ABSTRACT
Treatment options for patients with chronic kidney disease (CKD) are currently limited; therefore, there has been significant interest in applying mesenchymal stem/stromal cell (MSC)-based therapy to treat CKD. However, MSCs harvested from CKD patients tend to show diminished viability and proliferation due to sustained exposure to uremic toxins in the CKD environment, which limits their utility for cell therapy. The application of melatonin has been demonstrated to improve the therapeutic efficacy of MSCs derived from and engrafted to tissues in patients suffering from CKD, although the underlying biological mechanism has not been elucidated. In this study, we observed overexpression of hexokinase-2 (HK2) in serum samples of CKD patients and MSCs harvested from an adenine-fed CKD mouse model (CKD-mMSCs). HK2 upregulation led to increased production levels of methylglyoxal (MG), a toxic metabolic intermediate of abnormal glycolytic processes. The overabundance of HK2 and MG was associated with impaired mitochondrial function and low cell proliferation in CKD-mMSCs. Melatonin treatment inhibited the increases in HK2 and MG levels, and further improved mitochondrial function, glycolytic metabolism, and cell proliferation. Our findings suggest that identifying and characterizing metabolic regulators such as HK2 in CKD may improve the efficacy of MSCs for treating CKD and other kidney disorders.
ABSTRACT
Objectives@#This study aimed to identify the relationship between mental health problems and oral health in older adults. @*Methods@#The participants of this study were older adults aged 65 years or older. The study used the data of 16,489 people who responded to the 7th Korean National Health and Nutrition Examination Survey. Multiple logistic regression analysis was performed to evaluate the effect of depression on the frequency of tooth brushing when confounding factors such as income quintile and smoking were considered. Statistical software, SAS 9.4 ver. (SAS Institute, Cary, NC) was used. @*Results@#Depressed older adult participants were 1.3 times more likely to brush their teeth less than three times a day than non-depressed participants, which was statistically significant. In women with depression, the odds of brushing their teeth less than three times a day were 1.5 times higher than those without depression, which was statistically significant. @*Conclusions@#Depression in older adults is correlated with the number of tooth brushes per day. Moreover, depression in women affects their number of tooth brushes.
ABSTRACT
Objective@#To evaluate the accuracy of a deep learning-based automated segmentation of the left ventricle (LV) myocardium using cardiac CT. @*Materials and Methods@#To develop a fully automated algorithm, 100 subjects with coronary artery disease were randomly selected as a development set (50 training / 20 validation / 30 internal test). An experienced cardiac radiologist generated the manual segmentation of the development set. The trained model was evaluated using 1000 validation set generated by an experienced technician. Visual assessment was performed to compare the manual and automatic segmentations. In a quantitative analysis, sensitivity and specificity were calculated according to the number of pixels where two three-dimensional masks of the manual and deep learning segmentations overlapped. Similarity indices, such as the Dice similarity coefficient (DSC), were used to evaluate the margin of each segmented masks. @*Results@#The sensitivity and specificity of automated segmentation for each segment (1–16 segments) were high (85.5– 100.0%). The DSC was 88.3 ± 6.2%. Among randomly selected 100 cases, all manual segmentation and deep learning masks for visual analysis were classified as very accurate to mostly accurate and there were no inaccurate cases (manual vs. deep learning: very accurate, 31 vs. 53; accurate, 64 vs. 39; mostly accurate, 15 vs. 8). The number of very accurate cases for deep learning masks was greater than that for manually segmented masks. @*Conclusion@#We present deep learning-based automatic segmentation of the LV myocardium and the results are comparable to manual segmentation data with high sensitivity, specificity, and high similarity scores.
ABSTRACT
OBJECTIVE: The aim of our study was to develop and validate a convolutional neural network (CNN) architecture to convert CT images reconstructed with one kernel to images with different reconstruction kernels without using a sinogram. MATERIALS AND METHODS: This retrospective study was approved by the Institutional Review Board. Ten chest CT scans were performed and reconstructed with the B10f, B30f, B50f, and B70f kernels. The dataset was divided into six, two, and two examinations for training, validation, and testing, respectively. We constructed a CNN architecture consisting of six convolutional layers, each with a 3 × 3 kernel with 64 filter banks. Quantitative performance was evaluated using root mean square error (RMSE) values. To validate clinical use, image conversion was conducted on 30 additional chest CT scans reconstructed with the B30f and B50f kernels. The influence of image conversion on emphysema quantification was assessed with Bland–Altman plots. RESULTS: Our scheme rapidly generated conversion results at the rate of 0.065 s/slice. Substantial reduction in RMSE was observed in the converted images in comparison with the original images with different kernels (mean reduction, 65.7%; range, 29.5–82.2%). The mean emphysema indices for B30f, B50f, converted B30f, and converted B50f were 5.4 ± 7.2%, 15.3 ± 7.2%, 5.9 ± 7.3%, and 16.8 ± 7.5%, respectively. The 95% limits of agreement between B30f and other kernels (B50f and converted B30f) ranged from −14.1% to −2.6% (mean, −8.3%) and −2.3% to 0.7% (mean, −0.8%), respectively. CONCLUSION: CNN-based CT kernel conversion shows adequate performance with high accuracy and speed, indicating its potential clinical use.