Traditional Chinese medicine (TCM) has accumulated thousands years of knowledge in herbal therapy, but the use of herbal formulas is still characterized by reliance on personal experience. Due to the complex mechanism of herbal actions, it is challenging to discover effective herbal formulas for diseases by integrating the traditional experiences and modern pharmacological mechanisms of multi-target interactions. In this study, we propose a herbal formula prediction approach (TCMFP) combined therapy experience of TCM, artificial intelligence and network science algorithms to screen optimal herbal formula for diseases efficiently, which integrates a herb score (Hscore) based on the importance of network targets, a pair score (Pscore) based on empirical learning and herbal formula predictive score (FmapScore) based on intelligent optimization and genetic algorithm. The validity of Hscore, Pscore and FmapScore was verified by functional similarity and network topological evaluation. Moreover, TCMFP was used successfully to generate herbal formulae for three diseases, i.e. the Alzheimer’s disease, asthma and atherosclerosis. Functional enrichment and network analysis indicates the efficacy of targets for the predicted optimal herbal formula. The proposed TCMFP may provides a new strategy for the optimization of herbal formula, TCM herbs therapy and drug development.

Aristolochic acids (AAs) are a group of nitrophenanthrene carboxylic acids present in many medicinal herbs of the Aristolochia genus that may cause irreversible hepatotoxicity, nephrotoxicity, genotoxicity and carcinogenicity. However, the specific profile of AAs and their toxicity in Aristolochia plants, except for AAs Ι and ΙΙ, still remain unclear. In this study, a total of 52 batches of three medicinal herbs belonging to the Aristolochia family were analyzed for their AA composition profiles and AA contents using the UPLC-QTOF-MS/MS approach. The studied herbs were A. mollissima Hance (AMH), A. debilis Sieb.etZucc (ADS), and A. cinnabaria C.Y.Cheng (ACY). Chemometrics methods, including PCA and OPLS-DA, were used for the evaluation of the Aristolochia medicinal herbs. Additionally, cytotoxicity and genotoxicity of the selected AAs and the extracts of AMH and ADS were evaluated in a HepG2 cell line using the MTT method and a Comet assay, respectively. A total of 44 AAs, including 23 aristolochic acids and 21 aristolactams (ALs), were detected in A. mollissima. Moreover, 41 AAs (23 AAs and 18 ALs) were identified from A. debilis Sieb, and 45 AAs (29 AAs and 16 ALs) were identified in A. cinnabaria. Chemometrics results showed that 16, 19, and 22 AAs identified in AMH, ADS, and ACY, respectively, had statistical significance for distinguishing the three medicinal herbs of different origins. In the cytotoxicity assay, compounds AL-BΙΙ, AAΙ and the extract of AMH exhibited significant cytotoxicities against the HepG2 cell line with the IC₅₀ values of 0.2, 9.7 and 50.2 μM, respectively. The results of the Comet assay showed that AAΙ caused relatively higher damage to cellular DNA (TDNA 40–95%) at 50 μM, while AAΙΙ, AMH and ADS extracts (ranged from 10 to 131 μM) caused relatively lower damage to cellular DNA (TDNA 5–20%).

To evaluate the myocardial protective effect of Gualou Xiebai Banxia decoction GXBD) and explore the mechanisms of inhibition of NF-kappa B activation and blockade of inflammatory responses induced by ischemia-reperfusion in rats. Twenty-four Sprague Dawley (SD) rats were randomly divided into three groups. Rats in the treatment group received GXBD (13 g crude drug/kg) for three weeks, while rats in the model control and normal control groups received equal volumes of distilled water. On the 22nd day, rats in the ischemia-reperfusion (I/R) control and GXBD-treated groups underwent 30 min occlusion of the left anterior descending (LAD) coronary artery, followed by 120 min reperfusion. Electrocardiogram was recorded, and the activities of cardiac enzymes, cytokines, and NF-kappaB were assessed after I/R. Compared with the I/R control group, GXBD treatment restored the activity of the specific myocardial-injury marker creatine kinase (CK) and lactate dehydrogenase (LDH), and inhibited the inflammatory response involving the nuclear factor-kappaB (NF-KB) pathway, including down-regulation of interleukin (IL)-1beta and IL-6, and up-regulation of IL-10 gene expression. GXBD strongly reduced myocardial impairment in our I/R model, including inhibition of NF-kappaB activation and inflammatory cytokine responses.

Objective. This study aimed to develop a scale for evaluating and grading the evidence of prevention and treatment in ancient books of traditional Chinese medicine (TCM), in view of providing a reference for TCM clinicians, supporting the compilation or revision of evidence-based practice guidelines for TCM, improving the level of evidence-based research on ancient TCM books, and supplementing the development of evidence-based ancient TCM books. Methods. The Delphi method was used for consultation among 40 experts in relevant fields. Excel 2016 and SPSS 21.0 were used to analyze the positive coefficient, authority coefficient, degree of concentration, degree of coordination, and degree of expert consensus. Results. In the first round of the questionnaire, a total of 17 evaluation indexes were formulated in three aspects: 5 were deleted, 1 was modified according to the expert opinions, and no additional index was added. In addition, quantitative standards, weight assignment, and grading standards were developed according to the findings of the expert consultation. The positive coefficients of experts in the first and second rounds of questionnaires were 87.5% and 82.9%, respectively. The authority coefficient was 0.835 (>0.7). The coefficient of variation of the first and second rounds were 0.14∼0.29 and 0.09∼0.27, respectively. Kendall’s coefficient of concordance of the first and second rounds were 0.135 ( $p<0.05$ ) and 0.081 ( $p<0.05$ ), respectively. Conclusion. The evaluation indexes and quantitative reference values of the developed scale conform to the characteristics of prevention and treatment evidence in ancient TCM books. It can provide a useful exploration tool for the evaluation and grading of evidences in TCM ancient books.

Objective: The comorbidities of coronary artery disease (CAD) and rheumatoid arthritis (RA) are mutual risk factors, which lead to higher mortality, but the biological mechanisms connecting the two remain unclear. Here, we aimed to identify the risk genes for the comorbid presence of these two complex diseases using a network modularization approach, to offer insights into clinical therapy and drug development for these diseases. Method: The expression profile data of patients CAD with and without RA were obtained from the GEO database (GSE110008). Based on the differentially expressed genes (DEGs), weighted gene co-expression network analysis (WGCNA) was used to construct a gene network, detect co-expression modules, and explore their relation to clinical traits. The Z_summary index, gene significance (GS), and module membership (MM) were utilized to screen the important differentiated modules and hub genes. The GO and KEGG pathway enrichment analysis were applied to analyze potential mechanisms. Result: Based on the 278 DEGs obtained, 41 modules were identified, of which 17 and 24 modules were positively and negatively correlated with the comorbid occurrence of CAD and RA (CAD&RA), respectively. Thirteen modules with Z_summary < 2 were found to be the underlying modules, which may be related to CAD&RA. With GS ≥ 0.5 and MM ≥ 0.8, 49 hub genes were identified, such as ADO, ABCA11P, POT1, ZNF141, GPATCH8, ATF6 and MIA3, etc. The area under the curve values of the representative seven hub genes under the three models (LR, KNN, SVM) were greater than 0.88. Enrichment analysis revealed that the biological functions of the targeted modules were mainly involved in cAMP-dependent protein kinase activity, demethylase activity, regulation of calcium ion import, positive regulation of tyrosine, phosphorylation of STAT protein, and tissue migration, etc. Conclusion: Thirteen characteristic modules and 49 susceptibility hub genes were identified, and their corresponding molecular functions may reflect the underlying mechanism of CAD&RA, hence providing insights into the development of clinical therapies against these diseases.

Syndrome is a unique and crucial concept in traditional Chinese medicine (TCM). However, much of the syndrome knowledge lacks systematic organization and correlation, and current information technologies are unsuitable for TCM ancient texts.We aimed to develop a knowledge graph that presents this knowledge in a more orderly, structured, and semantically oriented manner, providing a foundation for computer-aided diagnosis and treatment.We developed a construction framework of TCM syndrome knowledge from ancient books, using a pretrained model and rules (TCMSF). We conducted fine-tuning training on Enhanced Representation through Knowledge Integration (ERNIE), Bidirectional Encoder Representation from Transformers pretrained language models, and chatGLM3–6b large language models for named entity recognition (NER) tasks. Furthermore, we employed the progressive entity relationship extraction method based on the dual pattern feature combination to extract and standardize entities and relationships between entities in these books.We selected Yin deficiency syndrome as a case study and constructed a model layer suitable for the expression of knowledge in these books. Compared with multiple NER methods, the combination of ERNIE and Conditional Random Fields performs the best. By utilizing this combination, we completed the entity extraction of Yin deficiency syndrome, achieving an average F1 value of 0.77. The relationship extraction method we proposed reduces the number of incorrectly connected relationships compared with fully connected pattern layers. We successfully constructed a knowledge graph of ancient books on Yin deficiency syndrome, including over 120,000 entities and over 1.18 million relationships.We developed TCMSF in line with the knowledge characteristics of ancient TCM books and improved the accuracy of knowledge graph construction.