기후 변화로 인한 해양 온도 상승으로 해양생물독소의 발생 빈도가 점점 증가하고 있으며, 이는 식품 안전과 공 중 보건에 중대한 위협을 가하고 있다. 해양생물독소를 검 출하기 위한 기존의 방법인 마우스 생체검사(MBA), 고성 능 액체 크로마토그래피(HPLC), 액체 크로마토그래피-질 량 분석법(LC-MS) 등은 절차가 오래 걸리고 비용이 많이 든다는 한계가 있다. 이러한 문제를 해결하기 위한 대안 으로 바이오센서 기술이 유망한 해결책으로 부상하고 있 다. 이러한 바이오센서는 세포, 항체, 압타머, 펩타이드와 같은 바이오리셉터를 이용해 해양생물독소를 신속하고 정 확하게 검출한다. 본 리뷰에서는 다양한 종류의 바이오리 셉터를 논의하고, 해양생물독소 검출을 위한 바이오센서 기술의 최근 발전을 탐구한다. 또한, 이러한 바이오리셉터 의 장점을 강조하며, 해양생물독소 검출을 위한 바이오센 서 성능 향상을 위한 미래 연구 방향을 고려한다.

Existing reinforced concrete (RC) building frames constructed before the seismic design was applied have seismically deficient structural details, and buildings with such structural details show brittle behavior that is destroyed early due to low shear performance. Various reinforcement systems, such as fiber-reinforced polymer (FRP) jacketing systems, are being studied to reinforce the seismically deficient RC frames. Due to the step-by-step modeling and interpretation process, existing seismic performance assessment and reinforcement design of buildings consume an enormous amount of workforce and time. Various machine learning (ML) models were developed using input and output datasets for seismic loads and reinforcement details built through the finite element (FE) model developed in previous studies to overcome these shortcomings. To assess the performance of the seismic performance prediction models developed in this study, the mean squared error (MSE), R-square (R2), and residual of each model were compared. Overall, the applied ML was found to rapidly and effectively predict the seismic performance of buildings according to changes in load and reinforcement details without overfitting. In addition, the best-fit model for each seismic performance class was selected by analyzing the performance by class of the ML models.

겨울과 같은 환경에서 곤충은 생존과 번성을 위해 생리학적, 생화학적 및 행동적 메커니즘을 이용하고 있다. 대부분의 곤충은 생리학적 적응가운데 급속내한성(Rapid cold hardiness, RCH) 유기를 통해 기온이 급격히 낮아 지는 외부 환경에 대해 빠르게 적응하고 저온조건에서 생존율을 높인다. 열대거세미나방의 경우 행동적 메커니 즘을 통해 따뜻한 곳을 찾아 장거리 비행을 하며, 생존에 유리한 환경으로 이동한다. 본 연구에서는 열대거세미나 방의 생리적 월동능력과 RCH 능력에 관해 조사하였다. 그 결과, RCH에 의해 혈중 글리세롤의 농도가 증가와 체내빙결점이 하강하는 것을 확인할 수 있었다. 또한, RCH(-10℃, 1h)에 노출된 2령 유충기를 대상으로 4령과 5령 유충기에 단기저온(5℃, 30min)에 노출 시 글리세롤 생합성에 관여하는 유전자(glycerol kinase 1, 2)의 발현이 RCH에 노출되지 않은 대조구와 비교하여 빠르게 발현되었다. 이는, 열대거세미나방의 유전자 수준에서 저온에 대한 단기기억이 존재하는 것을 제시한다.

Spodoptera eridania and S. ornithogalli (Lepidoptera: Noctuidae), which are polyphagous pests that damage various crops such as tomatoes and beans are regulated quarantine species that are highly likely to invade South Korea. Therefore, it is crucial to promptly and accurately identify the presence of S. eridania and S. ornithogalli in crop fields to effectively eradicate as a regulated quarantine species. In this study, we developed a loop-mediated isothermal amplification (LAMP) assay, which allows for rapid in-field identification. To develop the LAMP　assay, we selected target species-specific genomic regions from the whole-genome sequences of one target and 13 other lepidopteran species. We validated each five and six primer sets that consistently produced positive reactions in S. eridania and S. ornithogalli, respectively. To test the sensitivity of the each locus, LAMP reactions were performed using various reaction times using crude DNA, which was extracted from various types of adult tissues. All sensitivity tests were also successful.

Existing reinforced concrete buildings with seismically deficient column details affect the overall behavior depending on the failure type of column. This study aims to develop and validate a machine learning-based prediction model for the column failure modes (shear, flexure-shear, and flexure failure modes). For this purpose, artificial neural network (ANN), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models were used, considering previously collected experimental data. Using four machine learning methodologies, we developed a classification learning model that can predict the column failure modes in terms of the input variables using concrete compressive strength, steel yield strength, axial load ratio, height-to-dept aspect ratio, longitudinal reinforcement ratio, and transverse reinforcement ratio. The performance of each machine learning model was compared and verified by calculating accuracy, precision, recall, F1-Score, and ROC. Based on the performance measurements of the classification model, the RF model represents the highest average value of the classification model performance measurements among the considered learning methods, and it can conservatively predict the shear failure mode. Thus, the RF model can rapidly predict the column failure modes with simple column details.

In this research, reduced graphene oxide/polypyrrole (rGO/PPy) particles were synthesized and used to measure the amount of dopamine (DA) electrochemically. The obtained rGO/PPy particle was characterized by Fourier Transform Infrared Spectrophotometer (FTIR), UV–Visible Spectrophotometer (UV–Vis), and X-Ray Diffraction Diffractometry (XRD). To investigate the DA sensor performance, cyclic voltammetry (CV) and differential pulse voltammetry (DPV) were used to acquire electrochemical measurements of the sensor. Current values of 1.65 and 5.9 mA were observed in the CV at 0.2 mM and 1.2 mM concentrations of target molecule, respectively. Under optimized conditions, the linear calibration plots were found to exhibit significant sensitivity in the linear range of 0.2 and 1.2 mM, with a corresponding detection limit of 0.061 μM for DA. The results obtained were similar to the sensor results of DA made using precious metals. This work was a demonstration of the feasibility of high-sensitivity electrochemical analysis with conductive carbon materials without the use of precious metals. It was also observed that the cost-effective rGO/PPy exhibited a very high potential for DA detection.

Background: Single nucleotide polymorphisms (SNPs) are widely used genetic markers with applications in human disease diagnostics, animal breeding, and evolutionary studies, but existing genotyping methods can be labor-intensive and costly. The aim of this study is to develop a simple and rapid method for identification of a single nucleotide change. Methods: A modified Polymerase Chain Reaction Amplification of Multiple Specific Alleles (PAMSA) and high resolution melt (HRM) analysis was performed to discriminate a bovine polymorphism in the NCAPG gene (rs109570900, 1326T > G). Results: The inclusion of tails in the primers enabled allele discrimination based on PCR product lengths, detected through agarose gel electrophoresis, successfully determining various genotypes, albeit with some time and labor intensity due to the use of relatively costly high-resolution agarose gels. Additionally, high-resolution melt (HRM) analysis with tailed primers effectively distinguished the GG genotype from the TT genotype in bovine muscle cell lines, offering a reliable way to distinguish SNP polymorphisms without the need for time-consuming AS-PCR. Conclusions: Our experiments demonstrated the importance of incorporating unique mismatched bases in the allele-specific primers to prevent cross-amplification by fragmented primers. This efficient and cost-effective method, as presented here, enables genotyping laboratories to analyze SNPs using standard real-time PCR.

In this study, we developed Rapid Enrichment Broth for Vibrio parahaemolyticus (REB-V), a broth capable enriching V. parahaemolyticus from 100 CFU/mL to 106 CFU/mL within 6 hours, which greatly facilitates the rapid detection of V. parahaemolyticus. Using a modified Gompertz model and response surface methodology, we optimized supplement sources to rapidly enrich V. parahaemolyticus. The addition of 0.003 g/10 mL of D-(+)- mannose, 0.002 g/10 mL of L-valine, and 0.002 g/10 mL of magnesium sulfate to 2% (w/v) NaCl BPW was the most effective combination of V. parahaemolyticus enrichment. Optimal V. parahaemolyticus culture conditions using REB-V were at pH 7.84 and 37oC. To confirm REB-V culture efficiency compared to 2% (w/v) NaCl BPW, we assessed the amount of enrichment achieved in 7 hours in each medium and extracted DNA samples from each culture every hour. Real-time PCR was performed using the extracted DNA to verify the applicability of this REB-V culture method to molecular diagnosis. V. parahaemolyticus was enriched to 5.452±0.151 Log CFU/mL in 2% (w/v) NaCl BPW in 7 hours, while in REB-V, it reached 7.831±0.323 Log CFU/mL. This confirmed that REB-V enriched V. parahaemolyticus to more than 106 CFU/mL within 6 hours. The enrichment rate of REB-V was faster than that of 2% (w/v) NaCl BPW, and the amount of enrichment within the same time was greater than that of 2% (w/v) NaCl BPW, indicating that REB-V exhibits excellent enrichment efficiency.

Rapid and accurate detection of pathogenic bacteria is crucial for various applications, including public health and food safety. However, existing bacteria detection techniques have several drawbacks as they are inconvenient and require time-consuming procedures and complex machinery. Recently, the precision and versatility of CRISPR/Cas system has been leveraged to design biosensors that offer a more efficient and accurate approach to bacterial detection compared to the existing techniques. Significant research has been focused on developing biosensors based on the CRISPR/Cas system which has shown promise in efficiently detecting pathogenic bacteria or virus. In this review, we present a biosensor based on the CRISPR/Cas system that has been specifically developed to overcome these limitations and detect different pathogenic bacteria effectively including Vibrio parahaemolyticus, Salmonella, E. coli O157:H7, and Listeria monocytogenes. This biosensor takes advantage of the CRISPR/Cas system's precision and versatility for more efficiently accurately detecting bacteria compared to the previous techniques. The biosensor has potential to enhance public health and ensure food safety as the biosensor’s design can revolutionize method of detecting pathogenic bacteria. It provides a rapid and reliable method for identifying harmful bacteria and it can aid in early intervention and preventive measures, mitigating the risk of bacterial outbreaks and their associated consequences. Further research and development in this area will lead to development of even more advanced biosensors capable of detecting an even broader range of bacterial pathogens, thereby significantly benefiting various industries and helping in safeguard human health

The fall armyworm (FAW), Spodoptera frugiperda (Lepidoptera: Noctuidae), which is native to tropical and subtropical regions of the Western Hemisphere is now annually arrives in Korea. In this study, we developed loop-mediated isothermal amplification (LAMP) assay, one of the main merits of which is a rapid identification of target species. Five among 11 FAW-specific loci tested successfully provided a consistent reaction when ten FAWs, which were collected from eight localities in four countries were tested, whereas the 13 non-target species were not amplified. To increase in-field applicability of the method all life stages, reaction time, and different periods after death was tested using the quick extracted DNA. Our FAW diagnostic protocol can be completed within 30 min, from the process of extracting genomic DNA from an egg or a 1st instar larva to species determination.

The walking-stick insects, Ramulus mikado, are outbreaks in several mountainous areas from 2020 to 2022. In recent, some population of the insect are showed rapidly decline in their abundance, while some of them are still maintained high population density. In worldwide, insects belonging to Phasmida are reported to outbreak in their habitats as mentioned above environments, but knowledge about outbreak pattern of the walking-stick insects is still lacking. In this study, we aimed which biological and environmental factors are related to wax and wane of the insect population. From 2022 to 2023, we studied host tree preferences in natural conditions, ecological stoichiometry in major host trees, overwintering ecology of R. mikado eggs, and infection rate by entomophathogenic fungi during growing season.

Background: This study attempted a comparative analysis of three splint fabrication methods currently used in clinical fields. Traditional Orthotic Fabrication Method Utilizing Thermoplastic Resin, the Methodology for creating assistive devices using 3D scanner, commercial CAD software, and 3D printing technology, and the Fabrication Method of Arm Splint Based on XR (eXtended Reality) Algorithm. Objectives: The study recruited 12 undergraduate students majoring in physical therapy and occupational therapy who had sufficient knowledge of splints, with an equal gender distribution. The study randomized the participants and conducted the experiment and overall process using a stratified approach. Design: Clinical applied technology experiment Methods: The study used QUEST 2.0 (Quebec User Evaluation of Satisfaction with assistive Technology ver. 2.0) to survey standardization, weight, ease of use, safety, durability, usability, effectiveness, and patient satisfaction, and statistically analyzed all results as quantitative indicators. Results: The score of QUEST 2.0 showed different aspects in some items, and it is difficult to say that certain technologies are superior overall. Conclusion: The study attempted an intuitive interpretation of the results. Overall, it was concluded that the XR method, which allows for easy and fast fabrication, is likely to be more readily accepted in future clinical practice.

The rapid detection of bacteria in the oral cavity, its species identification, and bacterial count determination are important to diagnose oral diseases caused by pathogenic bacteria. The existing clinical microbial diagnosis methods are time-consuming as they involve observing patients’ samples under a microscope or culturing and confirming bacteria using polymerase chain reaction (PCR) kits, making the process complex. Therefore, it is required to analyze the development status of substances and systems that can rapidly detect and analyze pathogenic microorganisms in the oral cavity. With research advancements, a close relationship between oral and systemic diseases has been identified, making it crucial to identify the changes in the oral cavity bacterial composition. Additionally, an early and accurate diagnosis is essential for better prognosis in periodontal disease. However, most periodontal diseasecausing pathogens are anaerobic bacteria, which are difficult to identify using conventional bacterial culture methods. Further, the existing PCR method takes a long time to detect and involves complicated stages. Therefore, to address these challenges, the concept of point-of-care (PoC) has emerged, leading to the study and implementation of various chair-side test methods. This study aims to investigate the different PoC diagnostic methods introduced thus far for identifying pathogenic microorganisms in the oral cavity. These are classified into three categories: 1) microbiological tests, 2) microchemical tests, and 3) genetic tests. The microbiological tests are used to determine the presence or absence of representative causative bacteria of periodontal diseases, such as A. actinomycetemcomitans , P. gingivalis , P. intermedia , and T. denticola . However, the quantitative analysis remains impossible, and detecting pathogens other than the specific ones is challenging. The microchemical tests determine the activity of inflammation or disease by measuring the levels of biomarkers present in the oral cavity. Although this diagnostic method is based on increase in the specific biomarkers proportional to inflammation or disease progression in the oral cavity, its commercialization is limited due to low sensitivity and specificity. The genetic tests are based on the concept that differences in disease vulnerability and treatment response are caused by the patient’s DNA predisposition. Specifically, the IL-1 gene is used in such tests. PoC diagnostic methods developed to date serve as supplementary diagnostic methods and tools for patient education, in addition to existing diagnostic methods, although they have limitations in diagnosing oral diseases alone. Research on various PoC test methods that can analyze and manage the oral cavity bacterial composition is expected to become more active, aligning with the shift from treatmentoriented to prevention-oriented approaches in healthcare.