• Parasites, Hosts and Diseases
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• v.59 no.1
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• pp.103-108
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• 2021

To date, there is no report on the genetic diversity of ticks in these regions. A total of 370 representative ticks from the south and east regions of Kazakhstan (SERK) and Xinjiang Uygur Autonomous Region (XUAR) were selected for molecular comparison. A fragment of the mitochondrial cytochrome c oxidase subunit I (cox1) gene, ranging from 631 bp to 889 bp, was used to analyze genetic diversity among these ticks. Phylogenetic analyses indicated 7 tick species including Hyalomma asiaticum, Hyalomma detritum, Hyalomma anatolicum, Dermacentor marginatus, Rhipicephalus sanguineus, Rhipicephalus turanicus and Haemaphysalis erinacei from the SERK clustered together with conspecific ticks from the XUAR. The network diagram of haplotypes showed that i) Hy. asiaticum from Almaty and Kyzylorda Oblasts together with that from Yuli County of XUAR constituted haplogroup H-2, and the lineage from Chimkent City of South Kazakhstan was newly evolved; and ii) the R. turanicus ticks sampled in Israel, Almaty, South Kazakhstan, Usu City, Ulugqat and Baicheng Counties of XUAR were derivated from an old lineage in Alataw City of XUAR. These findings indicate that: i) Hy. asiaticum, R. turanicus and Ha. erinacei shared genetic similarities between the SERK and XUAR; and ii) Hy. marginatum and D. reticulatus show differences in their evolution.

• Health Policy and Management
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• v.32 no.2
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• pp.173-179
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• 2022

Background: This study aimed to identify patterns of elderly patients who transferred from long-term care hospitals to emergency rooms and provide the evidence of emergency medical systems to prepare for a super-aged society. Methods: The data source was the National Emergency Department Information System database from January 2014 to December 2019 in Korea. We performed a cross-sectional study among elderly patients (≥65 years) who transferred from a long-term care hospital to an emergency room. Trend analysis was conducted by year. Results: We identified 225,765 elderly patients who were transferred from long-term care hospitals to emergency rooms between January 1, 2014 and December 31, 2019. The proportion of the study population and their mean age were recently increased (p<0.001, respectively). The proportion of elderly patients being re-transferred (p=0.049) and the patients re-transferred to long-term care hospitals is significantly increased (p=0.005). Conclusion: The establishment of efficient emergency medical services for an aging society is important. It is necessary to develop a healthcare network with the government, long-term care hospitals, and medical institutions in the community suitable for preventing disease deterioration.

• BMB Reports
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• v.55 no.7
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• pp.354-359
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• 2022

MitoNEET, a mitochondrial outer membrane protein containing the Asn-Glu-Glu-Thr (NEET) sequence, controls the formation of intermitochondrial junctions and confers autophagy resistance. Moreover, mitoNEET as a mitochondrial substrate undergoes ubiquitination by activated Parkin during the initiation of mitophagy. Therefore, mitoNEET is linked to the regulation of autophagy and mitophagy. Mitophagy is the selective removal of the damaged or unnecessary mitochondria, which is crucial to sustaining mitochondrial quality control. In numerous human diseases, the accumulation of damaged mitochondria by impaired mitophagy has been observed. However, the therapeutic strategy targeting of mitoNEET as a mitophagy-enhancing mediator requires further research. Herein, we confirmed that mitophagy is indeed activated by mitoNEET inhibition. CCCP (carbonyl cyanide m-chlorophenyl hydrazone), which leads to mitochondrial depolarization, induces mitochondrial dysfunction and superoxide production. This, in turn, contributes to the induction of mitophagy; mitoNEET protein levels were initially increased before an increase in LC3-II protein following CCCP treatment. Pharmacological inhibition of mitoNEET using mitoNEET Ligand-1 (NL-1) promoted accumulation of Pink1 and Parkin, which are mitophagy-associated proteins, and activation of mitochondria-lysosome crosstalk, in comparison to CCCP alone. Inhibition of mitoNEET using NL-1, or mitoNEET shRNA transfected into RAW264.7 cells, abrogated CCCP-induced ROS and mitochondrial cell death; additionally, it activated the expression of PGC-1α and SOD2, regulators of oxidative metabolism. In particular, the increase in PGC-1α, which is a major regulator of mitochondrial biogenesis, promotes mitochondrial quality control. These results indicated that mitoNEET is a potential therapeutic target in numerous human diseases to enhance mitophagy and protect cells by maintaining a network of healthy mitochondria.

• Journal of the Korea Society of Computer and Information
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• v.26 no.5
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• pp.1-8
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• 2021

Coronavirus disease 2019 (COVID-19) has affected the world seriously. Every person is required for wearing a mask properly in a public area to prevent spreading the virus. However, many people are not wearing a mask properly. In this paper, we propose an efficient mask detection system. In our proposed system, we first detect the faces of input images using YOLOv5 and classify them as the one of three scene complexity classes (Simple, Moderate, and Complex) based on the number of detected faces. After that, the image is fed into the Faster-RCNN with the one of three ResNet (ResNet-18, 50, and 101) as backbone network depending on the scene complexity for detecting the face area and identifying whether the person is wearing the mask properly or not. We evaluated our proposed system using public mask detection datasets. The results show that our proposed system outperforms other models.

• Journal of Animal Science and Technology
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• v.63 no.6
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• pp.1453-1463
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• 2021

Feeding is the most important behavior that represents the health and welfare of weanling pigs. The early detection of feed refusal is crucial for the control of disease in the initial stages and the detection of empty feeders for adding feed in a timely manner. This paper proposes a real-time technique for the detection and recognition of small pigs using a deep-leaning-based method. The proposed model focuses on detecting pigs on a feeder in a feeding position. Conventional methods detect pigs and then classify them into different behavior gestures. In contrast, in the proposed method, these two tasks are combined into a single process to detect only feeding behavior to increase the speed of detection. Considering the significant differences between pig behaviors at different sizes, adaptive adjustments are introduced into a you-only-look-once (YOLO) model, including an angle optimization strategy between the head and body for detecting a head in a feeder. According to experimental results, this method can detect the feeding behavior of pigs and screen non-feeding positions with 95.66%, 94.22%, and 96.56% average precision (AP) at an intersection over union (IoU) threshold of 0.5 for YOLOv3, YOLOv4, and an additional layer and with the proposed activation function, respectively. Drinking behavior was detected with 86.86%, 89.16%, and 86.41% AP at a 0.5 IoU threshold for YOLOv3, YOLOv4, and the proposed activation function, respectively. In terms of detection and classification, the results of our study demonstrate that the proposed method yields higher precision and recall compared to conventional methods.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.3
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• pp.483-490
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• 2022

Malaria is a disease caused by a parasite and it is prevalent in all over the world. The usual method used to recognize malaria cells is a thick and thin blood smears examination methods, but this method requires a lot of manual calculation, so the efficiency and accuracy are very low as well as the lack of pathologists in impoverished country has led to high malaria mortality rates. In this paper, a malaria cell image recognition model using transfer learning is proposed, which consists in the feature extractor, the residual structure and the fully connected layers. When the pre-training parameters of the VGG-19 model are imported to the proposed model, the parameters of some convolutional layers model are frozen and the fine-tuning method is used to fit the data for the model. Also we implement another malaria cell recognition model without residual structure to compare with the proposed model. The simulation results shows that the model using the residual structure gets better performance than the other model without residual structure and the proposed model has the best accuracy of 97.33% compared to other recent papers.

• Journal of Trauma and Injury
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• v.35 no.2
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• pp.76-83
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• 2022

Purpose: Ventilator-associated pneumonia is the most common nosocomial infection in patients with mechanical ventilation. In 2013, the new concept of ventilator-associated events (VAEs) replaced the traditional concept of ventilator-associated pneumonia. We analyzed risk factors for VAE occurrence and in-hospital mortality in trauma patients who received mechanical ventilatory support. Methods: In this retrospective review, the study population comprised patients admitted to the Jeju Regional Trauma Center from January 2020 to January 2021. Data on demographics, injury characteristics, and clinical findings were collected from medical records. The subjects were categorized into VAE and no-VAE groups according to the Centers for Disease Control and Prevention/National Healthcare Safety Network VAE criteria. We identified risk factors for VAE occurrence and in-hospital mortality. Results: Among 491 trauma patients admitted to the trauma center, 73 patients who received ventilator care were analyzed. Patients with a chest Abbreviated Injury Scale (AIS) score ≥3 had a 4.7-fold higher VAE rate (odds ratio [OR], 4.73; 95% confidence interval [CI], 1.46-17.9), and those with a glomerular filtration rate (GFR) <75 mL/min/1.73 m² had 4.1-fold higher odds of VAE occurrence (OR, 4.15; 95% CI, 1.32-14.1) and a nearly 4.2-fold higher risk for in-hospital mortality (OR, 4.19; 95% CI, 1.30-14.3). The median VAE-free duration of patients with chest AIS ≥3 was significantly shorter than that of patients with chest AIS <3 (P=0.013). Conclusions: Trauma patients with chest AIS ≥3 or GFR <75 mL/min/1.73 m² on admission should be intensively monitored to detect at-risk patients for VAEs and modify the care plan accordingly. VAEs should be closely monitored to identify infections early and to achieve desirable results. We should also actively consider modalities to shorten mechanical ventilation in patients with chest AIS ≥3 to reduce VAE occurrence.

• The Korean Journal of Physiology and Pharmacology
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• v.26 no.3
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• pp.183-193
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• 2022

MicroRNAs (miRNAs) regulate gene expression and are biomarkers for coronary atherosclerosis (AS). A novel miRNA-mRNA regulation network of coronary AS still needs to be disclosed. The aim of this study was to analyze potential mRNAs in coronary AS patients and the role of their upstream miR-491-5p in vascular smooth muscle cells (VSMCs). We first confirmed top ten mRNAs according to the analysis from Gene Expression Omnibus database (GSE132651) and examined the expression levels of them in the plaques and serum from AS patients. Five mRNAs (UBE2G2, SLC16A3, POLR2C, PNO1, and AMDHD2) presented significantly abnormal expression in both plaques and serum from AS patients, compared with that in the control groups. Subsequently, they were predicted to be targeted by 11 miRNAs by bioinformatics analysis. Among all the potential upstream miRNAs, only miR-491-5p was abnormally expressed in the plaques and serum from AS patients. Notably, miR-491-5p overexpression inhibited viability and migration, and significantly increased the expression of contractile markers (α-SMA, calponin, SM22α, and smoothelin) in VSMCs. While silencing miR-491-5p promoted viability and migration, and significantly suppressed the expression of α-SMA, calponin, SM22α, and smoothelin. Overall, miR-491-5p targeted UBE2G2, SLC16A3, and PNO1 and regulated the dysfunctions in VSMCs.

• The Journal of Korean Academic Society of Nursing Education
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• v.28 no.4
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• pp.444-455
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• 2022

Purpose: This study explored the meaning of the social perceptions of nurses in online news articles during the coronavirus disease 2019 (COVID-19) pandemic. Methods: A total of 339 nurse-related articles published in Korean online newspapers from January 1 to December 31, 2020, were extracted by entering various combinations of OR and AND with the four words "Corona," "COVID," "Nursing," and "Nurse" as search keywords using BIGKinds, a news database provided by the Korea Press Foundation. The collected data were analyzed with a keyword network analysis and topic modeling using NetMiner 4. Results: The top keywords extracted from the nurse-related news articles were, in the following order, "metropolitan area," "protective clothing," "government," "task," and "admission." Four topics representing keywords were identified: "encouragement for dedicated nurses," "poor work environment," "front-line nurses working with obligation during the COVID-19 pandemic," and "nurses' efforts to prevent the spread of COVID-19." Conclusion: The media's attention to the dedication of nurses, the shortage of nursing resources, and the need for government support is encouraging in that it forms the public opinion necessary to lead to substantial improvements in treating nurses. The nursing community should actively promote policy proposals to improve treatment toward nurses by utilizing the net function of the media and proactively seek and apply strategies to improve the image of nurses working in various fields.

• Journal of Biomedical Engineering Research
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• v.44 no.2
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• pp.139-146
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• 2023

Osteoporosis is a disease in which the risk of bone fractures increases due to a decrease in bone density caused by aging. Osteoporosis is diagnosed by measuring bone density in the total hip, femoral neck, and lumbar spine. To accurately measure bone density in the lumbar spine, the vertebral region must be segmented from the lumbar X-ray image. Deep learning-based automatic spinal segmentation methods can provide fast and precise information about the vertebral region. In this study, we used 695 lumbar spine images as training and test datasets for a deep learning segmentation model. We proposed a lumbar automatic segmentation model, CM-Net, which combines the center point of the spine and the modified U-Net network. As a result, the average Dice Similarity Coefficient(DSC) was 0.974, precision was 0.916, recall was 0.906, accuracy was 0.998, and Area under the Precision-Recall Curve (AUPRC) was 0.912. This study demonstrates a high-performance automatic segmentation model for lumbar X-ray images, which overcomes noise such as spinal fractures and implants. Furthermore, we can perform accurate measurement of bone density on lumbar X-ray images using an automatic segmentation methodology for the spine, which can prevent the risk of compression fractures at an early stage and improve the accuracy and efficiency of osteoporosis diagnosis.