Visitsattapongse, Sarinporn

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Visitsattapongse, Sarinporn
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Visitsattapongse, S.
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sarinporn.vi@kmitl.ac.th

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    A Novel Approach for Optimizing Molecularly Imprinted Polymer Composition in Electrochemical Detection of Collagen Peptides
    (2025-11-01)
    Vongmanee, Naphatsawan
    ;
    Nampeng, Jindapa
    ;
    Rattanapithan, Katesirin
    ;
    Sriwichai, Phuritasinee
    ;
    Pintavirooj, Chuchart
    Collagen peptides are key structural proteins that play an important role in maintaining the integrity and proper function of multiple tissues in the human body. Their breakdown is recognized as an important biomarker for various degenerative conditions, including the loss of muscle mass, joint and bone disorders, and compromised skin health. Current analytical approaches for collagen detection, such as ultraviolet spectrometry, enzyme-linked immunosorbent assay (ELISA), high-performance liquid chromatography (HPLC), and histochemical staining, are widely used but often expensive, time-consuming, and reliant on specific laboratory instrumentation, limiting their practicality for routine or rapid diagnostics. This study reports a novel biosensor for collagen peptide detection based on molecularly imprinted polymers (MIPs) integrated with screen-printed electrodes (SPEs). Electrochemical measurements revealed a clear correlation between collagen concentration and current response, confirming effective molecular binding within the imprinted matrix. The optimized MIP-modified electrode exhibited a detection range of 0.1–1000 µg/mL with a limit of detection (LOD) of 1.0106 µg/mL, limit of quantification (LOQ) of 4.46 µg/mL, sensitivity of 8.3816, and correlation coefficient (R<sup>2</sup> = 0.9436). These results highlight strong selectivity and sensitivity toward collagen peptides. The proposed MIP-based biosensor provides a rapid, low-cost platform for detecting collagen degradation products and holds potential for early diagnosis and future clinical applications in degenerative disease monitoring.
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    Store-operated calcium entry facilitates LPS-induced superoxide anion-dependent macrophage extracellular traps
    (2025-07-09)
    Nguyen, Thang Ngoc
    ;
    Lin, Tzu Chien
    ;
    Chimphlee, Waratchaya
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    Siew, Kon Xuen
    ;
    Vongmanee, Naphatsawan
    Macrophage extracellular traps (METs) represent a recently discovered complex defence mechanism that is distinct from phagocytosis and involves the release of DNA and antibacterial proteins. They play an important role in pathogen removal, and calcium ions (Ca 2+) have also been reported to be involved. In the present study, we identified METotic cells using digitonin as an alternative to Triton X-100, coupled with immunofluorescence staining using lamin antibodies. The limited permeability of digitonin ensures exclusive intranuclear antibody labelling of MET cells, therefore providing a straightforward and intuitive differentiation method. We found that under lipopolysaccharide stimulation, macrophages undergo store-operated Ca 2+ entry (SOCE) to facilitate Ca 2+ influx. Elevation of cytoplasmic Ca 2+ levels by SOCE promotes the generation of superoxide anions by NADPH oxidase (NOX), ultimately leading to METosis. In summary, our study strengthens the role of Ca 2+ in NOX-dependent METosis, which differs from previous studies focusing on Ca 2+ in the NOX-independent pathway. Our research reveals that Ca 2+ -mediated regulation of NOX plays a crucial role in METosis, especially in SOCE, and provides novel ideas for future research.
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    Analysis of Deep Learning-Based Phase Retrieval Algorithm Performance for Quantitative Phase Imaging Microscopy
    (2022-05-01)
    Visitsattapongse, Sarinporn
    ;
    Thadson, Kitsada
    ;
    Pechprasarn, Suejit
    ;
    Thongpance, Nuntachai
    Quantitative phase imaging has been of interest to the science and engineering community and has been applied in multiple research fields and applications. Recently, the data-driven approach of artificial intelligence has been utilized in several optical applications, including phase retrieval. However, phase images recovered from artificial intelligence are questionable in their correctness and reliability. Here, we propose a theoretical framework to analyze and quantify the performance of a deep learning-based phase retrieval algorithm for quantitative phase imaging microscopy by comparing recovered phase images to their theoretical phase profile in terms of their correctness. This study has employed both lossless and lossy samples, including uniform plasmonic gold sensors and dielectric layer samples; the plasmonic samples are lossy, whereas the dielectric layers are lossless. The uniform samples enable us to quantify the theoretical phase since they are established and well understood. In addition, a context aggregation network has been employed to demonstrate the phase image regression. Several imaging planes have been simulated serving as input and the label for network training, including a back focal plane image, an image at the image plane, and images when the microscope sample is axially defocused. The back focal plane image plays an essential role in phase retrieval for the plasmonic samples, whereas the dielectric layer requires both image plane and back focal plane information to retrieve the phase profile correctly. Here, we demonstrate that phase images recovered using deep learning can be robust and reliable depending on the sample and the input to the deep learning.
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    Automated segmentation of infarct lesions in t1‐weighted mri scans using variational mode decomposition and deep learning
    (2021-03-02)
    Paing, May Phu
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    Tungjitkusolmun, Supan
    ;
    Bui, Toan Huy
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    Visitsattapongse, Sarinporn
    ;
    Pintavirooj, Chuchart
    Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions from T1‐weighted brain scans. As a key novelty, the proposed method combines variational mode decomposition and deep learning-based segmentation to take advantages of both methods and provide better results. There are three main technical contributions in this paper. First, variational mode decomposition is applied as a pre-processing to discriminate the infarct lesions from unwanted non‐infarct tissues. Second, overlapped patches strategy is proposed to reduce the workload of the deep‐learning‐based segmentation task. Finally, a three‐dimensional U‐Net model is developed to perform patch‐wise segmentation of infarct lesions. A total of 239 brain scans from a public dataset are utilized to develop and evaluate the proposed method. Empirical results reveal that the proposed automated segmentation can provide promising performances with an average dice similarity coefficient (DSC) of 0.6684, intersection over union (IoU) of 0.5022, and average symmetric surface distance (ASSD) of 0.3932, respectively.
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    Automatic detection of pulmonary nodules using three-dimensional chain coding and optimized random forest
    (2020-04-01)
    Paing, May Phu
    ;
    Hamamoto, Kazuhiko
    ;
    Tungjitkusolmun, Supan
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    Visitsattapongse, Sarinporn
    ;
    Pintavirooj, Chuchart
    The detection of pulmonary nodules on computed tomography scans provides a clue for the early diagnosis of lung cancer. Manual detection mandates a heavy radiological workload as it identifies nodules slice-by-slice. This paper presents a fully automated nodule detection with three significant contributions. First, an automated seeded region growing is designed to segment the lung regions from the tomography scans. Second, a three-dimensional chain code algorithm is implemented to refine the border of the segmented lungs. Lastly, nodules inside the lungs are detected using an optimized random forest classifier. The experiments for our proposed detection are conducted using 888 scans from a public dataset, and achieves a favorable result of 93.11% accuracy, 94.86% sensitivity, and 91.37% specificity, with only 0.0863 false positives per exam.
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    Emergency Blower-Based Ventilator with Novel-Designed Ventilation Sensor and Actuator
    (2022-03-01)
    Pintavirooj, Chuchart
    ;
    Maneerat, Areerat
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    Visitsattapongse, Sarinporn
    The ventilator, a life-saving device for COVID-19-infected patients, especially for pneu-monia patients whose lungs are infected, has overwhelmingly skyrocketed since the pandemic of COVID-19 diseases started in December 2019. As a result, many biomedical engineers have rushed to design and construct emergency ventilators, using the Ambu-bag squeezing ventilator to compensate for the insufficient ventilators supply. The Ambu-bag squeezing ventilator, however, suffers from the limitation of delivered tidal volume to the patient, the setting respiration rate and the noisy operational sound due to the movement of mechanic parts. The Ambu-bag based ventilator is, hence, not suitable for prolonged treatment of the patient. This paper presents a design and construction of a blower-based pressure-controlled ventilator for home-treatment COVID-19 patients featured with our novel-designed flow and pressure sensor, electronic peep valve and proportional controlled valve. Our proposed ventilator can be programmed with the suitable parameter setting depending upon the weight, height, gender, and blood oxygen saturation (SpO<inf>2</inf>) of the patients. This is useful in the current situation of COVID-19 pandemics, where trained medical staff is limited. The designed ventilator is also equipped with a safety mechanism, including an excessive-pressure-release valve, excessive flow rate, overpressure, and over-temperature blower to prevent any hazardous event. A home ventilator server is also set where all ventilator parameters will be acquired and broadcasted for remote access of the health provider. The designed blower-based ventilator has been calibrated and evaluated with a lung simulator and standard ventilator tester, including alarmed functions, safety mechanism, sound level, and regulated pressure. The respiration output graph is complied with the simulation. The blower-based ventilator for home-treatment COVID-19 patients is suitable for life support, commensurate with the strict requirements of the FDA for life-support ventilators, and ready to be tested with animal subjects in the next phase.
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    Ultrasensitive Label-Free Electrochemical Detection of Pseudomonas aeruginosa Using a Surface Molecularly Imprinted Polymer-Modified Screen-Printed Electrode
    (2026-06-01)
    Vongmanee, Naphatsawan
    ;
    Nampeng, Jindapa
    ;
    Pintavirooj, Chuchart
    ;
    Visitsattapongse, Sarinporn
    Pseudomonas aeruginosa is a major opportunistic pathogen frequently associated with nosocomial infections, such as pneumonia, urinary tract infections, and wound infections, particularly in immunocompromised or hospitalized patients. These infections are often difficult to treat due to the pathogen’s intrinsic antibiotic resistance and biofilm-forming ability. Therefore, rapid and selective detection of P. aeruginosa is essential for early diagnosis and effective infection control. In this study, a novel surface-imprinted MIP design uniquely combines methacrylamide (MAM), acrylamide (AAM), and vinylpyrrolidone (VP) monomers to generate recognition cavities that are complementary to the surface morphology and physicochemical properties of Pseudomonas aeruginosa cells. Unlike traditional MIP approaches, this surface imprinting strategy provides improved stability and reproducibility, without relying on biological recognition elements like antibodies or aptamers. This novel approach enabled us to achieve an ultralow LOD of 1 CFU/mL over a linear range of 1–10<sup>4</sup> CFU/mL, demonstrating excellent analytical performance. In addition, the sensor exhibited good reproducibility with an RSD of 5–12%. The novelty of this work lies in the use of a surface-imprinted MIP strategy combined with a multi-monomer system to enhance bacterial recognition and sensing performance. Overall, the proposed MIP-based electrochemical biomimetic sensor offers a rapid, cost-effective, and portable platform with strong potential for the detection of P. aeruginosa in clinical and environmental applications.
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    Electrochemical Characterization of a Molecularly Imprinted Polymer Sensor for the Selective Recognition of Type II Collagen in Joint Degeneration Monitoring
    (2026-02-01)
    Nampeng, Jindapa
    ;
    Vongmanee, Naphatsawan
    ;
    Pintavirooj, Chuchart
    ;
    Visitsattapongse, Sarinporn
    Type II collagen is a primary fibrillar component of articular cartilage, and its early degradation is a key biomarker of joint-degenerative disorders such as osteoarthritis, rheumatoid arthritis, gout, etc. Reliable detection at low concentrations remains challenging due to limited assay accessibility, complex analytical procedures, and nonspecific responses in multicomponent biological matrices. This research reports the development of a Molecularly Imprinted Polymer (MIP)–based electrochemical sensor engineered for the selective recognition of type II collagen. A series of monomer formulations were evaluated, and the 1AAM:2VP composition produced a well-defined imprinted layer on screen-printed carbon electrodes, yielding the highest electrochemical sensitivity and linearity. The optimized sensor exhibited strong anodic and cathodic responses proportional to increasing collagen concentrations, with a calibration slope corresponding to an R<sup>2</sup> value of 0.9394. Minimal signal interference was observed, confirming high molecular selectivity. The limit of detection (LOD) was calculated to be approximately 0.065 µg/mL. These characteristics demonstrate that the proposed MIP sensor provides a low-cost, accessible, and highly selective analytical platform suitable for early-stage cartilage degeneration monitoring.
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    Biosensors for Klebsiella pneumoniae with Molecularly Imprinted Polymer (MIP) Technique
    (2022-06-01)
    Pintavirooj, Chuchart
    ;
    Vongmanee, Naphatsawan
    ;
    Sukjee, Wannisa
    ;
    Sangma, Chak
    ;
    Visitsattapongse, Sarinporn
    Nosocomial infection is one of the most important problems that occurs in hospitals, as it directly affects susceptible patients or patients with immune deficiency. Klebsiella pneumoniae (K. pneu-moniae) is the most common cause of nosocomial infections in hospitals. K. pneumoniae can cause various diseases such as pneumonia, urinary tract infections, septicemias, and soft tissue infections, and it has also become highly resistant to antibiotics. The principal routes for the transmission of K. pneumoniae are via the gastrointestinal tract and the hands of hospital personnel via healthcare workers, patients, hospital equipment, and interventional procedures. These bacteria can spread rapidly in the hospital environment and tend to cause nosocomial outbreaks. In this research, we developed a MIP-based electrochemical biosensor to detect K. pneumoniae. Quantitative detection was performed using an electrochemical technique to measure the changes in electrical signals in different concentrations of K. pneumoniae ranging from 10 to 10<sup>5</sup> CFU/mL. Our MIP-based K. pneumoniae sensor was found to achieve a high linear response, with an R<sup>2</sup> value of 0.9919. A sensitivity test was also performed on bacteria with a similar structure to that of K. pneumoniae. The sensitivity results show that the MIP-based K. pneumoniae biosensor with a gold electrode was the most sensitive, with a 7.51 (% relative current/log concentration) when compared with the MIP sensor applied with Pseudomonas aeruginosa and Enterococcus faecalis, where the sensitivity was 2.634 and 2.226, respectively. Our sensor was also able to achieve a limit of detection (LOD) of 0.012 CFU/mL and limit of quantitation (LOQ) of 1.61 CFU/mL.
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    Cry4Aa and Cry4Ba Mosquito-Active Toxins Utilize Different Domains in Binding to a Particular Culex ALP Isoform: A Functional Toxin Receptor Implicating Differential Actions on Target Larvae
    (2022-10-01)
    Dechkla, Manussawee
    ;
    Charoenjotivadhanakul, Sathapat
    ;
    Imtong, Chompounoot
    ;
    Visitsattapongse, Sarinporn
    ;
    Li, Hui Chun
    The three-domain Cry4Aa toxin produced from Bacillus thuringiensis subsp. israelensis was previously shown to be much more toxic to Culex mosquito larvae than its closely related toxin—Cry4Ba. The interaction of these two individual toxins with target receptors on susceptible larval midgut cells is likely to be the critical determinant in their differential toxicity. Here, two full-length membrane-bound alkaline phosphatase (mALP) isoforms from Culex quinquefasciatus larvae, Cq-mALP1263and Cq-mALP1264, predicted to be GPI-linked was cloned and functionally expressed in Spodoptera frugiperda (Sf9) cells as 57- and 61-kDa membrane-bound proteins, respectively. Bioinformatics analysis disclosed that both Cq-mALP isoforms share significant sequence similarity to Aedes aegypti-mALP—a Cry4Ba toxin receptor. In cytotoxicity assays, Sf9 cells expressing Cq-mALP1264, but not Cq-mALP1263, showed remarkably greater susceptibility to Cry4Aa than Cry4Ba, while immunolocalization studies revealed that both toxins were capable of binding to each Cq-mALP expressed on the cell membrane surface. Molecular docking of the Cq-mALP1264-modeled structure with individual Cry4 toxins revealed that Cry4Aa could bind to Cq-mALP1264 primarily through particular residues on three surface-exposed loops in the receptor-binding domain—DII, including Thr<sup>512</sup>, Tyr<sup>513</sup> and Lys<sup>514</sup> in the β10-β11loop. Dissimilarly, Cry4Ba appeared to utilize only certain residues in its C-terminal domain—DIII to interact with such a Culex counterpart receptor. Ala-substitutions of selected β10-β11loop residues (T512A, Y513A and K514A) revealed that only the K514A mutant displayed a drastic decrease in biotoxicity against C. quinquefasciatus larvae. Further substitution of Lys<sup>514</sup> with Asp (K514D) revealed a further decrease in larval toxicity. Furthermore, in silico calculation of the binding affinity change (ΔΔG<inf>bind</inf>) in Cry4Aa-Cq-mALP1264 interactions upon these single-substitutions revealed that the K514D mutation displayed the largest ΔΔG<inf>bind</inf> value as compared to three other mutations, signifying an adverse impact of a negative charge at this critical receptor-binding position. Altogether, our present study has disclosed that these two related-Cry4 mosquito-active toxins conceivably exploited different domains in functional binding to the same Culex membrane-bound ALP isoform—Cq-mALP1264 for mediating differential toxicity against Culex target larvae.