Abstract：
Invasive mold infections (IMIs) are associated with high morbidity, particularly in immunocompromised patients, with mortality rates between 40% and 80%. Early initiation of appropriate antifungal therapy can substantially improve outcomes, yet early diagnosis remains difficult to establish and often requires multidisciplinary teams evaluating clinical and radiological findings plus supportive mycological findings. Universal digital high-resolution melting (U-dHRM) analysis may enable rapid and robust diagnoses of IMI. A universal fungal assay was developed for U-dHRM and used to generate a database of melt curve signatures for 19 clinically relevant fungal pathogens. A machine learning algorithm (ML) was trained to automatically classify these pathogen curves and detect novel melt curves. Performance was assessed on 73 clinical bronchoalveolar lavage samples from patients suspected of IMI. Novel curves were identified by micropipetting U-dHRM reactions and Sanger sequencing amplicons. U-dHRM achieved 97% overall fungal organism identification accuracy and a turnaround time of ~4 hrs. U-dHRM detected pathogenic molds (Aspergillus, Mucorales, Lomentospora, and Fusarium) in 73% of 30 samples classified as IMI, including mixed infections. Specificity was optimized by requiring the number of pathogenic mold curves detected in a sample to be >8 and a sample volume to be 1 mL, which resulted in 100% specificity in 21 at-risk patients without IMI. U-dHRM showed promise as a separate or combination diagnostic approach to standard mycological tests. U-dHRM\'s speed, ability to simultaneously identify and quantify clinically relevant mold pathogens in polymicrobial samples, and detect emerging opportunistic pathogens may aid treatment decisions, improving patient outcomes.
OBJECTIVE: Improvements in diagnostics for invasive mold infections are urgently needed. This work presents a new molecular detection approach that addresses technical and workflow challenges to provide fast pathogen detection, identification, and quantification that could inform treatment to improve patient outcomes.
OBJECTIVE: Improvements in diagnostics for invasive mold infections are urgently needed. This work presents a new molecular detection approach that addresses technical and workflow challenges to provide fast pathogen detection, identification, and quantification that could inform treatment to improve patient outcomes.
摘要：
侵袭性霉菌感染（IMI）与高发病率相关，特别是在免疫功能低下的患者中，死亡率在40%到80%之间。早期开始适当的抗真菌治疗可以显著改善预后。然而,早期诊断仍难以确立,通常需要多学科团队评估临床和放射学结果以及支持性真菌学结果.通用数字高分辨率熔化(U-dHRM)分析可以实现IMI的快速和稳健的诊断。针对U-dHRM开发了通用真菌测定，并用于生成19种临床相关真菌病原体的熔解曲线特征数据库。训练机器学习算法（ML）以自动分类这些病原体曲线并检测新的熔解曲线。对来自疑似IMI患者的73个临床支气管肺泡灌洗样品进行了性能评估。通过微量移液U-dHRM反应和Sanger测序扩增子鉴定新曲线。U-dHRM实现了97％的整体真菌生物鉴定准确性和〜4小时的周转时间。U-DHRM检测到致病性霉菌(曲霉，Mucorales,Lomentospora,和镰刀菌）在30个分类为IMI的样本中，有73%，包括混合感染。通过要求在样品中检测到的致病霉菌曲线的数量>8并且样品体积为1mL来优化特异性。在21例无IMI的高危患者中产生了100%的特异性。U-dHRM有望作为标准真菌学检查的单独或组合诊断方法。U-dHRM的速度,能够同时识别和量化微生物样品中临床相关的霉菌病原体，检测新出现的机会性病原体可能有助于治疗决策，改善患者预后。
目的：迫切需要改进侵袭性霉菌感染的诊断方法。这项工作提出了一种新的分子检测方法，解决了技术和工作流程的挑战，以提供快速病原体检测，identification,和量化，可以告知治疗，以改善患者的结果。
目的：迫切需要改进侵袭性霉菌感染的诊断方法。这项工作提出了一种新的分子检测方法，解决了技术和工作流程的挑战，以提供快速病原体检测，identification,和量化，可以告知治疗，以改善患者的结果。
