Rapid Phenotypic Antimicrobial Susceptibility Testing with Multichannel Large-Volume Scattering Imaging and a Bayesian Gaussian Process Model

Multidrug-resistant (MDR) bacterial infections present a great challenge to healthcare, particularly during life-threatening conditions, such as septic shock. In these critical cases, testing delays are life-threatening, as patient mortality increases by 7.6% for each hour without effective antibiotics. To overcome these issues, we have developed a multichannel <u>L</u>arge-<u>V</u>olume <u>S</u>cattering <u>im</u>aging (LVSim) system for rapid and precise phenotypic antimicrobial susceptibility testing (AST) and minimum inhibitory concentration (MIC) determination. This system simultaneously monitors up to eight sample/drug conditions over time. A Bayesian Gaussian process model is employed to analyze the temporal dynamics of bacterial growth for rapid MIC determination. We validated our method with <i>Escherichia coli</i>, a model bacterial strain, and two <i>Pseudomonas aeruginosa</i> strains─one model reference strain and one slow-growing MDR clinical isolate─and achieved rapid MIC determination within 2 h. The multiplexed LVSim system offers a promising rapid AST solution to combat MDR infections.

Authors

Institutions

Publication Details

Journal
Analytical Chemistry
Published
2025-12-01
DOI
https://doi.org/10.1021/acs.analchem.5c04615
Primary Topic
Bacterial Identification and Susceptibility Testing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Rapid Phenotypic Antimicrobial Susceptibility Testing with Multichannel Large-Volume Scattering Imaging and a Bayesian Gaussian Process Model

Jiapei Jiang, Michelle McBride, Nguyen Ly, Shelley E. Haydel et al.
Analytical Chemistry
Bacterial Identification and Susceptibility Testing
article

Rapid Phenotypic Antimicrobial Susceptibility Testing with Multichannel Large-Volume Scattering Imaging and a Bayesian Gaussian Process Model

Jiapei Jiang, Michelle McBride, Nguyen Ly, Shelley E. Haydel, Yannik A. Haller, Timothy Williams, Shaopeng Wang, Shuang Zhou, T. W. Jing, I. Hernández
article en

Abstract

Multidrug-resistant (MDR) bacterial infections present a great challenge to healthcare, particularly during life-threatening conditions, such as septic shock. In these critical cases, testing delays are life-threatening, as patient mortality increases by 7.6% for each hour without effective antibiotics. To overcome these issues, we have developed a multichannel <u>L</u>arge-<u>V</u>olume <u>S</u>cattering <u>im</u>aging (LVSim) system for rapid and precise phenotypic antimicrobial susceptibility testing (AST) and minimum inhibitory concentration (MIC) determination. This system simultaneously monitors up to eight sample/drug conditions over time. A Bayesian Gaussian process model is employed to analyze the temporal dynamics of bacterial growth for rapid MIC determination. We validated our method with <i>Escherichia coli</i>, a model bacterial strain, and two <i>Pseudomonas aeruginosa</i> strains─one model reference strain and one slow-growing MDR clinical isolate─and achieved rapid MIC determination within 2 h. The multiplexed LVSim system offers a promising rapid AST solution to combat MDR infections.

Analytical ChemistryVol. 97(48)
University Health System (US), Institute for Advanced Medical Research (US), Advanced Medical Research Institute (US), Biosensing Instrument (United States) (US), BioElectronics (United States) (US), Arizona State University (US)
Openalex Percentile: Top 59%
Bacterial Identification and Susceptibility Testing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.