This study examined the transverse relationship between the maxilla and mandible in children with mixed dentition. The study focused on the primary second molar and the permanent first molar in relation to the anteroposterior skeletal patterns using cone-beam computed tomography (CBCT). A total of 102 patients from the Pediatric Dentistry Department at Pusan Dental Hospital were classified into three skeletal groups (Class I, Ⅱ, Ⅲ) based on the ANB angle (angle formed by A-point-nasion-B-point). CBCT scans were analyzed to assess the transverse dimensions of basal bone and dento-alveolar measurements. The results showed that Class Ⅲ patients exhibited a significantly narrower maxillary basal bone compared with Class I and Ⅱ patients. The mandibular basal width was not significantly different between the classes. For primary second molars, Class Ⅲ patients showed significantly narrower maxillary alveolar bone width at the root bifurcation level (51.7 ± 2.5 mm, p < 0.05) compared with Class I and II patients. For permanent first molars, the maxillary occlusal fossa distance was smallest in Class II (45.4 ± 2.4 mm, p < 0.05), and was significantly different from Class I and Class Ⅲ. Maxillary first molar inclination was more lingually inclined in Class Ⅱ patients (99.0 ± 4.2°, p < 0.05) compared with Class Ⅲ patients, whereas Class Ⅲ patients exhibited more buccal inclination. This study highlights the correlation between transverse discrepancies and anteroposterior skeletal classifications, with Class Ⅲ showing a narrower maxillary base and Class Ⅱ patients presenting greater lingual compensation. These findings may aid pediatric dentists in diagnosing transverse relationships in mixed dentition.

This study examined the transverse relationship between the maxilla and mandible in children with mixed dentition. The study focused on the primary second molar and the permanent first molar in relation to the anteroposterior skeletal patterns using cone-beam computed tomography (CBCT). A total of 102 patients from the Pediatric Dentistry Department at Pusan Dental Hospital were classified into three skeletal groups (Class I, Ⅱ, Ⅲ) based on the ANB angle (angle formed by A-point-nasion-B-point). CBCT scans were analyzed to assess the transverse dimensions of basal bone and dento-alveolar measurements. The results showed that Class Ⅲ patients exhibited a significantly narrower maxillary basal bone compared with Class I and Ⅱ patients. The mandibular basal width was not significantly different between the classes. For primary second molars, Class Ⅲ patients showed significantly narrower maxillary alveolar bone width at the root bifurcation level (51.7 ± 2.5 mm, p < 0.05) compared with Class I and II patients. For permanent first molars, the maxillary occlusal fossa distance was smallest in Class II (45.4 ± 2.4 mm, p < 0.05), and was significantly different from Class I and Class Ⅲ. Maxillary first molar inclination was more lingually inclined in Class Ⅱ patients (99.0 ± 4.2°, p < 0.05) compared with Class Ⅲ patients, whereas Class Ⅲ patients exhibited more buccal inclination. This study highlights the correlation between transverse discrepancies and anteroposterior skeletal classifications, with Class Ⅲ showing a narrower maxillary base and Class Ⅱ patients presenting greater lingual compensation. These findings may aid pediatric dentists in diagnosing transverse relationships in mixed dentition.

This study examined the transverse relationship between the maxilla and mandible in children with mixed dentition. The study focused on the primary second molar and the permanent first molar in relation to the anteroposterior skeletal patterns using cone-beam computed tomography (CBCT). A total of 102 patients from the Pediatric Dentistry Department at Pusan Dental Hospital were classified into three skeletal groups (Class I, Ⅱ, Ⅲ) based on the ANB angle (angle formed by A-point-nasion-B-point). CBCT scans were analyzed to assess the transverse dimensions of basal bone and dento-alveolar measurements. The results showed that Class Ⅲ patients exhibited a significantly narrower maxillary basal bone compared with Class I and Ⅱ patients. The mandibular basal width was not significantly different between the classes. For primary second molars, Class Ⅲ patients showed significantly narrower maxillary alveolar bone width at the root bifurcation level (51.7 ± 2.5 mm, p < 0.05) compared with Class I and II patients. For permanent first molars, the maxillary occlusal fossa distance was smallest in Class II (45.4 ± 2.4 mm, p < 0.05), and was significantly different from Class I and Class Ⅲ. Maxillary first molar inclination was more lingually inclined in Class Ⅱ patients (99.0 ± 4.2°, p < 0.05) compared with Class Ⅲ patients, whereas Class Ⅲ patients exhibited more buccal inclination. This study highlights the correlation between transverse discrepancies and anteroposterior skeletal classifications, with Class Ⅲ showing a narrower maxillary base and Class Ⅱ patients presenting greater lingual compensation. These findings may aid pediatric dentists in diagnosing transverse relationships in mixed dentition.

Ischemic stroke is a medical emergency that requires precise diagnosis and prompt treatment. Nonetheless, it is essential to evaluate alternative conditions, such as seizure, peripheral neuropathy and malignancy, with special attention to lymphoma due to its variable clinical manifestations, imaging features, and prognosis. In this report, we present a case of a patient who initially exhibited symptoms and radiological findings considered as lacunar stroke but was ultimately diagnosed with diffuse large B cell lymphoma.

Background: Sleep apnea, characterized by interruptions in breathing during sleep, presents health risks and has been associated with various dietary patterns. This study aimed to investigate the relationship between dietary patterns, as measured by the Recommended Food Score (RFS), and the risk of sleep apnea, as assessed using the Berlin Questionnaire. Methods: A total of 11,586 adults from a general hospital in Korea were included in the analysis. Participants completed a questionnaire covering demographic factors, lifestyle behaviors, and dietary habits. Dietary quality was assessed using the RFS, while sleep apnea risk was evaluated using the Berlin Questionnaire. Logistic regression analysis was conducted to assess the impact of RFS on sleep quality, adjusting for potential confounding factors. Results: Higher RFS was negatively correlated with sleep apnea risk (odds ratio=0.91 [95% confidence interval, 0.82–1.00, P=0.048)]. Significant positive associations were observed between male sex, low education level, marital status, smoking, drinking, inactivity, high blood pressure, high waist circumference, high levels of triglycerides, and poor sleep quality. Hematologic markers such as C-reactive protein levels and glutamic pyruvic transaminase also displayed significant associations with sleep quality. Conclusions This study contributes to our understanding of the complex interplay between diet, lifestyle, metabolic health, and sleep quality. These findings underscore the importance of dietary interventions in managing sleep apnea and highlight avenues for further research.

The purpose of this study was to measure bone age using an artificial intelligence program based on the Greulich-Pyle (GP) method to find out the bone age corresponding to each stage of cervical vertebral maturation (CVM) and the middle phalanx of the third finger (MP3). This study was conducted on 3,118 patients who visited pediatric dentistry at Kyung Hee University Dental Hospital and Pusan National University Dental Hospital from 2013 to 2021. The CVM stage was divided into 5 stages according to the classification by Baccetti, and the MP3 stage was divided into 5 stages according to the methods of Hägg and Taranger. Based on the GP method, bone age was evaluated using an artificial intelligence program. The pubertal growth spurt in the CVM stage was CVM II and III. The mean bone age in CVM II was 11.00 ± 1.81 years for males and 10.00 ± 1.49 years for females, and in CVM III, 13.00 ± 1.46 years for males and 12.00 ± 1.44 years for females (p < 0.0001). The pubertal growth spurt in the MP3 stage was MP3 - G stage. The bone age at the MP3 - G stage was 13.14 ± 1.07 years for males and 11.40 ± 1.09 years for females (p < 0.0001). Bone age evaluation using artificial intelligence is worth using in clinical practice, and it is expected that a faster and more accurate diagnosis will be possible.

Objective: The need to perform genetic sequencing to diagnose the polymerase epsilon exonuclease (POLE) subtype of endometrial cancer (EC) hinders the adoption of molecular classification. We investigated clinicopathologic and protein markers that distinguish the POLE from the copy number (CN)-low subtype in EC. Methods: Ninety-one samples (15 POLE, 76 CN-low) were selected from The Cancer Genome Atlas EC dataset. Clinicopathologic and normalized reverse phase protein array expression data were analyzed for associations with the subtypes. A logistic model including selected markers was constructed by stepwise selection using area under the curve (AUC) from 5-fold cross-validation (CV). The selected markers were validated using immunohistochemistry (IHC) in a separate cohort. Results: Body mass index (BMI) and tumor grade were significantly associated with the POLE subtype. With BMI and tumor grade as covariates, 5 proteins were associated with the EC subtypes. The stepwise selection method identified BMI, cyclin B1, caspase 8, and X-box binding protein 1 (XBP1) as markers distinguishing the POLE from the CN-low subtype. The mean of CV AUC, sensitivity, specificity, and balanced accuracy of the selected model were 0.97, 0.91, 0.87, and 0.89, respectively. IHC validation showed that cyclin B1 expression was significantly higher in the POLE than in the CN-low subtype and receiver operating characteristic curve of cyclin B1 expression in IHC revealed AUC of 0.683. Conclusion BMI and expression of cyclin B1, caspase 8, and XBP1 are candidate markers distinguishing the POLE from the CN-low subtype. Cyclin B1 IHC may replace POLE sequencing in molecular classification of EC.

A survival prediction model has recently been developed to evaluate the prognosis of resected nonmetastatic pancreatic ductal adenocarcinoma based on a Cox model using two nationwide databases: Surveillance, Epidemiology and End Results (SEER) and Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP). In this study, we applied two machine learning methods—random survival forests (RSF) and support vector machines (SVM)—for survival analysis and compared their prediction performance using the SEER and KOTUS-BP datasets. Three schemes were used for model development and evaluation. First, we utilized data from SEER for model development and used data from KOTUS-BP for external evaluation. Second, these two datasets were swapped by taking data from KOTUS-BP for model development and data from SEER for external evaluation. Finally, we mixed these two datasets half and half and utilized the mixed datasets for model development and validation. We used 9,624 patients from SEER and 3,281 patients from KOTUS-BP to construct a prediction model with seven covariates: age, sex, histologic differentiation, adjuvant treatment, resection margin status, and the American Joint Committee on Cancer 8th edition T-stage and N-stage. Comparing the three schemes, the performance of the Cox model, RSF, and SVM was better when using the mixed datasets than when using the unmixed datasets. When using the mixed datasets, the C-index, 1-year, 2-year, and 3-year time-dependent areas under the curve for the Cox model were 0.644, 0.698, 0.680, and 0.687, respectively. The Cox model performed slightly better than RSF and SVM.

Purpose: Diagnostic biomarkers of pancreatic ductal adenocarcinoma (PDAC) have been used for early detection to reduce its dismal survival rate. However, clinically feasible biomarkers are still rare. Therefore, in this study, we developed an automated multi-marker enzyme-linked immunosorbent assay (ELISA) kit using 3 biomarkers (leucine-rich alpha-2-glycoprotein [LRG1], transthyretin [TTR], and CA 19-9) that were previously discovered and proposed a diagnostic model for PDAC based on this kit for clinical usage. Methods: Individual LRG1, TTR, and CA 19-9 panels were combined into a single automated ELISA panel and tested on 728 plasma samples, including PDAC (n = 381) and normal samples (n = 347). The consistency between individual panels of 3 biomarkers and the automated multi-panel ELISA kit were accessed by correlation. The diagnostic model was developed using logistic regression according to the automated ELISA kit to predict the risk of pancreatic cancer (high-, intermediate-, and low-risk groups). Results: The Pearson correlation coefficient of predicted values between the triple-marker automated ELISA panel and the former individual ELISA was 0.865. The proposed model provided reliable prediction results with a positive predictive value of 92.05%, negative predictive value of 90.69%, specificity of 90.69%, and sensitivity of 92.05%, which all simultaneously exceed 90% cutoff value. Conclusion This diagnostic model based on the triple ELISA kit showed better diagnostic performance than previous markers for PDAC. In the future, it needs external validation to be used in the clinic.

Background: A coronavirus disease 2019 (COVID-19) outbreak started in February 2020 and was controlled at the end of March 2020 in Daegu, the epicenter of the coronavirus outbreak in Korea. The aim of this study was to describe the clinical course and outcomes of patients with COVID-19 in Daegu. Methods: In collaboration with Daegu Metropolitan City and Korean Center for Diseases Control, we conducted a retrospective, multicenter cohort study. Demographic, clinical, treatment, and laboratory data, including viral RNA detection, were obtained from the electronic medical records and cohort database and compared between survivors and non-survivors. We used univariate and multi-variable logistic regression methods and Cox regression model and performed Kaplan–Meier analysis to determine the risk factors associated with the 28-day mortality and release from isolation among the patients. Results: In this study, 7,057 laboratory-confirmed patients with COVID-19 (total cohort) who had been diagnosed from February 18 to July 10, 2020 were included. Of the total cohort, 5,467 were asymptomatic to mild patients (77.4%) (asymptomatic 30.6% and mild 46.8%), 985 moderate (14.0%), 380 severe (5.4%), and 225 critical (3.2%). The mortality of the patients was 2.5% (179/7,057). The Cox regression hazard model for the patients with available clinical information (core cohort) (n = 2,254) showed the risk factors for 28-day mortality: age > 70 (hazard ratio [HR], 4.219, P = 0.002), need for O 2 supply at admission (HR, 2.995; P = 0.001), fever (> 37.5°C) (HR, 2.808; P = 0.001), diabetes (HR, 2.119; P = 0.008), cancer (HR, 3.043; P = 0.011), dementia (HR, 5.252; P = 0.008), neurological disease (HR, 2.084; P = 0.039), heart failure (HR, 3.234;P = 0.012), and hypertension (HR, 2.160; P = 0.017). The median duration for release from isolation was 33 days (interquartile range, 24.0–46.0) in survivors. The Cox proportional hazard model for the long duration of isolation included severity, age > 70, and dementia. Conclusion Overall, asymptomatic to mild patients were approximately 77% of the total cohort (asymptomatic, 30.6%). The case fatality rate was 2.5%. Risk factors, including older age, need for O 2 supply, dementia, and neurological disorder at admission, could help clinicians to identify COVID-19 patients with poor prognosis at an early stage.