Objective: Many studies have explored sense of self in individuals with autism spectrum disorder (ASD); however, few have reported on their experience of “disembodiment.” This study aimed to investigate the differences in brain activity between patients with ASD and neurotypicals (NTs) under conditions causing disembodiment and to examine the correlation between their interoceptive abilities and disembodiment-related brain activity. Methods: 18 Participants with ASD and 21 NTs completed psychological evaluations, interoceptive abilities measurement, and functional magnetic resonance imaging (fMRI). The fMRI images were taken while the participants performed tasks involving ball-throwing animations. The task focused on either self-agency related to ball-throwing (Agency Task) or the spatial location of a ball (Location Task). The animations were presented from constantly changing perspective (Changing View) or fixed perspective (Fixed View). The disembodiment-related condition was the interaction between the Agency Task and Changing View. Results: Participants with ASD exhibited higher activation than NTs in regions near the left parieto-temporo-occipital junction, left precuneus, left hippocampus, and other brain areas. Furthermore, interoceptive accuracy was negatively correlated with the activity of the left superior parietal and posterior midcingulate areas, whereas interoceptive trait prediction error was positively correlated with the activity of the left hippocampus, mid-temporal area, and left posterior cingulate area in participants with ASD. Conclusion These results suggest that disembodiment-related brain activation might be easily manifested by the interaction between perspective-shifting and the experience of agency, and that interoceptive abilities might be related to disembodiment-related brain activation in individuals with ASD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Objective: Many studies have explored sense of self in individuals with autism spectrum disorder (ASD); however, few have reported on their experience of “disembodiment.” This study aimed to investigate the differences in brain activity between patients with ASD and neurotypicals (NTs) under conditions causing disembodiment and to examine the correlation between their interoceptive abilities and disembodiment-related brain activity. Methods: 18 Participants with ASD and 21 NTs completed psychological evaluations, interoceptive abilities measurement, and functional magnetic resonance imaging (fMRI). The fMRI images were taken while the participants performed tasks involving ball-throwing animations. The task focused on either self-agency related to ball-throwing (Agency Task) or the spatial location of a ball (Location Task). The animations were presented from constantly changing perspective (Changing View) or fixed perspective (Fixed View). The disembodiment-related condition was the interaction between the Agency Task and Changing View. Results: Participants with ASD exhibited higher activation than NTs in regions near the left parieto-temporo-occipital junction, left precuneus, left hippocampus, and other brain areas. Furthermore, interoceptive accuracy was negatively correlated with the activity of the left superior parietal and posterior midcingulate areas, whereas interoceptive trait prediction error was positively correlated with the activity of the left hippocampus, mid-temporal area, and left posterior cingulate area in participants with ASD. Conclusion These results suggest that disembodiment-related brain activation might be easily manifested by the interaction between perspective-shifting and the experience of agency, and that interoceptive abilities might be related to disembodiment-related brain activation in individuals with ASD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Objective: Many studies have explored sense of self in individuals with autism spectrum disorder (ASD); however, few have reported on their experience of “disembodiment.” This study aimed to investigate the differences in brain activity between patients with ASD and neurotypicals (NTs) under conditions causing disembodiment and to examine the correlation between their interoceptive abilities and disembodiment-related brain activity. Methods: 18 Participants with ASD and 21 NTs completed psychological evaluations, interoceptive abilities measurement, and functional magnetic resonance imaging (fMRI). The fMRI images were taken while the participants performed tasks involving ball-throwing animations. The task focused on either self-agency related to ball-throwing (Agency Task) or the spatial location of a ball (Location Task). The animations were presented from constantly changing perspective (Changing View) or fixed perspective (Fixed View). The disembodiment-related condition was the interaction between the Agency Task and Changing View. Results: Participants with ASD exhibited higher activation than NTs in regions near the left parieto-temporo-occipital junction, left precuneus, left hippocampus, and other brain areas. Furthermore, interoceptive accuracy was negatively correlated with the activity of the left superior parietal and posterior midcingulate areas, whereas interoceptive trait prediction error was positively correlated with the activity of the left hippocampus, mid-temporal area, and left posterior cingulate area in participants with ASD. Conclusion These results suggest that disembodiment-related brain activation might be easily manifested by the interaction between perspective-shifting and the experience of agency, and that interoceptive abilities might be related to disembodiment-related brain activation in individuals with ASD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Objective: Many studies have explored sense of self in individuals with autism spectrum disorder (ASD); however, few have reported on their experience of “disembodiment.” This study aimed to investigate the differences in brain activity between patients with ASD and neurotypicals (NTs) under conditions causing disembodiment and to examine the correlation between their interoceptive abilities and disembodiment-related brain activity. Methods: 18 Participants with ASD and 21 NTs completed psychological evaluations, interoceptive abilities measurement, and functional magnetic resonance imaging (fMRI). The fMRI images were taken while the participants performed tasks involving ball-throwing animations. The task focused on either self-agency related to ball-throwing (Agency Task) or the spatial location of a ball (Location Task). The animations were presented from constantly changing perspective (Changing View) or fixed perspective (Fixed View). The disembodiment-related condition was the interaction between the Agency Task and Changing View. Results: Participants with ASD exhibited higher activation than NTs in regions near the left parieto-temporo-occipital junction, left precuneus, left hippocampus, and other brain areas. Furthermore, interoceptive accuracy was negatively correlated with the activity of the left superior parietal and posterior midcingulate areas, whereas interoceptive trait prediction error was positively correlated with the activity of the left hippocampus, mid-temporal area, and left posterior cingulate area in participants with ASD. Conclusion These results suggest that disembodiment-related brain activation might be easily manifested by the interaction between perspective-shifting and the experience of agency, and that interoceptive abilities might be related to disembodiment-related brain activation in individuals with ASD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Purpose: This study examined the characteristics and outcomes of resective epilepsy surgery following corpus callosotomy (CC) in children with Lennox-Gastaut syndrome (LGS). Methods: We retrospectively analyzed 17 children with LGS who underwent resective surgery (RS) after CC over a span of 10 years, with a minimum of 2 years of follow-up, at a single tertiary epilepsy center in Korea. Results: Of the 17 patients, 13 (73.5%) demonstrated favorable surgical outcomes (Engel class I or II) at 1 year after RS, and eight (47.1%) were ultimately free of seizures 2 years after surgery. A significantly larger decrease in the number of anti-seizure medications taken from before to 2 years after the final surgical procedure was observed in the group that became seizure-free than in the group with persistent seizures (P=0.062). Furthermore, a significantly greater decline in daily adaptive function was found in the persistent seizure group (P=0.059). The baseline characteristics, results of presurgical evaluation, and treatment-related factors assessed prior to surgery showed no significant differences between the seizure-free group and the group with persistent seizures. Conclusion In conclusion, RS may be a viable option for patients with LGS who exhibit lateralization and/or localization on presurgical evaluation after CC, as the procedure may reveal a concealed primary focus. The proactive implementation of two-stage epilepsy surgery could provide significant seizure reduction and preservation of cognitive function in carefully selected patients with LGS.

Background/Aims: Chronic hepatitis B (CHB) is related to an increased risk of extrahepatic malignancy (EHM), and antiviral treatment is associated with an incidence of EHM comparable to controls. We compared the risks of EHM and intrahepatic malignancy (IHM) between entecavir (ETV) and tenofovir disoproxil fumarate (TDF) treatment. Methods: Using data from the National Health Insurance Service of Korea, this nationwide cohort study included treatment-naïve CHB patients who initiated ETV (n=24,287) or TDF (n=29,199) therapy between 2012 and 2014. The primary outcome was the development of any primary EHM. Secondary outcomes included overall IHM development. E-value was calculated to assess the robustness of results to unmeasured confounders. Results: The median follow-up duration was 5.9 years, and all baseline characteristics were well balanced after propensity score matching. EHM incidence rate differed significantly between within versus beyond 3 years in both groups (P<0.01, Davies test). During the first 3 years, EHM risk was comparable in the propensity score-matched cohort (5.88 versus 5.84/1,000 person-years; subdistribution hazard ratio [SHR]=1.01, 95% confidence interval [CI]=0.88–1.17, P=0.84). After year 3, however, TDF was associated with a significantly lower EHM incidence compared to ETV (4.92 versus 6.91/1,000 person-years; SHR=0.70, 95% CI=0.60–0.81, P<0.01; E-value for SHR=2.21). Regarding IHM, the superiority of TDF over ETV was maintained both within (17.58 versus 20.19/1,000 person-years; SHR=0.88, 95% CI=0.81–0.95, P<0.01) and after year 3 (11.45 versus 16.20/1,000 person-years; SHR=0.68, 95% CI=0.62–0.75, P<0.01; E-value for SHR=2.30). Conclusions TDF was associated with approximately 30% lower risks of both EHM and IHM than ETV in CHB patients after 3 years of antiviral therapy.