• Biomedical Science Letters
• /
• v.27 no.4
• /
• pp.323-328
• /
• 2021

Naegleria fowleri is a free-living amoeba causing primary amoebic encephalitis. In this study, we analyzed how the N-aceytlglucosamine (GlcNAc) and D-galactose affected the interaction between Naegleria fowleri and methicillin-resistant Staphylococcus aureus (MRSA) or Escherichia coli O157:H7, and the interaction with bacteria when monosaccharides were treated with N. fowleri for a longer pre-incubation time. When GlcNAc was treated with N. fowleri for 1 hr, the E. coli association was almost the same as that of the control not treated with GlcNAc until the concentration of GlcNAc was 25 mM. However, the E. coli association was reduced by approximately 91% with 100 mM GlcNAc. E. coli invasion into N. fowleri showed statistical significance only in the group treated with 100 mM GlcNAc. The interaction when treated with galactose showed a very different pattern in the 50 mM galactose group than when treated with GlcNAc. In the MRSA interaction, a statistically significant decrease in association (76.3% by GlcNAc and 88.7% by galactose) and invasion (3.6% by GlcNAc and 9.3% by galactose) was shown by the concentration of two 100 mM monosaccharides. The group treated with monosaccharides at the same time showed almost no difference in all interactions from the group treated with monosaccharides at the same time. Taken together, it suggested that the effect of monosaccharides on the interaction of several Gram-negative or positive bacteria and the evidence that the interaction could be enhanced by longer pre-incubation time.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.62 no.7
• /
• pp.1009-1013
• /
• 2013

In this study, we developed a fiber-optic goniometer for the continuous measurement of knee joint angle which provides important medical information on Alzheimer's disease. The fiber-optic goniometer is composed of a light-emitting diode (LED), a plastic optical fiber (POF), and a voltage output photodiode (PD). As a sensing part of the fiber-optic goniometer, a unclad fiber with regular intervals of 1 mm was fabricated to improve efficiency of bending loss according to the angle variation of knee joint. The emitting light with a center wavelength of 470 nm from a LED is guided by a POF to the PD, the transmitted light is then attenuated by the bending loss inside the bent POF. The intensity variation of the light transmitted from the POF gives rise to a change in output voltage in the fiber-optic goniometer. Therefore, we measured the real-time output voltage of the proposed fiber-optic goniometer using the unclad fiber according to the knee joint angle. Through the repeated experiments, the fiber-optic goniometer shows that it has a reversibility and a wide measurable angle range.

• Journal of Biomedical Engineering Research
• /
• v.11 no.1
• /
• pp.171-178
• /
• 1990

This paper describes a software algorithm for automatic diagnosis of arrhythmia using the criteria of Minnesota code manual. This algorithm provides more accurate and more objective information to medical doctor by standardizing the criteria of diagnosis of arrhythmia. Because this algorithm doesn't need complicated mathematic processing, it carries out the real-time automatic diagnosis that is very important in clinic. The Decision-Table technology suggests the proper results for the given conditions. So it can express clearly the complicated medical problems those are not solved by the mathematical methods. The Decision-Tables have very simple structure. Therefore, it is very easy to correct or expand the system by adding or correcting some rules.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.12
• /
• pp.131-136
• /
• 2018

This paper proposes a computer-aided diagnostic algorithm in a non-invasive way. Currently, clinical diagnosis of jaundice is performed through blood sampling. Unlike the old methods, the non-invasive method will enable parents to measure newborns' jaundice by only using their mobile phones. The proposed algorithm enables high accuracy and quick diagnosis through machine learning. In here, we used the SVM model of machine learning that learned the feature extracted through image preprocessing and we used the international jaundice research data as the test data set. As a result of applying our developed algorithm, it took about 5 seconds to diagnose jaundice and it showed a 93.4% prediction accuracy. The software is real-time diagnosed and it minimizes the infant's pain by non-invasive method and parents can easily and temporarily diagnose newborns' jaundice. In the future, we aim to use the jaundice photograph of the newborn babies' data as our test data set for more accurate results.

• Journal of Biomedical Engineering Research
• /
• v.42 no.5
• /
• pp.201-210
• /
• 2021

In Korean medicine, tongue diagnosis is one of the important diagnostic methods for diagnosing abnormalities in the body. Representative features that are used in the tongue diagnosis include color, shape, texture, cracks, and tooth marks. When diagnosing a patient through these features, the diagnosis criteria may be different for each oriental medical doctor, and even the same person may have different diagnosis results depending on time and work environment. In order to overcome this problem, recent studies to automate and standardize tongue diagnosis using machine learning are continuing and the basic process of such a machine learning-based tongue diagnosis system is tongue segmentation. In this paper, image data is augmented based on the main tongue features, and backbones of various famous deep learning architecture models are used for automatic tongue segmentation. The experimental results show that the proposed augmentation technique improves the accuracy of tongue segmentation, and that automatic tongue segmentation can be performed with a high accuracy of 99.12%.

• Journal of Biomedical Engineering Research
• /
• v.36 no.5
• /
• pp.143-149
• /
• 2015

In this paper, we proposed a novel method for automatic detection for snoring and heart beat using a single piezoelectric sensor. For this study multi-rate signal processing technique was applied to detect snoring and heart beat from the single source signal. The sound event duration and intensity features were used to snore detection and heart beat was found by autocorrelation. The performance of the proposed method was evaluated on clinical database, which is the nocturnal piezoelectric snoring data of 30 patients that suffered obstructive sleep apnea. The method achieved sensitivity of 88.6%, specificity of 96.1% with accuracy of 95.6% for snoring and sensitivity of 94.1% and positive predictive value of 87.6% for heart beat, respectively. These results suggest that the proposed method can be a useful tool in sleep monitoring and sleep disordered breathing diagnosis.

• Journal of Biomedical Engineering Research
• /
• v.29 no.4
• /
• pp.329-336
• /
• 2008

Recently, causality analysis of source time series extracted from EEG or MEG signals is becoming of great importance in human brain mapping studies and noninvasive diagnosis of various brain diseases. Two approaches have been widely used for the analyses: one is independent component analysis (ICA), and the other is multiple signal classification (MUSIC). To the best of our knowledge, however, any comparison studies to reveal the difference of the two approaches have not been reported. In the present study, we compared the performance of the two different techniques, ICA and MUSIC, especially focusing on how accurately they can estimate and separate various brain electrical signals such as linear, nonlinear, and chaotic signals without a priori knowledge. Results of the realistic simulation studies, adopting directed transfer function (DTF) and Granger causality (GC) as measures of the accurate extraction of source time series, demonstrated that the MUSIC-based approach is more reliable than the ICA-based approach.

• Journal of Biomedical Engineering Research
• /
• v.39 no.4
• /
• pp.147-152
• /
• 2018

Lung cancer and pulmonary metastasis are the leading cause of cancer mortality worldwide. Survival for patients with lung metastases is about 5%. Nanoparticles have been developed for the imaging and treatment of various cancers, including pulmonary malignancies. In this work, we report lipid coated polymeric nanoparticles (LPNs) with an average diameter of 154 nm. In vivo performance of LPNs was characterized using optical imaging system. We expect this nanoparticle can be used for finding lung cancer or lung metastasis. Eventually loading therapeutic drug with the nanoparticle will be utilized for cancer diagnosis and effective therapy at the same time.

• Clinical Endoscopy
• /
• v.51 no.6
• /
• pp.587-590
• /
• 2018

Schwannomas are uncommon neoplasms that arise from Schwann cells of the neural sheath. Gastrointestinal schwannomas are rare among mesenchymal tumors of the gastrointestinal tract, and only a few cases have been reported to date. Duodenal schwannomas are usually discovered incidentally and achieving a preoperative diagnosis is difficult. Schwannomas can be distinguished from other subepithelial tumors on endoscopic ultrasonography; however, any typical endosonographic features of duodenal schwannomas have not been reported due to the rarity of these tumors. Immunohistochemistry is essential to distinguish schwannomas from gastrointestinal stromal tumors and leiomyomas. We report a case of duodenal schwannoma found incidentally during a health checkup endoscopy. On endoscopic ultrasonography, this tumor was suspected as a gastrointestinal stromal tumor; therefore, the patient underwent laparoscopic wedge resection of the tumor. Histopathology and immunohistochemistry confirmed that the duodenal lesion was a benign schwannoma.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.16 no.6
• /
• pp.526-532
• /
• 2023

Falls are fatal accidents that occur more than 420,000 times a year worldwide. Therefore, to study patients with falls, we found the association between extrinsic injury codes and principal diagnosis S-codes of patients with falls, and developed a prediction model to predict extrinsic injury codes based on the data of principal diagnosis S-codes of patients with falls. In this study, we received two years of data from 2020 and 2021 from Institution A, located in Gangneung City, Gangwon Special Self-Governing Province, and extracted only the data from W00 to W19 of the extrinsic injury codes related to falls, and developed a prediction model using W01, W10, W13, and W18 of the extrinsic injury codes of falls, which had enough principal diagnosis S-codes to develop a prediction model. 80% of the data were categorized as training data and 20% as testing data. The model was developed using MLP (Multi-Layer Perceptron) with 6 variables (gender, age, principal diagnosis S-code, surgery, hospitalization, and alcohol consumption) in the input layer, 2 hidden layers with 64 nodes, and an output layer with 4 nodes for W01, W10, W13, and W18 exogenous damage codes using the softmax activation function. As a result of the training, the first training had an accuracy of 31.2%, but the 30th training had an accuracy of 87.5%, which confirmed the association between the fall extrinsic code and the main diagnosis S code of the fall patient.