Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

Objective: This study hypothesized that physical status, temperament and characteristics, and neurocognitive functions of basketball players could predict the result of Korean Basketball League (KBL) draft selection. Methods: We recruited the number of 89 college elite basketball players (KBL selection, n=44; non-KBL selection, n=45), and the number of 82 age-matched healthy comparison subjects who major in sports education in college. All participants were assessed with the Temperament and Character Inventory, Sports Anxiety Scales, Beck Depression Inventory, Perceived Stress Scale-10, Trail Making Test, and Computerized Neuro-cognitive Test for Emotional Perception and Mental Rotation. Results: Current results showed that physical status, temperament and characteristics, and neurocognitive functions of college basketball players could predict the KBL draft selection. Among temperament and characteristics, novelty seeking and reward dependence were associated with KBL draft selection. The basketball performances including average scores and average rebound were associated with Emotional Perception and Mental Rotation. Conclusion In order to be a good basketball player for a long time, it was confirmed that temperamental factors and neurocognitive factors were very closely related. Furthermore, it is also judged that these results can be used as basic data to predict potential professional basketball players.

PURPOSE: This study aimed to evaluate the accuracy of static computer-assisted implant surgery (s-CAIS) by comparing the planned and actual positions of implants placed in a partially edentulous model using surgical templates with different sleeve designs. MATERIALS AND METHODS: Forty-nine mandibular partially edentulous models were scanned using a model scanner to create standard tessellation language (STL) files. The models were divided into seven groups based on sleeve design: Group 1–4 (Open non-metal sleeves with varying heights (3 mm, 6 mm) and buccal widths (5 mm, 6 mm)), Group 5–6 (Closed non-metal sleeves with heights of 3 mm and 6 mm), and Group 7 (Closed metal sleeve). After implant placement at #45 and #47, STL files were generated and analyzed using 3D measurement software. Deviations were measured in terms of horizontal deviation (coronal and apical), vertical deviation, and angular deviation. Statistical significance was evaluated using the Wilcoxon rank sum test, Kruskal-Wallis test, and multiple generalized linear models. RESULTS: While height differences showed no significant deviations (P > .05), there was a tendency for larger apical deviation with increased sleeve height (P < .1). Closed metal and closed non-metal sleeves showed smaller deviations compared to open non-metal sleeves, except in vertical deviation. CONCLUSION Closed sleeves demonstrated greater accuracy in s-CAIS compared to open sleeves. Higher sleeves may improve implant placement accuracy, but buccal insertion width did not significantly affect accuracy.

Objective: Extraction of the mandibular second molar (L7) and substitution by the mandibular third molar (L8) is an effective treatment option. This study aimed to evaluate spontaneously erupted L8 occlusion after L7 extraction, and identify the influencing factors. Methods: This study assessed 46 L8 from 28 patients using dental study models, panoramic radiographs, and lateral cephalograms obtained during L7 extraction (T1) and completion of L8 eruption (T2). At T2, samples were categorized as acceptable (A-group) or unacceptable (U-group) based on the American Board of Orthodontics index.L8 angulation and position, retromolar space, distance between the Xi point and mandibular first molar (L6), and Nolla stage were compared between the groups to identify the predictive factors for successful eruption. Results: At T2, 58.7% of L8 exhibited acceptable occlusion. Age at T1 was significantly higher in the U-group than that in the A-group. Angles ∠6-MnP and ∠8-MnP differed significantly between the groups at T2. Xi-L6 distance was considerably longer in the A-group than that in the U-group at T1 and T2. Younger age at extraction and Xi-L6 distance at T1 affected the acceptable occlusion. Conclusions Younger age at L7 extraction and adequate eruption space (Xi-L6 distance) appear to be the key factors for achieving acceptable L8 occlusion.

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Purpose: Obesity and metabolic syndrome are acknowledged as key factors contributing to the development of non-alcoholic fatty liver disease (NAFLD). Vitamin D (VitD) is a multifaceted secosteroid hormone known for its anti-fibrotic and anti-inflammatory properties, with its deficiency often linked to obesity. Our study aimed to investigate whether VitD supplementation could mitigate the liver pathology associated with NAFLD. Materials and Methods: The NAFLD model was developed by subjecting male C57BL/6 mice to a high-fat diet (HFD) for 14 weeks.These mice were supplemented with VitD through intraperitoneal injection at a dosage of 7 μg/kg, administered three times per week for 7 weeks. Results: HFD resulted in VitD deficiency, insulin resistance, and increased liver weight. It elevated serum levels of liver aminotransferases and triglyceride, ultimately leading to steatohepatitis with fibrosis. This model exhibited increased levels of transforming growth factor (TGF)-β1, pro-inflammatory cytokines, HNF4α transcription factors, reactive oxygen species (ROS), renin-angiotensin system activity, and epithelial-mesenchymal transitions (EMT) within the liver. Supplementation with VitD resulted in the recovery of liver weight, improvement in histologic features associated with steatohepatitis, and reduction in alanine aminotransferases and triglyceride levels induced by the HFD. Additionally, it mitigated the HFD-induced over-expressions of TGF-β1 and fibrosis-related genes, along with pro-inflammatory cytokines and ROS. Notably, no adverse effect was found due to VitD supplementation in this model. Conclusion VitD ameliorates steatohepatitis within obesity-induced NAFLD through its multifaceted pathways. VitD supplementation emerges as a potentially safe, cost-effective, and direct treatment approach for NAFLD patients dealing with obesity or metabolic dysfunction.

Objective: This study hypothesized that physical status, temperament and characteristics, and neurocognitive functions of basketball players could predict the result of Korean Basketball League (KBL) draft selection. Methods: We recruited the number of 89 college elite basketball players (KBL selection, n=44; non-KBL selection, n=45), and the number of 82 age-matched healthy comparison subjects who major in sports education in college. All participants were assessed with the Temperament and Character Inventory, Sports Anxiety Scales, Beck Depression Inventory, Perceived Stress Scale-10, Trail Making Test, and Computerized Neuro-cognitive Test for Emotional Perception and Mental Rotation. Results: Current results showed that physical status, temperament and characteristics, and neurocognitive functions of college basketball players could predict the KBL draft selection. Among temperament and characteristics, novelty seeking and reward dependence were associated with KBL draft selection. The basketball performances including average scores and average rebound were associated with Emotional Perception and Mental Rotation. Conclusion In order to be a good basketball player for a long time, it was confirmed that temperamental factors and neurocognitive factors were very closely related. Furthermore, it is also judged that these results can be used as basic data to predict potential professional basketball players.

PURPOSE: This study aimed to evaluate the accuracy of static computer-assisted implant surgery (s-CAIS) by comparing the planned and actual positions of implants placed in a partially edentulous model using surgical templates with different sleeve designs. MATERIALS AND METHODS: Forty-nine mandibular partially edentulous models were scanned using a model scanner to create standard tessellation language (STL) files. The models were divided into seven groups based on sleeve design: Group 1–4 (Open non-metal sleeves with varying heights (3 mm, 6 mm) and buccal widths (5 mm, 6 mm)), Group 5–6 (Closed non-metal sleeves with heights of 3 mm and 6 mm), and Group 7 (Closed metal sleeve). After implant placement at #45 and #47, STL files were generated and analyzed using 3D measurement software. Deviations were measured in terms of horizontal deviation (coronal and apical), vertical deviation, and angular deviation. Statistical significance was evaluated using the Wilcoxon rank sum test, Kruskal-Wallis test, and multiple generalized linear models. RESULTS: While height differences showed no significant deviations (P > .05), there was a tendency for larger apical deviation with increased sleeve height (P < .1). Closed metal and closed non-metal sleeves showed smaller deviations compared to open non-metal sleeves, except in vertical deviation. CONCLUSION Closed sleeves demonstrated greater accuracy in s-CAIS compared to open sleeves. Higher sleeves may improve implant placement accuracy, but buccal insertion width did not significantly affect accuracy.

Objective: Extraction of the mandibular second molar (L7) and substitution by the mandibular third molar (L8) is an effective treatment option. This study aimed to evaluate spontaneously erupted L8 occlusion after L7 extraction, and identify the influencing factors. Methods: This study assessed 46 L8 from 28 patients using dental study models, panoramic radiographs, and lateral cephalograms obtained during L7 extraction (T1) and completion of L8 eruption (T2). At T2, samples were categorized as acceptable (A-group) or unacceptable (U-group) based on the American Board of Orthodontics index.L8 angulation and position, retromolar space, distance between the Xi point and mandibular first molar (L6), and Nolla stage were compared between the groups to identify the predictive factors for successful eruption. Results: At T2, 58.7% of L8 exhibited acceptable occlusion. Age at T1 was significantly higher in the U-group than that in the A-group. Angles ∠6-MnP and ∠8-MnP differed significantly between the groups at T2. Xi-L6 distance was considerably longer in the A-group than that in the U-group at T1 and T2. Younger age at extraction and Xi-L6 distance at T1 affected the acceptable occlusion. Conclusions Younger age at L7 extraction and adequate eruption space (Xi-L6 distance) appear to be the key factors for achieving acceptable L8 occlusion.

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)