Myoung-Ock Cho

Automated Counting of Airborne Asbestos Fibers by a High-Throughput Microscopy (HTM) Method

Inhalation of airborne asbestos causes serious health problems such as lung cancer and malignant mesothelioma. The phase-contrast microscopy (PCM) method has been widely used for estimating airborne asbestos concentrations because it does not require complicated processes or high-priced equipment. However, the PCM method is time-consuming and laborious as it is manually performed off-site by an expert. We have developed a high-throughput microscopy (HTM) method that can detect fibers distinguishable from other spherical particles in a sample slide by image processing both automatically and quantitatively. A set of parameters for processing and analysis of asbestos fiber images was adjusted for standard asbestos samples with known concentrations. We analyzed sample slides containing airborne asbestos fibers collected at 11 different workplaces following PCM and HTM methods, and found a reasonably good agreement in the asbestos concentration. Image acquisition synchronized with the movement of the robotic sample stages followed by an automated batch processing of a stack of sample images enabled us to count asbestos fibers with greatly reduced time and labors. HTM should be a potential alternative to conventional PCM, moving a step closer to realization of on-site monitoring of asbestos fibers in air.

Selective detection and automated counting of fluorescently-labeled chrysotile asbestos using a dual-mode high-throughput microscopy (DM-HTM) method.

Phase contrast microscopy (PCM) is a widely used analytical method for airborne asbestos, but it is unable to distinguish asbestos from non-asbestos fibers and requires time-consuming and laborious manual counting of fibers. Previously, we developed a high-throughput microscopy (HTM) method that could greatly reduce human intervention and analysis time through automated image acquisition and counting of fibers. In this study, we designed a dual-mode HTM (DM-HTM) device for the combined reflection and fluorescence imaging of asbestos, and automated a series of built-in image processing commands of ImageJ software to test its capabilities. We used DksA, a chrysotile-adhesive protein, for selective detection of chrysotile fibers in the mixed dust-free suspension of crysotile and amosite prepared in the laboratory. We demonstrate that fluorescently-stained chrysotile and total fibers can be identified and enumerated automatically in a high-throughput manner by the DM-HTM system. Combined with more advanced software that can correctly identify overlapping and branching fibers and distinguish between fibers and elongated dust particles, the DM-HTM method should enable fully automated counting of airborne asbestos.

Analysis of Blood Cell Images Using Smartphone-based Mobile SmartScope

High-performance smartphones, equipped with a digital camera and an application software, can render conventional bench-top laboratory instruments mobile at affordable costs. As the smartphone-based devices are portable and wireless, they have wide applications especially in providing point-of-care (POC) tests in resource-constrained areas. We developed a hand-held diagnostic system, Mobile SmartScope, which consists of a small optical unit integrated with a smartphone. The performance of the SmartScope was favorably compared with that of conventional light microscopy in detecting and quantifying red blood cells. We also evaluated the fluorescence detection limit of the SmartScope incorporated with a blue light-emitting diode and appropriate optical filters by using fluorescently labeled microbeads for intensity calibration.

Performance evaluation of an automated image-based fluorescence CD4+ cell analyzer

BACKGROUND Although AIDS-related mortality has declined since the introduction of antiretroviral therapy (ART), HIV/ AIDS patients are predominantly present in developing countries that lack high-cost diagnostic devices and human expertise. OBJECTIVE New methods for counting CD4+ cells cost-effectively are needed to replace conventional flow cytometry-based diagnosis. METHODS We developed a CD4+ cell analyzer, ADAMII, which is a benchtop fluorescence image-based CD3+/4+ cell counting analyzer. It bears a three-channel light source and performs CD3+/4+ counting assays. The automatic 3D stage captures a maximum of 136 images that are subsequently processed and analyzed using a software integrated into the system. RESULTS Results obtained using ADAMII were compared with data obtained by conventional methods using a FACSCalibur flow cytometer and the point-of-care PIMA CD4 analyzer. Both comparisons between ADAMII vs. FACS and ADAMII vs. PIMA data yielded a strong correlation with an R2 value of 0.98, which ensures the feasibility of CD4 test by ADAMII. CONCLUSIONS The proposed method using ADAMII can be easily employed in resource-limited areas to replace conventional flow cytometers, which are expensive and require highly trained staff.

Improvement of Image Processing Algorithm of High-Throughput Microscopy for Automated Counting of Asbestos Fibers

We developed a high-throughput microscopy (HTM) method which enabled us to replace a conventional phase contrast microscopy (PCM) method that has been used as a standard analytical method for airborne asbestos. We could obtain the concentration of airborne asbestos fibers under detection limit by automated image processing and analysis using HTM method. Here we propose an improved image processing algorithm with variable parameters to enhance the accuracy of the HTM analysis. Since the variable parameters that compensate the difference of the brightness are applied to the individual images in our new image processing method, it is possible to enhance the accuracy of the automatic image analysis method for sample slides with low asbestos concentration that caused errors in binary image processing. We demonstrated that enumeration of fibers by improved image processing algorithm remarkably enhanced the accuracy of HTM analysis in comparison with PCM. The improved HTM method can be a potential alternative to conventional PCM.

Toward reducing uncertainty in Fluorescence Recovery After Photobleaching

We investigate the uncertainty associated with the Fluorescence Recovery After Photobleaching, FRAP, which is widely used in the determination of diffusion coefficient for bio molecules. The uncertainty of our FRAP technique stems from the measurement of the spot size and the half time. The uncertainties of the FRAP is evaluated by considering the uncertainty propagation through the measurements of both spot size and the half time. Finally, we suggest an approach to estimate the effective diffusion coefficient by considering slip conditions between the fluorescent beads and the fluid. The diffusion coefficients measured by the FRAP is close to those obtained from the Stokes-Einstein relation together with the slip correction factor rather than that obtained solely by the Stokes-Einstein equation.

Liquid-phase sample preparation method for real-time monitoring of airborne asbestos fibers by dual-mode high-throughput microscopy

Asbestos that had been used widely as a construction material is a first-level carcinogen recognized by the World Health Organization. It can be accumulated in body by inhalation causing virulent respiratory diseases including lung cancer. In our previous study, we developed a high-throughput microscopy (HTM) system that can minimize human intervention accompanied by the conventional phase contrast microscopy (PCM) through automated counting of fibrous materials and thus significantly reduce analysis time and labor. Also, we attempted selective detection of chrysotile using DksA protein extracted from Escherichia coli through a recombinant protein production technique, and developed a dual-mode HTM (DM-HTM) by upgrading the HTM device. We demonstrated that fluorescently-labeled chrysotile asbestos fibers can be identified and enumerated automatically among other types of asbestos fibers or non-asbestos particles in a high-throughput manner through a newly modified HTM system for both reflection and fluorescence imaging. However there is a limitation to apply DM-HTM to airborne sample with current air collecting method due to the difficulty of applying the protein to dried asbestos sample. Here, we developed a technique for preparing liquid-phase asbestos sample using an impinger normally used to collect odor molecules in the air. It would be possible to improve the feasibility of the dual-mode HTM by integrating a sample preparation unit for making collected asbestos sample dispersed in a solution. The new technique developed for highly sensitive and automated asbestos detection can be a potential alternative to the conventional manual counting method, and it may be applied on site as a fast and reliable environmental monitoring tool.

Asbestos concentration measurement using Differential Interference Contrast microscopy

We designed a new method for imaging and counting the concentration of asbestos fibers. In current research, we combined the principle of Differential Interference Contrast (DIC) microscopy with imaging program for counting their concentration automatically.

Selective and Automated Detection of Airborne Asbestos Fibers Using Chrysotile-Adhesive Protein and High-Throughput Microscopy (HTM)

There are several methods to detect asbestos including phase contrast microscopy (PCM), polarized light microscopy, X-ray diffraction, and electron microscopy. Although the PCM method is widely used due to its simple process and relatively low cost, it is a time-consuming and laborious process that is manually performed by a human counter. We developed a high-throughput microscopy (HTM) system for automated counting of airborne asbestos fibers to automate the conventional PCM method. Our results show that automatic image acquisition by synchronization of charge-coupled device (CCD) camera with movement of stages, and image analysis using image processing software, significantly reduced time consumption and labor. In this study, we used DksA chrysotile-adhesive protein for the selective detection of asbestos. DksA, known as the protein that specifically attaches to chrysotile, was extracted from Escherichia coli through a recombinant protein technique. We tried to detect chrysotile selectively from other fibers or particles, and we developed a highly selective and automated low-cost device for automated identification and enumeration of airborne asbestos fibers based on the HTM method.Copyright

Growth Responses of Swarming and Gliding Bacteria on Substrates With Different Levels of Stiffness

We conducted experiments to decipher the interplays among bacterial motility, surface stiffness of culture medium, and growth of colony when bacteria grow on semi-solid substrate. We observed the growth kinetics of two kinds of bacteria, swarming Escherichia coli (E.coli) and gliding Myxococcus Xanthus (M.xanthus), grown on semi-solid agar substrates with different stiffness. The colony of M.xanthus moved by traction force on the surface shows a tendency to grow larger on soft substrate. The colony of E.coli using flagella shows a similar tendency in the early phase but later grows smaller on substrate with lower stiffness. We found that the growth of bacterial colony is affected by the mechanical properties of the substrate and the type of bacterial motility as well.© 2010 ASME