数字聚合酶链反应
生物
多路复用
曲霉
多重聚合酶链反应
微生物学
隐球菌
熔化曲线分析
实时聚合酶链反应
聚合酶链反应
生物信息学
基因
生物化学
作者
Jian Guo,Wenxi Tian,Hui‐Ping Lin,Liang Hu,Xinjie Gao,Jiang Xia,Hao Yu,Hui Chen,Wei Li,Wenjuan Wu
出处
期刊:Mycology
[Informa]
日期:2023-12-29
卷期号:: 1-10
标识
DOI:10.1080/21501203.2023.2296941
摘要
Pulmonary invasive fungal infection in immunocompromised hosts is difficult to diagnose, and current tools for diagnosis or monitoring of response to antifungal treatments have inherent limitations. Droplet digital PCR (ddPCR) has emerged as a promising tool for pulmonary pathogen detection with high sensitivity. This study presents a novel ddPCR panel for rapid and sensitive identification of pulmonary fungal pathogens. First, a ddPCR method for detecting three fungal genera, including Pneumocystis, Aspergillus, and Cryptococcus, was established and evaluated. Then, the clinical validation performance of ddPCR was compared with that of qPCR using 170 specimens, and the 6 specimens with inconsistent results were further verified by metagenomics next-generation sequencing, which yielded results consistent with the ddPCR findings. Finally, the area under the ROC curve (AUC) was used to evaluate the efficiency of ddPCR. While the qPCR identified 16 (9.41%) cases of Aspergillus and 6 (3.53%) cases of Pneumocystis, ddPCR detected 20 (11.76%) Aspergillus cases and 8 (4.71%) Pneumocystis cases. The AUC for Aspergillus, Cryptococcus, and Pneumocystis was 0.974, 0.998, and 0.975, respectively. These findings demonstrated that the ddPCR assay is a highly sensitive method for identifying pathogens responsible for invasive fungal pulmonary infections, and is a promising tool for early diagnosis.
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