• The Journal of Pediatrics of Korean Medicine
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• v.35 no.1
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• pp.1-17
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• 2021

Objectives We aimed to analyze traditional Chinese medicine (TCM) literatures in regards to the pattern identification and related symptoms of idiopathic short stature (ISS). Methods We searched relevant literatures published up to September 29, 2020 through three Chinese electronic databases. We performed frequency analysis of the selected studies by extracting information on pattern identification, clinical symptoms, and TCM treatments presenting pattern identification of ISS. Results Sixteen studies were included. Spleen deficiency, kidney deficiency, dual deficiency of spleen-kidney, and liver-kidney yin deficiency were frequently reported. Clinical symptoms of the spleen deficiency include sallow complexion, body constituent weakness, anorexia, lack of qi and no desire to speak, and loose stools. Herbal medicines (HMs) such as Sijunzi-tang were frequently reported. Clinical symptoms of the kidney deficiency include cold limb and fear of cold, soreness and weakness of waist and knees, and clear and long urine. HMs such as Bishendihuang-wan were frequently reported. Clinical symptoms of the dual deficiency of spleen-kidney include body constituent weakness, spirit lassitude and lack of strength, anorexia, soreness and weakness of waist and knees, and cold limb and fear of cold. HMs such as Sijunzi-tang plus Bishendihuang-wan were frequently reported. Clinical symptoms of the liver-kidney yin deficiency include tidal fever and night sweating, heat in the palms and soles, dizziness, and dry throat. HMs such as Liuweidihuang-wan were frequently reported. Conclusions This was the first study to analyze the frequency of pattern identification and related symptoms on ISS. In the future, a standardized Korean medicine pattern identification system should be established.

• Animal Bioscience
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• v.35 no.7
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• pp.1048-1058
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• 2022

Objective: The present study was to investigate the extraction conditions of dietary fiber from dried cassava pulp (DCP) and cassava distiller's dried grains (CDG) under different NaOH concentrations, and the Fourier transform infrared (FTIR) was used to determine the dietary fiber components. Methods: The dried samples (DCP and CDG) were treated with various concentrations of NaOH at levels of 2%, 4%, 6%, and 8% using a completely randomized design with 4 replications of each. After extraction, the residual DCP and CDG dietary fiber were dried in a hot air oven at 55℃ to 60℃. Finally, the oven dried extracted dietary fiber was powdered to a particle size of 1 mm. Both extracted dietary fibers were analyzed for their chemical composition and determined by FTIR. Results: The DCP and CDG treated with NaOH linearly or quadratically or cubically (p<0.05) increased the total dietary fiber (TDF) and insoluble fiber (IDF). The optimal conditions for extracting dietary fiber from DCP and CDG were under treatment with 6% and 4% NaOH, respectively, as these conditions yielded the highest TDF and IDF contents. These results were associated with the FTIR spectra integration for a semi-quantitative analysis, which obtained the highest cellulose content in dietary fiber extracted from DCP and CDG with 6% and 4% NaOH solution, respectively. The principal component analysis illustrated clear separation of spectral distribution in cassava pulp extracted dietary fiber (DFCP) and cassava distiller's dried grains extracted dietary fiber (DFCDG) when treated with 6% and 4% NaOH, respectively. Conclusion: The optimal conditions for the extraction of dietary fiber from DCP and CDG were treatment with 6% and 4% NaOH solution, respectively. In addition, FTIR spectroscopy proved itself to be a powerful tool for fiber identification.

• Journal of dental hygiene science
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• v.22 no.2
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• pp.99-107
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• 2022

Background: Obesity weakens acquired immunity and causes infection. This study aimed to investigate the relationship between the inflammatory markers in the gingival crevicular fluid and serum and periodontal bacteria in saliva through obesity control for 4 weeks. Methods: Forty-six subjects with a body mass index (BMI) of ≥23 kg/m² stayed in the camp for 4 weeks, followed by exercise and a low salt-low fat diet. Body size measurements, oral examinations, blood, saliva, and gingival crevicular fluid were collected before and after the program. C-reactive protein (CRP) in serum, matrix metalloproteinase (MMP)-8, MMP-9, and interleukin (IL)-1β in the gingival sulcus fluid were measured. After extracting bacterial genomic DNA from saliva, the presence of periodontal bacteria were detected using Taq probe. The relationship of each index before and after the program was analyzed through paired t-test and partial correlation analysis. Results: Campylobacter rectus (Cr) increased after the program, and there was no significant change in other bacteria. Serum CRP and Fusobacterium nucleatum (Fn), Aggregatibacter actinomycetemcomitans, Cr, ratio of Fn, and ratio of Cr had a positive relationship at baseline; however, the relationship was not significant after the program. Ratio of Prevotella intermedia had a positive relationship with MMP-9, MMP-8, IL-1β at baseline. Moreover, the ratio of Treponema denticola and the ratio of Tannerella forsythia showed a positive relationship with MMP-8, MMP-9, and IL-1β. The relationship between the ratio of Porphyromonas gingivalis and IL-1β showed a constant positive relationship at baseline and after the program. Conclusion: Obesity control program in subjects with a BMI of ≥23 kg/m² accompanied by diet and exercise did not affect the changes in periodontal bacteria itself, but changes in the relationship between periodontal bacteria and serum CRP, the relationship between the inflammatory index in the gingival crevicular fluid and periodontal bacteria was observed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.662-667
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• 2022

Semantic role labeling task(SRL) is to extract predicate and arguments such as agent, patient, place, time. In the previously SRL task studies, a pipeline method extracting linguistic features of sentence has been proposed, but in this method, errors of each extraction work in the pipeline affect semantic role labeling performance. Therefore, methods using End-to-End neural network model have recently been proposed. In this paper, we propose a neural network model using the Biaffine Average Attention model for SRL task. The proposed model consists of a structure that can focus on the entire sentence information regardless of the distance between the predicate in the sentence and the arguments, instead of LSTM model that uses the surrounding information for prediction of a specific token proposed in the previous studies. For evaluation, we used F1 scores to compare two models based BERT model that proposed in existing studies using F1 scores, and found that 76.21% performance was higher than comparison models.

• Journal of Biomedical Engineering Research
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• v.44 no.1
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• pp.11-18
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• 2023

Currently, studies to predict the risk of rectal cancer surgery select MRI image slices based on the clinical experience of surgeons. The purpose of this study is to semi-automatically select and classify 2D MRI image slides to predict the risk of rectal cancer surgery using biomarkers. The data used were retrospectively collected MRI imaging data of 50 patients who underwent laparoscopic surgery for rectal cancer at Gachon University Gil Medical Center. Expert-selected MRI image slices and non-selected slices were screened and radiomics was used to extract a total of 102 features. A total of 16 approaches were used, combining 4 classifiers and 4 feature selection methods. The combination of Random Forest and Ridge performed with a sensitivity of 0.83, a specificity of 0.88, an accuracy of 0.85, and an AUC of 0.89±0.09. Differences between expert-selected MRI image slices and non-selected slices were analyzed by extracting the top five significant features. Selected quantitative features help expedite decision making and improve efficiency in studies to predict risk of rectal cancer surgery.

• The Korea Journal of Herbology
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• v.34 no.6
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• pp.117-124
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• 2019

Objective : Broccoli is edible green plant that has a wide variety of health benefits including cancer prevention and cholesterol reduction. However, leaves of broccoli are not eaten and are mostly left as waste. This study was conducted to evaluate the effects of the broccoli leaf extract (BLE) on prostaglandin E₂ (PGE₂) production related to nuclear factor kappa B (NF-κB) signaling in lipopolysaccharide (LPS)-activated macrophages. Methods : BLE was prepared by extracting dried leaf with ethanol. Cell viability was determined by 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. PGE₂ and inflammatory cytokines were detected by enzyme-linked immunosorbent assay (ELISA). Expression level of each protein was monitored by Western blot analysis. Results : In LPS-activated Raw264.7 cells, PGE₂ release into culture medium was dramatically enhanced compared to control cells. However, increased PGE₂ was attenuated dose-dependently by treatment with BLE. Inhibition of PGE₂ production by BLE was due to the suppression of cyclooxygenase-2 (COX-2) expression determined by Western blot analysis. BLE also inhibited the production of inflammatory cytokines such as interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α). Inhibition at PGE₂ and cytokine was mediated from inhibition of nuclear translocation of NF-κB due to the repression of inhibitory kappa B alpha (IκBα) phosphorylation and degradation. Conclusion : This study showed that BLE exerted inhibitory activities against PGE₂, which is critical for the initiation and resolution of inflammatory responses, and that inhibition of PGE₂ was mediated by suppression of NF-κB signaling. These results suggest that the waste broccoli leaves could be used for controlling inflammation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.3
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• pp.501-511
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• 2022

In 2021, ransomware attacks became popular, and the number is rapidly increasing every year. Since PowerShell is used as the primary ransomware technique, the need for PowerShell-based malware detection is ever increasing. However, the existing detection techniques have limits in that they cannot detect obfuscated scripts or require a long processing time for deobfuscation. This paper proposes a simple and fast deobfuscation method and a deep learning-based classification model that can detect PowerShell-based malware. Our technique is composed of Word2Vec and a convolutional neural network to learn the meaning of a script extracting important features. We tested the proposed model using 1400 malicious codes and 8600 normal scripts provided by the AI-based PowerShell malicious script detection track of the 2021 Cybersecurity AI/Big Data Utilization Contest. Our method achieved 5.04 times faster deobfuscation than the existing methods with a perfect success rate and high detection performance with FPR of 0.01 and TPR of 0.965.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.3
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• pp.211-216
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• 2022

Steganography technology protects the existence of hidden information by embedding a secret message in a specific location on the cover medium. Security and resistance are strengthened by applying various hybrid methods based on encryption and steganography. In particular, techniques to increase chaos and randomness are needed to improve security. In fact, the case where the shuffling method is applied based on the discrete cosine transform(DCT) and the least significant bit(LSB) is an area that needs to be studied. I propose a new approach to hide the bit information of Hangul messages by integrating the selective shuffling method that can add the complexity of message hiding and applying the spatial domain technique to steganography. Inverse shuffling is applied when extracting messages. In this paper, the Hangul message to be inserted is decomposed into the choseong, jungseong and jongseong. It improves security and chaos by applying a selective shuffling process based on the corresponding information. The correlation coefficient and PSNR were used to confirm the performance of the proposed method. It was confirmed that the PSNR value of the proposed method was appropriate when compared with the reference value.

• Smart Structures and Systems
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• v.31 no.4
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• pp.325-334
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• 2023

Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model.

• The Korean Journal of Pain
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• v.36 no.4
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• pp.425-440
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• 2023

Background: Muscimol's quick onset and GABAergic properties make it a promising candidate for the treatment of pain. This systematic review and meta-analysis of preclinical studies aimed at summarizing the evidence regarding the efficacy of muscimol administration in the amelioration of nerve injury-related neuropathic pain. Methods: Two independent researchers performed the screening process in Medline, Embase, Scopus and Web of Science extracting data were extracted into a checklist designed according to the PRISMA guideline. A standardized mean difference (SMD [95% confidence interval]) was calculated for each. To assess the heterogeneity between studies, ² and chi-square tests were utilized. In the case of heterogeneity, meta-regression and subgroup analyses were performed to identify the potential source. Results: Twenty-two articles met the inclusion criteria. Pooled data analysis showed that the administration of muscimol during the peak effect causes a significant reduction in mechanical allodynia (SMD = 1.78 [1.45-2.11]; P < 0.0001; I² = 72.70%), mechanical hyperalgesia (SMD = 1.62 [1.28-1.96]; P < 0.0001; I² = 40.66%), and thermal hyperalgesia (SMD = 2.59 [1.79-3.39]; P < 0.0001; I² = 80.33%). This significant amendment of pain was observed at a declining rate from 15 minutes to at least 180 minutes post-treatment in mechanical allodynia and mechanical hyperalgesia, and up to 30 minutes in thermal hyperalgesia (P < 0 .0001). Conclusions: Muscimol is effective in the amelioration of mechanical allodynia, mechanical hyperalgesia, and thermal hyperalgesia, exerting its analgesic effects 15 minutes after administration for up to at least 3 hours.