生物膜
多元分析
多元统计
医学
抗生素
微生物学
内科学
生物
细菌
计算机科学
遗传学
机器学习
作者
Junchen Liao,Shangjie Zou,Yanlin Deng,Yuan Jiang,Song Lin Chua,Bee Luan Khoo
标识
DOI:10.1016/j.cej.2022.139595
摘要
• A novel multivariate analysis platform to assess biofilm-associated infections. • Established a novel biosensor HMS indicator to identify patients at risk rapidly. • Has broad and robust applicability to various bacterial types with at least 80% efficiency. • Significant improvement in detection limit (more than 2-fold) and speed (within 2 h) compared to conventional methods. • Ease of operations to facilitate point-of-care management in clinics. Biofilm-associated infections (BAI) are chronic infections that are refractory to standard antibiotic therapy and challenging to diagnose. Detecting biofilms in patients remains a major challenge in the clinical field. Here, we introduced a microfluidic-based label-free and multivariate analysis (LF-MA) platform to assess biofilm-associated infection disease via multivariate analysis of severity parameters for point-of-care (POC) management. The integrated LF-MA platform consisted of two components: Biofilm Enrichment Device (BED) and Severity Detection Device (SDD), and allowed simultaneous real-time biofilm enrichment and viscosity-based BAI severity detection. High recovery efficiencies (> 80%) were observed for both gram-positive and gram-negative strains. A novel biosensor HMS indicator (Healthy: 1-3+, Mild: 1-3-, Severe: 1+3-) was developed to evaluate the severity of BAI based on microbeads distribution in target outlets SDD (for viscosity assessment) and the presence of biofilms. The one-step strategy was validated with patient-derived clinical isolates and could be completed within 2 h. We envisioned that the ease of operations and derived HMS biosensor indicator could facilitate new patient-centric approaches for rapid and multivariate assessment of patients with BAI.
科研通智能强力驱动
Strongly Powered by AbleSci AI