Background: and Purpose The Treat Stroke to Target (TST) was a randomized clinical trial involving French and Korean patients demonstrating that a lower low-density lipoprotein cholesterol (LDL-C, <70 mg/dL) target group (LT) experienced fewer cerebro-cardiovascular events than a higher target (90–110 mg/dL) group (HT). However, whether these results can be applied to Asian patients with different ischemic stroke subtypes remains unclear. Methods: Patients from 14 South Korean centers were analyzed separately. Patients with ischemic stroke or transient ischemic attack with evidence of atherosclerosis were randomized into LT and HT groups. The primary endpoint was a composite of ischemic stroke, myocardial infarction, coronary or cerebral revascularization, and cardiovascular death. Results: Among 712 enrolled patients, the mean LDL-C level was 71.0 mg/dL in 357 LT patients and 86.1 mg/dL in 355 HT patients. The primary endpoint occurred in 24 (6.7%) of LT and in 31 (8.7%) of HT group patients (adjusted hazard ratio [HR]=0.78; 95% confidence interval [CI]=0.45–1.33, P=0.353). Cardiovascular events alone occurred significantly less frequently in the LT than in the HT group (HR 0.26, 95% CI 0.09–0.80, P=0.019), whereas there were no significant differences in ischemic stroke events (HR 1.12, 95% CI 0.60–2.10, P=0.712). The benefit of LT was less apparent in patients with small vessel disease and intracranial atherosclerosis than in those with extracranial atherosclerosis. Conclusion In contrast to the French TST, the outcomes in Korean patients were neutral. Although LT was more effective in preventing cardiovascular diseases, it was not so in stroke prevention, probably attributed to the differences in stroke subtypes. Further studies are needed to elucidate the efficacy of statins and appropriate LDL-C targets in Asian patients with stroke.

Background: and Purpose The Treat Stroke to Target (TST) was a randomized clinical trial involving French and Korean patients demonstrating that a lower low-density lipoprotein cholesterol (LDL-C, <70 mg/dL) target group (LT) experienced fewer cerebro-cardiovascular events than a higher target (90–110 mg/dL) group (HT). However, whether these results can be applied to Asian patients with different ischemic stroke subtypes remains unclear. Methods: Patients from 14 South Korean centers were analyzed separately. Patients with ischemic stroke or transient ischemic attack with evidence of atherosclerosis were randomized into LT and HT groups. The primary endpoint was a composite of ischemic stroke, myocardial infarction, coronary or cerebral revascularization, and cardiovascular death. Results: Among 712 enrolled patients, the mean LDL-C level was 71.0 mg/dL in 357 LT patients and 86.1 mg/dL in 355 HT patients. The primary endpoint occurred in 24 (6.7%) of LT and in 31 (8.7%) of HT group patients (adjusted hazard ratio [HR]=0.78; 95% confidence interval [CI]=0.45–1.33, P=0.353). Cardiovascular events alone occurred significantly less frequently in the LT than in the HT group (HR 0.26, 95% CI 0.09–0.80, P=0.019), whereas there were no significant differences in ischemic stroke events (HR 1.12, 95% CI 0.60–2.10, P=0.712). The benefit of LT was less apparent in patients with small vessel disease and intracranial atherosclerosis than in those with extracranial atherosclerosis. Conclusion In contrast to the French TST, the outcomes in Korean patients were neutral. Although LT was more effective in preventing cardiovascular diseases, it was not so in stroke prevention, probably attributed to the differences in stroke subtypes. Further studies are needed to elucidate the efficacy of statins and appropriate LDL-C targets in Asian patients with stroke.

Background: and Purpose The Treat Stroke to Target (TST) was a randomized clinical trial involving French and Korean patients demonstrating that a lower low-density lipoprotein cholesterol (LDL-C, <70 mg/dL) target group (LT) experienced fewer cerebro-cardiovascular events than a higher target (90–110 mg/dL) group (HT). However, whether these results can be applied to Asian patients with different ischemic stroke subtypes remains unclear. Methods: Patients from 14 South Korean centers were analyzed separately. Patients with ischemic stroke or transient ischemic attack with evidence of atherosclerosis were randomized into LT and HT groups. The primary endpoint was a composite of ischemic stroke, myocardial infarction, coronary or cerebral revascularization, and cardiovascular death. Results: Among 712 enrolled patients, the mean LDL-C level was 71.0 mg/dL in 357 LT patients and 86.1 mg/dL in 355 HT patients. The primary endpoint occurred in 24 (6.7%) of LT and in 31 (8.7%) of HT group patients (adjusted hazard ratio [HR]=0.78; 95% confidence interval [CI]=0.45–1.33, P=0.353). Cardiovascular events alone occurred significantly less frequently in the LT than in the HT group (HR 0.26, 95% CI 0.09–0.80, P=0.019), whereas there were no significant differences in ischemic stroke events (HR 1.12, 95% CI 0.60–2.10, P=0.712). The benefit of LT was less apparent in patients with small vessel disease and intracranial atherosclerosis than in those with extracranial atherosclerosis. Conclusion In contrast to the French TST, the outcomes in Korean patients were neutral. Although LT was more effective in preventing cardiovascular diseases, it was not so in stroke prevention, probably attributed to the differences in stroke subtypes. Further studies are needed to elucidate the efficacy of statins and appropriate LDL-C targets in Asian patients with stroke.

Background and Objectives: Obstructive sleep apnea (OSA) is a prevalent sleep disorder characterized by recurrent upper airway obstruction, leading to disrupted sleep and various health complications. Positional OSA (POSA) refers to patients whose OSA severity is significantly influenced by body position, especially when lying supine. This study aimed to evaluate the polysomnographic characteristics of POSA and non-positional OSA (non-POSA) and to assess their clinical implications. Methods: This retrospective study included patients diagnosed with OSA who underwent type 1 polysomnography. Patients were categorized into POSA and non-POSA groups based on whether their apnea-hypopnea index (AHI) in the supine position was at least twice as high as that in the lateral position. We collected and analyzed clinical and polysomnographic parameters, including AHI, oxygen desaturation index, arousal index, nadir peripheral oxygen saturation (SpO2), and sleep position proportions. These were compared across different OSA severity levels—mild, moderate, and severe—to assess differences between the POSA and non-POSA groups. Results: In total, 500 patients with OSA were analyzed, of whom 63.4% were classified as having POSA. Patients with POSA exhibited milder disease severity than those without, with an average AHI of 23.3±15.3/h versus 43.9±27.9/h, respectively, and a higher nadir SpO2 of 82.8%±6.6% versus 77.1%±9.8%. POSA was more common in patients with mild OSA (76.5%) and moderate OSA (72.8%), while severe OSA cases were predominantly non-POSA (POSA was 47.4%). Moreover, patients with POSA spent significantly more sleep time in the lateral position (43.8%±22.7%) than non-POSA patients (27.2%±28.2%). Conclusion Patients with POSA generally exhibited milder disease and more favorable polysomnographic profiles than non-POSA patients. POSA is prevalent in mild-to-moderate OSA, and identifying it via polysomnography may inform tailored treatment strategies.

Background: This study was conducted to evaluate the potential of using ChatGPT in dental morphology education. Dentalmorphology is a fundamental subject in dental education that enables students to understand the structure and function of teeth, which is necessary for accurate diagnosis and treatment planning. Recently, artificial intelligence (AI) technology has gained attention as an educational support tool, and large language models like ChatGPT hold great potential to facilitate learners’ understanding through real-time interaction. Methods: This study involved asking GPT-4 questions from the national dental hygiene exam’s dental morphology section from 2021 to 2023, followed by an analysis of its response accuracy. Results: The results showed that GPT-4 demonstrated high accuracy in some questions but lacked consistency depending on thedifficulty and content of the questions. Specifically, GPT-4 was found to struggle with understanding complex tooth morphology and detailed academic concepts. Conclusion These findings suggest that ChatGPT could be a useful educational tool in dental morphology education, althoughsupplementary educational adjustments are necessary. Future research should explore developing a learning environment where AI and educators collaborate to address AI limitations and enhance students’ learning motivation and comprehension.

Background: This study was conducted to evaluate the potential of using ChatGPT in dental morphology education. Dentalmorphology is a fundamental subject in dental education that enables students to understand the structure and function of teeth, which is necessary for accurate diagnosis and treatment planning. Recently, artificial intelligence (AI) technology has gained attention as an educational support tool, and large language models like ChatGPT hold great potential to facilitate learners’ understanding through real-time interaction. Methods: This study involved asking GPT-4 questions from the national dental hygiene exam’s dental morphology section from 2021 to 2023, followed by an analysis of its response accuracy. Results: The results showed that GPT-4 demonstrated high accuracy in some questions but lacked consistency depending on thedifficulty and content of the questions. Specifically, GPT-4 was found to struggle with understanding complex tooth morphology and detailed academic concepts. Conclusion These findings suggest that ChatGPT could be a useful educational tool in dental morphology education, althoughsupplementary educational adjustments are necessary. Future research should explore developing a learning environment where AI and educators collaborate to address AI limitations and enhance students’ learning motivation and comprehension.

Background and Objectives: Obstructive sleep apnea (OSA) is a prevalent sleep disorder characterized by recurrent upper airway obstruction, leading to disrupted sleep and various health complications. Positional OSA (POSA) refers to patients whose OSA severity is significantly influenced by body position, especially when lying supine. This study aimed to evaluate the polysomnographic characteristics of POSA and non-positional OSA (non-POSA) and to assess their clinical implications. Methods: This retrospective study included patients diagnosed with OSA who underwent type 1 polysomnography. Patients were categorized into POSA and non-POSA groups based on whether their apnea-hypopnea index (AHI) in the supine position was at least twice as high as that in the lateral position. We collected and analyzed clinical and polysomnographic parameters, including AHI, oxygen desaturation index, arousal index, nadir peripheral oxygen saturation (SpO2), and sleep position proportions. These were compared across different OSA severity levels—mild, moderate, and severe—to assess differences between the POSA and non-POSA groups. Results: In total, 500 patients with OSA were analyzed, of whom 63.4% were classified as having POSA. Patients with POSA exhibited milder disease severity than those without, with an average AHI of 23.3±15.3/h versus 43.9±27.9/h, respectively, and a higher nadir SpO2 of 82.8%±6.6% versus 77.1%±9.8%. POSA was more common in patients with mild OSA (76.5%) and moderate OSA (72.8%), while severe OSA cases were predominantly non-POSA (POSA was 47.4%). Moreover, patients with POSA spent significantly more sleep time in the lateral position (43.8%±22.7%) than non-POSA patients (27.2%±28.2%). Conclusion Patients with POSA generally exhibited milder disease and more favorable polysomnographic profiles than non-POSA patients. POSA is prevalent in mild-to-moderate OSA, and identifying it via polysomnography may inform tailored treatment strategies.

Background: This study was conducted to evaluate the potential of using ChatGPT in dental morphology education. Dentalmorphology is a fundamental subject in dental education that enables students to understand the structure and function of teeth, which is necessary for accurate diagnosis and treatment planning. Recently, artificial intelligence (AI) technology has gained attention as an educational support tool, and large language models like ChatGPT hold great potential to facilitate learners’ understanding through real-time interaction. Methods: This study involved asking GPT-4 questions from the national dental hygiene exam’s dental morphology section from 2021 to 2023, followed by an analysis of its response accuracy. Results: The results showed that GPT-4 demonstrated high accuracy in some questions but lacked consistency depending on thedifficulty and content of the questions. Specifically, GPT-4 was found to struggle with understanding complex tooth morphology and detailed academic concepts. Conclusion These findings suggest that ChatGPT could be a useful educational tool in dental morphology education, althoughsupplementary educational adjustments are necessary. Future research should explore developing a learning environment where AI and educators collaborate to address AI limitations and enhance students’ learning motivation and comprehension.

Background and Objectives: Obstructive sleep apnea (OSA) is a prevalent sleep disorder characterized by recurrent upper airway obstruction, leading to disrupted sleep and various health complications. Positional OSA (POSA) refers to patients whose OSA severity is significantly influenced by body position, especially when lying supine. This study aimed to evaluate the polysomnographic characteristics of POSA and non-positional OSA (non-POSA) and to assess their clinical implications. Methods: This retrospective study included patients diagnosed with OSA who underwent type 1 polysomnography. Patients were categorized into POSA and non-POSA groups based on whether their apnea-hypopnea index (AHI) in the supine position was at least twice as high as that in the lateral position. We collected and analyzed clinical and polysomnographic parameters, including AHI, oxygen desaturation index, arousal index, nadir peripheral oxygen saturation (SpO2), and sleep position proportions. These were compared across different OSA severity levels—mild, moderate, and severe—to assess differences between the POSA and non-POSA groups. Results: In total, 500 patients with OSA were analyzed, of whom 63.4% were classified as having POSA. Patients with POSA exhibited milder disease severity than those without, with an average AHI of 23.3±15.3/h versus 43.9±27.9/h, respectively, and a higher nadir SpO2 of 82.8%±6.6% versus 77.1%±9.8%. POSA was more common in patients with mild OSA (76.5%) and moderate OSA (72.8%), while severe OSA cases were predominantly non-POSA (POSA was 47.4%). Moreover, patients with POSA spent significantly more sleep time in the lateral position (43.8%±22.7%) than non-POSA patients (27.2%±28.2%). Conclusion Patients with POSA generally exhibited milder disease and more favorable polysomnographic profiles than non-POSA patients. POSA is prevalent in mild-to-moderate OSA, and identifying it via polysomnography may inform tailored treatment strategies.

Background/Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by fat accumulation in the liver. MASLD encompasses both steatosis and MASH. Since MASH can lead to cirrhosis and liver cancer, steatosis and MASH must be distinguished during patient treatment. Here, we investigate the genomes, epigenomes, and transcriptomes of MASLD patients to identify signature gene set for more accurate tracking of MASLD progression. Methods: Biopsy-tissue and blood samples from patients with 134 MASLD, comprising 60 steatosis and 74 MASH patients were performed omics analysis. SVM learning algorithm were used to calculate most predictive features. Linear regression was applied to find signature gene set that distinguish the stage of MASLD and to validate their application into independent cohort of MASLD. Results: After performing WGS, WES, WGBS, and total RNA-seq on 134 biopsy samples from confirmed MASLD patients, we provided 1,955 MASLD-associated features, out of 3,176 somatic variant callings, 58 DMRs, and 1,393 DEGs that track MASLD progression. Then, we used a SVM learning algorithm to analyze the data and select the most predictive features. Using linear regression, we identified a signature gene set capable of differentiating the various stages of MASLD and verified it in different independent cohorts of MASLD and a liver cancer cohort. Conclusions We identified a signature gene set (i.e., CAPG, HYAL3, WIPI1, TREM2, SPP1, and RNASE6) with strong potential as a panel of diagnostic genes of MASLD-associated disease.