Study on material basis and anti-hypertensive metabolomics of different extraction methods of the Uncaria rhynchophylla Scrophularia Formula.

Hypertension is one of the most challenging public health problems worldwide. Previous studies suggested that the Uncaria rhynchophylla Scrophularia Formula (URSF), a medical institution preparation of the affiliated Hospital of Shandong University of Traditional Chinese Medicine, is effective for essential hypertension. However, the efficacy of URSF for hypertension remains unclear. We aimed to clarify the anti-hypertensive mechanism of the URSF. The material basis of URSF was identified by the LC-MS. We also evaluated the antihypertensive efficacy of URSF on SHR rats by body weight, blood pressure and biochemical indicators. The LC-MS spectrometry-based serum non-targeted metabolomics was used to seek potential biomarkers and relevant pathways for URSF in the treatment of SHR rats. 56 biomarkers were metabolically disturbed in SHR rats in the model group compared with the control group. After URSF intervention, 13 biomarkers showed a recovery in the optimal method compared with the other three groups. We identified 3 metabolic pathways in which URSF is involved: the arachidonic acid metabolism pathway, the niacin and nicotinamide metabolism pathway, and the purine metabolism pathway. These discoveries offer a basis for the study of URSF for the treatment of hypertension.

Machine learning in TCM with natural products and molecules: current status and future perspectives.

Traditional Chinese medicine (TCM) has been practiced for thousands of years with clinical efficacy. Natural products and their effective agents such as artemisinin and paclitaxel have saved millions of lives worldwide. Artificial intelligence is being increasingly deployed in TCM. By summarizing the principles and processes of deep learning and traditional machine learning algorithms, analyzing the application of machine learning in TCM, reviewing the results of previous studies, this study proposed a promising future perspective based on the combination of machine learning, TCM theory, chemical compositions of natural products, and computational simulations based on molecules and chemical compositions. In the first place, machine learning will be utilized in the effective chemical components of natural products to target the pathological molecules of the disease which could achieve the purpose of screening the natural products on the basis of the pathological mechanisms they target. In this approach, computational simulations will be used for processing the data for effective chemical components, generating datasets for analyzing features. In the next step, machine learning will be used to analyze the datasets on the basis of TCM theories such as the superposition of syndrome elements. Finally, interdisciplinary natural product-syndrome research will be established by unifying the results of the two steps outlined above, potentially realizing an intelligent artificial intelligence diagnosis and treatment model based on the effective chemical components of natural products under the guidance of TCM theory. This perspective outlines an innovative application of machine learning in the clinical practice of TCM based on the investigation of chemical molecules under the guidance of TCM theory.

Development and validation of predictive model based on deep learning method for classification of dyslipidemia in Chinese medicine.

Backgrounds: Dyslipidemia is a prominent risk factor for cardiovascular diseases and one of the primary independent modifiable factors of diabetes and stroke. Statins can significantly improve the prognosis of dyslipidemia, but its side effects cannot be ignored. Traditional Chinese Medicine (TCM) has been used in clinical practice for more than 2000 years in China and has certain traits in treating dyslipidemia with little side effect. Previous research has shown that Mutual Obstruction of Phlegm and Stasis (MOPS) is the most common dyslipidemia type classified in TCM. However, how to compose diagnostic factors in TCM into diagnostic rules relies heavily on the doctor's experience, falling short in standardization and objectiveness. This is a limit for TCM to play its advantages of treating dyslipidemia with MOPS. Methods: In this study, the syndrome diagnosis in TCM was transformed into the prediction and classification problem in artificial intelligence The deep learning method was employed to build the classification prediction models for dyslipidemia. The models were built and trained with a large amount of multi-centered clinical data on MOPS. The optimal model was screened out by evaluating the performance of prediction models through loss, accuracy, precision, recall, confusion matrix, PR and ROC curve (including AUC). Results: A total of 20 models were constructed through the deep learning method. All of them performed well in the prediction of dyslipidemia with MOPS. The model-11 is the optimal model. The evaluation indicators of model-11 are as follows: The true positive (TP), false positive (FP), true negative (TN) and false negative (FN) are 51, 15, 129, and 9, respectively. The loss is 0.3241, accuracy is 0.8672, precision is 0.7138, recall is 0.8286, and the AUC is 0.9268. After screening through 89 diagnostic factors of TCM, we identified 36 significant diagnosis factors for dyslipidemia with MOPS. The most outstanding diagnostic factors from the importance were dark purple tongue, slippery pulse and slimy fur, etc. Conclusions: This study successfully developed a well-performing classification prediction model for dyslipidemia with MOPS, transforming the syndrome diagnosis problem in TCM into a prediction and classification problem in artificial intelligence. Patients with dyslipidemia of MOPS can be accurately recognized through limited information from patients. We also screened out significant diagnostic factors for composing diagnostic rules of dyslipidemia with MOPS. The study is an avant-garde attempt at introducing the deep-learning method into the research of TCM, which provides a useful reference for the extension of deep learning method to other diseases and the construction of disease diagnosis model in TCM, contributing to the standardization and objectiveness of TCM diagnosis.

Rules of Chinese Herbal Intervention of Radiation Pneumonia Based on Network Pharmacology and Data Mining.

Objective: To explore the mechanism and principles of traditional Chinese medicine (TCM) in the management of radiation pneumonia. Methods: The targets of radiation pneumonia were obtained by screening the GeneCards, OMIM, TTD, DrugBank, and HERB databases, analyzing ADME parameters. In addition, compounds and Chinese herbs that can act on the targets were screened from the TCMSP database. The core target compounds for TCM were used to construct the target-compound, compound-traditional Chinese medicine, and target-compound-traditional Chinese medicine networks. These networks were further used to select the core targets, compounds, and TCM. The binding strength between the core targets and compounds was determined using AutoDock Vina. The trajectory for the molecular dynamics simulation was completed by Desmond version 2020. Results: A total of 55 active targets in radiation pneumonia were identified. Subsequently, 137 candidate compounds and 469 Chinese herbs were matched. Frequency statistics showed that the Chinese herbs that could interfere with radiation pneumonia were mainly bitter, spicy, and sweet, with both cold and warm properties. Moreover, they mainly belonged to liver and lung channels. The core targets included TNF, IL-6, TGF-ß1, and TP53. The most important components were quercetin, resveratrol, and (-)-epigallocatechin-3-gallate. Moreover, the most significant traditional Chinese herbs were Perilla pueraria, ephedra, Lonicerae japonicae, and sea buckthorn. Furthermore, analysis of 222 sets of receptor-ligand docking results suggested that the compounds had good docking activity to their core targets. By combining the docking binding energy, we determined that the chemical compounds had strong binding energy to the targets. Conclusion: Using network pharmacology, we explored the potential mechanism of TCM in the treatment of radiation pneumonia. The general rules for application of TCM in the treatment of radiation pneumonia were summarized. This study provides baseline information for future research on the development of TCM for the management of radiation pneumonia.

lncRNA7 and lncRNA2 modulate cell wall defense genes to regulate cotton resistance to Verticillium wilt.

In plants, long noncoding RNAs (lncRNAs) regulate disease resistance against fungi and other pathogens. However, the specific mechanism behind this regulation remains unclear. In this study, we identified disease resistance-related lncRNAs as well as their regulating genes and assessed their functions by infection of cotton (Gossypium) chromosome segment substitution lines with Verticillium dahliae. Our results demonstrated that lncRNA7 and its regulating gene Pectin methylesterase inhibitor 13 (GbPMEI13) positively regulated disease resistance via the silencing approach, while ectopic overexpression of GbPMEI13 in Arabidopsis (Arabidopsis thaliana) promoted growth and enhanced resistance to V. dahliae. In contrast, lncRNA2 and its regulating gene Polygalacturonase 12 (GbPG12) negatively regulated resistance to V. dahliae. We further found that fungal disease-related agents, including the pectin-derived oligogalacturonide (OG), could downregulate the expression of lncRNA2 and GbPG12, leading to pectin accumulation. Conversely, OG upregulated the expression of lncRNA7, which encodes a plant peptide phytosulfokine (PSK-α), which was confirmed by lncRNA7 overexpression and Ultra Performance Liquid Chromatography Tandem Mass Spectrometry (UPLC-MS) experiments. We showed that PSK-α promoted 3-Indoleacetic acid (IAA) accumulation and activated GbPMEI13 expression through Auxin Response Factor 5. Since it is an inhibitor of pectin methylesterase (PME), GbPMEI13 promotes pectin methylation and therefore increases the resistance to V. dahliae. Consistently, we also demonstrated that GbPMEI13 inhibits the mycelial growth and spore germination of V. dahliae in vitro. In this study, we demonstrated that lncRNA7, lncRNA2, and their regulating genes modulate cell wall defense against V. dahliae via auxin-mediated signaling, providing a strategy for cotton breeding.

Investigation of the Mechanism of Traditional Chinese Medicines in Angiogenesis through Network Pharmacology and Data Mining.

Although traditional Chinese medicine is effective and safe for the treatment of angiogenesis, the in vivo intervention mechanism is diverse, complex, and largely unknown. Therefore, we aimed to explore the active ingredients of traditional Chinese medicine and their mechanisms of action against angiogenesis. Data on angiogenesis-related targets were collected from GeneCards, Therapeutic Target Database, Online Mendelian Inheritance in Man, DrugBank, and DisGeNET. These were matched to related molecular compounds and ingredients in the traditional Chinese medicine system pharmacology platform. The data were integrated and based on the condition of degree > 1, and relevant literature, target-compound, compound-medicine, and target-compound-medicine networks were constructed using Cytoscape. Molecular docking was used to predict the predominant binding combination of core targets and components. We obtained 79 targets for angiogenesis; 41 targets were matched to 3839 compounds, of which 110 compounds were selected owing to their high correlation with angiogenesis. Fifty-five combinations in the network were obtained by molecular docking, among which PTGS2-astragalin (-9.18 kcal/mol), KDR-astragalin (-7.94 kcal/mol), PTGS2-quercetin (-7.41 kcal/mol), and PTGS2-myricetin (-7.21 kcal/mol) were top. These results indicated that the selected potential core compounds have good binding activity with the core targets. Eighty new combinations were obtained from the network, and the top combinations based on affinity were KDR-beta-carotene (-10.13 kcal/mol), MMP9-beta-sitosterol (-8.04 kcal/mol), MMP9-astragalin (-7.82 kcal/mol), and MMP9-diosgenin (-7.51 kcal/mol). The core targets included PTGS2, KDR, VEGFA, and MMP9. The essential components identified were astragalin, kaempferol, myricetin, quercetin, and ß-sitosterol. The crucial Chinese medicines identified included Polygoni Cuspidati Rhizoma et Radix, Morus alba Root Bark, and Forsythiae Fructus. By systematically analysing the ingredients of traditional Chinese medicine and their targets, it is possible to determine their potential mechanisms of action against pathological angiogenesis. Our study provides a basis for further research and the development of new therapeutics for angiogenesis.

The Use of Traditional Chinese Medicine in Relieving EGFR-TKI-Associated Diarrhea Based on Network Pharmacology and Data Mining.

In this study, the role of traditional Chinese medicine (TCM) in relieving epidermal growth factor receptor-tyrosine kinase inhibitor- (EGFR-TKI-) associated diarrhea was discussed by network pharmacology and data mining. Prediction of drug targets by introducing the EGFR-TKI molecular structures into the SwissTargetPrediction platform and diarrhea-related targets in the DrugBank, GeneCards, DisGeNET, and OMIM databases were obtained. Compounds in the drug-disease target intersection were screened by absorption, distribution, metabolism, and excretion parameters and Lipinski's rule in Traditional Chinese Medicine Systems Pharmacology. TCM-containing compounds were selected, and information on the property, taste, and meridian tropism of these TCMs was summarized and analyzed. A target-compound-TCM network diagram was constructed, and core targets, compounds, and TCMs were selected. The core targets and components were docked by AutoDock Vina (Version 1.1.2) to explore the target combinations of related compounds and evaluate the docking activity of related targets and compounds. Twenty-three potential therapeutic TCM targets for the treatment of EGFR-TKI-related diarrhea were obtained. There were 339 compounds acting on potential therapeutic targets, involving a total of 402 TCMs. The results of molecular docking showed good binding between the core targets and compounds, and the binding between the core targets and compounds was similar to that of the core target and the recommended drug loperamide. TCMs have multitarget characteristics and are present in a variety of compounds used for relieving EGFR-TKI-associated diarrhea. Antitumor activity and the efficacy of alleviating diarrhea are the pharmacological basis of combining TCMs with EGFR-TKI in the treatment of non-small-cell lung cancer. The core targets, compounds, and TCMs can provide data to support experimental and clinical studies on the relief of EGFR-TKI-associated diarrhea in the future.

Study on Medication Rules of Traditional Chinese Medicine against Antineoplastic Drug-Induced Cardiotoxicity Based on Network Pharmacology and Data Mining.

METHODS: The targets of antineoplastic drugs with cardiotoxicity were obtained from the National Center for Biotechnology Information (NCBI) database, China national knowledge infrastructure (CNKI) database, and Swiss Target Prediction platform. Then, the cardiotoxicity-related targets were derived from the Gene Cards, Disgenet, OMIM, and DrugBank databases, as well as the drug of current clinical guidelines. The targets both in these two sets were regarded as potential targets to alleviate ADIC. Then, candidate compounds and herbs were matched via Traditional Chinese Medicine Systems Pharmacology (TCMSP) platform. Cytoscape3.7.1 was used to set up the target-compound-herb network. Molecular docking between core targets and compounds was performed with AutodockVina1.1.2. The rules of herbs were summarized by analyzing their property, flavor, and channel tropism. RESULTS: Twenty-one potential targets, 332 candidate compounds, and 400 kinds of herbs were obtained. Five core targets including potassium voltage-gated channel subfamily H member 2 (KCNH2), cyclin-dependent kinase 1 (CDK1), matrix metalloproteinase 2 (MMP2), mitogen-activated protein kinase1 (MAPK1), and tumor protein p53 (TP53) and 29 core compounds (beta-sitosterol, quercetin, kaempferol, etc.) were collected. Five core herbs (Yanhusuo, Gouteng, Huangbai, Lianqiao, and Gancao) were identified. Also, the TCM against ADIC were mainly bitter and acrid in taste, warm in property, and distributed to the liver and lung meridians. CONCLUSION: TCM against ADIC has great potential. Our study provides a new method and ideas for clinical applications of integrated Chinese and western medicine in treating ADIC.

Phenolic Compounds from the Leaves of Castanopsis fargesii.

In the course of a phytochemical and chemotaxonomical investigation of Castanopsis species (Fagaceae), three new phenolic compounds, (3R,1'S)-[1'-(6″-O-galloyl-ß-d-gluco-pyranosyl)oxyethyl]-3-hydroxy-dihydrofuran-2(3H)-one (1), (2R,3S)-2-[2'-(galloyl)oxyethyl]-dihydroxybutanoic acid (2), and (3S,4S)-3-hydroxymethyl-3,4-dihydro-5,6,7-trihydroxy-4-(4'-hydroxy-3'-methoxyphenyl)-1H-[2]-benzopyran-1-one (3) were isolated from the fresh leaves of Castanopsis fargesii. In addition, a known phenolic glycoside, gentisic acid 5-O-α-l-rhamnopyranosyl-(1→2)-ß-d-glucopyranoside (4) was also isolated and identified. Their structures were elucidated by means of spectroscopic methods including one- and two-dimensional NMR techniques.

A new triterpenoid saponin from the root of Croton lachnocarpus Benth.

A new triterpenoid saponin, 3-O-ß-D-xylopyranosyl spathodic acid (1), was isolated from the EtOH extract of the root of Croton lachnocarpus Benth., together with four known compounds. These compounds were characterised on the basis of their spectral data and compatible with values in the literature. Compound 1 was the first triterpenoid glucoside isolated from the genus Croton. The known compound myriaboric acid (2) showed cytotoxic activity against human hepatocellular carcinoma SMMC-7721 cell line with an IC50 value of 42.2 µM.

A cytotoxic cardenolide and a saponin from the rhizomes of Tupistra chinensis.

A new cardenolide tupichinolide (1) and a new steroidal saponin tupichinin A (2), together with seven known compounds, were isolated from the rhizomes of Tupistra chinensis. Their structures were established using spectroscopic analysis and chemical methods. Compound 1 was the first cardenolide isolated from Tupistra chinensis and exhibited potent cytotoxicity against five human cancer cell lines: HL-60, SMMC-7721, A-549, MCF-7 and SW480.

Studies on chemical fingerprints of Siraitia grosvenorii fruits (Luo Han Guo) by HPLC.

A novel, efficient, and accurate fingerprinting method using high performance liquid chromatography-photodiode array detection has been developed and optimized for the investigation and demonstration of the variance in chemical properties among Siraitia grosvenorii fruits from different origins. The effects of growth stages, cultivated varieties, collection locations, and fruit portions of the herb on chromatographic fingerprints were examined. Eleven compounds were identified on chromatograms by comparing the retention time and UV spectrum of each peak separately with those of external references. The results revealed that chromatographic fingerprints, combining similarity or hierarchical clustering analysis along with reference compounds, could efficiently identify and distinguish S. grosvenorii fruits from different sources, which provided helpful clues for studying the plants' secondary metabolites and benefitted quality control.

[Simultaneous determination of six cucurbitane triterpene glycosides in Siraitia grosvenorii fruits using high performance liqid chromatography].

Siraitia grosvenorii, a traditional Chinese fruit, belongs to the family Cucurbitaceae and has been used as a pulmonary demulcent and emollient for the treatment of dry cough, sore throat, dire thirst, and constipation in folk medicine. A high performance liquid chromatographic method was developed for simultaneous determining the contents of mogroside V, mogroside IV A, mogroside III, 11-oxomogroside III, mogroside II E and 11-oxomogroside II E in Siraitia grosvenorii fruits. The chromatographic analysis was carried out on a ZORBAX SB-C18 column (150 mm x 4.6 mm, 5 microm). The mobile phase was water (A) and acetonitrile (B) with gradient elution (0-3 min, 20% B - 30% B; 3-8 min, 30% B - 35% B; 8-9 min, 35% B). The flow rate was maintained at 0. 8 mL/min. The detection wavelength was set at 203 nm and the column temperature was controlled at 30 degrees C. The sample injection volume was 10 microL. The calibration curves were linear over the ranges of 0.04 - 1.0 mg/mL, 0.011 - 0.68 mg/mL, 0.010 - 0.80 mg/mL, 0.0097 - 0.58 mg/mL, 0.025 - 1.0 mg/mL and 0.013 - 0.76 mg/mL (r > 0.999 1) for the above cucurbitane triterpene glycosides, respectively. The average recoveries were 99. 65% for mogroside V , 101.6% for mogroside IV A, 97. 05% for mogroside m, 103. 1% for 11-oxomogroside I, 99. 25% for mogroside II E, and 103.0% for 11-oxomogroside II E, with the relative standard deviations of 0.83%, 3.1%, 1.9%, 3.3%, 0.59% and 2.0%, respectively. This simple, rapid and accurate method is suitable for quality control and determination of raw materials and products of Siraitia grosvenorii fruits.