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. 2024 Dec 19;19:172. doi: 10.1186/s13020-024-01044-3

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© The Author(s) 2024

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 1 — High-throughput screening revealed PC as a metabolic regulatory agent. A Heat map of the gene expression profiles of 81 TCMs, together with Rhizoma Coptidis and berberine. Red represents the upregulated genes; blue shows the downregulated genes. B Correlation coefficient matrix of PC, C. chinensis, C. deltoidea and C. teeta, the color in the heat map represents the correlation coefficient between each two groups of samples, and the color ranges from white (low correlation coefficient) to red (high correlation coefficient). C Scatter plots between PC and C. chinensis from Shizhu; R² = 0.71. Each spot represents a differentially expressed gene