Multivariate analysis of liquid biopsies for real-time detection of patients with biofilm-associated infections (BAI)
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
---|---|
Article number | 139595 |
Number of pages | 28 |
Journal / Publication | Chemical Engineering Journal |
Volume | 453 |
Issue number | Part 1 |
Online published | 7 Oct 2022 |
Publication status | Online published - 7 Oct 2022 |
Link(s)
Abstract
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 outletsSDD (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.
Research Area(s)
- Biofilm-associated infections, Disease detection, Multivariate analysis, Personalized treatment, Viscosity
Citation Format(s)
Multivariate analysis of liquid biopsies for real-time detection of patients with biofilm-associated infections (BAI). / Liao, J. C.; Zou, S. J.; Deng, Y. L. et al.
In: Chemical Engineering Journal, Vol. 453, No. Part 1, 139595, 01.02.2023.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review