Objective determination of peripheral edema in heart failure patients using short-wave infrared molecular chemical imaging.

SIGNIFICANCE: Peripheral pitting edema is a clinician-administered measure for grading edema. Peripheral edema is graded 0, 1 + , 2 + , 3 + , or 4 + , but subjectivity is a major limitation of this technique. A pilot clinical study for short-wave infrared (SWIR) molecular chemical imaging (MCI) effectiveness as an objective, non-contact quantitative peripheral edema measure is underway. AIM: We explore if SWIR MCI can differentiate populations with and without peripheral edema. Further, we evaluate the technology for correctly stratifying subjects with peripheral edema. APPROACH: SWIR MCI of shins from healthy subjects and heart failure (HF) patients was performed. Partial least squares discriminant analysis (PLS-DA) was used to discriminate the two populations. PLS regression (PLSR) was applied to assess the ability of MCI to grade edema. RESULTS: Average spectra from edema exhibited higher water absorption than non-edema spectra. SWIR MCI differentiated healthy volunteers from a population representing all pitting edema grades with 97.1% accuracy (N = 103 shins). Additionally, SWIR MCI correctly classified shin pitting edema levels in patients with 81.6% accuracy. CONCLUSIONS: Our study successfully achieved the two primary endpoints. Application of SWIR MCI to monitor patients while actively receiving HF treatment is necessary to validate SWIR MCI as an HF monitoring technology.

Visible near infrared reflectance molecular chemical imaging of human ex vivo carcinomas and murine in vivo carcinomas.

SIGNIFICANCE: A key risk faced by oncological surgeons continues to be complete removal of tumor. Currently, there is no intraoperative imaging device to detect kidney tumors during excision. AIM: We are evaluating molecular chemical imaging (MCI) as a technology for real-time tumor detection and margin assessment during tumor removal surgeries. APPROACH: In exploratory studies, we evaluate visible near infrared (Vis-NIR) MCI for differentiating tumor from adjacent tissue in ex vivo human kidney specimens, and in anaesthetized mice with breast or lung tumor xenografts. Differentiation of tumor from nontumor tissues is made possible with diffuse reflectance spectroscopic signatures and hyperspectral imaging technology. Tumor detection is achieved by score image generation to localize the tumor, followed by application of computer vision algorithms to define tumor border. RESULTS: Performance of a partial least squares discriminant analysis (PLS-DA) model for kidney tumor in a 22-patient study is 0.96 for area under the receiver operating characteristic curve. A PLS-DA model for in vivo breast and lung tumor xenografts performs with 100% sensitivity, 83% specificity, and 89% accuracy. CONCLUSION: Detection of cancer in surgically resected human kidney tissues is demonstrated ex vivo with Vis-NIR MCI, and in vivo on mice with breast or lung xenografts.

Evaluation of algorithm development approaches: Development of biomarker panels for early detection of colorectal lesions.

INTRODUCTION: Colorectal cancer (CRC) is the third most common cancer in the U.S. Early detection of CRC can substantially increase survival rates. Test compliance may be improved by offering a blood-based test option. METHODS: Endoscopy II trial specimens were tested for AFP, CA19-9, CEA, hs-CRP, CyFra 21-1, Ferritin, Galectin-3, and TIMP-1 levels. These biomarkers, as well as patient demographic information (e.g., age, gender), were included in algorithm development. Six statistical methods were utilized to develop algorithms that would discriminate cancer vs. noncancers. Statistical methods included logistic regression, adaptive index modeling, partial least-squares discriminant analysis, feature vector (weighted and unweighted), and random forest. The performance of these algorithms was compared against benchmark criteria established for stool-based tests. RESULTS: Using several statistical methods, the presence of CRC and high-risk adenomas was detected with an AUCs of at least 0.65-0.76, with a few of models approaching the stool-based tests benchmark performance. Further, common markers were utilized across the different statistical techniques, with model complexities ranging from 3 to 9 markers. CONCLUSIONS: Predictive models identified subjects with CRC and high-risk adenomas with the similar levels of statistical accuracy. Clinical performance differences were minimal across the statistical techniques, although the intuitive interpretations, model complexity, clinical adoption and implementation varied.

Raman imaging.

The past decade has seen an enormous increase in the number and breadth of imaging techniques developed for analysis in many industries, including pharmaceuticals, food, and especially biomedicine. Rather than accept single-dimensional forms of information, users now demand multidimensional assessment of samples. High specificity and the need for little or no sample preparation make Raman imaging a highly attractive analytical technique and provide motivation for continuing advances in its supporting technology and utilization. This review discusses the current tools employed in Raman imaging, the recent advances, and the major applications in this ever-growing analytical field.

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.

Raman chemical imaging for ingredient-specific particle size characterization of aqueous suspension nasal spray formulations: a progress report.

PURPOSE: This study was conducted to evaluate the feasibility of using Raman chemical imaging (i.e., Raman imaging microspectroscopy) to establish chemical identity, particle size and particle size distribution (PSD) for a representative corticosteroid in aqueous nasal spray suspension formulations. MATERIALS AND METHODS: The Raman imaging PSD protocol was validated using polystyrene (PS) microsphere size standards (NIST-traceable). A Raman spectral library was developed for the active and inactive compounds in the formulation. Four nasal sprays formulated with beclomethasone dipropionate (BDP) ranging in size from 1.4 to 8.3 microm were imaged by both Raman and brightfield techniques. The Raman images were then processed to calculate the PSD for each formulation. RESULTS: Within each region examined, active pharmaceutical ingredient (API) particles are unambiguously identified and the total number of those particles, particle size and PSD of API free of excipients and PSD of API particles adhered to other excipients are reported. CONCLUSIONS: Good statistical agreement is obtained between the reported and measured sizes of the PS microspheres. BDP particles were clearly distinguishable from those of excipients. Raman chemical imaging (RCI) is able to differentiate between and identify the chemical makeup of multiple components in complex BDP sample and placebo mixtures. The Raman chemical imaging method (coupled Raman and optical imaging) shows promise as a method for characterizing particle size and shape of corticosteroid in aqueous nasal spray suspension formulations. However, rigorous validation of RCI for PSD analysis is incomplete and requires additional research effort. Some specific areas of concern are discussed.

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.

Raman spectroscopy and chemical imaging for quantification of filtered waterborne bacteria.

Rapid and reliable assessment of pathogenic microbial contamination in water is critically important. In the present work we evaluated the suitability of Raman Spectroscopy and Chemical Imaging as enumeration techniques for waterborne pathogens. The prominent C-H stretching band observed between 2800-3000 cm(-1) of the spectrum is used for quantification purposes. This band provides the highest intensity of the bacterial-spectrum bands facilitating the detection of low number of microorganisms. The intensity of the Raman response correlates with number of cells present in drops of sample water on aluminum-coated slides. However, concentration of pathogens in drinking and recreational water is low, requiring a concentration step, i.e., filtering. Subsequent evaluation of filtering approaches for water sampling for Raman detection showed significant background signal from alumina and silver membranes that reduces method sensitivity. Samples concentrated by filtration show good correlation between Raman spectroscopy and other quantification methods including turbidity (R(2)=0.92), plate counts (R(2)=0.87) and dry weight (R(2)=0.97). Background interferences did not allow for evaluation of this relationship at low cell concentrations.

Forensic applications of chemical imaging: latent fingerprint detection using visible absorption and luminescence.

Chemical imaging technology is a rapid examination technique that combines molecular spectroscopy and digital imaging, providing information on morphology, composition, structure, and concentration of a material. Among many other applications, chemical imaging offers an array of novel analytical testing methods, which limits sample preparation and provides high-quality imaging data essential in the detection of latent fingerprints. Luminescence chemical imaging and visible absorbance chemical imaging have been successfully applied to ninhydrin, DFO, cyanoacrylate, and luminescent dye-treated latent fingerprints, demonstrating the potential of this technology to aid forensic investigations. In addition, visible absorption chemical imaging has been applied successfully to visualize untreated latent fingerprints.