Collagen organization and structure in FBLN5-/- mice using label-free microscopy: implications for pelvic organ prolapse.

Pelvic organ prolapse (POP) is a gynecological disorder described by the descent of superior pelvic organs into or out of the vagina as a consequence of disrupted muscles and tissue. A thorough understanding of the etiology of POP is limited by the availability of clinically relevant samples, restricting longitudinal POP studies on soft-tissue biomechanics and structure to POP-induced models such as fibulin-5 knockout (FBLN5-/- ) mice. Despite being a principal constituent in the extracellular matrix, little is known about structural perturbations to collagen networks in the FBLN5-/- mouse cervix. We identify significantly different collagen network populations in normal and prolapsed cervical cross-sections using two label-free, nonlinear microscopy techniques. Collagen in the prolapsed mouse cervix tends to be more isotropic, and displays reduced alignment persistence via 2-D Fourier transform analysis of images acquired using second harmonic generation microscopy. Furthermore, coherent Raman hyperspectral imaging revealed elevated disorder in the secondary structure of collagen in prolapsed tissues. Our results underscore the need for in situ multimodal monitoring of collagen organization to improve POP predictive capabilities.

Collagen organization and structure in FLBN5-/- mice using label-free microscopy: implications for pelvic organ prolapse.

Pelvic organ prolapse (POP) is a gynecological disorder described by the descent of superior pelvic organs into or out of the vagina as a consequence of disrupted muscles and tissue. A thorough understanding of the etiology of POP is limited by the availability of clinically relevant samples, restricting longitudinal POP studies on soft-tissue biomechanics and structure to POP-induced models such as fibulin-5 knockout (FBLN5-/-) mice. Despite being a principal constituent in the extracellular matrix, little is known about structural perturbations to collagen networks in the FBLN5-/- mouse cervix. We identify significantly different collagen network populations in normal and prolapsed cervical cross-sections using two label-free, nonlinear microscopy techniques. Collagen in the prolapsed mouse cervix tends to be more isotropic, and displays reduced alignment persistence via 2-D Fourier Transform analysis of images acquired using second harmonic generation microscopy. Furthermore, coherent Raman hyperspectral imaging revealed elevated disorder in the secondary structure of collagen in prolapsed tissues. Our results underscore the need for in situ multimodal monitoring of collagen organization to improve POP predictive capabilities.

Capillary flow velocity profile analysis on paper-based microfluidic chips for screening oil types using machine learning.

We conceived a novel approach to screen oil types on a wax-printed paper-based microfluidic platform. Various oil samples spontaneously flowed through a micrometer-scale channel via capillary action while their components were filtered and partitioned. The resulting capillary flow velocity profile fluctuated during the flow, which was used to screen oil types. Raspberry Pi camera captured the video clips, and a custom Python code analyzed them to obtain the capillary flow velocity profiles. 106 velocity profiles (each with 125 frames for 5 s) were recorded from various oil samples to build a training database. Principal component analysis (PCA), support vector machine (SVM), and linear discriminant analysis (LDA) were used to classify the oil types into heavy-to-medium crude, light crude, marine fuel, lubricant, and diesel oils. The second-order polynomial SVM model with PCA as a pre-processing step showed the highest accuracy: 90% in classifying crude oils and 81% in classifying non-crude oils. The assay took less than 30 s from the sample to answer, with 5 s of the capillary action-driven flow. This simple and effective assay will allow rapid preliminary screening of oil types, enable early tracking, and reduce the number of suspect samples to be analyzed by laboratory fingerprinting analysis.

Norovirus detection in water samples at the level of single virus copies per microliter using a smartphone-based fluorescence microscope.

Norovirus is a widespread public health threat and has a very low infectious dose. This protocol presents the extremely sensitive mobile detection of norovirus from water samples using a custom-built smartphone-based fluorescence microscope and a paper microfluidic chip. Antibody-conjugated fluorescent particles are immunoagglutinated and spread over the paper microfluidic chip by capillary action for individual counting using a smartphone-based fluorescence microscope. Smartphone images are analyzed using intensity- and size-based thresholding for the elimination of background noise and autofluorescence as well as for the isolation of immunoagglutinated particles. The resulting pixel counts of particles are correlated with the norovirus concentration of the tested sample. This protocol provides detailed guidelines for the construction and optimization of the smartphone- and paper-based assay. In addition, a 3D-printed enclosure is presented to incorporate all components in a dark environment. On-chip concentration and the assay of higher concentrations are presented to further broaden the assay range. This method is the first to be presented as a highly sensitive mobile platform for norovirus detection using low-cost materials. With all materials and reagents prepared, a single standard assay takes under 20 min. Although the method described is used for detection of norovirus, the same protocol could be adapted for detection of other pathogens by using different antibodies.

Mie scattering and microparticle-based characterization of heavy metal ions and classification by statistical inference methods.

A straightforward method for classifying heavy metal ions in water is proposed using statistical classification and clustering techniques from non-specific microparticle scattering data. A set of carboxylated polystyrene microparticles of sizes 0.91, 0.75 and 0.40 µm was mixed with the solutions of nine heavy metal ions and two control cations, and scattering measurements were collected at two angles optimized for scattering from non-aggregated and aggregated particles. Classification of these observations was conducted and compared among several machine learning techniques, including linear discriminant analysis, support vector machine analysis, K-means clustering and K-medians clustering. This study found the highest classification accuracy using the linear discriminant and support vector machine analysis, each reporting high classification rates for heavy metal ions with respect to the model. This may be attributed to moderate correlation between detection angle and particle size. These classification models provide reasonable discrimination between most ion species, with the highest distinction seen for Pb(II), Cd(II), Ni(II) and Co(II), followed by Fe(II) and Fe(III), potentially due to its known sorption with carboxyl groups. The support vector machine analysis was also applied to three different mixture solutions representing leaching from pipes and mine tailings, and showed good correlation with single-species data, specifically with Pb(II) and Ni(II). With more expansive training data and further processing, this method shows promise for low-cost and portable heavy metal identification and sensing.

Distance versus Capillary Flow Dynamics-Based Detection Methods on a Microfluidic Paper-Based Analytical Device (µPAD).

In recent years, there has been high interest in paper-based microfluidic sensors or microfluidic paper-based analytical devices (µPADs) towards low-cost, portable, and easy-to-use sensing for chemical and biological targets. µPAD allows spontaneous liquid flow without any external or internal pumping, as well as an innate filtration capability. Although both optical (colorimetric and fluorescent) and electrochemical detection have been demonstrated on µPADs, several limitations still remain, such as the need for additional equipment, vulnerability to ambient lighting perturbation, and inferior sensitivity. Herein, alternative detection methods on µPADs are reviewed to resolve these issues, including relatively well studied distance-based measurements and the newer capillary flow dynamics-based method. Detection principles, assay performance, strengths, and weaknesses are explained for these methods, along with their potential future applications towards point-of-care medical diagnostics and other field-based applications.