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.

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.

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.

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.

ObjectiveThe nucleolar protein PES1 (Pescadillo homolog 1) plays critical roles in ribosome biogenesis and cell cycle regulation, yet its involvement in cellular senescence remains poorly understood. This study aimed to comprehensively investigate the functional consequences of PES1 suppression in cellular senescence and elucidate the molecular mechanisms underlying its regulatory role. MethodsInitially, we assessed PES1 expression patterns in two distinct senescence models: replicative senescent mouse embryonic fibroblasts (MEFs) and doxorubicin-induced senescent human hepatocellular carcinoma HepG2 cells. Subsequently, PES1 expression was specifically downregulated using siRNA-mediated knockdown in these cell lines as well as additional relevant cell types. Cellular proliferation and senescence were assessed by EdU incorporation and SA-β-gal staining assays, respectively. The expression of senescence-associated proteins (p53, p21, and Rb) and SASP factors (IL-6, IL-1β, and IL-8) were analyzed by Western blot or qPCR. Furthermore, Northern blot and immunofluorescence were employed to evaluate pre-rRNA processing and nucleolar morphology. ResultsPES1 expression was significantly downregulated in senescent MEFs and HepG2 cells. PES1 knockdown resulted in decreased EdU-positive cells and increased SA‑β‑gal-positive cells, indicating proliferation inhibition and senescence induction. Mechanistically, PES1 suppression activated the p53-p21 pathway without affecting Rb expression, while upregulating IL-6, IL-1β, and IL-8 production. Notably, PES1 depletion impaired pre-rRNA maturation and induced nucleolar stress, as evidenced by aberrant nucleolar morphology. ConclusionOur findings demonstrate that PES1 deficiency triggers nucleolar stress and promotes p53-dependent (but Rb-independent) cellular senescence, highlighting its crucial role in maintaining nucleolar homeostasis and regulating senescence-associated pathways.

Objective:To explore the role of NK cells in allogeneic hematopoietic stem cell micro-transplantation(MST)in the treatment of patients with acute myeloid leukemia(AML).Methods:Data from 93 AML patients treated with MST at our center from 2013-2018 were retrospectively analyzed.The induction regimen was anthracycline and cytarabine combined with peripheral blood stem cells transplantation mobilization by granulocyte colony stimulating factor(GPBSC),followed by 2-4 courses of intensive treatment with medium to high doses of cytarabine combined with GPBSC after achieving complete remission(CR).The therapeutic effects of one and two courses of MST induction therapy on 42 patients who did not reach CR before transplantation were evaluated.Cox proportional hazards regression analysis was used to analyze the impact of donor NK cell dose and KIR genotype,including KIR ligand mismatch,2DS1,haplotype,and HLA-Cw ligands on survival prognosis of patients.Results:Forty-two patients received MST induction therapy,and the CR rate was 57.1％after 1 course and 73.7％after 2 courses.Multivariate analysis showed that,medium and high doses of NK cells was significantly associated with improved disease-free survival(DFS)of patients(HR=0.27,P=0.005;HR=0.21,P=0.001),and high doses of NK cells was significantly associated with improved overall survival(OS)of patients(HR=0.15,P=0.000).Donor 2DS1 positive significantly increases OS of patients(HR=0.25,P=0.011).For high-risk patients under 60 years old,patients of the donor-recipient KIR ligand mismatch group had longer DFS compared to the nonmismatch group(P=0.036);donor 2DS1 positive significantly prolonged OS of patients(P=0.009).Conclusion:NK cell dose,KIR ligand mismatch and 2DS1 influence the therapeutic effect of MST,improve the survival of AML patients.

Purpose: This study aimed to evaluate the impact of facilitating target delineation of continuous positive airway pressure (CPAP) in patients undergoing stereotactic ablative radiation therapy (SABR) for lung tumors by lung expansion and respiratory motion management. Materials and Methods: We performed a prospective single-institutional trial of patients who were diagnosed with either primary lung cancer or lung metastases and received SABR with a dose of 40 to 60 Gy in 4 fractions. Four-dimensional computed tomography simulations were conducted for each patient: once without CPAP and again with CPAP. Results: Thirty-two patients with 39 tumors were analyzed, after the withdrawal of five patients due to discomfort. For 26 tumors separated from the diaphragm, CPAP significantly increased the superoinferior distance between the tumor and the diaphragm (5.96 cm vs. 8.06 cm; p < 0.001). For 13 tumors located adjacent to the diaphragm, CPAP decreased the overlap of planning target volume (PTV) with the diaphragm significantly (6.32 cm3 vs. 4.09 cm3; p = 0.002). PTV showed a significant reduction with CPAP (25.06 cm3 vs. 22.52 cm3, p = 0.017). In dosimetric analyses, CPAP expanded lung volume by 58.4% with a significant reduction in mean dose and V5 to V40. No more than grade 2 adverse events were reported. Conclusion This trial demonstrated significant improvement of CPAP in target delineation uncertainties for lung SABR, with dosimetric benefits, a favorable safety profile and tolerability. Further investigation is warranted to explore the role of CPAP as a novel strategy for respiratory motion management.

Purpose: Cybersickness is a type of motion sickness induced by virtual reality (VR) or augmented reality (AR) environments that presents symptoms including nausea, dizziness, and headaches. This study aimed to investigate how cathodal transcranial direct current stimulation (tDCS) alleviates motion sickness symptoms and modulates brain activity in individuals experiencing cybersickness after exposure to a VR environment. Methods: This study was performed on two groups of healthy adults with cybersickness symptoms. Subjects were randomly assigned to receive either cathodal tDCS intervention or sham tDCS intervention. Brain activity during VR stimulation was measured by 38-channel functional near-infrared spectroscopy (fNIRS). tDCS was administered to the right temporoparietal junction (TPJ) for 20 minutes at an intensity of 2mA, and the severity of cybersickness was assessed pre- and post-intervention using a simulator sickness questionnaire (SSQ).Result: Following the experiment, cybersickness symptoms in subjects who received cathodal tDCS intervention were reduced based on SSQ scores, whereas those who received sham tDCS showed no significant change. fNIRS analysis revealed that tDCS significantly diminished cortical activity in subjects with high activity in temporal and parietal lobes, whereas high cortical activity was maintained in these regions after intervention in subjects who received sham tDCS. Conclusion These findings suggest that cathodal tDCS applied to the right TPJ region in young adults experiencing cybersickness effectively reduces motion sickness induced by VR environments.

Purpose: Cybersickness is a type of motion sickness induced by virtual reality (VR) or augmented reality (AR) environments that presents symptoms including nausea, dizziness, and headaches. This study aimed to investigate how cathodal transcranial direct current stimulation (tDCS) alleviates motion sickness symptoms and modulates brain activity in individuals experiencing cybersickness after exposure to a VR environment. Methods: This study was performed on two groups of healthy adults with cybersickness symptoms. Subjects were randomly assigned to receive either cathodal tDCS intervention or sham tDCS intervention. Brain activity during VR stimulation was measured by 38-channel functional near-infrared spectroscopy (fNIRS). tDCS was administered to the right temporoparietal junction (TPJ) for 20 minutes at an intensity of 2mA, and the severity of cybersickness was assessed pre- and post-intervention using a simulator sickness questionnaire (SSQ).Result: Following the experiment, cybersickness symptoms in subjects who received cathodal tDCS intervention were reduced based on SSQ scores, whereas those who received sham tDCS showed no significant change. fNIRS analysis revealed that tDCS significantly diminished cortical activity in subjects with high activity in temporal and parietal lobes, whereas high cortical activity was maintained in these regions after intervention in subjects who received sham tDCS. Conclusion These findings suggest that cathodal tDCS applied to the right TPJ region in young adults experiencing cybersickness effectively reduces motion sickness induced by VR environments.

Objective: Previous studies have reported that vitamin D deficiency increased the risk of Alzheimer’s disease (AD) dementia in older adults. However, little is known about how vitamin D is involved in the pathophysiology of AD. Thus, this study aimed to examine the association and interaction of serum vitamin D levels with in vivo AD pathologies including cerebral beta-amyloid (Aβ) deposition and neurodegeneration in nondemented older adults. Methods: 428 Nondemented older adults were recruited from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s Disease, a prospective cohort that began in 2014. All participants underwent comprehensive clinical assessments, measurement of serum 25-hydroxyvitamin D (25[OH]D), and multimodal brain imaging including Pittsburgh compound B (PiB) positron emission tomography and magnetic resonance imaging. Global PiB deposition was measured for the Aβ biomarker. Intracranial volume-adjusted hippocampal volume (HVa) was used as a neurodegeneration biomarker. Results: Overall, serum 25(OH)D level was not associated with either Aβ deposition or HVa after controlling for age, sex, apolipoprotein E ε4 positivity, and vascular risk factors. However, serum 25(OH)D level had a significant moderating effect on the association between Aβ and neurodegeneration, with lower serum 25(OH)D level significantly exacerbating cerebral Aβ-associated hippocampal volume loss (B = 34.612, p = 0.008). Conclusion Our findings indicate that lower serum vitamin D levels may contribute to AD by exacerbating Aβ-associated neurodegeneration in nondemented older adults. Further studies to explore the potential therapeutic effect of vitamin D supplementation on the progression of AD pathology will be necessary.