Arora Mehak, Zambrzycki Stephen C, Levy Joshua M, Esper Annette, Frediani Jennifer K, Quave Cassandra L, Fernández Facundo M, Kamaleswaran Rishikesan
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA 30332, USA.
Metabolites. 2022 Mar 8;12(3):232. doi: 10.3390/metabo12030232.
Point-of-care screening tools are essential to expedite patient care and decrease reliance on slow diagnostic tools (e.g., microbial cultures) to identify pathogens and their associated antibiotic resistance. Analysis of volatile organic compounds (VOC) emitted from biological media has seen increased attention in recent years as a potential non-invasive diagnostic procedure. This work explores the use of solid phase micro-extraction (SPME) and ambient plasma ionization mass spectrometry (MS) to rapidly acquire VOC signatures of bacteria and fungi. The MS spectrum of each pathogen goes through a preprocessing and feature extraction pipeline. Various supervised and unsupervised machine learning (ML) classification algorithms are trained and evaluated on the extracted feature set. These are able to classify the type of pathogen as bacteria or fungi with high accuracy, while marked progress is also made in identifying specific strains of bacteria. This study presents a new approach for the identification of pathogens from VOC signatures collected using SPME and ambient ionization MS by training classifiers on just a few samples of data. This ambient plasma ionization and ML approach is robust, rapid, precise, and can potentially be used as a non-invasive clinical diagnostic tool for point-of-care applications.
即时检测筛查工具对于加快患者护理速度以及减少对缓慢诊断工具（如微生物培养）的依赖至关重要，这些诊断工具用于识别病原体及其相关的抗生素耐药性。近年来，对生物介质释放的挥发性有机化合物（VOC）进行分析作为一种潜在的非侵入性诊断程序受到了越来越多的关注。这项工作探索了使用固相微萃取（SPME）和常压等离子体电离质谱（MS）来快速获取细菌和真菌的VOC特征。每种病原体的质谱图都要经过一个预处理和特征提取流程。在提取的特征集上对各种监督和无监督机器学习（ML）分类算法进行训练和评估。这些算法能够高精度地将病原体类型分类为细菌或真菌，同时在识别特定细菌菌株方面也取得了显著进展。本研究提出了一种新方法，通过仅对少量数据样本进行分类器训练，从使用SPME和常压电离MS收集的VOC特征中识别病原体。这种常压等离子体电离和ML方法具有稳健、快速、精确的特点，并且有可能用作即时检测应用的非侵入性临床诊断工具。
