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Exploration of salivary biomarkers for accurate diagnosis of anxiety, depression, and somatic symptom disorder by the integration of microfluidic chip and machine learning 期刊论文

编号：

552F276AAB98222A23435E34519A1DD8
作者：

Wang, Rongjie#^[1]Xu, Fei(徐飞)^[1]Wang, Lin^[2];Zhang, Xue^[1];Huang, Miao^[1];Wei, Zhun^[3];Jiang, Yan^[4];Zhang, Yunhong^[1];Chen, Xiaoshuang^[1];Liang, Yanbo^[1];Zhang, Yu^[1];Mao, Xueqin(毛雪琴)*^[1]Han, Lin(韩琳)*^[1,5]
语种：

英文
期刊：

CHEMICAL ENGINEERING JOURNAL ISSN：1385-8947 2026 年 535 卷 ; MAY 1
收录：
关键词：

Depression Saliva Machine learning Microfluidic chip
摘要：

Depression represents a major global public health concern, affecting hundreds of millions of individuals worldwide. The World Health Organization forecasts that, by 2030, depression will emerge as the primary driver of the global healthcare challenge. Consequently, the identification of biomarkers specific to anxiety (ANX), depression (DEP), and somatic symptom disorder (SSD) is crucial for early and accurate diagnosis and timely treatment. Here, we explicited a diagnosis model to screen the patients by a salivary biomarkers panel in the of a cohort consisting of healthy individuals and patients with ANX, DEP, and SSD, through a microfluidic chip. The detection results show that the expression levels of inflammatory cytokines were elevated in patients compared with healthy individuals. A four-biomarker panel was employed to train the machine learning (ML) model (M4D) based on the random forest (RF) algorithm for early and accurate differential diagnosis, which includes tumor necrosis factor-alpha (TNF-alpha), interleukin-8 (IL-8), interleukin-1 beta (IL-1 beta) and Interferon-gamma (IFN-gamma). It can identify the specific type of mental disorder for an individual with AUC >0.98, without invasive procedures. The diagnostic accuracy in the test set reached 92.22%. The proposed non-invasive diagnostic method presents promising applications in making precise diagnosis of mental disorders.
推荐引用方式
GB/T 7714：

Wang Rongjie,Xu Fei,Wang Lin, et al. Exploration of salivary biomarkers for accurate diagnosis of anxiety, depression, and somatic symptom disorder by the integration of microfluidic chip and machine learning [J].CHEMICAL ENGINEERING JOURNAL,2026,535.
APA：

Wang Rongjie,Xu Fei,Wang Lin,Zhang Xue,&Han Lin.(2026).Exploration of salivary biomarkers for accurate diagnosis of anxiety, depression, and somatic symptom disorder by the integration of microfluidic chip and machine learning .CHEMICAL ENGINEERING JOURNAL,535.
MLA：

Wang Rongjie, et al. "Exploration of salivary biomarkers for accurate diagnosis of anxiety, depression, and somatic symptom disorder by the integration of microfluidic chip and machine learning" .CHEMICAL ENGINEERING JOURNAL 535(2026).
入库时间：

4/22/2026 10:07:50 PM
更新时间：

4/23/2026 12:09:28 AM

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