Purpose: This study aimed to identify reproducible locations for evaluating masseter muscle function by measuring its thickness using ultrasonography (US). The study focused on comparing two measurement locations: the thickest part of the masseter muscle during ultrasonographic scanning (TMUS) and the most prominent part during clenching (PMC). Methods: Forty healthy adults (20 males and 20 females) participated in the study. US images were obtained from both sides of the masseter muscle under resting and clenching conditions. Measurements were taken at the TMUS and PMC locations, and the clenching-to-resting (C/R) ratio was calculated. Intra- and inter-rater reliability were assessed using intraclass correlation coefficients (ICCs), and the agreement between the two locations was further analyzed using Bland–Altman (BA) plots. Results: The measurements at both TMUS and PMC showed high intra- and inter-rater agreement, with no significant difference in measurements between the two locations.However, the PMC location demonstrated slightly higher ICC values (0.94) compared to TMUS (0.91). The C/R ratio for PMC showed higher consistency (0.89) compared to TMUS (0.65). BA plots indicated that the agreement between TMUS and PMC was slightly better during clenching than at rest, with smaller mean differences in clenching (–0.06 mm) than resting (–0.13 mm). Additionally, the number of measurements outside the upper and lower limits was lower during clenching (10) than at rest (13). Conclusions Both TMUS and PMC locations demonstrated reliable measurements, but the PMC location showed slightly better consistency across different muscle states. The findings suggest that PMC provides a more reproducible and standardized approach for masseter muscle assessment, making it a better choice for both clinical practice and research in evaluating masticatory function.

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: Some studies suggest that TP53 mutations are associated with the response to immune checkpoint inhibitors (ICI) in patients with non–small cell lung cancer (NSCLC) and also contribute to sex disparities in several cancers. Thus, we hypothesized that TP53 mutations might serve as sex-dependent genomic biomarkers of ICI treatment response in patients with NSCLC. Materials and Methods: Clinical data of 100 patients with metastatic NSCLC treated with ICI monotherapy at Seoul National University Bundang Hospital (SNUBH) were retrospectively reviewed. Genomic and clinical datasets of The Cancer Genome Atlas and an ICI-treated lung cancer cohort (cBioPortal) were also analyzed. Results: In SNUBH cohort, no statistically significant difference was observed in the median progression-free survival (PFS) according to TP53 mutation status (p=0.930); however, female patients with TP53 mutations (MT) had a significantly prolonged median PFS compared to wild-type (WT) (6.1 months in TP53 MT vs. 2.6 months in TP53 WT; p=0.021). Programmed death-ligand 1 (PD-L1) high (≥ 50%) expression was significantly enriched in female patients with TP53 MT (p=0.005). The analysis from publicly available dataset also revealed that females with NSCLC with TP53 MT showed significantly longer PFS than those with TP53 WT (p < 0.001). In The Cancer Genome Atlas analysis, expression of immune-related genes, and tumor mutation burden score in TP53 MT females were higher than in males without TP53 MT. Conclusion Female patients with NSCLC with TP53 mutations had high PD-L1 expression and showed favorable clinical outcomes following ICI therapy, suggesting a need for further research to explore the role of TP53 mutations for sex disparities in response to ICI therapy.

Background: The exposome encompasses all factors people encounter through life, with the skin constantly exposed. While particulate matter (PM) and sleep deprivation are known to contribute to barrier dysfunction, their combined effects remain unclear. Objective: To evaluate the independent and combined effects of PM exposure and short-term sleep deprivation on skin barrier function. Methods: Forty healthy Korean women (aged 24–58 years) were enrolled in this study. Forearms were divided into 4 sites: control, PM exposure, sleep deprivation, and PM plus sleep deprivation. Parameters such as trans-epidermal water loss (TEWL), hydration, elasticity, roughness, and redness were measured at baseline and post-exposure. RNA sequencing and reverse transcription-polymerase chain reaction were conducted on tape-stripped skin samples. Results: PM exposure significantly increased TEWL (+25.59%, p<0.01), roughness (+21.9%, p<0.01), and redness (+13.7%, p<0.0001) while reducing elasticity (−3.98%, p<0.01). Sleep deprivation modestly reduced elasticity (−1.39%, p<0.05) without affecting other parameters.Combined PM and sleep deprivation did not further exacerbate barrier dysfunction compared to PM alone. RNA sequencing revealed reduced FLG and LORICRIN expression and upregulated endoplasmic reticulum (ER) stress markers (HSP90B1, CANX) in both PM and sleep deprivation conditions. Conclusion PM exposure impaired skin barrier function, while short-term sleep deprivation alone did not significantly affect the barrier, either independently or in combination with PM.However, it was observed that the sleep deprivation-only, while not directly causing barrier damage, induced changes in ER stress-related gene expression in tape-stripped skin samples, like the PM exposure-only. This suggests that such signaling pathways could potentially exacerbate skin barrier deterioration.

Gastric cancer with peritoneal metastasis (GCPM) is a common and lethal manifestation of advanced gastric cancer, with a median survival of only 5-11 months. This consensus was developed by 30 experts from Asia (China, Japan, Korea, and Singapore) using the Delphi method and the GRADE evidence grading system. A total of 29 statements were formulated, covering the diagnosis and assessment of GCPM, indications for laparoscopic exploration and NIPS (normothermic intraperitoneal and systemic treatment), treatment regimens, prevention and management of complications, criteria for conversion surgery, and postoperative intraperitoneal therapy. The consensus aims to standardize clinical practice and improve the prognosis of patients with GCPM.

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: Some studies suggest that TP53 mutations are associated with the response to immune checkpoint inhibitors (ICI) in patients with non–small cell lung cancer (NSCLC) and also contribute to sex disparities in several cancers. Thus, we hypothesized that TP53 mutations might serve as sex-dependent genomic biomarkers of ICI treatment response in patients with NSCLC. Materials and Methods: Clinical data of 100 patients with metastatic NSCLC treated with ICI monotherapy at Seoul National University Bundang Hospital (SNUBH) were retrospectively reviewed. Genomic and clinical datasets of The Cancer Genome Atlas and an ICI-treated lung cancer cohort (cBioPortal) were also analyzed. Results: In SNUBH cohort, no statistically significant difference was observed in the median progression-free survival (PFS) according to TP53 mutation status (p=0.930); however, female patients with TP53 mutations (MT) had a significantly prolonged median PFS compared to wild-type (WT) (6.1 months in TP53 MT vs. 2.6 months in TP53 WT; p=0.021). Programmed death-ligand 1 (PD-L1) high (≥ 50%) expression was significantly enriched in female patients with TP53 MT (p=0.005). The analysis from publicly available dataset also revealed that females with NSCLC with TP53 MT showed significantly longer PFS than those with TP53 WT (p < 0.001). In The Cancer Genome Atlas analysis, expression of immune-related genes, and tumor mutation burden score in TP53 MT females were higher than in males without TP53 MT. Conclusion Female patients with NSCLC with TP53 mutations had high PD-L1 expression and showed favorable clinical outcomes following ICI therapy, suggesting a need for further research to explore the role of TP53 mutations for sex disparities in response to ICI therapy.