Network Patterns of Herbal Combinations in Traditional Chinese Clinical Prescriptions.

As a well-established multidrug combinations schema, traditional Chinese medicine (herbal prescription) has been used for thousands of years in real-world clinical settings. This paper uses a complex network approach to investigate the regularities underlying multidrug combinations in herbal prescriptions. Using five collected large-scale real-world clinical herbal prescription datasets, we construct five weighted herbal combination networks with herb as nodes and herbal combinational use in herbal prescription as links. We found that the weight distribution of herbal combinations displays a clear power law, which means that most herb pairs were used in low frequency and some herb pairs were used in very high frequency. Furthermore, we found that it displays a clear linear negative correlation between the clustering coefficients and the degree of nodes in the herbal combination network (HCNet). This indicates that hierarchical properties exist in the HCNet. Finally, we investigate the molecular network interaction patterns between herb related target modules (i.e., subnetworks) in herbal prescriptions using a network-based approach and further explore the correlation between the distribution of herb combinations and prescriptions. We found that the more the hierarchical prescription, the better the corresponding effect. The results also reflected a well-recognized principle called "Jun-Chen-Zuo-Shi" in TCM formula theories. This also gives references for multidrug combination development in the field of network pharmacology and provides the guideline for the clinical use of combination therapy for chronic diseases.

Herb Target Prediction Based on Representation Learning of Symptom related Heterogeneous Network.

Traditional Chinese Medicine (TCM) has received increasing attention as a complementary approach or alternative to modern medicine. However, experimental methods for identifying novel targets of TCM herbs heavily relied on the current available herb-compound-target relationships. In this work, we present an Herb-Target Interaction Network (HTINet) approach, a novel network integration pipeline for herb-target prediction mainly relying on the symptom related associations. HTINet focuses on capturing the low-dimensional feature vectors for both herbs and proteins by network embedding, which incorporate the topological properties of nodes across multi-layered heterogeneous network, and then performs supervised learning based on these low-dimensional feature representations. HTINet obtains performance improvement over a well-established random walk based herb-target prediction method. Furthermore, we have manually validated several predicted herb-target interactions from independent literatures. These results indicate that HTINet can be used to integrate heterogeneous information to predict novel herb-target interactions.

A comprehensive review of phosphorus recovery from wastewater by crystallization processes.

The presence of phosphorus (P) in discharged wastewater can lead to water pollution events and eutrophication. Given the increasing consumption of phosphate (PO43-) rocks, wastewater containing large quantities of P is deemed as a potential source of P recovery. Crystallization of P is an ideal way to recover P because of its simple design, ease of operation, high efficiency, and limited environmental impact. This paper provides a comprehensive review of P recovery by crystallization processes with respect to the mechanisms involved, operational parameters that influence the quality of the crystal, and available seed materials for inducing crystallization. Various operational parameters including pH, molar ratio of participating ions, mixing intensity, reactor type, and seeding conditions, were detailedly investigated. Different kinds of seeds were reviewed critically with regard to their principal properties, application, and long-term prospects. Crystallized products with a high P content can be used directly as slow-release fertilizers for agricultural production, and some test methods have been developed to determine their efficiency as a fertilizer and to evaluate their availability for plants. Further, the feasibility of P recovery by crystallization was evaluated in terms of economic benefits and environmental sustainability. This work serves as a basis for future research of P recovery by crystallization processes and responses to the increasingly stringent problems of eutrophication and the growing depletion of P resources.

Effects of supersaturation control strategies on hydroxyapatite (HAP) crystallization for phosphorus recovery from wastewater.

The HAP crystallization for phosphorus removal from wastewater contributes to an environmental friendly production due to the fact that it helps reduce or eliminate the water eutrophication as well as increases the recovery of mineral resources. However, the generated microcrystalline with poor settleability in high levels of supersaturation solution has a negative effect on the phosphorus recovery efficiency. To overcome the drawback, multiple reagent feed ports (four feed ports) and different recirculation ratio (1.0, 1.5, 2.0, 2.5, 3.0) were investigated to control the levels of supersaturation in an air-agitated reactor with calcite as seeds. Results showed that the approach of multiple reagent feed ports could improve the conversion ratio of orthophosphate, but it had a limited effect (∼3% improvement) on phosphorus recovery efficiency (deposition on the seeds). With the increase of the recirculation ratio, the recovery efficiency was increased gradually and reached an optimal value of 85.63% under the recirculation ratio of 2.5 and four feed ports. This is because the adopted strategies could reduce the level of supersaturation by diluting the concentration of the reagents and inhibit large numbers of microcrystalline coinstantaneous occurrence. Meanwhile, the crystallized products were detected and analyzed by scanning electron micrograph (SEM) with energy-dispersive spectrometry (EDS) and X-ray diffraction (XRD), which were proved to be HAP with a high purity. Collectively, these results demonstrated that supersaturation control using conventional approaches had a limited improvement on the phosphorus recovery efficiency in the form of HAP, and the new control strategies for supersaturation dispersion should be developed in the further study.

An efficient approach for phosphorus recovery from wastewater using series-coupled air-agitated crystallization reactors.

Homogeneous nucleation of hydroxyapatite (HAP) crystallization in high levels of supersaturation solution has a negative effect on phosphorus recovery efficiency because of the poor settleability of the generated HAP microcrystalline. In this study, a new high-performance approach for phosphorus recovery from anaerobic supernatant using three series-coupled air-agitated crystallization reactors was developed and characterized. During 30-day operation, the proposed process showed a high recovery efficiency (∼95.82%) and low microcrystalline ratio (∼3.11%). Particle size analysis showed that the microcrystalline size was successively increased (from 5.81 to 26.32 µm) with the sequence of series-coupled reactors, confirming the conjectural mechanism that a multistage-induced crystallization system provided an appropriate condition for the growth, aggregation, and precipitation of crystallized products. Furthermore, the new process showed a broad spectrum of handling ability for different concentrations of phosphorus-containing solution in the range of 5-350 mg L-1, and the obtained results of phosphorus conversion ratio and recovery efficiency were more than 92% and 80%, respectively. Overall, these results showed that the new process exhibited an excellent ability of efficient phosphorus recovery as well as wide application scope, and might be used as an effective approach for phosphorus removal and recovery from wastewater.

Detection of herb-symptom associations from traditional chinese medicine clinical data.

Background. Traditional Chinese medicine (TCM) is an individualized medicine by observing the symptoms and signs (symptoms in brief) of patients. We aim to extract the meaningful herb-symptom relationships from large scale TCM clinical data. Methods. To investigate the correlations between symptoms and herbs held for patients, we use four clinical data sets collected from TCM outpatient clinical settings and calculate the similarities between patient pairs in terms of the herb constituents of their prescriptions and their manifesting symptoms by cosine measure. To address the large-scale multiple testing problems for the detection of herb-symptom associations and the dependence between herbs involving similar efficacies, we propose a network-based correlation analysis (NetCorrA) method to detect the herb-symptom associations. Results. The results show that there are strong positive correlations between symptom similarity and herb similarity, which indicates that herb-symptom correspondence is a clinical principle adhered to by most TCM physicians. Furthermore, the NetCorrA method obtains meaningful herb-symptom associations and performs better than the chi-square correlation method by filtering the false positive associations. Conclusions. Symptoms play significant roles for the prescriptions of herb treatment. The herb-symptom correspondence principle indicates that clinical phenotypic targets (i.e., symptoms) of herbs exist and would be valuable for further investigations.

Clinical phenotype network: the underlying mechanism for personalized diagnosis and treatment of traditional Chinese medicine.

Traditional Chinese medicine (TCM) investigates the clinical diagnosis and treatment regularities in a typical schema of personalized medicine, which means that individualized patients with same diseases would obtain distinct diagnosis and optimal treatment from different TCM physicians. This principle has been recognized and adhered by TCM clinical practitioners for thousands of years. However, the underlying mechanisms of TCM personalized medicine are not fully investigated so far and remained unknown. This paper discusses framework of TCM personalized medicine in classic literatures and in real-world clinical settings, and investigates the underlying mechanisms of TCM personalized medicine from the perspectives of network medicine. Based on 246 well-designed outpatient records on insomnia, by evaluating the personal biases of manifestation observation and preferences of herb prescriptions, we noted significant similarities between each herb prescriptions and symptom similarities between each encounters. To investigate the underlying mechanisms of TCM personalized medicine, we constructed a clinical phenotype network (CPN), in which the clinical phenotype entities like symptoms and diagnoses are presented as nodes and the correlation between these entities as links. This CPN is used to investigate the promiscuous boundary of syndromes and the co-occurrence of symptoms. The small-world topological characteristics are noted in the CPN with high clustering structures, which provide insight on the rationality of TCM personalized diagnosis and treatment. The investigation on this network would help us to gain understanding on the underlying mechanism of TCM personalized medicine and would propose a new perspective for the refinement of the TCM individualized clinical skills.

Network based integrated analysis of phenotype-genotype data for prioritization of candidate symptom genes.

BACKGROUND: Symptoms and signs (symptoms in brief) are the essential clinical manifestations for individualized diagnosis and treatment in traditional Chinese medicine (TCM). To gain insights into the molecular mechanism of symptoms, we develop a computational approach to identify the candidate genes of symptoms. METHODS: This paper presents a network-based approach for the integrated analysis of multiple phenotype-genotype data sources and the prediction of the prioritizing genes for the associated symptoms. The method first calculates the similarities between symptoms and diseases based on the symptom-disease relationships retrieved from the PubMed bibliographic database. Then the disease-gene associations and protein-protein interactions are utilized to construct a phenotype-genotype network. The PRINCE algorithm is finally used to rank the potential genes for the associated symptoms. RESULTS: The proposed method gets reliable gene rank list with AUC (area under curve) 0.616 in classification. Some novel genes like CALCA, ESR1, and MTHFR were predicted to be associated with headache symptoms, which are not recorded in the benchmark data set, but have been reported in recent published literatures. CONCLUSIONS: Our study demonstrated that by integrating phenotype-genotype relationships into a complex network framework it provides an effective approach to identify candidate genes of symptoms.

Effects of Ficus hirta Vahl. (Wuzhimaotao) extracts on growth inhibition of HeLa cells.

Ficus hirta Vahl. (Wuzhimaotao) is widely used as a folk medicine by Hakka people in southern China. In order to ascertain if any major fraction can be attributed to have pronounced anticancer effect, extracts of Wuzhimaotao on cytotoxic and apoptosis of HeLa cell lines were evaluated. HeLa cells were cultured and incubated with different concentrations of crude aqueous extracts (CAE), ethyl acetate extracts (EAE), and butyl alcohol extracts (BAE). It showed CAE, EAE, and BAE decreased cell viability on HeLa cells as a dose-dependent manner, and induced a sub-G1 peak in flow cytometry histogram of treated cells compared to the control. Apoptotic cell death is involved in CAE, EAE, and BAE toxicity, with EAE having a significant decrease in G1 population. An over all analysis of results showed that Wuzhimaotao extracts exert antiproliferative action and growth inhibition on HeLa cells through apoptosis induction which indicates its anticancer properties and deserves further investigation.

Text mining for traditional Chinese medical knowledge discovery: a survey.

Extracting meaningful information and knowledge from free text is the subject of considerable research interest in the machine learning and data mining fields. Text data mining (or text mining) has become one of the most active research sub-fields in data mining. Significant developments in the area of biomedical text mining during the past years have demonstrated its great promise for supporting scientists in developing novel hypotheses and new knowledge from the biomedical literature. Traditional Chinese medicine (TCM) provides a distinct methodology with which to view human life. It is one of the most complete and distinguished traditional medicines with a history of several thousand years of studying and practicing the diagnosis and treatment of human disease. It has been shown that the TCM knowledge obtained from clinical practice has become a significant complementary source of information for modern biomedical sciences. TCM literature obtained from the historical period and from modern clinical studies has recently been transformed into digital data in the form of relational databases or text documents, which provide an effective platform for information sharing and retrieval. This motivates and facilitates research and development into knowledge discovery approaches and to modernize TCM. In order to contribute to this still growing field, this paper presents (1) a comparative introduction to TCM and modern biomedicine, (2) a survey of the related information sources of TCM, (3) a review and discussion of the state of the art and the development of text mining techniques with applications to TCM, (4) a discussion of the research issues around TCM text mining and its future directions.