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AbstractContext Qing Cuo Formula (QCF) is a traditional Chinese medicine for treating acne, but its active compounds and molecular mechanisms are unclear.Objective To investigate the material basis and molecular mechanism of QCF.Materials and methods In vivo experiments were conducted on 60 male golden hamsters with damp-heat acne, with a blank group, a spironolactone group and 3 QCF administration groups (given high, medium and low doses) over a 30-day period. Serum androgen and inflammatory cytokine levels were tested by ELISA. In vitro, chemical compositions of QCF were investigated by UPLC-LTQ-Orbitrap-MS. Network pharmacology approaches were used to analyse the protein–protein interaction (PPI) network and QCF active compounds-intersection targets-acne network. GO enrichment and KEGG pathway analysis was conducted subsequently.Results Low-dose QCF group (11.4 g/kg/day) showed significantly reduced levels of serum T (4.94 ± 0.36; 5.51 ± 0.36 ng/mL), DHT (6.67 ± 0.61; 8.09 ± 0.59 nmol/L), E2 (209.01 ± 20.92; 237.08 ± 13.94 pg/mL), IL-1α (36.84 ± 3.23; 44.07 ± 4.00 pg/mL) and FFA (128.32 ± 10.94; 148.00 ± 12.12 µmol/L) compared to the blank group (p

Qing Cuo Formula (QCF) is a traditional Chinese medicine for treating acne, but its active compounds and molecular mechanisms are unclear. To investigate the material basis and molecular mechanism of QCF. In vivo experiments were conducted on 60 male golden hamsters with damp-heat acne, with a blank group, a spironolactone group and 3 QCF administration groups (given high, medium and low doses) over a 30-day period. Serum androgen and inflammatory cytokine levels were tested by ELISA. In vitro, chemical compositions of QCF were investigated by UPLC-LTQ-Orbitrap-MS. Network pharmacology approaches were used to analyse the protein–protein interaction (PPI) network and QCF active compounds-intersection targets-acne network. GO enrichment and KEGG pathway analysis was conducted subsequently. Low-dose QCF group (11.4 g/kg/day) showed significantly reduced levels of serum T (4.94 ± 0.36; 5.51 ± 0.36 ng/mL), DHT (6.67 ± 0.61; 8.09 ± 0.59 nmol/L), E2 (209.01 ± 20.92; 237.08 ± 13.94 pg/mL), IL-1α (36.84 ± 3.23; 44.07 ± 4.00 pg/mL) and FFA (128.32 ± 10.94; 148.00 ± 12.12 µmol/L) compared to the blank group (p < 0.05). In vitro experiments identified 75 compounds in QCF decoction, with 27 active compounds absorbed in serum. Network pharmacology identified 6 active components connecting 17 targets. GO enrichment and KEGG pathway analysis indicated that QCF's anti-acne targets mainly regulate extracellular matrix function, inflammatory processes, immune response and endocrine function. This study provides evidence of the molecular mechanism and material basis of QCF in treating androgen-related damp-heat acne, paving the way for further research on its potential in treating other conditions related to damp-heat constitution. [ABSTRACT FROM AUTHOR]

Yupingfeng San (YPFS) is a famous and commonly used traditional Chinese medicine (TCM) formula for the treatment of chronic obstructive pulmonary disease, asthma, respiratory tract infections, and pneumonia in China. It is composed of three Chinese herbs, including Astragali Radix, Atractylodis Macrocephalae Rhizoma and Saposhnikoviae Radix. In this review, the relevant references on YPFS were searched in the Web of Science, PubMed, China National Knowledge Infrastructure (CNKI), and other databases. Literatures published from 2000 to 2022 were screened and summarized. The constituents in YPFS could be classified into nine groups according to their structures, including flavonoids, saponins, essential oils, coumarins, lactones, amino acids, organic acids, saccharides, chromones and others. The importance of chemical constituents in YPFS were demonstrated for specific pathological processes including immunoregulatory, anti-inflammatory, anti-tumor and pulmonary diseases. This article systematically reviewed the up-to-date information on its chemical compositions, pharmacology and safety, that could be used as essential data and reference for clinical applications of YPFS. [Display omitted] [ABSTRACT FROM AUTHOR]

Traditional Chinese medicine (TCM) formula is widely used for thousands of years in clinical practice. With the development of artificial intelligence, deep learning models may help doctors prescribe reasonable formulas. Meanwhile, current studies of formula recommendation only focus on the observable clinical symptoms and lack of molecular information. Here, inspired by the theory of TCM network pharmacology, we propose an intelligent formula recommendation system based on deep learning (FordNet), fusing the information of phenotype and molecule. We collected more than 20,000 electronic health records from TCM Master Li Jiren's experience from 2013 to March 2020. In the FordNet system, the feature of diagnosis description is extracted by convolution neural network and the feature of TCM formula is extracted by network embedding, which fusing the molecular information. A hierarchical sampling strategy for data augmentation is designed to effectively learn training samples. Based on the expanded samples, a deep neural network based quantitative optimization model is developed for TCM formula recommendation. FordNet performs significantly better than baseline methods (hit ratio of top 10 improved by 46.9% compared with the best baseline random forest method). Moreover, the molecular information helps FordNet improve 17.3% hit ratio compared with the model using only macro information. Clinical evaluation shows that FordNet can well learn the effective experience of TCM Master and obtain excellent recommendation results. Our study, for the first time, proposes an intelligent recommendation system for TCM formula integrating phenotype and molecule information, which has potential to improve clinical diagnosis and treatment, and promote the shift of TCM research pattern from "experience based, macro" to "data based, macro-micro combined" as well as the development of TCM network pharmacology. [Display omitted] [ABSTRACT FROM AUTHOR]