Paper
6 December 2022 Study on the anti-inflammatory effect of traditional Chinese medicine on gingivitis
Author Affiliations +
Proceedings Volume 12458, International Conference on Biomedical and Intelligent Systems (IC-BIS 2022); 1245805 (2022) https://doi.org/10.1117/12.2660527
Event: International Conference on Biomedical and Intelligent Systems, 2022, Chengdu, China
Abstract
Gingivitis is a kind of oral disease caused by local inflammation. The periodontal diseases with the highest clinical incidence in adults are chronic gingivitis and periodontitis. However, most of the antibiotics used in clinical treatment of periodontal disease are those that produce bacterial resistance and interfere with the ecological balance of oral flora. Research shows that honeysuckle, forsythia, coptis and other heat-clearing and detoxifying traditional Chinese medicine can assist periodontal basic treatment has a good effect on periodontitis and can be applied for a long time. In order to study the anti-inflammatory effects of common Chinese medicines on gingivitis, 27 kinds of Chinese medicines were selected and extracted by enzymolysis and boiling method. The antibacterial effects on bacteria and fungi were detected by the AGAR plate Oxford cup method. The best antibacterial effects of rhubarb, honeysuckle, coptis chinensis, galla chinensis, liquorice, forsythia, and clove were screened, and their inhibitory effects on inflammatory factors were further detected by human gingival epithelial cells. The results showed that rhubarb, honeysuckle, coptis chinensis, galla chinensis, glycyrrhiza, forsythia suspensa, and clove could inhibit inflammatory factors.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Minghan Zhao, Qiulin Yue, Chen Zhao, Baojun Li, Qun Su, Lin Zhao, and Le Su "Study on the anti-inflammatory effect of traditional Chinese medicine on gingivitis", Proc. SPIE 12458, International Conference on Biomedical and Intelligent Systems (IC-BIS 2022), 1245805 (6 December 2022); https://doi.org/10.1117/12.2660527
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KEYWORDS
Medicine

Inflammation

Modeling

Pathogens

Statistical analysis

Biotechnology

Liquids

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