Simon K. Poon

Synergistic interaction between Astragali Radix and Rehmanniae Radix in a Chinese herbal formula to promote diabetic wound healing

ETHNOPHARMACOLOGICAL RELEVANCE Astragali Radix (AR) and Rehmanniae Radix (RR) are two traditional Chinese medicines widely used in China for treating diabetes mellitus and its complications, such as diabetic foot ulcer. AIM OF STUDY In our previous study, a herbal formula NF3 comprising AR and RR in the ratio of 2:1 was found effective in enhancing diabetic wound healing in rats through the actions of tissue regeneration, angiogenesis promotion and inflammation inhibition. The aims of the present study were to investigate the herb-herb interaction (or the possible synergistic effect) between AR and RR in NF3 to promote diabetic wound healing and to identify the principal herb in the formula by evaluating the potencies of individual AR and RR in different mechanistic studies. MATERIALS AND METHODS A chemically induced diabetic foot ulcer rat model was used to examine the wound healing effect of NF3 and its individual herbs AR and RR. For mechanistic studies, murine macrophage cell (RAW 264.7) inflammation, human fibroblast (Hs27) proliferation and human endothelial cell (HMEC-1) migration assays were adopted to investigate the anti-inflammatory, granulation formation and angiogenesis-promoting activities of the herbal extracts, respectively. RESULTS In the foot ulcer animal model, neither AR nor RR at clinical relevant dose (0.98g/kg) promoted diabetic wound healing. However, when they were used in combination as NF3, synergistic interaction was demonstrated, of which NF3 could significantly reduce the wound area of rats when compared to water group (p<0.01). For anti-inflammation and granulation formation, AR was more effective than RR in inhibiting lipopolysaccharide (LPS)-induced nitric oxide production from RAW 264.7 cells and promoting Hs27 fibroblast proliferation. In the aspect of angiogenesis promotion, only NF3 promoted cell migration of HMEC-1 cells. CONCLUSIONS AR plays a preeminent role in the anti-inflammatory and fibroblast-proliferating activities of NF3. The inclusion of RR, however, is crucial for NF3 to exert its overall wound-healing as well as the underlying angiogenesis-promoting effects. The results of present study justified the combined usage of AR and RR in the ratio of 2:1 as NF3 to treat diabetic foot ulcer and illustrated that AR is the principal herb in this herbal formula.

The roots of Salvia miltiorrhiza (Danshen) and Pueraria lobata (Gegen) inhibit atherogenic events: a study of the combination effects of the 2-herb formula.

ETHNOPHARMACOLOGICAL RELEVANCE The roots of Salvia miltiorrhiza (Danshen) and Pueraria lobata (Gegen) are principle herbs of Chinese herbal formulae which have long been used to treat cardiovascular diseases. AIM OF STUDY The present study validated the anti-atherogenic effects of three extracts, Danshen alone (DE), Gegen alone (GE) as well as DGE and interpreted their combination effects statistically. MATERIALS AND METHODS The anti-atherogenic effects of the three extracts were studied in three assays with regards to inflammation, foam cell formation and vascular smooth muscle cell (vSMC) proliferation using lipopolysaccharide (LPS)-induced nitric oxide production model, macrophage foam cell formation model and platelet-derived growth factor (PDGF)-induced vSMC proliferation model, respectively. The combination effects of DGE were statistically analyzed using combination index (CI) and fixed-ratio experimental design. RESULTS The anti-atherogenic effects of the three extracts including anti-inflammation, anti-foam cell formation and anti-vSMC proliferation were demonstrated in this study. Their combination effects in anti-inflammation, anti-foam cell formation and anti-vSMC proliferation were found to be synergistic, additive and antagonistic, respectively. CONCLUSIONS This study provided scientific support for the combination use of DGE on atherosclerosis and presented one of the first applications of statistical interpretations of the combination effects of the 2-herb formula.

A novel approach in discovering significant interactions from TCM patient prescription data

The efficacy of a traditional Chinese medicine medication derives from the complex interactions of herbs or Chinese Materia Medica in a formula. The aim of this paper is to propose a new approach to systematically generate combinations of interacting herbs that might lead to good outcome. Our approach was tested on a data set of prescriptions for diabetic patients to verify the effectiveness of detected combinations of herbs. This approach is able to detect effective higher orders of herb-herb interactions with statistical validation. We present an exploratory analysis of clinical records using a pattern mining approach called Interaction Rules Mining.

Novel two-stage analytic approach in extraction of strong herb-herb interactions in TCM clinical treatment of insomnia

In this paper, we aim to investigate strong herb-herb interactions in TCM for effective treatment of insomnia. Given that extraction of herb interactions is quite similar to gene epistasis study due to non-linear interactions among their study factors, we propose to apply Multifactor Dimensionality Reduction (MDR) that has shown useful in discovering hidden interaction patterns in biomedical domains. However, MDR suffers from high computational overhead incurred in its exhaustive enumeration of factors combinations in its processing. To address this drawback, we introduce a two-stage analytical approach which first uses hierarchical core sub-network analysis to pre-select the subset of herbs that have high probability in participating in herb-herb interactions, which is followed by applying MDR to detect strong attribute interactions in the pre-selected subset. Experimental evaluation confirms that this approach is able to detect effective high order herb-herb interaction models in high dimensional TCM insomnia dataset that also has high predictive accuracies.

Analyzing IT business values - A Dominance based Rough Sets Approach perspective

The impact of information technology (IT) on the business value of a cooperation has been an active research area for more than two decades. Although it is widely agreed that IT has a positive impact on the business values of cooperations an in-depth understanding of the underlying structures is still missing. Especially due to the huge investments in IT, there is still a need to better understand how IT influences the performance of cooperations and business values. Generally, the data collected in IT business value research to be quantitative as well as of qualitative nature. While quantitative data can be examined by classic econometric methods the analysis of qualitative data requires special methods. In the case of ordinal data DRSA - Dominance based Rough Sets Approach has been proposed. DRSA can be applied to induce rules out of a decision table containing ordinal data. This method has already successfully applied to such diverse areas like customer relationship management and satisfaction analysis, or the technical diagnostic of a fleet of vehicles besides others. In this article we apply it for the first time to the analysis of IT business value. We use ordinal data of a survey on IT management strategies of Australian firms conducted by the Australian Department of Communications, Information Technology and the Arts. The induces rules are interpreted and provide important insights into the impact of information technology on the business values of cooperations. Furthermore our study shows the potential of DRSA for information systems research where questionnaire are a widely applied technique to collect ordinal data.

Ensemble learning for prediction of the bioactivity capacity of herbal medicines from chromatographic fingerprints

BackgroundRecent quality control of complex mixtures, including herbal medicines, is not limited to chemical chromatographic definition of one or two selected compounds; multivariate linear regression methods with dimension reduction or regularisation have been used to predict the bioactivity capacity from the chromatographic fingerprints of the herbal extracts. The challenge of this type of analysis requires a multi-dimensional approach at two levels: firstly each herb comprises complex mixtures of active and non-active chemical components; and secondly there are many factors relating to the growth, production, and processing of the herbal products. All these factors result in the significantly diverse concentrations of bioactive compounds in the herbal products. Therefore, it is imminent to have a predictive model with better generalisation that can accurately predict the bioactivity capacity of samples when only the chemical fingerprints data are available.ResultsIn this study, the algorithm of Stacking Multivariate Linear Regression (SMLR) and a few other commonly used chemometric approaches were evaluated. They were to predict the Cluster of Differentiation 80 (CD80) expression bioactivity of a commonly used herb, Astragali Radix (AR), from the corresponding chemical chromatographic fingerprints. SMLR provides a superior prediction accuracy in comparison with the other multivariate linear regression methods of PCR, PLSR, OPLS and EN in terms of MSEtest and the goodness of prediction of test samples.ConclusionsSMLR is a better platform than some multivariate linear regression methods. The first advantage of SMLR is that it has better generalisation to predict the bioactivity capacity of herbal medicines from their chromatographic fingerprints. Future studies should aim to further improve the SMLR algorithm. The second advantage of SMLR is that single chemical compounds can be effectively identified as highly bioactive components which demands further CD80 bioactivity confirmation..

Data Analytics for Traditional Chinese Medicine Research

This contributed volume explores how data mining, machine learning, and similar statistical techniques can analyze the types of problems arising from Traditional Chinese Medicine (TCM) research. The book focuses on the study of clinical data and the analysis of herbal data. Challenges addressed include diagnosis, prescription analysis, ingredient discoveries, network based mechanism deciphering, pattern-activity relationships, and medical informatics. Each author demonstrates how they made use of machine learning, data mining, statistics and other analytic techniques to resolve their research challenges, how successful if these techniques were applied, any insight noted and how these insights define the most appropriate future work to be carried out. Readers are given an opportunity to understand the complexity of diagnosis and treatment decision, the difficulty of modeling of efficacy in terms of herbs, the identification of constituent compounds in an herb, the relationship between these compounds and biological outcome so that evidence-based predictions can be made. Drawing on a wide range of experienced contributors, Data Analytics for Traditional Chinese Medicine Research is a valuable reference for professionals and researchers working in health informatics and data mining. The techniques are also useful for biostatisticians and health practitioners interested in traditional medicine and data analytics.

Application of Genetic Algorithm for Discovery of Core Effective Formulae in TCM Clinical Data

Research on core and effective formulae (CEF) does not only summarize traditional Chinese medicine (TCM) treatment experience, it also helps to reveal the underlying knowledge in the formulation of a TCM prescription. In this paper, CEF discovery from tumor clinical data is discussed. The concepts of confidence, support, and effectiveness of the CEF are defined. Genetic algorithm (GA) is applied to find the CEF from a lung cancer dataset with 595 records from 161 patients. The results had 9 CEF with positive fitness values with 15 distinct herbs. The CEF have all had relative high average confidence and support. A herb-herb network was constructed and it shows that all the herbs in CEF are core herbs. The dataset was divided into CEF group and non-CEF group. The effective proportions of former group are significantly greater than those of latter group. A Synergy index (SI) was defined to evaluate the interaction between two herbs. There were 4 pairs of herbs with high SI values to indicate the synergy between the herbs. All the results agreed with the TCM theory, which demonstrates the feasibility of our approach.

Mining symptom-herb patterns from patient records using tripartite graph

Unlike the western medical approach where a drug is prescribed against specific symptoms of patients, traditional Chinese medicine (TCM) treatment has a unique step, which is called syndrome differentiation (SD). It is argued that SD is considered as patient classification because prior to the selection of the most appropriate formula from a set of relevant formulae for personalization, a practitioner has to label a patient belonging to a particular class (syndrome) first. Hence, to detect the patterns between herbs and symptoms via syndrome is a challenging problem; finding these patterns can help prepare a prescription that contributes to the efficacy of a treatment. In order to highlight this unique triangular relationship of symptom, syndrome, and herb, we propose a novel three-step mining approach. It first starts with the construction of a heterogeneous tripartite information network, which carries richer information. The second step is to systematically extract path-based topological features from this tripartite network. Finally, an unsupervised method is used to learn the best parameters associated with different features in deciding the symptom-herb relationships. Experiments have been carried out on four real-world patient records (Insomnia, Diabetes, Infertility, and Tourette syndrome) with comprehensive measurements. Interesting and insightful experimental results are noted and discussed.

Chinese Herbal Medicine and Salmeterol and Fluticasone Propionate for Chronic Obstructive Pulmonary Disease: Systematic Review and Network Meta-Analysis

Abstract Among Chinese populations worldwide, Chinese herbal medicines (CHMs) are often used as an adjunct to pharmacotherapy in managing chronic obstructive pulmonary disease (COPD). However, the relative performance among different CHM is unknown. The aim of this study was to evaluate comparative effectiveness of different CHM when used with salmeterol and fluticasone propionate (SFP), compared with SFP alone. This study is a systematic review of randomized controlled trials (RCTs) with network meta-analyses (NMAs). Eight electronic databases were searched. Data from RCTs were extracted for random effect pairwise meta-analyses. Pooled relative risk (RR) with 95% confidence interval (CI) was used to quantify the impact of CHM and SFP on forced expiratory volume in 1 second (FEV1), St Georges Respiratory Questionnaire (SGRQ) scoring, and 6-Minute Walk Test (6MWT). NMA was used to explore the most effective CHM when used with SFP. Eleven RCTs (n = 925) assessing 11 different CHM were included. Result from pairwise meta-analyses indicated favorable, clinically relevant benefit of CHM and SFP on FEV1 [7 studies, pooled weighted mean difference (WMD) = 0.20 L, 95% CI: 0.06–0.34 L], SGRQ scoring (5 studies, pooled WMD = −4.99, 95% CI: −7.73 to −2.24), and 6MWT (3 studies, pooled WMD = 32.84 m, 95% CI: 18.26–47.42). Results from NMA showed no differences on the comparative effectiveness among CHM formulations for improving FEV1. For SGRQ, NMA suggested that Runfeijianpibushen decoction and Renshenbufei pills performed best. Use of CHM on top of SFP can provide clinically relevant benefit for COPD patients on FEV1 and SGRQ. Additional use of Runfeijianpibushen decoction and Renshenbufei pills showed better effect on improving SGRQ. Use of CHM and SFP may provide clinically relevant benefit for COPD patients on FEV1, SGRQ, and 6MWT. Use of different CHM formulae included in this NMA showed similar effect for increasing FEV1, while the additional use of Runfeijianpibushen formula and Renshenbufei Pills showed better effect on improving SGRQ. Well conducted, adequately powered trials are needed to confirm their effectiveness in the future.