Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, TN, USA.
Department of Medicine, The University of Tennessee Graduate School of Medicine, Knoxville, TN, USA.
J Hosp Infect. 2023 Apr;134:80-88. doi: 10.1016/j.jhin.2023.01.008. Epub 2023 Jan 20.
Microbial contamination of aerosol facemasks could be a source of nosocomial infections during nebulization therapy in hospital, prompting efforts to identify these contaminants. Identification of micro-organisms in medical devices has traditionally relied on culture-dependent methods, which are incapable of detecting the majority of these microbial contaminants. This challenge could be overcome with culture-independent sequencing-based techniques that are suited for the profiling of complex microbiomes.
To characterize the microbial contaminants in aerosol facemasks used for nebulization therapy, and identify factors influencing the composition of these microbial contaminants with the acquisition and analysis of comprehensive microbiome-scale profiles using culture-independent high-throughput sequencing.
Used aerosol facemasks collected from hospitalized patients were analysed with culture-independent 16S rRNA gene-based amplicon sequencing to acquire microbiome-scale comprehensive profiles of the microbial contaminants. Microbiome-based analysis was performed to identify potential sources of microbial contamination in facemasks.
Culture-independent high-throughput sequencing was demonstrated for the capacity to acquire microbiome-scale profiles of microbial contaminants on aerosol facemasks. Microbial source identification enabled by the microbiome-scale profiles linked microbial contamination on aerosol facemasks to the human skin and oral microbiota. Antibiotic treatment with levofloxacin was found to reduce contamination of the facemasks by oral microbiota.
Sequencing-based microbiome-scale analysis is capable of providing comprehensive characterization of microbial contamination in aerosol facemasks. Insight gained from microbiome-scale analysis facilitates the development of effective strategies for the prevention and mitigation of the risk of nosocomial infections arising from exposure to microbial contamination of aerosol facemasks, such as targeted elimination of potential sources of contamination.
在医院进行雾化治疗时,气溶胶面罩的微生物污染可能成为医院感染的源头,因此需要努力识别这些污染物。传统上,医疗器械中的微生物鉴定依赖于依赖培养的方法,但这些方法无法检测到大多数微生物污染物。这一挑战可以通过适合复杂微生物组分析的非依赖培养的基于测序的技术来克服。
使用非依赖培养的高通量测序技术,对用于雾化治疗的气溶胶面罩中的微生物污染物进行特征描述,并通过获取和分析综合微生物组规模的图谱来识别影响这些微生物污染物组成的因素。
使用非依赖培养的 16S rRNA 基因扩增子测序对从住院患者收集的气溶胶面罩进行分析,以获取微生物污染物的综合微生物组规模图谱。采用基于微生物组的分析方法来识别面罩中微生物污染的潜在来源。
非依赖培养的高通量测序技术能够获取气溶胶面罩上微生物污染物的微生物组规模图谱。通过微生物组规模图谱进行微生物源识别,将气溶胶面罩上的微生物污染与人体皮肤和口腔微生物群联系起来。发现左氧氟沙星的抗生素治疗可减少面罩口腔微生物群的污染。
基于测序的微生物组规模分析能够全面描述气溶胶面罩上的微生物污染情况。微生物组规模分析提供的见解有助于制定有效的策略来预防和减轻因暴露于气溶胶面罩的微生物污染而导致的医院感染风险,例如有针对性地消除潜在的污染来源。