Biomedical subjects

Melissa D Krebs

Publications and source records attributed to Melissa D Krebs.

3 recordsLinked to original sources

PubMed2006

Novel technology for rapid species-specific detection of Bacillus spores.

There is an urgent need for a small, inexpensive sensor that can rapidly detect bio-warfare agents with high specificity. Bacillus anthracis, the causative agent of anthrax, would be a perilous disease-causing organism in the event of a release. Currently, most anthrax detection research is based on nucleic acid detection, immunoassays and mass spectrometry, with few detection levels reported below 10(5) spores. Here, we show the ability to distinguish Bacillus spores to a level approaching 10(3) spores, below the reported median infectious dose of B. anthracis, using pyrolysis--micromachined differential mobility spectrometry and novel pattern recognition algorithms that combine lead cluster mapping with genetic algorithms.

Algorithms↗

PubMed2005

Species-specific bacteria identification using differential mobility spectrometry and bioinformatics pattern recognition.

As bacteria grow and proliferate, they release a variety of volatile compounds that can be profiled and used for speciation, providing an approach amenable to disease diagnosis through quick analysis of clinical cultures as well as patient breath analysis. As a practical alternative to mass spectrometry detection and whole cell pyrolysis approaches, we have developed methodology that involves detection via a sensitive, micromachined differential mobility spectrometer (microDMx), for sampling headspace gases produced by bacteria growing in liquid culture. We have applied pattern discovery/recognition algorithms (ProteomeQuest) to analyze headspace gas spectra generated by microDMx to reliably discern multiple species of bacteria in vitro: Escherichia coli, Bacillus subtilis, Bacillus thuringiensis, and Mycobacterium smegmatis. The overall accuracy for identifying volatile profiles of a species within the 95% confidence interval for the two highest accuracy models evolved was between 70.4 and 89.3% based upon the coordinated expression of between 5 and 11 features. These encouraging in vitro results suggest that the microDMx technology, coupled with bioinformatics data analysis, has potential for diagnosis of bacterial infections.

Bacillus↗

PubMed2004

Autoregressive modeling of analytical sensor data can yield classifiers in the predictor coefficient parameter space.

SUMMARY: The analysis of chromatographic data resulting from complex chemical mixtures is challenging. Components may co-elute, causing their signals to overlap. An algorithm that will increase the signal-to-noise ratio so compounds present in low abundance can be better distinguished from noise is useful in this type of analysis. The autoregressive (AR) filter offers the advantage of smoothing chromatograms to increase this ratio, while also offering data compression and increased resolution. Furthermore, this filter can be useful for classification, as the roots of the predictor coefficient vectors represent features present in the data and can therefore be used for pattern recognition. In this paper, we present a novel method for applying AR filtering to chromatogram data. We show that the AR filter outperforms the Savitzky-Golay filter for smoothing noise while retaining important information within chromatograms, and also that AR correlation coefficients have the potential to be used to classify chromatogram data into groups. CONTACT: cdavis@draper.com.

Algorithms↗