Digital Chinese Medicine最新文献

Objective

To establish the knowledge graph of “disease-syndrome-symptom-method-formula” in Treatise on Febrile Diseases (Shang Han Lun,《伤寒论》) for reducing the fuzziness and uncertainty of data, and for laying a foundation for later knowledge reasoning and its application.

Methods

Under the guidance of experts in the classical formula of traditional Chinese medicine (TCM), the method of “top-down as the main, bottom-up as the auxiliary” was adopted to carry out knowledge extraction, knowledge fusion, and knowledge storage from the five aspects of the disease, syndrome, symptom, method, and formula for the original text of Treatise on Febrile Diseases, and so the knowledge graph of Treatise on Febrile Diseases was constructed. On this basis, the knowledge structure query and the knowledge relevance query were realized in a visual manner.

Results

The knowledge graph of “disease-syndrome-symptom-method-formula” in the Treatise on Febrile Diseases was constructed, containing 6 469 entities and 10 911 relational triples, on which the query of entities and their relationships can be carried out and the query result can be visualized.

Conclusion

The knowledge graph of Treatise on Febrile Diseases systematically realizes its digitization of the knowledge system, and improves the completeness and accuracy of the knowledge representation, and the connection between “disease-syndrome-symptom-treatment-formula”, which is conducive to the sharing and reuse of knowledge can be obtained in a clear and efficient way.

Cardiovascular diseases (CVDs) are major disease burdens with high mortality worldwide. Early prediction of cardiovascular events can reduce the incidence of acute myocardial infarction and decrease the mortality rates of patients with CVDs. The pathological mechanisms and multiple factors involved in CVDs are complex; thus, traditional data analysis is insufficient and inefficient to manage multidimensional data for the risk prediction of CVDs and heart attacks, medical image interpretations, therapeutic decision-making, and disease prognosis prediction. Meanwhile, traditional Chinese medicine (TCM) has been widely used for treating CVDs. TCM offers unique theoretical and practical applications in the diagnosis and treatment of CVDs. Big data have been generated to investigate the scientific basis of TCM diagnostic methods. TCM formulae contain multiple herbal items. Elucidating the complicated interactions between the active compounds and network modulations requires advanced data-analysis capability. Recent progress in artificial intelligence (AI) technology has allowed these challenges to be resolved, which significantly facilitates the development of integrative diagnostic and therapeutic strategies for CVDs and the understanding of the therapeutic principles of TCM formulae. Herein, we briefly introduce the basic concept and current progress of AI and machine learning (ML) technology, and summarize the applications of advanced AI and ML for the diagnosis and treatment of CVDs. Furthermore, we review the progress of AI and ML technology for investigating the scientific basis of TCM diagnosis and treatment for CVDs. We expect the application of AI and ML technology to promote synergy between western medicine and TCM, which can then boost the development of integrative medicine for the diagnosis and treatment of CVDs.

Objective

For computer-aided Chinese medical diagnosis and aiming at the problem of insufficient segmentation, a novel multi-level method based on the multi-scale fusion residual neural network (MF2ResU-Net) model is proposed.

Methods

To obtain refined features of retinal blood vessels, three cascade connected U-Net networks are employed. To deal with the problem of difference between the parts of encoder and decoder, in MF2ResU-Net, shortcut connections are used to combine the encoder and decoder layers in the blocks. To refine the feature of segmentation, atrous spatial pyramid pooling (ASPP) is embedded to achieve multi-scale features for the final segmentation networks.

Results

The MF2ResU-Net was superior to the existing methods on the criteria of sensitivity (Sen), specificity (Spe), accuracy (ACC), and area under curve (AUC), the values of which are 0.8013 and 0.8102, 0.9842 and 0.9809, 0.9700 and 0.9776, and 0.9797 and 0.9837, respectively for DRIVE and CHASE DB1. The results of experiments demonstrated the effectiveness and robustness of the model in the segmentation of complex curvature and small blood vessels.

Conclusion

Based on residual connections and multi-feature fusion, the proposed method can obtain accurate segmentation of retinal blood vessels by refining the segmentation features, which can provide another diagnosis method for computer-aided Chinese medical diagnosis.

With the widespread use of Internet, the amount of data in the field of traditional Chinese medicine (TCM) is growing exponentially. Consequently, there is much attention on the collection of useful knowledge as well as its effective organization and expression. Knowledge graphs have thus emerged, and knowledge reasoning based on this tool has become one of the hot spots of research. This paper first presents a brief introduction to the development of knowledge graphs and knowledge reasoning, and explores the significance of knowledge reasoning. Secondly, the mainstream knowledge reasoning methods, including knowledge reasoning based on traditional rules, knowledge reasoning based on distributed feature representation, and knowledge reasoning based on neural networks are introduced. Then, using stroke as an example, the knowledge reasoning methods are expounded, the principles and characteristics of commonly used knowledge reasoning methods are summarized, and the research and applications of knowledge reasoning techniques in TCM in recent years are sorted out. Finally, we summarize the problems faced in the development of knowledge reasoning in TCM, and put forward the importance of constructing a knowledge reasoning model suitable for the field of TCM.

Objective

To construct symptom-formula-herb heterogeneous graphs structured Treatise on Febrile Diseases (Shang Han Lun,《伤寒论》) dataset and explore an optimal learning method represented with node attributes based on graph convolutional network (GCN).

Methods

Clauses that contain symptoms, formulas, and herbs were abstracted from Treatise on Febrile Diseases to construct symptom-formula-herb heterogeneous graphs, which were used to propose a node representation learning method based on GCN − the Traditional Chinese Medicine Graph Convolution Network (TCM-GCN). The symptom-formula, symptom-herb, and formula-herb heterogeneous graphs were processed with the TCM-GCN to realize high-order propagating message passing and neighbor aggregation to obtain new node representation attributes, and thus acquiring the nodes’ sum-aggregations of symptoms, formulas, and herbs to lay a foundation for the downstream tasks of the prediction models.

Results

Comparisons among the node representations with multi-hot encoding, non-fusion encoding, and fusion encoding showed that the Precision@10, Recall@10, and F1-score@10 of the fusion encoding were 9.77%, 6.65%, and 8.30%, respectively, higher than those of the non-fusion encoding in the prediction studies of the model.

Conclusion

Node representations by fusion encoding achieved comparatively ideal results, indicating the TCM-GCN is effective in realizing node-level representations of heterogeneous graph structured Treatise on Febrile Diseases dataset and is able to elevate the performance of the downstream tasks of the diagnosis model.

In the study, a quantum resonant cavity model based on wave-particle duality was proposed for the explanation of the dynamic processes of essence, vigor, and spirit in the human body in traditional Chinese medicine (TCM). It is assumed that there is a macro human order parameter (wave function), and its dynamics are governed by a macro potential field reflecting influences from heaven, earth, and society, and satisfy the generalized Schrodinger equation. This proposed model was applied in the study to interpret basic concepts of human body in TCM, with an aim to unfold the TCM development in the future.

Following the quantum theory-based physical model of the human body, a new interpretation of the traditional Chinese medicine (TCM) principle of “Cunkou reads viscera” is presented. Then, a Gaussian pulse wave model as a solution to the Schrodinger equation is shown to accurately describe 19 different pulse shapes, and to quantitatively capture the degree of Yin-Yang attributes of 13 pulse shapes. Furthermore, the model suggests using pulse depth and strength as leading-order quantity and pulse shape as first-order quantity, to characterize the hierarchical resonance between the human body and the environment. The future pulse informatics will focus on determining an individual’s unique quantum human equilibrium state, and diagnose its health state according to the pulse deviation from its equilibrium state, to truly achieve the high level of TCM: “knowing the normal state and reaching the change”.

Objective

Bouchardatine (1) is a β-indoloquinazoline alkaloid isolated from the plant Bouchardatia neurococca, acting as a modulator of adipogenesis and lipogenesis, and as an anticancer agent. The natural product functions as an activator of proteins adenosine 5’-monophosphate (AMP)-activated protein kinase (AMPK) and sirtuin 1 (SIRT1). We used molecular modeling to investigate the SIRT1-binding capacity of compound 1 and various structural analogues, such as orirenierine A (2) and orirenierine B (3) isolated from the medicinal plant Oricia renieri.

Methods

We investigated the binding to human SIRT1 (hSIRT1) of 25 natural products including the β-indoloquinazoline alkaloids 1 − 3 and analogues, in comparison with the reference product sirtinol (R and S isomers). A sirtinol binding model was elaborated starting from the closed and open state conformations of the catalytic domain of hSIRT1 (PDB structures 4KXQ and 4IG9). For each compound bound to SIRT1, the empirical energy of interaction (ΔE) was calculated and compared to that of sirtinol.

Results

In our model, compound1 was found to bind modestly to the sirtinol site of SIRT1. In contrast, the presence of a phenolic OH group at position 7 on the quinazolinone moiety conferred a much higher binding capacity. Compound 2 provided SIRT1 protein complexes as stable as those observed with sirtinol. The replacement of the hydroxy substituent (2) with a methoxy group (3) reduced the SIRT1 binding capacity. Other SIRT1-binding natural products were identified, such as the alkaloids orisuaveolines A and B. Structure-binding relationships were discussed.

Conclusion

The study underlines the capacity of β-indoloquinazoline alkaloids to interact with SIRT1. This deacetylase enzyme could represent a molecular target for the alkaloid 2. This compound merits further attention for the design of drugs active against SIRT1-dependent pathologies.

Objective

To explore the effect and mechanism of Chaihu Longgu Muli Decoction (柴胡龙骨牡蛎汤, CHLGMLD) in rats with temporal lobe epilepsy (TLE).

Methods

A total of 80 Sprague-Dawley (SD) male rats were randomized into control (CON), model (MOD), carbamazepine (CBZ, 0.1 g/kg), CHLGMLD low dose (CHLGMLD-L, 12.5 g/kg), and high dose (CHLGMLD-H, 25 g/kg) groups, with 16 rats in each group. TLE rat models were established in the four groups with the use of lithium-pilocarpine except for the CON group. After the successful establishment of TLE models, all drugs were administered through gavage, and distilled water was given to rats in the CON and MOD groups for four weeks. The frequency and duration of seizures before and after treatment were recorded for the evaluation of the alleviation degree. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect the expression levels of miR-146a-3p and miR-146a-5p. The expression levels of toll-like receptor 4 (TLR4), interleukin-1 receptor-associated kinase 1 (IRAK1), tumor necrosis factor (TNF) receptor-associated factor 6 (TRAF6), TAK1-binding protein (TAB), nuclear factor-kappa B (NF-κB), and interleukin-1 beta (IL-1β) in hippocampus were tested by immunofluorescence assay. Correlation analysis between the above factors and expressions of miR-146a-3p and miR-146a-5p were performed separately.

Results

CHLGMLD decreased the frequency (P < 0.05) and duration (P < 0.01) of seizures in rats. CHLGMLD down-regulated the expression levels of miR-146a-5p and miR-146a-3p (P < 0.05), and inhibited the expression levels of TLR4, IRAK1, TRAF6, TAB, NF-κB, and IL-1β (P < 0.01). The correlation analysis revealed that the expression levels of TLR4, IRAK1, TRAF6, TAB, NF-κB, and IL-1β were positively correlated with the expression levels of miR-146a-3p and miR-146a-5p detected by qRT-PCR, respectively (P < 0.01).

Conclusion

CHLGMLD can inhibite the TLR4 signaling pathway by lowering the expression levels of miR-146a-3p and miR-146a-5p to alleviate hippocampal dentate gyrus inflammation in TLE rats, thus relieving seizures.