Quantitative Assessment of Fungal Biomarkers in Clinical Samples via an Interface‐Modulated Optical Fiber Biosensor

Author:

Chen Pengwei12,Wu Haotian12,Zhao Yajing34,Zhong Lv5,Zhang Yujiao34,Zhan Xundi12,Xiao Aoxiang12,Huang Yunyun12ORCID,Zhang Hong34,Guan Bai‐Ou12

Affiliation:

1. Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications Institute of Photonics Technology Jinan University Guangzhou 511443 China

2. College of Physics & Optoelectronic Engineering Jinan University Guangzhou 510632 China

3. Department of Dermatology The First Affiliated Hospital of Jinan University Guangzhou 510632 China

4. Institute of Mycology Jinan University Guangzhou 510632 China

5. Department of Critical Care Medicine The First Affiliated Hospital of Jinan University Guangzhou Guangdong 510632 China

Abstract

AbstractInvasive fungal infections pose a significant public health threat. The lack of precise and timely diagnosis is a primary factor contributing to the significant increase in patient mortality rates. Here, an interface‐modulated biosensor utilizing an optical fiber for quantitative analysis of fungal biomarkers at the early stage of point‐of‐care testing (POCT), is reported. By integrating surface refractive index (RI) modulation and plasmon enhancement, the sensor to achieve high sensitivity in a directional response to the target analytes, is successfully optimized. As a result, a compact fiber‐optic sensor with rapid response time, cost‐effectiveness, exceptional sensitivity, stability, and specificity, is developed. This sensor can successfully identify the biomarkers of specific pathogens from blood or other tissue specimens in animal models. It quantifies clinical blood samples with precision and effectively discriminates between negative and positive cases, thereby providing timely alerts to potential patients. It significantly reduces the detection time of fungal infection to only 30 min. Additionally, this approach exhibits remarkable stability and achieves a limit of detection (LOD) three orders of magnitude lower than existing methods. It overcomes the limitations of existing detection methods, including a high rate of misdiagnosis, prolonged detection time, elevated costs, and the requirement for stringent laboratory conditions.

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Wiley

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