Background: Swine dorsum is commonly utilized as a model for studying skin wounds and assessment of dermatological and cosmetic medicaments. The human abdomen is a common location for dermatological intervention.Objective: This study provides a correlation between spectral, mechanical, and structural characterization techniques, utilized for evaluating human abdominal skin and swine dorsum. Methods: Raman spectroscopy (RS), tensile testing, ballistometry, AFM, SEM, and MPM were utilized to characterize and compare full-thickness skin properties in swine and human model.Results: RS of both species' skin types revealed a similar assignment of vibrations in the fingerprint and the high wavenumber spectral regions. Structural imaging and mechanical characterization using ballistometry and tensile testing displayed differences in the inherent functional properties of human and swine skin. These differences correlated with variations in the Raman peak ratios, collagen intensity measured using SEM and MPM and collagen density measured using AFM. Conclusion:A comprehensive evaluation of swine skin as a suitable substitute for human skin for mechanical and structural comparisons was performed. This data should be considered for better understanding the swine skin model for cutaneous drug delivery and wound applications. Additionally, correlation between RS, tensile testing, AFM, SEM, and MPM was performed as skin characterization tools.
Raman spectroscopy has been used for decades to detect and identify biological substances as it provides specific molecular information. Spectra collected from biological samples are often complex, requiring the aid of data truncation techniques such as principal component analysis (PCA) and multivariate classification methods. Classification results depend on the proper selection of principal components (PCs) and how PCA is performed (scaling and/or centering). There are also guidelines for choosing the optimal number of PCs such as a scree plot, Kaiser criterion, or cumulative percent variance. The goal of this research is to evaluate these methods for best implementation of PCA and PC selection to classify Raman spectra of bacteria. Raman spectra of three different isolates of mycobacteria ( Mycobacterium sp. JLS, Mycobacterium sp. KMS, Mycobacterium sp. MCS) were collected and then passed through PCA and linear discriminant analysis for classification. Principal component analysis implementation as well as PC selection was evaluated by comparing the highest possible classification accuracies against accuracies determined by PC selection methods for each centering and scaling option. Centered and unscaled data provided the best results when selecting PCs based on cumulative percent variance.
Immunoassays are used to detect proteins based on the presence of associated antibodies. Because of their extensive use in research and clinical settings, a large infrastructure of immunoassay instruments and materials can be found. For example, 96- and 384-well polystyrene plates are available commercially and have a standard design to accommodate ultraviolet-visible (UV-Vis) spectroscopy machines from various manufacturers. In addition, a wide variety of immunoglobulins, detection tags, and blocking agents for customized immunoassay designs such as enzyme-linked immunosorbent assays (ELISA) are available. Despite the existing infrastructure, standard ELISA kits do not meet all research needs, requiring individualized immunoassay development, which can be expensive and time-consuming. For example, ELISA kits have low multiplexing (detection of more than one analyte at a time) capabilities as they usually depend on fluorescence or colorimetric methods for detection. Colorimetric and fluorescent-based analyses have limited multiplexing capabilities due to broad spectral peaks. In contrast, Raman spectroscopy-based methods have a much greater capability for multiplexing due to narrow emission peaks. Another advantage of Raman spectroscopy is that Raman reporters experience significantly less photobleaching than fluorescent tags. Despite the advantages that Raman reporters have over fluorescent and colorimetric tags, protocols to fabricate Raman-based immunoassays are limited. The purpose of this paper is to provide a protocol to prepare functionalized probes to use in conjunction with polystyrene plates for direct detection of analytes by UV-Vis analysis and Raman spectroscopy. This protocol will allow researchers to take a do-it-yourself approach for future multi-analyte detection while capitalizing on pre-established infrastructure.
Traditional bacterial analyses take one to two days under favorable conditions where the bulk of the time is spent waiting for bacteria to divide and grow until visual colonies can be observed for identification. In the case of bacteria with slow doubling times, this process can take weeks. This delay in analysis is unacceptable, especially in cases of life threatening diseases or emergencies. It is clear that in order to decrease the analysis time of the bacteria, the culturing and growth step must be circumvented. The goal of this research is to design, build, and test a device that could decrease the analysis time of bacteria using label-free methods of dielectrophoresis and Raman spectroscopy.Testing for device design was performed with clinical samples in mind, which consist of bacteria grown in a variety of environmental conditions (i.e. available food sources, growth stage, temperature, etc.) and accompanied by sample debris. Raman spectra of bacteria grown in varying media and metabolic stages were collected and analyzed. Results indicate that growth phase and media have an impact on Raman spectra iv and is distinguishable by linear discriminant analysis (LDA). Despite these spectral differences, it was found that LDA classification of closely related bacteria remains fairly high (90%) regardless of growth phase. Sample debris were also considered in device design and accommodated for by dielectrophoresis. Devices were built with the goal to isolate bacteria from a mixed sample and simultaneously acquire Raman spectra for identification.For this dissertation, a device was designed, built, and tested that incorporates dielectrophoresis for particle isolation and Raman spectroscopy for identification. The device was modeled in COMSOL to ensure that an appropriate electrical field gradient could be obtained to isolate bacteria from 5 µm diameter polystyrene spheres. The device was built and successfully trapped bacteria away from polystyrene spheres and Raman spectra of the bacteria were collected while trapped. These results indicate a clear potential for contactless dielectrophoresis-Raman devices to isolate and identify bacteria from sample debris, and thereby decrease the analysis time of bacteria. Typical bacterial analysis involves culturing and visualizing colonies on an array of agar plates. The growth patterns and colors among the array are used to identify the bacteria. For fast growing bacteria such as Escherichia coli, analysis will take one to two days. However, slow growing bacteria such as mycobacteria can take weeks to identify.In addition, there are some species of bacteria that are viable but nonculturable. This lengthy analysis time is unacceptable for life-threatening infections and emergency situations. It is clear that to decrease the analysis of the bacteria, the culturing and growth steps must be avoided. The goal of this research is to design, build, and test a device that could decrease the analysis time of bacteria.
When studying bone fragility diseases, it is difficult to identify which factors reduce bone’s resistance to fracture because these diseases alter bone at many length scales. Here, we investigate the contribution of nanoscale collagen behavior on macroscale toughness and microscale toughening mechanisms using a bovine heat-treatment fragility model. This model is assessed by developing an in situ toughness testing technique for synchrotron radiation micro-computed tomography to study the evolution of microscale crack growth in 3D. Low-dose imaging is employed with deep learning to denoise images while maintaining bone’s innate mechanical properties. We show that collagen damage significantly reduces macroscale toughness and post-yield properties. We also find that bone samples with a compromised collagen network have reduced amounts of crack deflection, the main microscale mechanism of fracture resistance. This research demonstrates that collagen damage at the nanoscale adversely affects bone’s toughening mechanisms at the microscale and reduces the overall toughness of bone.
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