Pediatric kidney disease has a relatively lower prevalence than do other pediatric conditions and has a notably different etiology from kidney diseases observed in adults. Furthermore, the pediatric population is unique in that they experience ongoing growth and development, distinguishing them from adult patients. Consequently, pediatric patients with kidney disease require more specialized and meticulous nutritional management than do adults. To address this need and promote optimal dietary practices for pediatric patients with kidney disease, pediatric nephrologists from the Korean Society of Pediatric Nephrology and nutritionists from the Korean Society of Clinical Nutrition have collaborated to establish nutritional guidelines specifically tailored to Korean dietary patterns. These guidelines offer detailed, nutrient-specific recommendations covering energy, protein, calcium, phosphorus, and potassium consumption while providing practical, culturally relevant guidance intended to support both pediatric patients and their caregivers.

This study investigated the clinical characteristics and kidney outcomes of childhood-onset lupus nephritis (LN), and risk factors associated with prognosis. Methods: We enrolled 216 patients with histologically diagnosed LN during childhood. The Korean Society of Pediatric Nephrology organized a retrospective cohort study of childhood-onset LN in 13 major pediatric nephrology centers in South Korea. Results: The mean age at kidney biopsy was 13.2 ± 3.22 years. The main forms of presentation were nephrotic syndrome and/or hematuria in 152 patients (70.4%), and the most common histological finding was World Health Organization (WHO) class IV in 138 patients (63.9%), followed by WHO class III in 34 patients (15.7%). In the outcome analysis, the mean follow-up period of the patients was 7.8 ± 5.11 years. At last follow-up, 32 patients (14.8%) developed advanced chronic kidney disease (CKD). Male sex and failure to achieve remission at 12 months of treatment were significant risk factors for developing advanced CKD (hazard ratio of 2.57 and 2.29, respectively). Conclusion: Our study demonstrated the clinical characteristics and long-term outcomes of patients with childhood-onset LN. Male sex and failure to achieve remission in the first year of treatment were predictive of advanced CKD. Therefore, prompt awareness and close monitoring of these high-risk patients are needed, which may further improve the prognosis of children with LN.

Pediatric kidney disease has a relatively lower prevalence than do other pediatric conditions and has a notably different etiology from kidney diseases observed in adults. Furthermore, the pediatric population is unique in that they experience ongoing growth and development, distinguishing them from adult patients. Consequently, pediatric patients with kidney disease require more specialized and meticulous nutritional management than do adults. To address this need and promote optimal dietary practices for pediatric patients with kidney disease, pediatric nephrologists from the Korean Society of Pediatric Nephrology and nutritionists from the Korean Society of Clinical Nutrition have collaborated to establish nutritional guidelines specifically tailored to Korean dietary patterns. These guidelines offer detailed, nutrient-specific recommendations covering energy, protein, calcium, phosphorus, and potassium consumption while providing practical, culturally relevant guidance intended to support both pediatric patients and their caregivers.

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.

This article provides a comprehensive examination of Bertolotti syndrome (BS), a disorder characterized by back pain due to a lumbosacral transitional vertebra, to facilitate surgical decision-making by exploring various surgical options, including the innovative approach of endoscopic spine surgery. A review of existing literature and studies on BS published until December 2023 was undertaken, utilizing databases such as PubMed and Google Scholar to identify relevant information. The review offers an integrated overview of the essential knowledge of BS and a comprehensive range of surgical treatments. Symptomatic BS can manifest as pain originating from pseudoarticulation and the facet joints, discs, adjacent segments, and the L5 root, indicating a diverse distribution of pain sources. Furthermore, various surgical strategies are tailored to the specific origin of pain, including pseudoarticulation resection, transverse processectomy, decompression, nerve root decompression, fusion, and endoscopic spine surgery. For individuals with BS contemplating surgical solutions, performing a detailed assessment of symptoms and physical evaluations is imperative to accurately identify the origin of the pain. The choice of a surgical strategy must be meticulously customized according to the identified source of pain, guaranteeing a tailored and efficacious treatment for each patient.

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.

Background: Chronic kidney disease (CKD) has a negative impact on growth and development in children and is a risk factor for neurocognitive impairment; however, there is limited research on the cognitive function of children and adolescents with CKD. This study therefore aimed to investigate the mean intelligence and risk factors for low intelligence in children and adolescents with CKD. Methods: Eighty-one patients with CKD under 18 years old were included in the KoreaN cohort study for Outcomes in patients With Pediatric Chronic Kidney Disease (KNOW-Ped CKD). Participants completed either the Wechsler Intelligence Scale for Children (6–16 years), or Wechsler Adult Intelligence Scale (> 16 years). Results: The mean full-scale intelligence quotient (IQ) was 91 ± 19; 24.7% of participants scored a full-scale IQ below 80. Participants with a short stature (height Z scores < −1.88), failure to thrive (weight Z scores < −1.65), more severe CKD stage (≥ IIIb), longer duration of CKD (≥ 5 years), and those who were Medicare or Medicaid beneficiaries, had significantly lower mean full-scale IQs. Conclusion On linear regression analysis, the association between the full-scale IQ, and longer duration of CKD and growth failure, remained significant after controlling for demographic and clinical variables. It is therefore necessary to investigate cognitive impairment in pediatric patients with CKD who exhibit growth failure or for a longer postmorbid period. It is believed that early interventions, such as kidney transplantation, will have a positive effect on IQ in children with CKD, as the disease negatively affects IQ due to poor glomerular filtration rate over time.

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.

Background: Chronic kidney disease (CKD) has a negative impact on growth and development in children and is a risk factor for neurocognitive impairment; however, there is limited research on the cognitive function of children and adolescents with CKD. This study therefore aimed to investigate the mean intelligence and risk factors for low intelligence in children and adolescents with CKD. Methods: Eighty-one patients with CKD under 18 years old were included in the KoreaN cohort study for Outcomes in patients With Pediatric Chronic Kidney Disease (KNOW-Ped CKD). Participants completed either the Wechsler Intelligence Scale for Children (6–16 years), or Wechsler Adult Intelligence Scale (> 16 years). Results: The mean full-scale intelligence quotient (IQ) was 91 ± 19; 24.7% of participants scored a full-scale IQ below 80. Participants with a short stature (height Z scores < −1.88), failure to thrive (weight Z scores < −1.65), more severe CKD stage (≥ IIIb), longer duration of CKD (≥ 5 years), and those who were Medicare or Medicaid beneficiaries, had significantly lower mean full-scale IQs. Conclusion On linear regression analysis, the association between the full-scale IQ, and longer duration of CKD and growth failure, remained significant after controlling for demographic and clinical variables. It is therefore necessary to investigate cognitive impairment in pediatric patients with CKD who exhibit growth failure or for a longer postmorbid period. It is believed that early interventions, such as kidney transplantation, will have a positive effect on IQ in children with CKD, as the disease negatively affects IQ due to poor glomerular filtration rate over time.

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.