Objectives: Allergic rhinitis (AR) is a chronic inflammation of the nasal mucosa triggered by environmental allergens. Although its pathophysiology has been extensively investigated, the influence of environmental aggravating factors—particularly combined pollutant exposure—remains insufficiently characterized. This study aimed to assess the impact of coexposure to PM2.5, formaldehyde, and Zn on allergic inflammation in a murine AR model and to delineate the associated immunological and histopathological responses. Methods: Female BALB/c mice were sensitized with ovalbumin (OVA) and challenged intranasally to induce AR. On days 21–24, the mice were exposed to PM2.5, formaldehyde, and Zn, either individually or in combination with OVA. Allergic symptoms were evaluated through behavioral observation, while immune responses were assessed by analyzing nasal and bronchoalveolar lavage fluids (NALF and BALF), serum immunoglobulin levels, nasal histopathology, and cytokine profiles. Results: Combined exposure to PM2.5, formaldehyde, and Zn significantly intensified allergic inflammation compared with single exposures. Coexposure to PM2.5 and Zn led to synergistic increases in total and OVA-specific immunoglobulin E levels, eosinophilic infiltration, nasal rubbing, sneezing, and Th2/Th17 cytokine levels in NALF and BALF. Histological analysis demonstrated epithelial thickening and goblet cell hyperplasia after combined exposure. Other combinations, including PM2.5 with formaldehyde, also produced additive or modestly amplified inflammatory responses. Conclusions Coexposure to PM2.5, formaldehyde, and Zn aggravated allergic inflammation in an OVA-induced murine model, with PM2.5+Zn yielding the strongest synergistic effects. These findings emphasize the role of pollutant–pollutant interactions in allergic airway diseases and highlight the need for further research to clarify long-term health effects and relevance to human disease.

Purpose: This study compared the diagnostic performance of two attenuation imaging (ATI) modes—low-frequency (3 MHz) and high-frequency (4 MHz)—for assessing hepatic steatosis, with histopathological hepatic fat fraction (HFF) as the reference standard. Methods: This prospective single-center study enrolled participants with suspected metabolic dysfunction-associated steatotic liver disease (MASLD) scheduled for liver biopsy or surgery between June 2023 and June 2024. Attenuation coefficient (AC) values were consecutively measured using low- and high-frequency ATI modes, while the skin-to-region of interest distance (SRD) was measured simultaneously. Spearman correlation analysis evaluated the relationships of AC with HFF and SRD, and linear regression identified factors affecting AC. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Results: In total, 119 participants (mean age, 37.2±12.0 years; 87 men) were included, with 73 (61.3%) diagnosed with MASLD. HFF ranged from 0% to 50%. The AC values in the lowfrequency mode were significantly higher than those in the high-frequency mode (0.61 vs. 0.54 dB/cm/MHz, P<0.001). HFF significantly influenced AC in both modes, whereas SRD affected AC only in the high-frequency mode (P<0.001). AC correlated positively with HFF in both modes (r_s≥0.514, P<0.001) and negatively with SRD in the high-frequency mode (r_s=-0.338, P<0.001). The AUROC for hepatic steatosis did not differ significantly between the two modes (0.751 vs. 0.771; P=0.609). Conclusion The low-frequency mode produced higher AC values than the high-frequency mode and demonstrated comparable diagnostic accuracy for assessing hepatic steatosis. Unlike the high-frequency mode, the low-frequency mode was not influenced by SRD.

Four soluble factors—putrescine, glucosamine, nicotinamide, and signal transducer and activator of transcription 3 (STAT3) inhibitor BP-1-102—were shown to differentiate bone marrow mononucleated cells (BMNCs) into functional insulin-producing cells (IPCs) in vitro. Transplantation of these IPCs improved hyperglycemia in diabetic mice. However, the role of endogenous BMNC regeneration in this effect was unclear. This study aimed to evaluate the effect of these factors on in vivo BMNC differentiation into IPCs in diabetic mice. Mice were orally administered the factors for 5 days, twice at 2-week intervals, and monitored for 45–55 days. Glucose tolerance, glucose-stimulated insulin secretion, and pancreatic insulin content were measured. Chimeric mice harboring BMNCs from insulin promoter luciferase/green fluorescent protein (GFP) transgenic mice were used to track endogenous BMNC fate. These factors lowered blood glucose levels, improved glucose tolerance, and enhanced insulin secretion. Immunostaining confirmed IPCs in the pancreas, showing the potential of these factors to induce β-cell regeneration and improve diabetes treatment.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Four soluble factors—putrescine, glucosamine, nicotinamide, and signal transducer and activator of transcription 3 (STAT3) inhibitor BP-1-102—were shown to differentiate bone marrow mononucleated cells (BMNCs) into functional insulin-producing cells (IPCs) in vitro. Transplantation of these IPCs improved hyperglycemia in diabetic mice. However, the role of endogenous BMNC regeneration in this effect was unclear. This study aimed to evaluate the effect of these factors on in vivo BMNC differentiation into IPCs in diabetic mice. Mice were orally administered the factors for 5 days, twice at 2-week intervals, and monitored for 45–55 days. Glucose tolerance, glucose-stimulated insulin secretion, and pancreatic insulin content were measured. Chimeric mice harboring BMNCs from insulin promoter luciferase/green fluorescent protein (GFP) transgenic mice were used to track endogenous BMNC fate. These factors lowered blood glucose levels, improved glucose tolerance, and enhanced insulin secretion. Immunostaining confirmed IPCs in the pancreas, showing the potential of these factors to induce β-cell regeneration and improve diabetes treatment.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Background: Coronavirus disease 2019 (COVID-19) is a newly emerged infectious disease that needs further clinical investigation. Characterizing the temporal pattern of combined infections in patients with COVID-19 may help clinicians understand the clinical nature of this disease and provide valuable diagnostic and therapeutic guidelines. Methods: We retrospectively analyzed COVID-19 patients isolated in four study hospitals in Korea for one year period from May 2021 to April 2022 when the delta and omicron variants were dominant. The temporal characteristics of combined infections based on specific diagnostic tests were analyzed. Results: A total of 16,967 COVID-19 patients were screened, 2,432 (14.3%) of whom underwent diagnostic microbiologic tests according to the clinical decision-making, 195 of whom had positive test results, and 0.55% (94/16,967) of whom were ultimately considered to have clinically meaningful combined infections. The median duration for the diagnosis of combined infections was 15 (interquartile range [IQR], 5–25) days after admission. The proportion of community-acquired coinfections (≤ 2 days after admission) was 11.7% (11/94), which included bacteremia (10/94, 10.63%) and tuberculosis (1/94, 1.06%). Combined infections after 2 days of admission were diagnosed at median 16 (IQR, 9–26) days, and included bacteremia (72.3%), fungemia (19.3%), cytomegalovirus (CMV) diseases (8.4%), Pneumocystis jerovecii pneumonia (PJP, 8.4%) and invasive pulmonary aspergillosis (IPA, 4.8%). Conclusion Among COVID-19 patients with risk factors for severe complications, 0.55% had laboratory-confirmed combined infections, which included community and nosocomial pathogens in addition to unusual pathogens such as CMV disease, PJP and IPA.

Objective: The treatment rate for alcohol use disorder (AUD) in Korea is significantly lower than its prevalence rate. Because untreated AUD can have harmful consequences, it is important to identify the factors that contribute to individuals with AUD seeking mental health services. Methods: We collected nationally representative data from the National Mental Health Survey of Korea 2021 and analyzed responses from 643 individuals with AUD, of which 76.8% were male. Factors related to mental health service utilization among individuals with AUD were classified into three categories: sociodemographic (such as sex, age, marital status, education, and monthly household income), clinical (including symptom severity, psychiatric comorbidity, suicidality, and physical illness), and psychological characteristics (like perceived stigma, loneliness and social isolation, and resilience). We used multiple logistic regression analyses to examine each characteristic separately and combined in a single model to determine the most significant factors. Results: The three logistic regression models revealed that sex, psychiatric comorbidity, physical illness, and perceived stigma are significantly linked to the utilization of mental health services among individuals with AUD. Results from the comprehensive model indicated that only physical illness and perceived stigma have significant associations with mental health service utilization. Conclusion These findings can assist in developing targeted interventions for individuals with AUD.

Objective: The treatment rate for alcohol use disorder (AUD) in Korea is significantly lower than its prevalence rate. Because untreated AUD can have harmful consequences, it is important to identify the factors that contribute to individuals with AUD seeking mental health services. Methods: We collected nationally representative data from the National Mental Health Survey of Korea 2021 and analyzed responses from 643 individuals with AUD, of which 76.8% were male. Factors related to mental health service utilization among individuals with AUD were classified into three categories: sociodemographic (such as sex, age, marital status, education, and monthly household income), clinical (including symptom severity, psychiatric comorbidity, suicidality, and physical illness), and psychological characteristics (like perceived stigma, loneliness and social isolation, and resilience). We used multiple logistic regression analyses to examine each characteristic separately and combined in a single model to determine the most significant factors. Results: The three logistic regression models revealed that sex, psychiatric comorbidity, physical illness, and perceived stigma are significantly linked to the utilization of mental health services among individuals with AUD. Results from the comprehensive model indicated that only physical illness and perceived stigma have significant associations with mental health service utilization. Conclusion These findings can assist in developing targeted interventions for individuals with AUD.

Purpose: This study compared the diagnostic performance of two attenuation imaging (ATI) modes—low-frequency (3 MHz) and high-frequency (4 MHz)—for assessing hepatic steatosis, with histopathological hepatic fat fraction (HFF) as the reference standard. Methods: This prospective single-center study enrolled participants with suspected metabolic dysfunction-associated steatotic liver disease (MASLD) scheduled for liver biopsy or surgery between June 2023 and June 2024. Attenuation coefficient (AC) values were consecutively measured using low- and high-frequency ATI modes, while the skin-to-region of interest distance (SRD) was measured simultaneously. Spearman correlation analysis evaluated the relationships of AC with HFF and SRD, and linear regression identified factors affecting AC. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Results: In total, 119 participants (mean age, 37.2±12.0 years; 87 men) were included, with 73 (61.3%) diagnosed with MASLD. HFF ranged from 0% to 50%. The AC values in the lowfrequency mode were significantly higher than those in the high-frequency mode (0.61 vs. 0.54 dB/cm/MHz, P<0.001). HFF significantly influenced AC in both modes, whereas SRD affected AC only in the high-frequency mode (P<0.001). AC correlated positively with HFF in both modes (r_s≥0.514, P<0.001) and negatively with SRD in the high-frequency mode (r_s=-0.338, P<0.001). The AUROC for hepatic steatosis did not differ significantly between the two modes (0.751 vs. 0.771; P=0.609). Conclusion The low-frequency mode produced higher AC values than the high-frequency mode and demonstrated comparable diagnostic accuracy for assessing hepatic steatosis. Unlike the high-frequency mode, the low-frequency mode was not influenced by SRD.