• Journal of Biomedical Engineering Research
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• v.43 no.1
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• pp.27-34
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• 2022

Drunk driving defines a driver as unable to drive a vehicle safely due to drinking. To crack down on drunk driving, alcohol concentration evaluates through breathing and crack down on drinking using S-shaped courses. A method for assessing drunk driving without using BAC or BrAC is measurement via biosignal. Depending on the individual specificity of drinking, alcohol evaluation studies through various biosignals need to be conducted. In this study, we measure biosignals that are related to alcohol concentration, predict BrAC through SVM, and verify the effectiveness of the S-shaped course. Participants were 8 men who have a driving license. Subjects conducted a d2 test and a scenario evaluation of driving an S-shaped course when they attained BrAC's certain criteria. We utilized SVR to predict BrAC via biosignals. Statistical analysis used a one-way Anova test. Depending on the amount of drinking, there was a tendency to increase pupil size, HR, normLF, skin conductivity, body temperature, SE, and speed, while normHF tended to decrease. There was no apparent change in the respiratory rate and TN-E. The result of the D2 test tended to increase from 0.03% and decrease from 0.08%. Measured biosignals have enabled BrAC predictions using SVR models to obtain high Figs in primary and secondary cross-validations. In this study, we were able to predict BrAC through changes in biosignals and SVMs depending on alcohol concentration and verified the effectiveness of the S-shaped course drinking control method.

• Journal of Veterinary Science
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• v.21 no.5
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• pp.71.1-71.10
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• 2020

Background: Feline calicivirus (FCV) is a major and highly infectious pathogen in cats worldwide. However, there have been limited studies about the status of FCV infections in Korea. Objectives: To investigate the current status of FCV infections in stray cats in Korea. Methods: A novel reverse transcription polymerase chain reaction (RT-PCR) assay was developed based on the conserved nucleotide sequences of reported FCV strains. Field swab samples were collected from 122 cats (2 hospital admitted cats and 120 stray cats) in 2016 and 2017. All the samples were tested by virus isolation and 2 different RT-PCRs, including the novel RT-PCR, for the detection of FCV. Results: The novel RT-PCR assay showed no cross-reactivity to the nucleic acids of the other feline pathogens tested, and the limit of detection was calculated as 10⁰ TCID₅₀/mL based on an in vitro assessment. The novel RT-PCR assay detected 5 positive samples from the 122 field samples, which showed perfect agreement with the results of the virus isolation method. In contrast, another RT-PCR assay used in a previous study in Korea detected no positive samples. The prevalence of FCV infection in stray cats was 2.5% (3/120) based on the results of virus isolation and the novel RT-PCR assays. Conclusions: The current study is the first report of the detection and prevalence of FCV in stray cats in Korea. The novel RT-PCR assay developed in this study showed high sensitivity and specificity, which indicates a useful diagnostic assay to identify FCV infection in cats.

• Parasites, Hosts and Diseases
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• v.60 no.1
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• pp.25-34
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• 2022

Alveolar echinococcosis (AE) caused by infection with E. multilocularis metacestode, represents one of the most fatal helminthic diseases. AE is principally manifested with infiltrative, proliferating hepatic mass, resembling primary hepatocellular carcinoma. Sometimes metastatic lesions are found in nearby or remote tissue. AE diagnosis largely depends on imaging studies, but atypical findings of imaging features frequently require differential diagnosis from other hepatic lesions. Serological tests may provide further evidence, while obtaining reliable AE materials is not easy. In this study, alternative antigens, specific to AE were identified by analyzing E. granulosus protoscolex proteins. An immunoblot analysis of E. granulosus protoscolex showed that a group of low-molecular-weight proteins in the range from 14 kDa to 16 kDa exhibited a sensitive and specific immune response to AE patient sera. Partial purification and proteomic analysis indicated that this protein group contained myosin, tubulin polymerization promoting protein, fatty-acid binding protein, uncharacterized DM9, heat shock protein 90 cochaperone tebp P-23, and antigen S. When the serological applicability of recombinant forms of these proteins was assessed using enzyme-linked immunosorbent assay, DM9 protein (rEgDM9) showed 90.1% sensitivity (73/81 sera tested) and 94.5% specificity (172/181 sera tested), respectively. rEgDM9 showed weak cross-reactions with patient sera from the transitional and chronic stages of cystic echinococcosis (3 to 5 stages). rEgDM9 would serve as a useful alternative antigen for serodiagnosis of both early- and advanced-stage AE cases.

• 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 radiological science and technology
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• v.46 no.4
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• pp.277-285
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• 2023

In this study, pneumonia identification networks with the small number of layers were constructed by using chest X-ray images. The networks had similar trainable-parameters, and the performance of the trained models was quantitatively evaluated with the modification of the network architectures. A total of 6 networks were constructed: convolutional neural network (CNN), VGGNet, GoogleNet, residual network with identity blocks, ResNet with bottleneck blocks and ResNet with identity and bottleneck blocks. Trainable parameters for the 6 networks were set in a range of 273,921-294,817 by adjusting the output channels of convolution layers. The network training was implemented with binary cross entropy (BCE) loss function, sigmoid activation function, adaptive moment estimation (Adam) optimizer and 100 epochs. The performance of the trained models was evaluated in terms of training time, accuracy, precision, recall, specificity and F1-score. The results showed that the trained models with the small number of layers precisely detect pneumonia from chest X-ray images. In particular, the overall quantitative performance of the trained models based on the ResNets was above 0.9, and the performance levels were similar or superior to those based on the CNN, VGGNet and GoogleNet. Also, the residual blocks affected the performance of the trained models based on the ResNets. Therefore, in this study, we demonstrated that the object detection networks with the small number of layers are suitable for detecting pneumonia using chest X-ray images. And, the trained models based on the ResNets can be optimized by applying appropriate residual-blocks.

• 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 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.