• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.175-181
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• 2022

COVID-19 is an acute respiratory syndrome that affects the host's breathing and respiratory system. The novel disease's first case was reported in 2019 and has created a state of emergency in the whole world and declared a global pandemic within months after the first case. The disease created elements of socioeconomic crisis globally. The emergency has made it imperative for professionals to take the necessary measures to make early diagnoses of the disease. The conventional diagnosis for COVID-19 is through Polymerase Chain Reaction (PCR) testing. However, in a lot of rural societies, these tests are not available or take a lot of time to provide results. Hence, we propose a COVID-19 classification system by means of machine learning and transfer learning models. The proposed approach identifies individuals with COVID-19 and distinguishes them from those who are healthy with the help of Deep Visual Embeddings (DVE). Five state-of-the-art models: VGG-19, ResNet50, Inceptionv3, MobileNetv3, and EfficientNetB7, were used in this study along with five different pooling schemes to perform deep feature extraction. In addition, the features are normalized using standard scaling, and 4-fold cross-validation is used to validate the performance over multiple versions of the validation data. The best results of 88.86% UAR, 88.27% Specificity, 89.44% Sensitivity, 88.62% Accuracy, 89.06% Precision, and 87.52% F1-score were obtained using ResNet-50 with Average Pooling and Logistic regression with class weight as the classifier.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.6
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• pp.594-602
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• 2022

Cast-resin transformers are widely installed in various electrical power systems because of their low operating cost and low influence on external environmental factors. However, when they have an internal defect during the manufacturing process or operation, a partial discharge (PD) occurs, and eventually destroys the insulation. In this paper, a Rogowski-type PD sensor was studied to replace commercial PD sensors used for the insulation diagnosis of power apparatus. The proposed PD sensor was manufactured with four different types of PCB-based winding structures, and it was analyzed in terms of the detection characteristics for standard calibration pulses and the changes of the output voltage according to the distance. The output increased linearly in accordance with the applied discharge amount. It was confirmed that the hexagon structure sensor had the highest sensitivity, because the winding cross-sectional area of the sensor was larger than others. In addition, as the distance from the defect increased, the output voltage of the sensors decreased by 7.32% on average. It was also confirmed that the attenuation rate according to the distance decreased as the input discharge amount increased. For the application of this new type sensor, PD electrode system was designed to simulate the void defect. Waveforms and PRPD patterns measured by the proposed PD sensors at DIV and 120% of DIV were the same as the results measured by MPD 600 based on IEC 60270. The proposed PD sensors can be installed on the inner wall of the transformer tank by coating its surfaces with a non-conductive material; therefore, it is possible to detect internal defects more effectively at a closer distance from the defect than the conventional sensors.

• BMB Reports
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• v.56 no.2
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• pp.78-83
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• 2023

Chronic myeloid leukemia (CML) has a markedly improved prognosis with the use of breakpoint cluster region-abelson 1 (BCR-ABL1) tyrosine kinase inhibitors (BCR-ABL1 TKIs). However, approximately 40% of patients are resistant or intolerant to BCR-ABL1 TKIs. Hypoxia-inducible factor 1α (HIF-1α) is a hypoxia response factor that has been reported to be highly expressed in CML patients, making it a therapeutic target for BCR-ABL1 TKI-sensitive CML and BCR-ABL1 TKI-resistant CML. In this study, we examined whether HIF-1α inhibitors induce cell death in CML cells and BCR-ABL1 TKI-resistant CML cells. We found that echinomycin and PX-478 induced cell death in BCR-ABL1 TKIs sensitive and resistant CML cells at similar concentrations while the cell sensitivity was not affected with imatinib or dasatinib in BCR-ABL1 TKIs resistant CML cells. In addition, echinomycin and PX-478 inhibited the c-Jun N-terminal kinase (JNK), Akt, and extracellular-regulated protein kinase 1/2 (ERK1/2) activation via suppression of BCR-ABL1 and Met expression in BCR-ABL1 sensitive and resistant CML cells. Moreover, treatment with HIF-1α siRNA induced cell death by inhibiting BCR-ABL1 and Met expression and activation of JNK, Akt, and ERK1/2 in BCR-ABL1 TKIs sensitive and resistant CML cells. These results indicated that HIF-1α regulates BCR-ABL and Met expression and is involved in cell survival in CML cells, suggesting that HIF-1α inhibitors induce cell death in BCR-ABL1 TKIs sensitive and resistant CML cells and therefore HIF-1α inhibitors are potential candidates for CML treatment.

• Journal of Preventive Medicine and Public Health
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• v.56 no.1
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• pp.41-49
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• 2023

Objectives: Rwanda reported a stunting rate of 33% in 2020, decreasing from 38% in 2015; however, stunting remains an issue. Globally, child deaths from malnutrition stand at 45%. The best options for the early detection and treatment of stunting should be made a community policy priority, and health services remain an issue. Hence, this research aimed to develop a model for predicting stunting in Rwandan children. Methods: The Rwanda Demographic and Health Survey 2019-2020 was used as secondary data. Stratified 10-fold cross-validation was used, and different machine learning classifiers were trained to predict stunting status. The prediction models were compared using different metrics, and the best model was chosen. Results: The best model was developed with the gradient boosting classifier algorithm, with a training accuracy of 80.49% based on the performance indicators of several models. Based on a confusion matrix, the test accuracy, sensitivity, specificity, and F1 were calculated, yielding the model's ability to classify stunting cases correctly at 79.33%, identify stunted children accurately at 72.51%, and categorize non-stunted children correctly at 94.49%, with an area under the curve of 0.89. The model found that the mother's height, television, the child's age, province, mother's education, birth weight, and childbirth size were the most important predictors of stunting status. Conclusions: Therefore, machine-learning techniques may be used in Rwanda to construct an accurate model that can detect the early stages of stunting and offer the best predictive attributes to help prevent and control stunting in under five Rwandan children.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.148-162
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• 2023

Classification systems can significantly assist the medical sector by allowing for the precise and quick diagnosis of diseases. As a result, both doctors and patients will save time. A possible way for identifying risk variables is to use machine learning algorithms. Non-surgical technologies, such as machine learning, are trustworthy and effective in categorizing healthy and heart-disease patients, and they save time and effort. The goal of this study is to create a medical intelligent decision support system based on machine learning for the diagnosis of heart disease. We have used a mixed feature creation (MFC) technique to generate new features from the UCI Cleveland Cardiology dataset. We select the most suitable features by using Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination with Random Forest feature selection (RFE-RF) and the best features of both LASSO RFE-RF (BLR) techniques. Cross-validated and grid-search methods are used to optimize the parameters of the estimator used in applying these algorithms. and classifier performance assessment metrics including classification accuracy, specificity, sensitivity, precision, and F1-Score, of each classification model, along with execution time and RMSE the results are presented independently for comparison. Our proposed work finds the best potential outcome across all available prediction models and improves the system's performance, allowing physicians to diagnose heart patients more accurately.

• Journal of Microbiology and Biotechnology
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• v.33 no.8
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• pp.1091-1100
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• 2023

Human papillomavirus (HPV) types 16 and 18 are the major causes of cervical lesions and are associated with 71% of cervical cancer cases globally. However, public health infrastructures to support cervical cancer screening may be unavailable to women in low-resource areas. Therefore, sensitive, convenient, and cost-efficient diagnostic methods are required for the detection of HPV16/18. Here, we designed two novel methods, real-time ERA and ERA-LFD, based on enzymatic recombinase amplification (ERA) for quick point-of-care identification of the HPV E6/E7 genes. The entire detection process could be completed within 25 min at a constant low temperature (35-43℃), and the results of the combined methods could be present as the amplification curves or the bands presented on dipsticks and directly interpreted with the naked eye. The ERA assays evaluated using standard plasmids carrying the E6/E7 genes and clinical samples exhibited excellent specificity, as no cross-reaction with other common HPV types was observed. The detection limits of our ERA assays were 10⁰ and 10¹ copies/µl for HPV16 and 18 respectively, which were comparable to those of the real-time PCR assay. Assessment of the clinical performance of the ERA assays using 114 cervical tissue samples demonstrated that they are highly consistent with real-time PCR, the gold standard for HPV detection. This study demonstrated that ERA-based assays possess excellent sensitivity, specificity, and repeatability for HPV16 and HPV18 detection with great potential to become robust diagnostic tools in local hospitals and field studies.

• Nutrition Research and Practice
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• v.17 no.5
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• pp.997-1006
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• 2023

BACKGROUND/OBJECTIVES: Asthma and allergic rhinitis (AR) are closely related and considered as allergic respiratory diseases (ARD), and their prevalence has recently increased. Data on the association of dietary antioxidant vitamin intake with asthma and AR in adults are limited. The present study aimed to investigate the associations of vitamin A and C intake with asthma, AR, and all cases of both diseases in young adults who participated in a cross-sectional national survey, with the use of high-sensitivity C-reactive protein (hs-CRP) level as an effect modifier. SUBJECTS/METHODS: This study included 6,293 male and female adults aged 20-49 years from the Korea National Health and Nutrition Examination Survey (KNHANES) conducted between 2016 and 2018. The questionnaire-based reports on asthma and AR diagnosis were used to determine outcome variables. Further, 24-h recall data on dietary vitamin A and C, carotene, and retinol intake were acquired. Logistic regression analysis was performed to calculate odds ratios (ORs) and 95% confidence interval (CI). RESULTS: Dietary vitamin C intake was inversely associated with asthma prevalence among participants with hs-CRP levels (≥ 1 mg/L); the OR of asthma prevalence was 0.27 (95% CI, 0.08-0.84) for participants with vitamin C consumption ≥ 75 mg/day compared with those consuming < 20 mg/day. Similar association analyses limiting to non-users of dietary supplements were performed to rule out the potential effects of supplement intake on the outcomes; results showed a stronger association. However, the association between vitamin C and asthma was not significant in participants with hs-CRP levels < 1 mg/L; the OR of asthma was 1.44 (95% CI, 0.66-3.16) for participants with vitamin C consumption ≥ 75 mg/day compared with those consuming < 20 mg/day. Vitamin C intake was not associated with AR. Moreover, there was no association between vitamin A intake and neither asthma nor AR. CONCLUSIONS: These findings suggest that higher vitamin C intake may play a potential role in reducing asthma prevalence. Nevertheless, further studies should be conducted to evaluate whether this association is causal.

• Journal of Veterinary Science
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• v.25 no.3
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• pp.37.1-37.10
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• 2024

Importance: The portal vein to aorta (PV/Ao) ratio is used to assess the clinical significance of extrahepatic portosystemic shunt (EHPSS). Previous studies using computed tomography (CT) were conducted in dogs but not in cats. Objective: This study aimed to establish normal reference values for PV indices (PV/Ao ratio and PV diameter) in cats and determine the usefulness of these for predicting symptomatic EHPSS. Methods: This study included 95 dogs and 114 cats that underwent abdominal CT. The canine normal (CN) group included dogs without EHPSS. The cats were classified into feline normal (FN, 88/114), feline asymptomatic (FA, 16/114), and feline symptomatic (FS, 10/114) groups. The PV and Ao diameters were measured in axial cross-sections. Results: The group FN had a higher PV/Ao ratio than the group CN (p < 0.001). Within the feline groups, the PV indices were in the order FN > FA > FS (both p < 0.001). The mean PV diameter and PV/Ao ratio for group FN were 5.23±0.77 mm and 1.46±0.19, respectively. The cutoff values between groups FN and FS were 4.115 mm for PV diameter (sensitivity, 100%; specificity, 97.7%) and 1.170 for PV/Ao ratio (90%, 92.1%). The cutoff values between group FA and FS were 3.835 mm (90%, 93.8%) and 1.010 (70%, 100%), respectively. Conclusions and Relevance: The results demonstrated significant differences in PV indices between dogs and cats. In cats, the PV/Ao ratio demonstrated high diagnostic performance for symptomatic EHPSS. The PV diameter also performed well, in contrast to dogs.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.27 no.3
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• pp.176-185
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• 2024

Purpose: Acute pancreatitis (AP) is common among children in Bangladesh. Its management depends mainly on risk stratification. This study aimed to assess the severity of pediatric AP using computed tomography (CT). Methods: This cross-sectional, descriptive study was conducted in pediatric patients with AP at the Department of Pediatric Gastroenterology and Nutrition, BSMMU, Dhaka, Bangladesh. Results: Altogether, 25 patients with AP were included, of whom 18 (mean age, 10.27±4.0 years) were diagnosed with mild AP, and 7 (mean age, 10.54±4.0 years) with severe AP. Abdominal pain was present in all the patients, and vomiting was present in 88% of the patients. Etiology was not determined. No significant differences in serum lipase, serum amylase, BUN, and CRP levels were observed between the mild and severe AP groups. Total and platelet counts as well as hemoglobin, hematocrit, serum creatinine, random blood sugar, and serum alanine aminotransferase levels (p>0.05) were significantly higher in the mild AP group than in the severe AP group (p=0.001). The sensitivity, specificity, positive predictive value, and negative predictive value of CT severity index (CTSI) were 71.4%, 72.2%, 50%, and 86.7%, respectively. In addition, significant differences in pancreatic appearance and necrosis were observed between the two groups on CT. Conclusion: CT can be used to assess the severity of AP. In the present study, the CTSI effectively assessed the severity of AP in pediatric patients.

• Korean Journal of Radiology
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• v.20 no.6
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• pp.931-938
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• 2019

Objective: To compare inferior capsular redundancy by using magnetic resonance arthrography (MRA) images in patients with multidirectional instability (MDI) of the shoulder and control subjects without instability and thereby develop a screening method to identify the presence of shoulder MDI. Materials and Methods: The MRA images of patients with MDI of the shoulder (n = 65, 57 men, 8 women; mean age, 24.5 years; age range, 18-42 years) treated over an eight-year period were retrospectively reviewed; a control group (n = 65, 57 men, 8 women; mean age, 27.4 years; age range, 18-45 years) without instability was also selected. The inferior capsular redundancy was measured using a new method we named the glenocapsular (GC) ratio method. MRA images of both groups were randomly mixed together, and two orthopedic surgeon reviewers measured the cross-sectional areas (CSAs) and sagittal capsule-head ratios on oblique sagittal images, as well as the axial capsule-head ratios on axial images and GC ratios on oblique coronal images. Results: The CSAs and GC ratios were significantly higher in patients than in controls (both, p < 0.001); however, the sagittal capsule-head ratios and axial capsule-head ratios were not significantly different (p = 0.317, p = 0.053, respectively). In addition, GC ratios determined the presence of MDI more sensitively and specifically than did CSAs. A GC ratio of > 1.42 was found to be most suggestive of MDI of the shoulder, owing to its high sensitivity (92.3%) and specificity (89.2%). Conclusion: GC ratio can be easily measured and used to accurately screen for MDI of the shoulder.