 Cold- and 10 Hot-Propertied Chinese Herbal Medicines

According to the traditional Chinese medicine (TCM) system, Chinese herbal medicines (HMs) can be divided into four categories: hot, warm, cold, and cool. A cool nature usually is categorized as a cold nature, and a warm nature is classified as a hot nature. However, the detectable characteristics of the gene expression profile associated with the cold and hot properties have not been studied. To address this question, a strategy for the cross-species annotation of conserved genes was established in the present study by using transcriptome data of 20 HMs with cold and hot properties. Functional enrichment analysis was performed on group-specific expressed genes inferred from the functional genome of the reference species (i.e., Arabidopsis). Results showed that metabolic pathways relevant to chrysoeriol, luteolin, paniculatin, and wogonin were enriched for cold-specific genes, and pathways of inositol, heptadecane, lauric acid, octanoic acid, hexadecanoic acid, and pentadecanoic acid were enriched for hot-specific genes. Six functional modules were identified in the HMs with the cold property: nucleotide biosynthetic process, peptidy-L-cysteine S-palmitoylation, lipid modification, base-excision repair, dipeptide transport, and response to endoplasmic reticulum stress. For the hot HMs, another six functional modules were identified: embryonic meristem development, embryonic pattern specification, axis specification, regulation of RNA polymerase II transcriptional preinitiation complex assembly, mitochondrial RNA modification, and cell redox homeostasis. The research provided a new insight into HMs’ cold and hot properties from the perspective of the gene expression profile of plants.

Applications of Network Pharmacology in Traditional Chinese Medicine Research

Human diseases, especially infectious ones, have been evolving constantly. However, their treatment strategies are not developing quickly. Some diseases are caused by a variety of factors with very complex pathologies, and the use of a single drug cannot solve these problems. Traditional Chinese Medicine (TCM) medication is a unique treatment method in China. TCM formulae contain multiple herbs with multitarget, multichannel, and multilink characteristics. In recent years, with the flourishing development of network pharmacology, a new method for searching therapeutic drugs has emerged. The multitarget action in network pharmacology is consistent with the complex mechanisms of disease and drug action. Using network pharmacology to understand TCM is an emerging trend.

A Novel Discovery: Holistic Efficacy at the Special Organ Level of Pungent Flavored Compounds from Pungent Traditional Chinese Medicine

Pungent traditional Chinese medicines (TCMs) play a vital role in the clinical treatment of hepatobiliary disease, gastrointestinal diseases, cardiovascular diseases, diabetes, skin diseases and so on. Pungent TCMs have a vastness of pungent flavored (with pungent taste or smell) compounds. To elucidate the molecular mechanism of pungent flavored compounds in treating cardiovascular diseases (CVDs) and liver diseases, five pungent TCMs with the action of blood-activating and stasis-resolving (BASR) were selected. Here, an integrated systems pharmacology approach is presented for illustrating the molecular correlations between pungent flavored compounds and their holistic efficacy at the special organ level. First, we identified target proteins that are associated with pungent flavored compounds and found that these targets were functionally related to CVDs and liver diseases. Then, based on the phenotype that directly links human genes to the body parts they affect, we clustered target modules associated with pungent flavored compounds into liver and heart organs. We applied systems-based analysis to introduce a pungent flavored compound-target-pathway-organ network that clarifies mechanisms of pungent substances treating cardiovascular diseases and liver diseases by acting on the heart/liver organ. The systems pharmacology also suggests a novel systematic strategy for rational drug development from pungent TCMs in treating cardiovascular disease and associated liver diseases.

A systematic literature review and classification of knowledge discovery in traditional medicine

INTRODUCTION AND OBJECTIVE

Despite the importance of machine learning methods application in traditional medicine there is a no systematic literature review and a classification for this field. This is the first comprehensive literature review of the application of data mining methods in traditional medicine.

METHOD

We reviewed 5 database between 2000 to 2017 based on the Kitchenham systematic review methodology. 502 articles were identified and reviewed for their relevance to application of machine learning methods in traditional medicine, 42 selected papers were classified and categorized on four dimension; 1) application domain of data mining techniques in traditional medicine; 2) the data mining methods most frequently used in traditional medicine; 3) main strength and limitation of data mining techniques in traditional medicine; 4) the performance evaluation methods in data mining methods in traditional medicine.

RESULT

The result obtained showed that main application domain of data mining techniques in traditional medicine was related to syndrome differentiation. Bayesian Networks (BNs), Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) were recognized as being the methods most frequently applied in traditional medicine. Furthermore, each data mining techniques has its own strength and limitations when applied in traditional medicine. Single scaler methods were frequently used for performance evaluation of data mining methods.

CONCLUSION

Machine learning methods have become an important research field in traditional medicine. Our research provides information about this methods by examining the related articles.

Development of models for classification of action between heat-clearing herbs and blood-activating stasis-resolving herbs based on theory of traditional Chinese medicine

Background

Action (“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.

Methods

One 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.

Results

As 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.

Conclusions

The 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.

Electronic supplementary material

The online version of this article (10.1186/s13020-018-0169-x) contains supplementary material, which is available to authorized users.

Adjuvant effects mediated by the carbohydrate recognition domain of Agrocybe aegerita lectin interacting with avian influenza H9N2 viral surface glycosylated proteins

OBJECTIVE

To evaluate the potential adjuvant effect of Agrocybe aegerita lectin (AAL), which was isolated from mushroom, against a virulent H₉N₂ strain in vivo and in vitro.

METHODS

In trial 1, 50 BALB/c male mice (8 weeks old) were divided into five groups (n=10 each group) which received a subcutaneous injection of inactivated H₉N₂ (control), inactivated H₉N₂+0.2% (w/w) alum, inactivated H₉N₂+0.5 mg recombinant AAL/kg body weight (BW), inactivated H₉N₂+1.0 mg AAL/kg BW, and inactivated H₉N₂+2.5 mg AAL/kg BW, respectively, four times at 7-d intervals. In trial 2, 30 BALB/c male mice (8 weeks old) were divided into three groups (n=10 each group) which received a subcutaneous injection of inactivated H₉N₂ (control), inactivated H₉N₂+2.5 mg recombinant wild-type AAL (AAL-wt)/kg BW, and inactivated H₉N₂+2.5 mg carbohydrate recognition domain (CRD) mutant AAL (AAL-mutR63H)/kg BW, respectively, four times at 7-d intervals. Seven days after the final immunization, serum samples were collected from each group for analysis. Hemagglutination assay, immunogold electron microscope, lectin blotting, and co-immunoprecipitation were used to study the interaction between AAL and H₉N₂ in vitro.

RESULTS

IgG, IgG1, and IgG2a antibody levels were significantly increased in the sera of mice co-immunized with inactivated H₉N₂ and AAL when compared to mice immunized with inactivated H₉N₂ alone. No significant increase of the IgG antibody level was detected in the sera of the mice co-immunized with inactivated H₉N₂ and AAL-mutR63H. Moreover, AAL-wt, but not mutant AAL-mutR63H, adhered to the surface of H₉N₂ virus. The interaction between AAL and the H₉N₂ virus was further demonstrated to be associated with the CRD of AAL binding to the surface glycosylated proteins, hemagglutinin and neuraminidase.

CONCLUSIONS

Our findings indicated that AAL could be a safe and effective adjuvant capable of boosting humoral immunity against H₉N₂ viruses in mice through its interaction with the viral surface glycosylated proteins, hemagglutinin and neuraminidase.

Co-adjuvant effects of plant polysaccharide and propolis on chickens inoculated with Bordetella avium inactivated vaccine

Taishan Pinus massoniana pollen polysaccharide (TPPPS), propolis (PP) and aloe polysaccharide (AP), used as adjuvants, have been proven to possess immunity-enhancing functions. However, their collaborative immunomodulatory effects are largely unknown. To determine which combination can induce the best effects, the three adjuvants were separately or conjointly added into Bordetella avium inactivated vaccines to investigate their co-adjuvant effects on vaccinated chickens. We found that, among all six adjuvant-treated vaccine inoculated groups (TPPPS, PP, AP, TPPPS-PP, PP-AP and TPPPS-AP), the chickens inoculated with TPPPS, PP or TPPPS-PP adjuvant vaccines showed significantly higher levels of antibody titre, cytokine, lymphocyte transformation and peripheral blood T-lymphocyte count than those of non-adjuvant vaccine inoculated groups (P < 0.05), indicating the good immune-enhancing effects of TPPPS and PP. The TPPPS-PP group showed the highest levels of antibody titres and interleukin-2 (IL-2) at 14-28 days post the first inoculation (dpi), lymphocyte transformation rates (LTRs) at 14-35 dpi, CD4(+) T-lymphocyte counts at 14-42 dpi, and CD8(+) T-lymphocyte counts at 28 dpi. The results revealed that B. avium inactivated vaccine used conjointly with TPPPS and PP induced the strongest humoral and cellular immune responses. Thus, there was a synergistic effect between TPPPS and PP on enhancing immunity, which suggests that they can be used as a novel adjuvant formulation for the development of poultry vaccines.

The traditional medical uses and cytotoxic activities of sixty-one Egyptian plants: discovery of an active cardiac glycoside from Urginea maritima

ETHNOPHARMACOLOGICAL RELEVANCE

Medicinal plants from the Sinai desert are widely used in traditional Bedouin medicine to treat a range of conditions including, cancers, and may thus be useful sources of novel anti-tumor compounds. Information on plants used in this way was obtained through collaboration with Bedouin herbalists.

AIM OF THE STUDY

To document the traditional uses of 61 species from 29 families of Egyptian medicinal plants and to investigate their biological activity using a cytotoxicity assay.

MATERIAL AND METHODS

MeOH extracts of the 61 plant species investigated were dissolved in 10% DMSO and their cytotoxic activity was evaluated. The extracts were tested in duplicate on three separate occasions at three different concentrations (1, 10 and 100μg/ml) against human lymphoma U-937 GTB. The most active extract was subjected to bioassay-guided fractionation using HPLC and LC/ESI-MS to isolate and identify its active components.

RESULTS AND DISCUSSION

The most potent extracts were those from Asclepias sinaica, Urginea maritima, Nerium oleander and Catharanthus roseus, followed by those from Cichorium endivia, Pulicaria undulate and Melia azedarach. Literature reports indicate that several of these plants produce cardiac glycosides. Bioassay-guided fractionation of alcoholic U. maritima extracts led to the isolation of a bioactive bufadienolide that was subsequently shown to be proscillaridin A, as determined by 1D and 2D NMR spectroscopy. This result demonstrates the value of plants used in traditional medicine as sources of medicinally interesting cytotoxic compounds.

Adjuvant activity of Sargassum pallidum polysaccharides against combined Newcastle disease, infectious bronchitis and avian influenza inactivated vaccines

This study evaluates the effects of Sargassum pallidum polysaccharides (SPP) on the immune responses in a chicken model. The adjuvanticity of Sargassum pallidum polysaccharides in Newcastle disease (ND), infectious bronchitis (IB) and avian influenza (AI) was investigated by examining the antibody titers and lymphocyte proliferation following immunization in chickens. The chickens were administrated combined ND, IB and AI inactivated vaccines containing SPP at 10, 30 and 50 mg/mL, using an oil adjuvant vaccine as a control. The ND, IB and AI antibody titers and the lymphocyte proliferation were enhanced at 30 mg/mL SPP. In conclusion, an appropriate dose of SPP may be a safe and efficacious immune stimulator candidate that is suitable for vaccines to produce early and persistent prophylaxis.

Study of the distribution patterns of the constituent herbs in classical Chinese medicine prescriptions treating respiratory disease by data mining methods

OBJECTIVE

To provide the distribution pattern and compatibility laws of the constituent herbs in prescriptions, for doctor's convenience to make decision in choosing correct herbs and prescriptions for treating respiratory disease.

METHODS

Classical prescriptions treating respiratory disease were selected from authoritative prescription books. Data mining methods (frequent itemsets and association rules) were used to analyze the regular patterns and compatibility laws of the constituent herbs in the selected prescriptions.

RESULTS

A total of 562 prescriptions were selected to be studied. The result exhibited that, Radix glycyrrhizae was the most frequently used in 47.2% prescriptions, other frequently used were Semen armeniacae amarum, Fructus schisandrae Chinese, Herba ephedrae, and Radix ginseng. Herbal ephedrae was always coupled with Semen armeniacae amarum with the confidence of 73.3%, and many herbs were always accompanied by Radix glycyrrhizae with high confidence. More over, Fructus schisandrae Chinese, Herba ephedrae and Rhizoma pinelliae was most commonly used to treat cough, dyspnoea and associated sputum respectively besides Radix glycyrrhizae and Semen armeniacae amarum. The prescriptions treating dyspnoea often used double herb group of Herba ephedrae & Radix glycyrrhizae, while prescriptions treating sputum often used double herb group of Rhizoma pinelliae & Radix glycyrrhizae and Rhizoma pinelliae & Semen armeniacae amarum, triple herb groups of Rhizoma pinelliae & Semen armeniacae amarum & Radix glycyrrhizae and Pericarpium citri reticulatae & Rhizoma pinelliae & Radix glycyrrhizae.

CONCLUSIONS

The prescriptions treating respiratory disease showed common compatibility laws in using herbs and special compatibility laws for treating different respiratory symptoms. These principle patterns and special compatibility laws reported here could be useful for doctors to choose correct herbs and prescriptions in treating respiratory disease.

Compound Chinese herbal medicinal ingredients can enhance immune response and efficacy of RHD vaccine in rabbit

In order to validate the immune-enhancement efficacy of compound Chinese herbal medicinal ingredients (cCHMIs), made with astragalus polysaccharide (APS), epimedium polysaccharide (EPS), propolis flavone (PF) and ginsenosides (GS), as immune potentiator or vaccine adjuvants for rabbits, the effects of two cCHMIs on rabbit lymphocyte proliferation and IFN-gamma and IL-10 mRNA expression of T lymphocyte in vitro were determined. At the same time, two cCHMIs were injected into 35-day-old rabbits after mixed with rabbit hemorrhagic disease (RHD) vaccine taking aluminum adjuvant and phosphate-buffered saline (PBS) as controls. On days 7, 14, 21, 35 and 49 after the vaccination, the dynamic changes of peripheral lymphocyte proliferation and serum antibody titers of the rabbits were analyzed. On day 63, all rabbits were challenged with RHD virus. The results showed that the two cCHMIs could significantly promote rabbit lymphocyte proliferation and IFN-gamma and IL-10 mRNA expression of T lymphocyte in vitro. In vivo, two cCHMIs could significantly enhance serum antibody titers and lymphocyte proliferation. Their adjuvanticity was slightly superior to aluminum adjuvant. All the rabbits vaccinated with the cCHMIs adjuvant vaccine were protected. These findings confirmed that two cCHMIs possessed better immune-enhancement efficacy and would be used as effective immune adjuvant of RHD vaccine.

Are herb-pairs of traditional Chinese medicine distinguishable from others? Pattern analysis and artificial intelligence classification study of traditionally defined herbal properties

Multi-herb prescriptions of traditional Chinese medicine (TCM) often include special herb-pairs for mutual enhancement, assistance, and restraint. These TCM herb-pairs have been assembled and interpreted based on traditionally defined herbal properties (TCM-HPs) without knowledge of mechanism of their assumed synergy. While these mechanisms are yet to be determined, properties of TCM herb-pairs can be investigated to determine if they exhibit features consistent with their claimed unique synergistic combinations. We analyzed distribution patterns of TCM-HPs of TCM herb-pairs to detect signs indicative of possible synergy and used artificial intelligence (AI) methods to examine whether combination of their TCM-HPs are distinguishable from those of non-TCM herb-pairs assembled by random combinations and by modification of known TCM herb-pairs. Patterns of the majority of 394 known TCM herb-pairs were found to exhibit signs of herb-pair correlation. Three AI systems, trained and tested by using 394 TCM herb-pairs and 2470 non-TCM herb-pairs, correctly classified 72.1-87.9% of TCM herb-pairs and 91.6-97.6% of the non-TCM herb-pairs. The best AI system predicted 96.3% of the 27 known non-TCM herb-pairs and 99.7% of the other 1,065,100 possible herb-pairs as non-TCM herb-pairs. Our studies suggest that TCM-HPs of known TCM herb-pairs contain features distinguishable from those of non-TCM herb-pairs consistent with their claimed synergistic or modulating combinations.