Chronic renal disease or acute renal injury could result in end-stage renal disease or renal failure. Sonoporation, induced by ultrasound-targeted microbubble destruction (UTMD), has evolved as a new technology for gene delivery. It increases the transfection efficiency of the genes into target kidney tissues. Moreover, UTMD-mediated gene delivery can directly repair the damaged tissues or improve the recruitment and homing of stem cells in the recovery of injured tissues, which has the potential to act as a non-viral and effective method to current gene therapy. This article reviews the mechanisms and applications of UTMD in terms of renal disease, including diabetic nephropathy, renal carcinoma, acute kidney injury, renal interstitial fibrosis, nephrotoxic nephritis, urinary stones, and acute rejection.
Purpose: Depression is a sickening psychiatric condition that is prevalent worldwide. To manage depression, the underlying modes of antidepressant effect of herbals are important to be explored for the development of natural drugs. Tiansi Liquid is a traditional Chinese medicine (TCM) that is prescribed for the management of depression, however its underlying mechanism of action is still uncertain. The purpose of this study was to systematically investigate the pharmacological mode of action of a herbal formula used in TCM for the treatment of depression. Methods: Based on literature search, an ingredients-targets database was developed for Tiansi Liquid, followed by the identification of targets related to depression. The interaction between these targets was evaluated on the basis of protein-protein interaction network constructed by STITCH and gene ontology (GO) enrichment analysis using ClueGO plugin. Results: As a result of literature search, 57 components in Tiansi Liquid formula and 106 potential targets of these ingredients were retrieved. A careful screening of these targets led to the identification of 42 potential targets associated with depression. Ultimately, 327 GO terms were found by analysis of gene functional annotation clusters and abundance value of these targets. Most of these terms were found to be closely related to depression. A significant number of protein targets such as IL10, MAPK1, PTGS2, AKT1, APOE, PPARA, MAPK1, MIF, NOS3 and TNF-α were found to be involved in the functioning of Tiansi Liquid against depression. Conclusions: The findings elaborate that Tiansi Liquid can be utilized to manage depression, however, multiple molecular mechanisms of action could be proposed for this effect. The observed core mechanisms could be the sensory perception of pain, regulation of lipid transport and lipopolysaccharide-mediated signaling pathway.
Abstract Alzheimer's disease (AD) is characterized by progressive cognitive decline. Besides cognitive deficit, AD is also characterized by behavioral and psychological symptoms in dementia (BPSD). However, therapeutic management of BPSD remains challenging. HuanglianJiedu decoction (HLJDD), a traditional Chinese prescription, consisting of four herbs, is applied to treat AD, especially AD with BPSD. Though HLJDD, has the traditional combination with the principal herb Coptidis rhizoma (Huang-lian), it might, however, not be suitable for treating BPSD. Elucidating the mechanism underlying each herb is critical to the disease-matched combination of HLJDD. In this study, network pharmacology was used to determine the targets and biological processes regulated by HLJDD in the treatment of BPSD. Moreover, molecular docking was utilized to evaluate the binding activity between the herbs' main active ingredients and neurotransmitter receptors. The results showed that Scutellariae radix (Huang-qin) and Phellodendri chinrnsis cortex (Huang-bai) exhibited better anti-BPSD effects when compared to Coptidis rhizoma and Gardeniae fructus (Zhi-zi). Scutellariae radi x exhibited superior anti-neuroinflammation functions, with better blood vessel regulation effects. Phellodendri chinrnsis cortex showed a higher binding affinity to the dopamine D2 receptor (DRD2) and 5-hydroxytryptamine receptor 2A (HTR2A). Coptidis rhizoma and Gardeniae fructus were better in neuronal signaling. In conclusion, for treating BPSD, Scutellariae radix and Phellodendri chinrnsis cortex are the principal herbs while Coptidis rhizoma and Gardeniae fructus are the ancillary herbs.
Traditional Chinese medicine (TCM) syndrome differentiation, as the core of the traditional Chinese medical system, has played an indispensable role in guaranteeing the health of the Chinese nation for thousands of years.Recently, with the collaborative promotion of multiple departments, the TCM technology innovation capability of China has been continuously enhanced.The integration of TCM syndrome differentiation with artificial intelligence AI, big data, and other fields has made new progress.Engineering frontier methods and technologies have provided an effective route for breaking through the theoretical bottlenecks of TCM syndrome differentiation.Against the backdrop of the modernization and intelligent development of TCM diagnosis in the new era, this study summarizes the fundamental theories, basic processes, and key technical links of AI-assisted TCM syndrome differentiation.收稿日期：2024-02-26；修回日期：2024-03-27 通讯作者： * 陆军，浙江省医学电子与数字健康重点实验室研究员，中国工程院院士，研究方向为综合电子信息系统、人工智能、数字健康；
Background: So far, only a few researchers have systematically analyzed the constituents of the traditional Chinese medicine prescription Xixin Decoction (XXD) and its potential mechanism of action in treating Alzheimer’s disease (AD). This study aimed to explore the potential mechanism of XXD in the treatment of AD using network pharmacology and molecular docking. Methods: The compounds of XXD were searched within the Traditional Chinese Medicine System Pharmacology Database (TCMSP) and the Traditional Chinese Medicine Integrated Database (TCMID) databases. Overlapping AD-related targets obtained from the two databases and the predicted targets of XXD obtained from SwissTargetPrediction platform were imported into the STRING database to build PPI networks including hub targets; Cytoscape software was used to construct the herb-compound-target network while its plug-in CytoNCA was used to screen the main active compounds of XXD. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses explored the core biological mechanism and pathways via the Metascape platform. In addition, we used AutoDock Vina and PyMOL software to investigate the molecular docking of main compounds to hub targets. Results: We determined 114 active compounds, 973 drug targets, and 973 disease targets. However, intersection analysis screened out 208 shared targets.Protein-protein interaction (PPI) network identified 9 hub targets. The hub targets were found to be majorly enriched in several biological processes (positive regulation of kinase activity, positive regulation of cell death, regulation of MAPK cascade, trans-synaptic signaling, synaptic signaling, etc.) and the relevant pathways of Alzheimer's disease, including neuroactive ligand-receptor interaction, dopaminergic synapse, serotonergic synapse, and the MAPK signaling pathway, etc. The pathway-target-compound network of XXD for treating AD was then constructed. 8 hub targets exhibited good binding activity with 9 main active compounds of XXD in molecular docking. Conclusion: In this study, we found multi-compound-multi-target-multi-pathway regulation to reveal the mechanism of XXD for treating AD based on network pharmacology and molecular docking. XXD may play a therapeutic role through regulating the Alzheimer's disease pathway, its downstream PI3K/Akt signaling pathway or the MAPK signaling pathway, thereby treating AD. This provides new insights for further experiments on the pharmacological effects of XXD.
Abstract 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.
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