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.

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.

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.

Purpose: Angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers (ARBs) are frequently employed to counteract the detrimental effects of proteinuria on glomerular diseases. However, the effects of ARBs remain poorly examined in pediatric patients with immunoglobulin A (IgA) nephropathy. Herein, we evaluated the efficacy and safety of losartan, an ARB, in pediatric IgA nephropathy with proteinuria. Methods: This prospective, single-arm, multicenter study included children with IgA nephropathy exhibiting proteinuria. Changes in proteinuria, blood pressure, and kidney function were prospectively evaluated before and 4 and 24 weeks after losartan administration. The primary endpoint was the difference in proteinuria between baseline and 24 weeks. Results: In total, 29 patients were enrolled and received losartan treatment. The full analysis set included 28 patients who received losartan at least once and had pre- and post-urinary protein to creatinine ratio measurements (n=28). The per-protocol analysis group included 22 patients who completed all scheduled visits without any serious violations during the study period. In both groups, the mean log (urine protein to creatinine ratio) value decreased significantly at 6 months. After 24 weeks, the urinary protein to creatinine ratio decreased by more than 50% in approximately 40% of the patients. The glomerular filtration rate was not significantly altered during the observation period. Conclusions Losartan decreased proteinuria without decreasing kidney function in patients with IgA nephropathy over 24 weeks. Losartan could be safely employed to reduce proteinuria in this patient population. ClinicalTrials.gov trial registration (NCT0223277)

Background: and Purpose We aimed to develop a model predicting early recanalization after intravenous tissue plasminogen activator (t-PA) treatment in large-vessel occlusion. Methods: Using data from two different multicenter prospective cohorts, we determined the factors associated with early recanalization immediately after t-PA in stroke patients with large-vessel occlusion, and developed and validated a prediction model for early recanalization. Clot volume was semiautomatically measured on thin-section computed tomography using software, and the degree of collaterals was determined using the Tan score. Follow-up angiographic studies were performed immediately after t-PA treatment to assess early recanalization. Results: Early recanalization, assessed 61.0±44.7 minutes after t-PA bolus, was achieved in 15.5% (15/97) in the derivation cohort and in 10.5% (8/76) in the validation cohort. Clot volume (odds ratio [OR], 0.979; 95% confidence interval [CI], 0.961 to 0.997; P=0.020) and good collaterals (OR, 6.129; 95% CI, 1.592 to 23.594; P=0.008) were significant factors associated with early recanalization. The area under the curve (AUC) of the model including clot volume was 0.819 (95% CI, 0.720 to 0.917) and 0.842 (95% CI, 0.746 to 0.938) in the derivation and validation cohorts, respectively. The AUC improved when good collaterals were added (derivation cohort: AUC, 0.876; 95% CI, 0.802 to 0.950; P=0.164; validation cohort: AUC, 0.949; 95% CI, 0.886 to 1.000; P=0.036). The integrated discrimination improvement also showed significantly improved prediction (0.097; 95% CI, 0.009 to 0.185; P=0.032). Conclusions The model using clot volume and collaterals predicted early recanalization after intravenous t-PA and had a high performance. This model may aid in determining the recanalization treatment strategy in stroke patients with large-vessel occlusion.