Waterborne pathogen detection using Raman spectroscopy.

Raman spectroscopy is being evaluated as a candidate technology for waterborne pathogen detection. We have investigated the impact of key experimental and background interference parameters on the bacterial species level identification performance of Raman detection. These parameters include laser-induced photodamage threshold, composition of water matrix, and organism aging in water. The laser-induced photodamage may be minimized by operating a 532 nm continuous wave laser excitation at laser power densities below 2300 W/cm(2) for Grampositive Bacillus atrophaeus (formerly Bacillus globigii, BG) vegetative cells, 2800 W/cm(2) for BG spores, and 3500 W/cm(2) for Gram-negative E. coli (EC) organisms. In general, Bacillus spore microorganism preparations may be irradiated with higher laser power densities than the equivalent Bacillus vegetative preparations. In order to evaluate the impact of background interference and organism aging, we selected a biomaterials set comprising Gram-positive (anthrax simulants) organisms, Gram-negative (plague simulant) organisms, and proteins (toxin simulants) and constructed a Raman signature classifier that identifies at the species level. Subsequently, we evaluated the impact of tap water and storage time in water (aging) on the classifier performance when characterizing B. thuringiensis spores, BG spores, and EC cell preparations. In general, the measured Raman signatures of biological organisms exhibited minimal spectral variability with respect to the age of a resting suspension and water matrix composition. The observed signature variability did not substantially degrade discrimination performance at the genus and species levels. In addition, Raman chemical imaging spectroscopy was used to distinguish a mixture of BG spores and EC cells at the single cell level.

Strain imaging of corneal tissue with an ultrasound elasticity microscope.

PURPOSE: We hypothesize that high-resolution elasticity measurements can guide corrective refractive surgery of the cornea. Elasticity measurements would improve surgical outcomes by adding biomechanical information not used in existing clinical nomograms. As an initial investigation, we determined the usefulness and evaluated the ability of our ultrasound elasticity microscope by measuring strain ex vivo in an intact porcine eye globe. METHODS: Strain was predicted with a finite element model guided by direct mechanical measurements of corneal elasticity. Next, a porcine cornea was deformed with a slitted plate while being imaged with ultrasound. For high spatial resolution, the ultrasound elasticity microscope uses a 50 MHz transducer with a 1.4 f/number. It produces high-quality conventional ultrasonic B-scans over large thicknesses by confocal processing. Strain was calculated from tracking speckle in these images after deformation. This technique is compatible with in vivo measurements. RESULTS: Compressional and expansional deformations were the same order of magnitude from -3.5% to as great as +3.5%. Strain imaging indicated the stroma expanded into the slit of the deformation plate while Bowman's layer compressed. This bipolar variation within a specimen is unusual. Within the stroma, a variation of strain with depth was measured suggesting a distribution of elasticity. Results compared favorably with the finite element model. CONCLUSION: An ultrasound elasticity microscope can produce high-resolution strain images throughout the corneal depth. Various layers with different elastic properties appeared as different strains in the images.

Raman chemical imaging spectroscopy reagentless detection and identification of pathogens: signature development and evaluation.

An optical detection method, Raman chemical imaging spectroscopy (RCIS), is reported, which combines Raman spectroscopy, fluorescence spectroscopy, and digital imaging. Using this method, trace levels of biothreat organisms are detected in the presence of complex environmental backgrounds without the use of amplification or enhancement techniques. RCIS is reliant upon the use of Raman signatures and automated recognition algorithms to perform species-level identification. The rationale and steps for constructing a pathogen Raman signature library are described, as well as the first reported Raman spectra from live, priority pathogens, including Bacillus anthracis, Yersinia pestis, Burkholderia mallei, Francisella tularensis, Brucella abortus, and ricin. Results from a government-managed blind trial evaluation of the signature library demonstrated excellent specificity under controlled laboratory conditions.