- Portable breath analysis device for clinical diagnosis and disease monitoring
- Non-invasive, accurate, and rapid biomarker detection in complex sample mixtures
Portable gas chromatography (GC) systems have been intensively investigated for a broad range of field applications such as environmental sampling, indoor air quality monitoring, and clinical diagnostics. However, current portable GC systems are simply miniaturized versions of the standard, one-dimensional benchtop GC instrument. While portable GCs are field-deployable and rapid in vapor analysis, they suffer severely from deteriorated separation capability, peak capacity, and peak width due to their miniature size. As a result, most can separate only a small set of well-defined vapors and often fail when complex sample matrixes or analyte mixtures are present.
Researchers at the University of Michigan have developed a fully automated, portable GC system that overcomes these limitations. The GC system integrates several innovative technologies developed at Michigan, including a micro photoionization detector (microPID), a micro helium discharge photoionization detector (HDPID), a multichannel two-dimensional GC column design, peak focusing technology, and sophisticated machine learning algorithms for peak reconstruction, biomarker identification, and disease diagnosis. The result is a high performance, portable GC that excels in the analysis of complex samples.
The GC technology is suitable for practically any application requiring automated, portable GC chemical analysis. The Michigan team has specifically designed the system for use as a breath analysis device for clinical diagnostic applications. The technology has been demonstrated to accurately diagnose a number of conditions, including certain cancers, COVID-19, acute respiratory distress syndrome (ARDS), and asthma.
- Portability while retaining ability to analyze complex samples
- Rapid (diagnosis in 30 minutes or less)
- High peak capacity, high sensitivity
- Accurate and intelligent
- Continuous and non-invasive
- Cancer detection
- Early diagnosis, trajectory tracking, and outcome prediction for ARDS
- Asthma diagnosis and phenotyping
- COVID-19 detection
- Sepsis monitoring
Name: Jeremy Nelson