内型
鼻息肉
医学
慢性鼻-鼻窦炎
生物标志物
鼻科学
疾病
鼻窦炎
美波利祖马布
嗜酸性
生物标志物发现
精密医学
哮喘
内科学
病理
免疫学
嗜酸性粒细胞
外科
耳鼻咽喉科
生物化学
化学
蛋白质组学
基因
作者
Tsuguhisa Nakayama,Shinichi Haruna
标识
DOI:10.1080/1744666x.2023.2200164
摘要
Chronic rhinosinusitis (CRS) is a heterogeneous disease with a variety of cellular and molecular pathophysiologic mechanisms. Biomarkers have been explored in CRS using various phenotypes, such as polyp recurrence after surgery. Recently, the presence of regiotype in CRS with nasal polyps (CRSwNP) and the introduction of biologics for the treatment of CRSwNP has indicated the importance of endotypes, and there is a need to elucidate endotype-based biomarkers.Biomarkers for eosinophilic CRS, nasal polyps, disease severity, and polyp recurrence have been identified. Additionally, endotypes are being identified for CRSwNP and CRS without nasal polyps using cluster analysis, an unsupervised learning technique.Endotypes in CRS have still being established, and biomarkers capable of identifying endotypes of CRS are not yet clear. When identifying endotype-based biomarkers, it is necessary to first identify endotypes clarified by cluster analysis for outcomes. With the application of machine learning, the idea of predicting outcomes using a combination of multiple integrated biomarkers, rather than a single biomarker, will become mainstream.
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