PresRecST: a novel herbal prescription recommendation algorithm for real-world patients with integration of syndrome differentiation and treatment planning.

OBJECTIVES: Herbal prescription recommendation (HPR) is a hot topic and challenging issue in field of clinical decision support of traditional Chinese medicine (TCM). However, almost all previous HPR methods have not adhered to the clinical principles of syndrome differentiation and treatment planning of TCM, which has resulted in suboptimal performance and difficulties in application to real-world clinical scenarios. MATERIALS AND METHODS: We emphasize the synergy among diagnosis and treatment procedure in real-world TCM clinical settings to propose the PresRecST model, which effectively combines the key components of symptom collection, syndrome differentiation, treatment method determination, and herb recommendation. This model integrates a self-curated TCM knowledge graph to learn the high-quality representations of TCM biomedical entities and performs 3 stages of clinical predictions to meet the principle of systematic sequential procedure of TCM decision making. RESULTS: To address the limitations of previous datasets, we constructed the TCM-Lung dataset, which is suitable for the simultaneous training of the syndrome differentiation, treatment method determination, and herb recommendation. Overall experimental results on 2 datasets demonstrate that the proposed PresRecST outperforms the state-of-the-art algorithm by significant improvements (eg, improvements of P@5 by 4.70%, P@10 by 5.37%, P@20 by 3.08% compared with the best baseline). DISCUSSION: The workflow of PresRecST effectively integrates the embedding vectors of the knowledge graph for progressive recommendation tasks, and it closely aligns with the actual diagnostic and treatment procedures followed by TCM doctors. A series of ablation experiments and case study show the availability and interpretability of PresRecST, indicating the proposed PresRecST can be beneficial for assisting the diagnosis and treatment in real-world TCM clinical settings. CONCLUSION: Our technology can be applied in a progressive recommendation scenario, providing recommendations for related items in a progressive manner, which can assist in providing more reliable diagnoses and herbal therapies for TCM clinical task.

Efficacy and safety of Qingda granule versus valsartan capsule in Chinese grade 1 hypertensive patients with low-moderate risk: A randomized, double-blind, double dummy, non-inferiority, multi-center trial.

BACKGROUND: The efficacy and safety of Qingda granule (QDG) in managing blood pressure (BP) among grade 1 hypertensive patients with low-moderate risk remain uncertain. METHODS: In the randomized, double-blind, double dummy, non-inferiority and multicenter trial, 552 patients with grade 1 hypertension at low-moderate risk were assigned at a ratio of 1:1 to receive either QDG or valsartan for 4 weeks, followed up by a subsequent 4 weeks. RESULTS: Post-treatment, clinic systolic/diastolic BPs (SBP/DBP) were reduced by a mean change of 9.18/4.04 mm Hg in the QDG group and 9.85/5.05 mm Hg in the valsartan group (SBP P = 0.47, DBP P = 0.16). Similarly, 24-hour, daytime and nighttime BPs were proportional in both groups (P > 0.05) after 4 weeks treatment. After discontinuing medications for 4 weeks, the mean reduction of clinic SBP/DBP were 0.29/0.57 mm Hg in the QDG group compared to -1.59/-0.48 mm Hg in the valsartan group (SBP P = 0.04, DBP P = 0.04). Simultaneously, the 24-hour SBP/DBP were reduced by 0.9/0.31 mm Hg in the QDG group and -1.66/-1.08 mm Hg in the valsartan group (SBP P = 0.006, DBP P = 0.02). And similar results were observed regarding the outcomes of daytime and nighttime BPs. There was no difference in occurrence of adverse events between two groups (P > 0.05). CONCLUSION: QDG proves to be efficacious for grade 1 hypertension at a low-to-medium risk, even after discontinuation of the medication for 4 weeks. These findings provide a promising option for managing grade 1 hypertension and suggest the potential for maintaining stable BP through intermittent administration of QDG. TRIAL REGISTRATION: ChiCTR2000033890.

Dietary Saturated, Monounsaturated, or Polyunsaturated Fatty Acids and Estimated 10-Year Risk of a First Hard Cardiovascular Event.

BACKGROUND: The effects of dietary saturated, monounsaturated, or polyunsaturated fatty acids on the risk of cardiovascular events remain controversial. METHODS: This cross-sectional study was performed in 4211 patients, aged 40 to 79 years, from the National Health and Nutrition Examination Survey between 1999 and 2018. The independent variables were saturated fatty acids, monounsaturated fatty acids, and polyunsaturated fatty acids. The dependent variable was the 10-year risk of a first hard atherosclerotic cardiovascular event. The other variables were considered as the potential confounding factors. Multivariate linear regression models and smooth curve fittings were used to evaluate the association between saturated fatty acids, polyunsaturated fatty acids, or monounsaturated fatty acids and the 10-year risk. RESULTS: There was no association between dietary saturated fatty acids and 10-year risk after adjusting for all the potential confounding factors; 10-year risk decreased by 0.022% each 1-g increase in monounsaturated fatty acids intake from 0 to 153.772 g, and 0.025% each 1-g increase in polyunsaturated fatty acids intake from 0 to 98.323 g, respectively. Moreover, subgroup analysis showed that monounsaturated fatty acids and polyunsaturated fatty acids were both negatively correlated to 10-year risk in nondiabetes and non-high-low-density lipoprotein patients; monounsaturated fatty acids were also negatively associated with 10-year risk in hypertensive patients. CONCLUSIONS: There was no association between dietary saturated fatty acids and 10-year risk. Increased dietary intake of monounsaturated fatty acids or polyunsaturated fatty acids decreased 10-year risk, particularly in nondiabetes, non-high-low density lipoprotein patients.

The mechanisms of Qizhu Tangshen formula in the treatment of diabetic kidney disease: Network pharmacology, machine learning, molecular docking and experimental assessment.

BACKGROUND: Qizhu Tangshen Formula (QZTS) has been shown therapeutic effects on diabetic kidney disease (DKD). However, to date, the pharmacological mechanisms remain vague. METHODS: To explore the underlying mechanisms of QZTS in treating DKD using network pharmacology, machine learning, molecular docking and experimental assessment. RESULTS: First, we found that QZTS improved glycolipid metabolism disorder, decreased proteinuria and alleviated kidney tissue injury in DKD model KKAy mice. Then, by integrating multiple databases, a total of 96 targets of 74 active compounds in QZTS and 759 DKD-related genes were acquired. Next, we identified 13 hub targets of QZTS in DKD by three rank algorithms, including functional similarity, topological similarity and shortest path. Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses demonstrated that the pathways mainly centered on the processes of glycolipid metabolism disorder, inflammation and angiogenesis. Among them, VEGF signaling pathway was significantly enriched. Molecular docking showed that key active compounds of QZTS all had relatively good binding affinity with predicted hub targets. Finally, animal experiments found that QZTS significantly inhibited the secretion of plasma VEGF and downregulated the protein and mRNA expression levels of AKT, p38MAPK and VEGFR2. CONCLUSION: Our results indicated that QZTS treated DKD via multiple targets and pathways and the VEGF signaling pathway may be highly involved in this process.

Therapeutic potential of traditional Chinese medicine for vascular endothelial growth factor.

Vascular endothelial growth factor (VEGF) is the main regulator of physiological angiogenesis during embryonic development, bone growth, and reproductive function, and it also participates in a series of pathological changes. Traditional Chinese medicine (TCM), with a history of more than 2000 years, has been widely used in clinical practice, while the exploration of its mechanisms has only begun. This review summarizes the research of recent years on the influence of TCM on VEGF. It is found that many Chinese medicines and recipes have a regulatory effect on VEGF, indicating that Chinese medicine has broad prospects as a complementary and alternative therapy, providing new treatment ideas for clinical applications and the theoretical basis for research on the mechanisms of TCM.

TCMPR: TCM Prescription Recommendation Based on Subnetwork Term Mapping and Deep Learning.

Traditional Chinese medicine (TCM) has played an indispensable role in clinical diagnosis and treatment. Based on a patient's symptom phenotypes, computation-based prescription recommendation methods can recommend personalized TCM prescription using machine learning and artificial intelligence technologies. However, owing to the complexity and individuation of a patient's clinical phenotypes, current prescription recommendation methods cannot obtain good performance. Meanwhile, it is very difficult to conduct effective representation for unrecorded symptom terms in an existing knowledge base. In this study, we proposed a subnetwork-based symptom term mapping method (SSTM) and constructed a SSTM-based TCM prescription recommendation method (termed TCMPR). Our SSTM can extract the subnetwork structure between symptoms from a knowledge network to effectively represent the embedding features of clinical symptom terms (especially the unrecorded terms). The experimental results showed that our method performs better than state-of-the-art methods. In addition, the comprehensive experiments of TCMPR with different hyperparameters (i.e., feature embedding, feature dimension, subnetwork filter threshold, and feature fusion) demonstrate that our method has high performance on TCM prescription recommendation and potentially promote clinical diagnosis and treatment of TCM precision medicine.

A comprehensive update: gastrointestinal microflora, gastric cancer and gastric premalignant condition, and intervention by traditional Chinese medicine.

With the recent upsurge of studies in the field of microbiology, we have learned more about the complexity of the gastrointestinal microecosystem. More than 30 genera and 1000 species of gastrointestinal microflora have been found. The structure of the normal microflora is relatively stable, and is in an interdependent and restricted dynamic equilibrium with the body. In recent years, studies have shown that there is a potential relationship between gastrointestinal microflora imbalance and gastric cancer (GC) and precancerous lesions. So, restoring the balance of gastrointestinal microflora is of great significance. Moreover, intervention in gastric premalignant condition (GPC), also known as precancerous lesion of gastric cancer (PLGC), has been the focus of current clinical studies. The holistic view of traditional Chinese medicine (TCM) is consistent with the microecology concept, and oral TCM can play a two-way regulatory role directly with the microflora in the digestive tract, restoring the homeostasis of gastrointestinal microflora to prevent canceration. However, large gaps in knowledge remain to be addressed. This review aims to provide new ideas and a reference for clinical practice.

An innovative targeted therapy for fluoroscopy-induced chronic radiation dermatitis.

Fluoroscopy-induced chronic radiation dermatitis (FICRD) is a complication of fluoroscopy-guided intervention. Unlike acute radiation dermatitis, FICRD is different as delayed onset and usually appears without preexisting acute dermatitis. Unfortunately, the chronic and progressive pathology of FICRD makes it difficult to treat, and some patients need to receive wide excision and reconstruction surgery. Due to lack of standard treatment, investigating underlying mechanism is needed in order to develop an effective therapy. Herein, the Hippo pathway is specifically identified using an RNA-seq analysis in mild damaged skin specimens of patients with FICRD. Furthermore, specific increase of the Yes-associated protein (YAP1), an effector of the Hippo pathway, in skin region with mild damage plays a protective role for keratinocytes via positively regulating the numerous downstream genes involved in different biological processes. Interestingly, irradiated-keratinocytes inhibit activation of fibroblasts under TGF-ß1 treatment via remote control by an exosome containing YAP1. More importantly, targeting one of YAP1 downstream genes, nuclear receptor subfamily 3 group C member 1 (NR3C1), which encodes glucocorticoid receptor, has revealed its therapeutic potential to treat FICRD by inhibiting fibroblasts activation in vitro and preventing formation of radiation ulcers in a mouse model and in patients with FICRD. Taken together, this translational research demonstrates the critical role of YAP1 in FICRD and identification of a feasible, effective therapy for patients with FICRD. KEY MESSAGES: • YAP1 overexpression in skin specimens of radiation dermatitis from FICRD patient. • Radiation-induced YAP1 expression plays protective roles by promoting DNA damage repair and inhibiting fibrosis via remote control of exosomal YAP1. • YAP1 positively regulates NR3C1 which encodes glucocorticoid receptor expression. • Targeting glucocorticoid receptor by prednisolone has therapeutic potential for FICRD patient.

Efficacy and Safety of TCMI in Patients With Combined Coronary Heart Disease and Heart Failure: A Systematic Review and Network Meta-Analysis.

Objective: To compare the efficacy and safety of conventional treatments (CTs) to those that included traditional Chinese medicine injections (TCMIs) in patients with combined coronary heart disease and heart failure (CHD-HF). Methods: Eight electronic literature databases (PubMed, Embase, Cochrane Central Register of Controlled Trials, Web of Science, China National Knowledge Infrastructure Database, Chinese Scientific Journal Database, Wanfang Database, Chinese Biomedical Database) were searched from their inceptions to May 18, 2021, to identify relevant randomised controlled trials (RCTs). The primary outcomes analyzed included the total effectiveness rate and adverse events (ADRs). The secondary outcomes analyzed included the left ventricular ejection fraction (LVEF), N-terminal pro-brain natriuretic peptide (NT-proBNP), brain natriuretic peptide (BNP), and 6-min walk test (6MWT). Cochrane risk-of-bias tool was used to assess quality of the analyzed RCTs. Stata and OpenBUGS software were used to prior to the systematic review and network meta-analysis. Results: Sixty-one eligible trials involved 5,567 patients and one of the following 15 TCMIs: Shuxuetong, Shenmai, Shenfu, Shengmai, Danshenduofenyansuan, Danhong, Dazhuhongjingtian, Xinmailong, Dengzhanxixin, Gualoupi, Shuxuening, Xuesaitong, Yiqi Fumai, Shenqi Fuzheng, Huangqi. Network meta-analysis revealed that Shuxuetong injection + CT group was superior to CT only in improving the total effectiveness rate [odds ratio (OR): 7.8, 95% confidence interval (CI): 1.17-27.41]. Shenmai injection + CT was superior to CT only for LVEF (OR: 8.97, CI: 4.67-13.18), Xinmailong injection + CT was superior to CT only for NT-proBNP (OR: -317.70, CI: -331.10-303.10), Shenqi Fuzheng injection + CT was superior to CT only for BNP (OR: -257.30, CI: -308.40-242.80); and Danhong injection + CT was superior to CT only for 6MWT (OR: 84.40, CI: 62.62-106.20). Different TCMIs had different toxicity spectrums. Conclusion: TCMIs combined with CT are better than CT alone in treating CHD-HF. Different TCMIs improve different outcomes. Additional properly designed RCTs are needed to conduce a more refined comparison of various TCMIs. Systematic Review Registration: [https://www.crd.york.ac.uk/PROSPERO/], identifier [CRD42021258263].

FordNet: Recommending traditional Chinese medicine formula via deep neural network integrating phenotype and molecule.

Traditional Chinese medicine (TCM) formula is widely used for thousands of years in clinical practice. With the development of artificial intelligence, deep learning models may help doctors prescribe reasonable formulas. Meanwhile, current studies of formula recommendation only focus on the observable clinical symptoms and lack of molecular information. Here, inspired by the theory of TCM network pharmacology, we propose an intelligent formula recommendation system based on deep learning (FordNet), fusing the information of phenotype and molecule. We collected more than 20,000 electronic health records from TCM Master Li Jiren's experience from 2013 to March 2020. In the FordNet system, the feature of diagnosis description is extracted by convolution neural network and the feature of TCM formula is extracted by network embedding, which fusing the molecular information. A hierarchical sampling strategy for data augmentation is designed to effectively learn training samples. Based on the expanded samples, a deep neural network based quantitative optimization model is developed for TCM formula recommendation. FordNet performs significantly better than baseline methods (hit ratio of top 10 improved by 46.9% compared with the best baseline random forest method). Moreover, the molecular information helps FordNet improve 17.3% hit ratio compared with the model using only macro information. Clinical evaluation shows that FordNet can well learn the effective experience of TCM Master and obtain excellent recommendation results. Our study, for the first time, proposes an intelligent recommendation system for TCM formula integrating phenotype and molecule information, which has potential to improve clinical diagnosis and treatment, and promote the shift of TCM research pattern from "experience based, macro" to "data based, macro-micro combined" as well as the development of TCM network pharmacology.

Add-On Chinese Medicine for Coronavirus Disease 2019 (ACCORD): A Retrospective Cohort Study of Hospital Registries.

Chinese medicine (CM) was extensively used to treat COVID-19 in China. We aimed to evaluate the real-world effectiveness of add-on semi-individualized CM during the outbreak. A retrospective cohort of 1788 adult confirmed COVID-19 patients were recruited from 2235 consecutive linked records retrieved from five hospitals in Wuhan during 15 January to 13 March 2020. The mortality of add-on semi-individualized CM users and non-users was compared by inverse probability weighted hazard ratio (HR) and by propensity score matching. Change of biomarkers was compared between groups, and the frequency of CMs used was analyzed. Subgroup analysis was performed to stratify disease severity and dose of CM exposure. The crude mortality was 3.8% in the semi-individualized CM user group and 17.0% among the non-users. Add-on CM was associated with a mortality reduction of 58% (HR = 0.42, 95% CI: 0.23 to 0.77, [Formula: see text] = 0.005) among all COVID-19 cases and 66% (HR = 0.34, 95% CI: 0.15 to 0.76, [Formula: see text] = 0.009) among severe/critical COVID-19 cases demonstrating dose-dependent response, after inversely weighted with propensity score. The result was robust in various stratified, weighted, matched, adjusted and sensitivity analyses. Severe/critical patients that received add-on CM had a trend of stabilized D-dimer level after 3-7 days of admission when compared to baseline. Immunomodulating and anti-asthmatic CMs were most used. Add-on semi-individualized CM was associated with significantly reduced mortality, especially among severe/critical cases. Chinese medicine could be considered as an add-on regimen for trial use.

Topological Analysis of the Language Networks of Ancient Traditional Chinese Medicine Books.

This study aims to explore the topological regularities of the character network of ancient traditional Chinese medicine (TCM) book. We applied the 2-gram model to construct language networks from ancient TCM books. Each text of the book was separated into sentences and a TCM book was generated as a directed network, in which nodes represent Chinese characters and links represent the sequential associations between Chinese characters in the sentences (the occurrence of identical sequential associations is considered as the weight of this link). We first calculated node degrees, average path lengths, and clustering coefficients of the book networks and explored the basic topological correlations between them. Then, we compared the similarity of network nodes to assess the specificity of TCM concepts in the network. In order to explore the relationship between TCM concepts, we screened TCM concepts and clustered them. Finally, we selected the binary groups whose weights are greater than 10 in Inner Canon of Huangdi (ICH, ) and Treatise on Cold Pathogenic Disease (TCPD, ), hoping to find the core differences of these two ancient TCM books through them. We found that the degree distributions of ancient TCM book networks are consistent with power law distribution. Moreover, the average path lengths of book networks are much smaller than random networks of the same scale; clustering coefficients are higher, which means that ancient book networks have small-world patterns. In addition, the similar TCM concepts are displayed and linked closely, according to the results of cosine similarity comparison and clustering. Furthermore, the core words of Inner Canon of Huangdi and Treatise on Cold Pathogenic Diseases have essential differences, which might indicate the significant differences of language and conceptual patterns between theoretical and clinical books. This study adopts language network approach to investigate the basic conceptual characteristics of ancient TCM book networks, which proposes a useful method to identify the underlying conceptual meanings of particular concepts conceived in TCM theories and clinical operations.

Identification of herbal categories active in pain disorder subtypes by machine learning help reveal novel molecular mechanisms of algesia.

Chronic pain is highly prevalent and poorly controlled, of which the accurate underlying mechanisms need be further elucidated. Herbal drugs have been widely used for controlling various pain disorders. The systematic integration of pain herbal data resources might be promising to help investigate the molecular mechanisms of pain phenotypes. Here, we integrated large-scale bibliographic literatures and well-established data sources to obtain high-quality pain relevant herbal data (i.e. 426 pain related herbs with their targets). We used machine learning method to identify three distinct herb categories with their specific indications of symptoms, targets and enriched pathways, which were characterized by the efficacy of treatment to the chronic cough related neuropathic pain, the reproduction and autoimmune related pain, and the cancer pain, respectively. We further detected the novel pathophysiological mechanisms of the pain subtypes by network medicine approach to evaluate the interactions between herb targets and the pain disease modules. This work increased the understanding of the underlying molecular mechanisms of pain subtypes that herbal drugs are participating and with the ultimate aim of developing novel personalized drugs for pain disorders.

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.

SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping.

Recently, the pharmaceutical industry has heavily emphasized phenotypic drug discovery (PDD), which relies primarily on knowledge about phenotype changes associated with diseases. Traditional Chinese medicine (TCM) provides a massive amount of information on natural products and the clinical symptoms they are used to treat, which are the observable disease phenotypes that are crucial for clinical diagnosis and treatment. Curating knowledge of TCM symptoms and their relationships to herbs and diseases will provide both candidate leads and screening directions for evidence-based PDD programs. Therefore, we present SymMap, an integrative database of traditional Chinese medicine enhanced by symptom mapping. We manually curated 1717 TCM symptoms and related them to 499 herbs and 961 symptoms used in modern medicine based on a committee of 17 leading experts practicing TCM. Next, we collected 5235 diseases associated with these symptoms, 19 595 herbal constituents (ingredients) and 4302 target genes, and built a large heterogeneous network containing all of these components. Thus, SymMap integrates TCM with modern medicine in common aspects at both the phenotypic and molecular levels. Furthermore, we inferred all pairwise relationships among SymMap components using statistical tests to give pharmaceutical scientists the ability to rank and filter promising results to guide drug discovery. The SymMap database can be accessed at http://www.symmap.org/ and https://www.bioinfo.org/symmap.

Heterogeneous network propagation for herb target identification.

BACKGROUND: Identifying targets of herbs is a primary step for investigating pharmacological mechanisms of herbal drugs in Traditional Chinese medicine (TCM). Experimental targets identification of herbs is a difficult and time-consuming work. Computational method for identifying herb targets is an efficient approach. However, how to make full use of heterogeneous network data about herbs and targets to improve the performance of herb targets prediction is still a dilemma. METHODS: In our study, a random walk algorithm on the heterogeneous herb-target network (named heNetRW) has been proposed to identify protein targets of herbs. By building a heterogeneous herb-target network involving herbs, targets and their interactions and simulating random walk algorithm on the network, the candidate targets of the given herb can be predicted. RESULTS: The experimental results on large-scale dataset showed that heNetRW had higher performance of targets prediction than PRINCE (improved F1-score by 0.08 and Hit@1 by 21.34% in one validation setting, and improved F1-score by 0.54 and Hit@1 by 69.08% in the other validation setting). Furthermore, we evaluated novel candidate targets of two herbs (rhizoma coptidis and turmeric), which showed our approach could generate potential targets that are valuable for further experimental investigations. CONCLUSIONS: Compared with PRINCE algorithm, heNetRW algorithm can fuse more known information (such as, known herb-target associations and pathway-based similarities of protein pairs) to improve prediction performance. Experimental results also indicated heNetRW had higher performance than PRINCE. The prediction results not only can be used to guide the selection of candidate targets of herbs, but also help to reveal the molecule mechanisms of herbal drugs.

Integrated Modules Analysis to Explore the Molecular Mechanisms of Phlegm-Stasis Cementation Syndrome with Ischemic Heart Disease.

Background: Ischemic heart disease (IHD) has been the leading cause of death for several decades globally, IHD patients usually hold the symptoms of phlegm-stasis cementation syndrome (PSCS) as significant complications. However, the underlying molecular mechanisms of PSCS complicated with IHD have not yet been fully elucidated. Materials and Methods: Network medicine methods were utilized to elucidate the underlying molecular mechanisms of IHD phenotypes. Firstly, high-quality IHD-associated genes from both human curated disease-gene association database and biomedical literatures were integrated. Secondly, the IHD disease modules were obtained by dissecting the protein-protein interaction (PPI) topological modules in the String V9.1 database and the mapping of IHD-associated genes to the PPI topological modules. After that, molecular functional analyses (e.g., Gene Ontology and pathway enrichment analyses) for these IHD disease modules were conducted. Finally, the PSCS syndrome modules were identified by mapping the PSCS related symptom-genes to the IHD disease modules, which were further validated by both pharmacological and physiological evidences derived from published literatures. Results: The total of 1,056 high-quality IHD-associated genes were integrated and evaluated. In addition, eight IHD disease modules (the PPI sub-networks significantly relevant to IHD) were identified, in which two disease modules were relevant to PSCS syndrome (i.e., two PSCS syndrome modules). These two modules had enriched pathways on Toll-like receptor signaling pathway (hsa04620) and Renin-angiotensin system (hsa04614), with the molecular functions of angiotensin maturation (GO:0002003) and response to bacterium (GO:0009617), which had been validated by classical Chinese herbal formulas-related targets, IHD-related drug targets, and the phenotype features derived from human phenotype ontology (HPO) and published biomedical literatures. Conclusion: A network medicine-based approach was proposed to identify the underlying molecular modules of PSCS complicated with IHD, which could be used for interpreting the pharmacological mechanisms of well-established Chinese herbal formulas (e.g., Tao Hong Si Wu Tang, Dan Shen Yin, Hunag Lian Wen Dan Tang and Gua Lou Xie Bai Ban Xia Tang). In addition, these results delivered novel understandings of the molecular network mechanisms of IHD phenotype subtypes with PSCS complications, which would be both insightful for IHD precision medicine and the integration of disease and TCM syndrome diagnoses.

Multistage analysis method for detection of effective herb prescription from clinical data.

Determining effective traditional Chinese medicine (TCM) treatments for specific disease conditions or particular patient groups is a difficult issue that necessitates investigation because of the complicated personalized manifestations in real-world patients and the individualized combination therapies prescribed in clinical settings. In this study, a multistage analysis method that integrates propensity case matching, complex network analysis, and herb set enrichment analysis was proposed to identify effective herb prescriptions for particular diseases (e.g., insomnia). First, propensity case matching was applied to match clinical cases. Then, core network extraction and herb set enrichment were combined to detect core effective herb prescriptions. Effectiveness-based mutual information was used to detect strong herb-symptom relationships. This method was applied on a TCM clinical data set with 955 patients collected from well-designed observational studies. Results revealed that groups of herb prescriptions with higher effectiveness rates (76.9% vs. 42.8% for matched samples; 94.2% vs. 84.9% for all samples) compared with the original prescriptions were found. Particular patient groups with symptom manifestations were also identified to help investigate the indications of the effective herb prescriptions.

The efficacy of blueberry and grape seed extract combination on triple therapy for Helicobacter pylori eradication: a randomised controlled trial.

Helicobacter pylori is a major risk factor for gastritis, gastric ulcers and gastric cancer. Traditional therapy with proton pump inhibitor and antibiotics is regarded as optimal for H. pylori eradication whereas, the eradication rate is unsatisfactory. Studies have reported that cranberry may inhibit H. pylori adhesion to the human gastric mucus but lack of other berry extracts have been evaluated in clinical study. Thus, a 9-week add-on randomised controlled trial was conducted to explore the impact of blueberry and grape seed extract (BGE) combinations traditional therapy for H. pylori eradication. In results, we found that there was no significant difference of eradication rate between the berry extract group and placebo group in the intention-to-treat analysis and in the per-protocol analysis (94.64% versus 84.62%, p = 0.085). Diarrhoea, constipation and epigastric pain were observed increasing during ingestion of the berry extract in some cases. In conclusion, this study indicated that no significant difference existed between the BGE extract group and placebo group in eradication rate under triple therapy.