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.

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.

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.

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.

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.