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Author: search.filters.author.Jangpatarapongsa, Kulachart

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    A deep learning model (FociRad) for automated detection of γ-H2AX foci and radiation dose estimation
    (2022-12-01)
    Wanotayan, Rujira
    ;
    Chousangsuntorn, Khaisang
    ;
    Petisiwaveth, Phasit
    ;
    Anuttra, Thunchanok
    ;
    Lertchanyaphan, Waritsara
    DNA double-strand breaks (DSBs) are the most lethal form of damage to cells from irradiation. γ-H2AX (phosphorylated form of H2AX histone variant) has become one of the most reliable and sensitive biomarkers of DNA DSBs. However, the γ-H2AX foci assay still has limitations in the time consumed for manual scoring and possible variability between scorers. This study proposed a novel automated foci scoring method using a deep convolutional neural network based on a You-Only-Look-Once (YOLO) algorithm to quantify γ-H2AX foci in peripheral blood samples. FociRad, a two-stage deep learning approach, consisted of mononuclear cell (MNC) and γ-H2AX foci detections. Whole blood samples were irradiated with X-rays from a 6 MV linear accelerator at 1, 2, 4 or 6 Gy. Images were captured using confocal microscopy. Then, dose–response calibration curves were established and implemented with unseen dataset. The results of the FociRad model were comparable with manual scoring. MNC detection yielded 96.6% accuracy, 96.7% sensitivity and 96.5% specificity. γ-H2AX foci detection showed very good F1 scores (> 0.9). Implementation of calibration curve in the range of 0–4 Gy gave mean absolute difference of estimated doses less than 1 Gy compared to actual doses. In addition, the evaluation times of FociRad were very short (< 0.5 min per 100 images), while the time for manual scoring increased with the number of foci. In conclusion, FociRad was the first automated foci scoring method to use a YOLO algorithm with high detection performance and fast evaluation time, which opens the door for large-scale applications in radiation triage.
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    PMMA particles coated with chitosan-silver nanoparticles as a dual antibacterial modifier for natural rubber latex films
    (2019-02-01)
    Suteewong, Teeraporn
    ;
    Wongpreecha, Jitrada
    ;
    Polpanich, Duangporn
    ;
    Jangpatarapongsa, Kulachart
    ;
    Kaewsaneha, Chariya
    The antibacterial activity in sulphur prevulcanized natural rubber (SPNR) latex film was effectively improved by deposition of poly(methyl methacrylate) (PMMA) particles encircled with chitosan-coated silver nanoparticles (AgNPs-CS). With the focus on a green process, CS was selected as a safe reducing and stabilizing agent for the one-step synthesis of AgNPs-CS (38 nm, +40.4 mV) in an autoclave. The adsorption of small-sized AgNPs-CS directly onto rubber film did not provide an inhibitory effect on S. aureus. It also had a low antibacterial effect on E. coli. This is because of the particles becoming completely/partially submerged into the soft rubber matrix upon drying. Hence, the AgNPs-CS were fabricated as a shell surrounding a rigid PMMA core (496 nm, -30.9 mV). This was done using a heterocoagulation technique prior to coating on SPNR film. The presence of PMMA/AgNPs-CS on the surface of SPNR film effectively increased the surface roughness from ca. 44 to 150 nm. This substantially promoted the antibacterial activity against E. coli and S. aureus by way of contact killing and repelling mechanisms. The cytotoxicity on L-929 fibroblasts was also suppressed. This study would be, therefore, applicable to the development of antibacterial SPNR film with high surface roughness, low cytotoxicity. It could also be applied for other soft substrates.
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