MDL-HTI: A Multimodal Deep Learning Approach for Predicting Herb-Target Interactions.

The biological targets of traditional Chinese medicine (TCM) are the core effectors mediating the interaction between TCM and the human body. Identification of TCM targets is essential to elucidate the chemical basis and mechanisms of TCM for treating diseases. Given the chemical complexity of TCM, both in silico high-throughput compound-target interaction predicting models and biological profile-based methods have been commonly applied for identifying TCM targets based on the structural information of TCM chemical components and biological information, respectively. However, the existing methods lack the integration of TCM chemical and biological information, resulting in difficulty in the systematic discovery of TCM action pathways. To solve this problem, we propose a novel target identification model NP-TCMtarget to explore the TCM target path by combining the overall chemical and biological profiles. First, NP-TCMtarget infers TCM effect targets by calculating associations between herb/disease inducible gene expression profiles and specific gene signatures for 8233 targets. Then, NP-TCMtarget utilizes a constructed binary classification model to predict binding targets of herbal ingredients. Finally, we can distinguish TCM direct and indirect targets by comparing the effect targets and binding targets to establish the action pathways of herbal component-direct target-indirect target by mapping TCM targets in the biological molecular network. We apply NP-TCMtarget to the formula XiaoKeAn to demonstrate the power of revealing the action pathways of herbal formula. We expect that this novel model could provide a systematic framework for exploring the molecular mechanisms of TCM at the target level. NP-TCMtarget is available at http://www.bcxnfz.top/NP-TCMtarget.

Interview with Dr. Liu Qingquan: Traditional Chinese Medicine for COVID-19 Diagnosis and Treatment

Aim at the two problems in the field of traditional Chinese medicine (TCM) mechanism elucidation, one is the lack of detailed biological processes information, next is the low efficient in constructing network models, we constructed an auxiliary elucidation system for the TCM mechanism and realize the automatic establishment of biological network model. This study used the Entity Grammar Systems (EGS) as the theoretical framework, integrated the data of formulae, herbs, chemical components, targets of component, biological reactions, signaling pathways and disease related proteins, established the formal models, wrote the reasoning engine, constructed the auxiliary elucidation system for the TCM mechanism elucidation. The platform provides an automatic modeling method for biological network model of TCM mechanism. It would be benefit to perform the in-depth research on TCM theory of natures and combination and provides the scientific references for R&D of TCM.

Network Pharmacology in Traditional Chinese Medicine

Traditional Chinese Medicine (TCM), as a life science rooted in unique philosophical principles, offers significant intellectual property advantages and substantial economic potential, positioning it as a growing area of global interest. As key instruments in representing its economic value, the international standards for TCM terminology have garnered widespread attention from the global community. International organizations, experts, scholars, and governmental bodies have engaged in this process, yielding valuable outcomes. In 2007, the World Health Organization released the "International Standard Terminology for Traditional Medicine" (IST), while the World Federation of Chinese Medicine Societies simultaneously published the "Chinese-English Bilingual Standard for Basic TCM Terminology" (ISN). However, discrepancies in the translation of specific terms within these two standards have undermined the scientific accuracy of TCM and sparked new debates. As such, it has become imperative to unify TCM terminology standards to present a cohesive, authoritative voice for TCM on the global stage. Based on a classification of TCM terminology, this article employs a cyclic equidistant sampling method to extract 500 entries (excluding categories such as herbal medicine, prescriptions, and acupuncture) from the ISN. It then compares and analyzes these terms against their counterparts in the IST. Drawing on the Theory of Adaptive Selection in Translation, the paper investigates the various translations of key TCM concepts, including fundamental theories and diagnostic methods, from linguistic, cultural, and communicative perspectives. The study identifies optimal translation choices and proposes a comprehensive framework for TCM terminology translation, encapsulated by the principles of "One Shift," "Three Dimensions," and "Several Translation Methods." Given that TCM has its origins in China, China needs to take a leading role in the global efforts to establish international standards for traditional medicine, ensuring that the unique characteristics of TCM are fully reflected. By analyzing and comparing the two major international TCM terminology standards, this research aims to provide valuable insights that will guide future initiatives in the global standardization of TCM terminology.

A national survey on how to improve traditional Chinese medicine learning internationally: Perceptions from both teachers and students

Regulatory mechanisms of natural compounds from traditional Chinese herbal medicines on the microglial response in ischemic stroke

Recently, huge amounts of data are generated in the domain of biology. Embedded with domain knowledge from different disciplines, the isolated biological resources are implicitly connected. Thus it has shaped a big network of versatile biological knowledge. Faced with such massive, disparate, and interlinked biological data, providing an efficient way to model, integrate, and analyze the big biological network becomes a challenge. In this paper, we present a general OWL (web ontology language) reasoning framework to study the implicit relationships among biological entities. A comprehensive biological ontology across traditional Chinese medicine (TCM) and western medicine (WM) is used to create a conceptual model for the biological network. Then corresponding biological data is integrated into a biological knowledge network as the data model. Based on the conceptual model and data model, a scalable OWL reasoning method is utilized to infer the potential associations between biological entities from the biological network. In our experiment, we focus on the association discovery between TCM and WM. The derived associations are quite useful for biologists to promote the development of novel drugs and TCM modernization. The experimental results show that the system achieves high efficiency, accuracy, scalability, and effectivity.

It is one of the key task for the strategy of the modernization of traditional Chinese medicine(TCM) to develop an efficient methodology.Based on systems biology and network biology, we proposed the methodology for the study of TCM compound prescription under the theory of traditional Chinese medicine and modern biological techniques.The methodology involves both theories of reductionism and system theory, both levels of macroscopy and microscopy, and both analyses in vivo and in vitro.The proposed methodology is composed of four research platforms, including chemical research platform, pharmacology research platform, systems biology and network biology research platform.The proposed methodology provides powerful tool for the modernization study of TCM compound prescription, which may be meaningful to fully reveal the essential principals of TCM, and also may promote the new drug discovery of TCM compound prescription, the inheritance and development of TCM theory.

Cancer is the second leading cause of death by disease in the world. Chemotherapy is one of three major therapeutic methods for cancer treatment, but cancer cells gradually evolve resistance to chemotherapeutic reagents. For centuries, traditional Chinese medicine (TCM) was used to fight against cancer. In recent years, a number of effective component mechanisms of TCM have been increasingly illuminated. As we know, chemical structures of reagents decide or affect their activities on target pathways. Thus, we classified some antitumor-related TCM components reported in the last five years into thirteen groups by their chemical structures, such as, alkaloids, diterpenoids, triterpenes, sesquiterpenes, anthraquinones, benzoquinones, flavonoids, berbamines, xanthones, saponins, steroids, polysaccharides, and glycosides. In various cancer cell lines, these constituents target dozens of signaling pathways in vitro and in vivo. Among these components, there are three sets: i) mainly apoptosis-related groups, such as, alkaloids, diterpenoids, anthraquinones, berbamines, and xanthones, target pathways like the mitochondrial pathway, NF-κB pathway, p53 pathway and so on; ii) mainly proliferation, invasion and metastasis-related groups, such as, triterpenes, sesquiterpenes, polysaccharides, and glycosides, target pathways like the mTOR pathway, β-catenin pathway, ERK pathway and so on; iii) both apoptosis and proliferation, invasion and metastasis-related groups, such as benzoquinones, flavonoids, saponins, and steroids, target the pathways in i) and ii) synchronously. These will provide association information between TCM components and signaling pathways to promote studies on mechanisms of effective constituents, target drug development, and combinational chemotherapy. TCM could be alternative medicine for cancer treatment in the future.

Traditional Chinese medicines differentially modulate the gut microbiota based on their nature (Yao-Xing)

Learning from the multimodal brain imaging data attracts a large amount of attention in medical image analysis due to the proliferation of multimodal data collection. It is widely accepted that multimodal data can provide complementary information than mining from a single modality. However, unifying the image-based knowledge from the multimodal data is very challenging due to different image signals, resolution, data structure, etc.. In this study, we design a supervised deep model to jointly analyze brain morphometry and functional connectivity on the cortical surface and we name it deep multimodal brain network learning (DMBNL). Two graph-based kernels, i.e., geometry-aware surface kernel (GSK) and topology-aware network kernel (TNK), are proposed for processing the cortical surface morphometry and brain functional network. The vertex features on the cortical surface from GSK is pooled and feed into TNK as its initial regional features. In the end, the graph-level feature is computed for each individual and thus can be applied for classification tasks. We test our model on a large autism imaging dataset. The experimental results prove the effectiveness of our model.

Gut Microbiota-targeted, Whole-Body Systems Biology for Understanding Traditional Chinese Medicine

Development of the disease is the result of several factors involved in biological network changes. The nature of drug intervention is to regulate these pathological changes to the normal range. Advantages of traditional Chinese medicine (TCM) are to integrally and systematically regulate this biological networks and systematic pathology through multi-targets, multi-levels, multi-channels. Structural components TCM provides the controlled and precise basis "substance" for this regulation and also to clarify the "truth" of the nature of the regulation by the network pharmacology. Network pharmacology provides new strategy for the research on mechanism of structural components TCM. This study not only reflects the overall characteristics of the development of the disease, but also fully embodies the essence of TCM for preventing and treating diseases through changing traditional model on "one drug, one gene, one disease". This paper explores systematically the integration essence, features and research strategies of structural components TCM and the network pharmacology, understand the interaction of structural components TCM and body from the perspective of the overall concept of improving or restoring the balance of.biological networks. It is effective measure to reveal the structure of a multi-component for regulating biological networks mechanisms, and also provide new ideas and methods for further scientific research and innovation of structural component TCM.

ZHENG, also known as traditional Chinese medicine (TCM) syndrome or TCM pattern, is an integral and essential part of TCM theory. A TCM ZHENG, in essence, is a characteristic profile of all clinical manifestations that can be identified by a TCM practitioner. Clinical treatments of a patient rely on the successful differentiation of a specific ZHENG. Recently, some new technologies and methods such as the System‐ Omics approach were introduced in ZHENG research, which significantly facilitate the development of ZHENG theory. This review focuses on a brief introduction of these new technologies and methods and their application in TCM ZHENG differentiation research. Also, some of the latest progress and applications in this area, such as ZHENG measurement, information collection, data analysis and mining, ZHENG differentiation based TCM treatment, and mechanisms of ZHENG differentiation based on biological networks