2022
DOI: 10.3390/plants11192470
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Molecular Mechanisms Underlying Qi-Invigorating Effects in Traditional Medicine: Network Pharmacology-Based Study on the Unique Functions of Qi-Invigorating Herb Group

Abstract: Qi-invigorating herbs (QIHs) are a group of herbs that invigorate Qi, the most vital force for maintaining the physiological functions of the human body in traditional medicine. However, the mechanism underlying the Qi-invigorating effects remains unclear. This study aimed to elucidate the unique mechanisms of QIHs based on unique compounds, using a network pharmacology approach. QIHs and their compounds were identified using existing literature and the TCMSP database, respectively. Subsequently, a method was … Show more

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Cited by 4 publications
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“…The mechanisms underlying herbs and prescriptions have been revealed and further detailed through “multi-target–multi-pathway” paradigms of network pharmacology ( Liang et al, 2014 ; Zhang et al, 2019 ; Tran and Lee, 2022 ). These methods are also useful for understanding specific concepts of traditional medicine at the molecular scale such as the synergism of herbal pairs ( Wang et al, 2017 ), or the Qi and Blood-tonifying effects of a group of herbs ( Sun et al, 2016 ; Tran et al, 2022 ). The common feature of these studies is that the molecular mechanisms were determined using databases to generate large gene sets of interest analyses.…”
Section: Introductionmentioning
confidence: 99%
“…The mechanisms underlying herbs and prescriptions have been revealed and further detailed through “multi-target–multi-pathway” paradigms of network pharmacology ( Liang et al, 2014 ; Zhang et al, 2019 ; Tran and Lee, 2022 ). These methods are also useful for understanding specific concepts of traditional medicine at the molecular scale such as the synergism of herbal pairs ( Wang et al, 2017 ), or the Qi and Blood-tonifying effects of a group of herbs ( Sun et al, 2016 ; Tran et al, 2022 ). The common feature of these studies is that the molecular mechanisms were determined using databases to generate large gene sets of interest analyses.…”
Section: Introductionmentioning
confidence: 99%