Detection of serum CC16 by a rapid and ultrasensitive magnetic chemiluminescence immunoassay for lung disease diagnosis

免疫分析 检出限 单克隆抗体 慢性阻塞性肺病 化学发光 生物标志物 医学 抗体 免疫学 化学 色谱法 内科学 生物化学
作者
Kaili Duan,Yu Xiang,Yilong Deng,Junman Chen,Ping Liu
出处
期刊:Clinical Chemistry and Laboratory Medicine [De Gruyter]
标识
DOI:10.1515/cclm-2024-0724
摘要

Abstract Objectives It has been reported that serum Clara cell secreted protein 16 (CC16) is a potential biomarker for lung injury diseases, but currently, there is no other method that is faster, more accurate, or more sensitive being applied in clinical practice apart from ELISA. The current study was designed to established a magnetic nanoparticles chemiluminescence immunoassay (MNPs-CLIA) for highly sensitive automated detection of serum Clara cell secretory protein 16 (CC16), and validated its diagnostic performance for lung disease. Methods The study included the expression of CC16 recombinant protein, the preparation and screening of its monoclonal antibody (MAb), as well as the construction, optimization and analytical evaluation of the MNPs-CLIA method. The clinical application value of this method was investigated by detecting CC16 level in 296 serum samples. Results The linear range of the MNPs-CLIA assay system was 0.2–50 ng/mL, and the limit of detection was 0.037 ng/mL. Performance parameters such as specificity, recovery rate, and precision can meet the industry standards of in vitro diagnostic reagents. The established method reveals consistent results with ELISA (R 2 =0.9962) currently used clinically, and it also exhibits satisfactory diagnostic efficacy of silicosis, chronic obstructive pulmonary disease (COPD), and pulmonary sarcoidosis, with areas under the curve (AUC) of 0.9748, 0.8428 and 0.9128, respectively. Conclusions Our established MNPs-CLIA method has the advantages of automation, high throughput, rapidity, and simplicity, and can be promoted for widely popularized in clinical applications. MNPs-CLIA detection of serum CC16 has efficient diagnostic potentiality for predicting and diagnosing lung diseases.
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