探索MALDI-TOF质谱法和抗菌药物耐药性在医院暴发检测中的应用
Exploring the Utility of MALDI-TOF Mass Spectrometry and Antimicrobial Resistance in Hospital Outbreak Detection
AI总结：
本研究探讨了MALDI-TOF质谱法和抗菌药物耐药性模式在医院暴发检测中的应用，开发了机器学习框架以提高检测效率和可及性。
摘要：
准确且及时识别医院暴发集群对于防止具有流行潜力的感染传播至关重要。尽管通过全基因组测序（WGS）评估病原体相似性被认为是暴发检测的黄金标准，但其高成本和长的周转时间阻碍了在临床实验室中的常规应用。我们探讨了两种快速且成本效益高的替代方法，即基质辅助激光解吸电离飞行时间（MALDI-TOF）质谱法和抗菌药物耐药性（AR）模式。我们开发了一个机器学习框架，从MALDI-TOF光谱和AR模式中提取信息表示用于暴发检测，并探索其融合。通过多物种分析，我们证明在某些情况下，MALDI-TOF和AR有潜力减少对WGS的依赖，使更可及且快速的暴发监测成为可能。
英文摘要：
Accurate and timely identification of hospital outbreak clusters is crucial for preventing the spread of infections that have epidemic potential. While assessing pathogen similarity through whole genome sequencing (WGS) is considered the gold standard for outbreak detection, its high cost and lengthy turnaround time preclude routine implementation in clinical laboratories. We explore the utility of two rapid and cost-effective alternatives to WGS, matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry and antimicrobial resistance (AR) patterns. We develop a machine learning framework that extracts informative representations from MALDI-TOF spectra and AR patterns for outbreak detection and explore their fusion. Through multi-species analyses, we demonstrate that in some cases MALDI-TOF and AR have the potential to reduce reliance on WGS, enabling more accessible and rapid outbreak surveillance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
