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Research on quality markers of Prunella vulgaris from different origins and parts by HPLC and multi-dimensional discrimination methods
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Chinese Traditional and Herbal Drugs | 2026, 57(5) : 1864 - 1876
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Chinese Traditional and Herbal Drugs | 2026, 57(5): 1864-1876
Research on quality markers of Prunella vulgaris from different origins and parts by HPLC and multi-dimensional discrimination methods
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LI Yuanyuan, LI Tianjiao, WANG Shuai, BAO Yongrui, ZHANG Tiejun, XU Haiyu, MENG Xiansheng
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doi: 10.7501/j.issn.0253-2670.2026.05.023
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Objective To initially analyze and determine the levels of potential quality Markers (Q-Markers) in four medicinal parts (spikes, stems, leaves, and roots) of Prunella vulgaris based on fingerprinting, network pharmacology, and molecular docking techniques.Methods Fingerprint profiles of the spikes, stems, leaves, and roots of P. vulgaris were established for eight different origins. The common and non-common peaks in the fingerprints of different parts were identified. To predict Q-Markers of P. vulgaris, similarity analysis, principal component analysis (PCA), TOPSIS analysis, and orthogonal partial least squares discriminant analysis (OPLS-DA) were employed, utilizing network pharmacology. Additionally, quantitative analysis was conducted to provide a comprehensive quality evaluation. Results A total of 17 common peaks were identified in the spikes of eight batches of P. vulgaris, 22 in the stems, 21 in the leaves, and 16 in the roots. Peaks 5, 11, 12, 13, 14, 16, and 20 were identified as caffeic acid, rutin, hyperoside, isoquercitrin, salviaflaside, rosmarinic acid, and luteolin, respectively. Caffeic acid and rosmarinic acid were present in all four parts, with similarity values exceeding 0.97 within the same medicinal part, while the fingerprints of different medicinal parts showed significant differences. Network pharmacology screening identified 14 core targets, including PTGS2 and EGFR. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis revealed involvement in protein polysaccharides in cancer, COVID-19, lipid metabolism, and atherosclerosis. A “component-target-pathway” network diagram was developed. For molecular docking, 14 core targets and two components were chosen. The results indicated that the components exhibited strong binding properties with the proteins. Conclusion The established fingerprinting method is both accurate and reliable. Combined with network pharmacology and molecular docking technology, it predicted the activity of two potential Q-Markers in the four medicinal parts of P. vulgaris. This study provides a theoretical foundation for the comprehensive quality evaluation of P. vulgaris and the exploration of new medicinal parts.
Prunella vulgaris L.  /  fingerprinting  /  caffeic acid  /  rutin  /  hyperoside  /  isoquercitrin  /  salviaflaside  /  rosmarinic acid  /  luteolin  /  principal component analysis  /  TOPSIS  /  quality evaluation
LI Yuanyuan, LI Tianjiao, WANG Shuai, BAO Yongrui, ZHANG Tiejun, XU Haiyu, MENG Xiansheng. Research on quality markers of Prunella vulgaris from different origins and parts by HPLC and multi-dimensional discrimination methods[J]. Chinese Traditional and Herbal Drugs, 2026 , 57 (5) : 1864 -1876 . DOI: 10.7501/j.issn.0253-2670.2026.05.023
Year 2026 volume 57 Issue 5
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doi: 10.7501/j.issn.0253-2670.2026.05.023
  • Receive Date:2025-10-03
  • Online Date:2026-09-09
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  • Received:2025-10-03
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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