The rapid development of computer vision and deep learning has enabled these technologies to be applied to the automated classification and counting of microscope images, thereby relieving of some burden from pathologists in terms of performing tedious microscopic examination for analysis of a large number of slides for pathological lesions. Recently, the use of these digital methods has expanded into the field of medical image analysis. In this study, the Inception-v3 deep learning model was used for classification of chondrocytes from knee joints of rats. Knee joints were extracted, fixed in neutral buffered formalin, decalcified, processed and embedded in paraffin, and hematoxylin and eosin (H&E) stained. The H&E stained slides were converted into whole slide imaging (WSI), and the images were cropped to 79 × 79 pixels. The images were divided into training (60.42%) and test (39.58%) sets (46,349 and 30,360 images, respectively). Then, images containing chondrocytes were classified by Inception-v3 and accuracy was calculated. We visualized the images containing chondrocytes in WSIs by adding colored dots to patches. When images of chondrocytes in knee joints were evaluated, the accuracy was within the range of 91.20 ± 8.43%. Therefore, it is considered that the Inception-v3 deep learning model was able to distinguish chondrocytes from non-chondrocytes in knee joints of rats with a relatively high accuracy. The above results taken together confirmed that this deep learning model could classify the chondrocytes and this promising approach will provide pathologists a fast and accurate analysis of diverse tissue structures.
Mercury and its compounds are globally managed chemicals as risks to the human body and ecosystem. It mainly enters the human body through ingestion of seafood, especially, methylmercury causes serious diseases such as central nervous system (CNS) disorder and renal dysfunction. In this study, total mercury (Hg) and methyl mercury (MeHg) were determined in seafood (16 species, n = 97) commonly consumed in Daejeon, using a gold amalgamation method. The average concentration (Hg/MeHg mean ± S.D. (minimum–maximun) mg/kg) of total Hg and MeHg in the samples was as follows; Fish 0.038 ± 0.058 (0.004 – 0.272) / 0.028 ± 0.047 (N.D. – 0.236), Crustacea 0.023 ± 0.021 (0.003 – 0.078) / 0.016 ± 0.018 (N.D. – 0.055), Mollusks 0.015 ± 0.015 (0.002 – 0.056) / 0.008 ± 0.013 (N.D. – 0.040). The concentration of MeHg in seafood were significantly correlated with total Hg concentration (p<0.001). The species with the highest average concentration of Hg was the Korean rockfish, but there was no sample that exceeded the maximum residual limit. The total %provisional tolerable weekly intake (%PTWI) value of MeHg for all of the samples was 3.76%, compared with the JECFA’s reference value, which indicates that there is almost no health risk from heavy MeHg intake through the consumption of seafood distributed in Daejeon.
Periodontal disease is a chronic but treatable condition which often does not cause pain during the initial stages of the illness. Lack of awareness of symptoms can delay initiation of treatment and worsen health. The aim of this study was to develop and compare different risk prediction models for periodontal disease using machine learning algorithms. We obtained information on risk factors for periodontal disease from the Korea National Health and Nutrition Examination Survey (KNHANES) dataset. Principal component analysis and an auto-encoder were used to extract data on risk factors for periodontal disease. A synthetic minority oversampling technique algorithm was used to solve the problem of data imbalance. We used a combination of logistic regression analysis, support vector machine (SVM) learning, random forest, and AdaBoost to classify and compare risk prediction models for periodontal disease. In cases where we used principal component analysis (PCA) to extract risk factors, the recall was higher than the feature selection method in the logistic regression and support-vector machine learning models. AdaBoost’s recall was 0.98, showing the highest performance of both feature selection and PCA. The F1 score showed relatively high performance in Ada- Boost, logistic regression, and SVM learning models. By using the risk factors extracted from the research results and the predictive model based on machine learning, it will be able to help in the prevention and diagnosis of periodontal disease, and it will be used to study the relationship with various diseases related to periodontal disease.
Coccidiosis is caused by infection of Eimeria species and an significant parasitic disease in poultry. Various kinds of natural products have been studied to find alternative treatments for coccidiosis in chickens, but the effect of Houttuynia cordata on Eimeria infection has not been investigated. The aim of this study is to study the anticoccidial effect of H. cordata extract (HCE) in chickens after oral infection by Eimeria tenella. Anticoccidial effects of the HCE was evaluated in chickens after oral infection with E. tenella. This study was performed on threeday- old chicks (n = 30). These animals were divided into 3 groups; HCE 0.2% treated/infected (n = 10), HCE untreated/infected (n = 10) and non-infected control (n = 10). The effect of HCE on E. tenella infection was assessed by two parameters; fecal oocysts shedding and body weights gain. the chicks fed HCE significantly reduced fecal oocysts when compared to the E. tenella-infected group fed standard diets (p<0.05). Furthermore, the HCE-based diet improved weight loss due to E. tenella infection. Our data shows that HCE had significant antiprotozoal activity against E. tenella. These findings may have implications for the development of anticoccidial drugs.