An image quality matrix provides a significant principle for objectively observing an image based on an alteration between the original and distorted images. During the past two decades, a novel universal image quality assessment has been developed with the ability of adaptation with human visual perception for measuring the difference of a degraded image from the reference image, namely a structural similarity index. Structural similarity has since been widely used in various sectors, including medical image evaluation. Although numerous studies have reported the use of structural similarity as an evaluation strategy for computer-based medical images, reviews on the prospects of using structural similarity for medical imaging applications have been rare. This paper presents previous studies implementing structural similarity in analyzing medical images from various imaging modalities. In addition, this review describes structural similarity from the perspective of a family’s historical background, as well as progress made from the original to the recent structural similarity, and its strengths and drawbacks. Additionally, potential research directions in applying such similarities related to medical image analyses are described. This review will be beneficial in guiding researchers toward the discovery of potential medical image examination methods that can be improved through structural similarity index.

Separation of micro- and nano-sized biological particles, such as cells, proteins, and nucleotides, is at the heart of most biochemical sensing/analysis, including in vitro biosensing, diagnostics, drug development, proteomics, and genomics. However, most of the conventional particle separation techniques are based on membrane filtration techniques, whose efficiency is limited by membrane characteristics, such as pore size, porosity, surface charge density, or biocompatibility, which results in a reduction in the separation efficiency of bioparticles of various sizes and types. In addition, since other conventional separation methods, such as centrifugation, chromatography, and precipitation, are difficult to perform in a continuous manner, requiring multiple preparation steps with a relatively large minimum sample volume is necessary for stable bioprocessing. Recently, microfluidic engineering enables more efficient separation in a continuous flow with rapid processing of small volumes of rare biological samples, such as DNA, proteins, viruses, exosomes, and even cells. In this paper, we present a comprehensive review of the recent advances in microfluidic separation of micro-/nano-sized bioparticles by summarizing the physical principles behind the separation system and practical examples of biomedical applications.

Surface-Enhanced Raman Spectroscopy (SERS) is often used for heavy metal ion detection. However, large variations in signal strength, spectral profile, and nonlinearity of measurements often cause problems that produce varying results. It raises concerns about the reproducibility of the results. Consequently, the manual classification of the SERS spectrum requires carefully controlled experimentation that further hinders the large-scale adaptation. Recent advances in machine learning offer decent opportunities to address these issues. However, well-documented procedures for model development and evaluation, as well as benchmark datasets, are missing. Towards this end, we provide the SERS spectral benchmark dataset of lead(II) nitride (Pb(NO₃)₂) for a heavy metal ion detection task and evaluate the classification performance of several machine learning models. We also perform a comparative study to find the best combination between the preprocessing methods and the machine learning models. The proposed model can successfully identify the Pb(NO₃)₂ molecule from SERS measurements of independent test experiments. In particular, the proposed model shows an $84.6\%$ balanced accuracy for the cross-batch testing task.

Polydimethylsiloxane (PDMS) is a widely used material in laboratories for fabricating microfluidic devices with a rapid and reproducible prototypingability, owing to its inherent properties (e.g., flexibility, air permeability, and transparency). However, the PDMS channel is easily deformed under pressures applied to generate flows because of its elasticity, which can affect the robustness of experiments. In addition, air permeability of PDMS causes the pervaporation of water, and its porous structure absorbs oil and even small hydrophobic molecules, rendering it inappropriate for chemically demanding or day-long experiments. In this study, we develop a rapid and reproducible fabrication method for polymer-based rigid microfluidic devices, using epoxy resin that can overcome the limitations of PDMS channels, which are structurally and chemically robust. We first optimize a high-resolution fabrication protocol to achieve convenient and repeatable prototyping of polymeric devices via epoxy casting using PDMS soft molds. In addition, we compare the velocity changes in PDMS microchannels by tracking fluorescent particles in various flows (~133 μL/min) to demonstrate the structural robustness of the polymeric device. Furthermore, by comparing the adsorption of fluorescent hydrophobic chemicals and the pervaporation through channel walls, we demonstrate the excellent chemical resistance of the polymeric device and its suitability for day-long experiments. The rigid polymeric device can facilitate lab-on-chip research and enable various applications, such as high-performance liquid chromatography, anaerobic bacterial culture, and polymerase chain reaction, which require chemically or physically demanding experiments.

We developed a novel multiphysics-based nanoparticle separation technique utilizing low-electric-potential-assisted diffusiophoresis (LEPDP).