Ketone bodies produced by sodium-glucose cotransporter 2 (SGLT2) inhibitors can be advantageous, providing an efficient and stable energy source for the brain and muscles. However, in patients with diabetes, ketogenesis induced by SGLT2 inhibitors may be harmful, potentially resulting in severe diabetic ketoacidosis (DKA). During fasting, ketone body production serves as an alternative and efficient energy source for the brain by utilizing stored fat, promoting mental clarity, and reducing dependence on glucose. The concurrent use of SGLT2 inhibitors during perioperative fasting may further elevate the risk of euglycemic DKA. We describe a case of DKA that occurred during perioperative fasting in a patient receiving empagliflozin, an SGLT2 inhibitor. This case underscores the importance of recognizing the potential risk of DKA in patients with diabetes using SGLT2 inhibitors during perioperative fasting.

Ketone bodies produced by sodium-glucose cotransporter 2 (SGLT2) inhibitors can be advantageous, providing an efficient and stable energy source for the brain and muscles. However, in patients with diabetes, ketogenesis induced by SGLT2 inhibitors may be harmful, potentially resulting in severe diabetic ketoacidosis (DKA). During fasting, ketone body production serves as an alternative and efficient energy source for the brain by utilizing stored fat, promoting mental clarity, and reducing dependence on glucose. The concurrent use of SGLT2 inhibitors during perioperative fasting may further elevate the risk of euglycemic DKA. We describe a case of DKA that occurred during perioperative fasting in a patient receiving empagliflozin, an SGLT2 inhibitor. This case underscores the importance of recognizing the potential risk of DKA in patients with diabetes using SGLT2 inhibitors during perioperative fasting.

Ketone bodies produced by sodium-glucose cotransporter 2 (SGLT2) inhibitors can be advantageous, providing an efficient and stable energy source for the brain and muscles. However, in patients with diabetes, ketogenesis induced by SGLT2 inhibitors may be harmful, potentially resulting in severe diabetic ketoacidosis (DKA). During fasting, ketone body production serves as an alternative and efficient energy source for the brain by utilizing stored fat, promoting mental clarity, and reducing dependence on glucose. The concurrent use of SGLT2 inhibitors during perioperative fasting may further elevate the risk of euglycemic DKA. We describe a case of DKA that occurred during perioperative fasting in a patient receiving empagliflozin, an SGLT2 inhibitor. This case underscores the importance of recognizing the potential risk of DKA in patients with diabetes using SGLT2 inhibitors during perioperative fasting.

Ketone bodies produced by sodium-glucose cotransporter 2 (SGLT2) inhibitors can be advantageous, providing an efficient and stable energy source for the brain and muscles. However, in patients with diabetes, ketogenesis induced by SGLT2 inhibitors may be harmful, potentially resulting in severe diabetic ketoacidosis (DKA). During fasting, ketone body production serves as an alternative and efficient energy source for the brain by utilizing stored fat, promoting mental clarity, and reducing dependence on glucose. The concurrent use of SGLT2 inhibitors during perioperative fasting may further elevate the risk of euglycemic DKA. We describe a case of DKA that occurred during perioperative fasting in a patient receiving empagliflozin, an SGLT2 inhibitor. This case underscores the importance of recognizing the potential risk of DKA in patients with diabetes using SGLT2 inhibitors during perioperative fasting.

Purpose: Recently, deep learning techniques have been introduced for age estimation, with automated methods based on radiographic analysis demonstrating high accuracy. In this study, we applied convolutional neural network (CNN) techniques to the lower dentition area on orthopantomograms (OPGs) of children to develop an automated age estimation model and evaluate its accuracy for use in forensic dentistry. Methods: In this study, OPGs of 2,856 subjects aged 3-14 years were analyzed. The You Only Look Once (YOLO) V8 object detection technique was applied to extract the mandibular dentition area on OPGs, designating it as the region of interest (ROI). First, 200 radiographs were randomly selected, and were used to train a model for extracting the ROI. The trained model was then applied to the entire dataset. For the CNN image classification task, 80% of OPGs were allocated to the training set, while the remaining 20% were used as the test set. A transfer learning approach was employed using the ResNet50 and VGG19 backbone models, with an ensemble technique combining these models to improve performance. The mean absolute error (MAE) on the test set was used as the validation metric, and the model with the lowest MAE was selected. Results: In this study, the age estimation model developed using mandibular dentition region from OPGs achieved MAE and root mean squared error (RMSE) values of 0.501 and 0.742, respectively, on the test set, and MAE and RMSE values of 0.273 and 0.354, respectively, on the training set. Conclusions The automated age estimation model developed in this study demonstrated accuracy comparable to that of previous research and shows potential for applications in forensic investigations. Increasing the sample size and incorporating diverse deep learning techniques are expected to further enhance the accuracy of future age estimation models.

As extensive as the concept of and the resources required for ‘Health for Korean Unification’ are, and due to the limited access to information on the state of health and medical care in North Korea, discussion on ‘Health for Korean Unification’ has tended to be intermittent and lacked concrete action plans. In this article, we specifically distinguished areas of cooperation and selected five executable agenda that meet the goals of international development cooperation: 1) Health security; 2) Easing the burden of major diseases; 3) Resilient healthcare system; 4) R&D cooperation; 5) Sustainable cooperation system. Then we provided corresponding strategic priorities and operative directions, in consideration of future military and political sanctions against North Korea. The strategies we outline are sustainable, preemptive for problems that might affect lives of South and North Korean citizens, and satisfy the unmet needs of the North Korean health system. Throughout the process, we utilized a special platform, the ‘Korean Peninsula Healthcare Cooperation Platform,’ designed to enable continual communication across sectors engaged in public health and medical care. By doing so, we take the first step to actually carry out the 'Health for Korean Unification,’ which tended to have remained on the discussion agenda.

Purpose: With the revision of the Organ and Transplantation Act in 2018, the hand has become legal as an area of transplantable organs in Korea. In January 2021, the first hand allotransplantation since legalization was successfully performed, and we have performed a total of three successful hand transplantation since then. By comparing and incorporating our experiences, this study aimed to provide a comprehensive reconstructive solution for hand amputation in Korea. Materials and Methods: Recipients were selected through a structured preoperative evaluation, and hand transplantations were performed at the distal forearm level. Postoperatively, patients were treated with three-drug immunosuppressive regimen, and functional outcomes were monitored. Results: The hand transplantations were performed without intraoperative complications. All patients had partial skin necrosis and underwent additional surgical procedures in 2 months after transplantation. After additional operations, no further severe complications were observed. Also, patients developed acute rejection within 3 months of surgery, but all resolved within 2 weeks after steroid pulse therapy. Motor and sensory function improved dramatically, and patients were very satisfied with the appearance and function of their transplanted hands. Conclusion Hand transplantation is a viable reconstructive option, and patients have shown positive functional and psychological outcomes. Although this study has limitations, such as the small number of patients and short follow-up period, we should focus on continued recovery of hand function, and be careful not to develop side effects from immunosuppressive drugs. Through the present study, we will continue to strive for a bright future regarding hand transplantation in Korea.

In children and adolescents, dental age estimation is performed with the development of the teeth. Various statistical analysis methods have been used to determine the relationship between age and dental maturity and develop an accurate method of age calculation. This study attempted to apply a neural network model for the statistical analysis of dental age estimation in children and evaluated its applicability. This study used 1196 panoramic radiographs of patients aged 3–16 years, and 996 and 200 were randomly classified into training and test sets, respectively. The dental maturity of the mandibular left teeth was evaluated using Demirjian's method, the neural network model using the backpropagation algorithm was derived using training sets, and the errors were evaluated using 100 radiographs of each male and female as test sets. In addition, multiple linear regression analysis was conducted on the same training set, and the error was calculated by applying it to the test set and comparing it with the error of the neural network model. In the neural network model, the mean absolute error (MAE) and root mean squared error (RMSE) were 0.589 and 0.783 in male subjects and 0.529 and 0.760 in female subjects, respectively. In the multiple linear regression model, the MAE and RMSE were 0.600 and 0.748 in male subjects and 0.566 and 0.789 in female subjects, respectively. When applying the neural network model to the statistical analysis of the dental developmental stage, the results were as accurate as those of conventional statistical analysis methods. This study’s approach is expected to be useful for estimating the ages of children.

Objective: To assess whether computed tomography (CT) conversion across different scan parameters and manufacturers using a routable generative adversarial network (RouteGAN) can improve the accuracy and variability in quantifying interstitial lung disease (ILD) using a deep learning-based automated software. Materials and Methods: This study included patients with ILD who underwent thin-section CT. Unmatched CT images obtained using scanners from four manufacturers (vendors A-D), standard- or low-radiation doses, and sharp or medium kernels were classified into groups 1–7 according to acquisition conditions. CT images in groups 2–7 were converted into the target CT sty le (Group 1: vendor A, standard dose, and sharp kernel) using a RouteGAN. ILD was quantified on original and converted CT images using a deep learning-based software (Aview, Coreline Soft). The accuracy of quantification was analyzed using the dice similarity coefficient (DSC) and pixel-wise overlap accuracy metrics against manual quantification by a radiologist. Five radiologists evaluated quantification accuracy using a 10-point visual scoring system. Results: Three hundred and fifty CT slices from 150 patients (mean age: 67.6 ± 10.7 years; 56 females) were included. The overlap accuracies for quantifying total abnormalities in groups 2–7 improved after CT conversion (original vs. converted: 0.63vs. 0.68 for DSC, 0.66 vs. 0.70 for pixel-wise recall, and 0.68 vs. 0.73 for pixel-wise precision; P < 0.002 for all). The DSCs of fibrosis score, honeycombing, and reticulation significantly increased after CT conversion (0.32 vs. 0.64, 0.19 vs. 0.47, and 0.23 vs. 0.54, P < 0.002 for all), whereas those of ground-glass opacity, consolidation, and emphysema did not change significantly or decreased slightly. The radiologists’ scores were significantly higher (P < 0.001) and less variable on converted CT. Conclusion CT conversion using a RouteGAN can improve the accuracy and variability of CT images obtained using different scan parameters and manufacturers in deep learning-based quantification of ILD.