Objective: In this study, we aimed to investigate the incidence and risk factors for interstitial nephritis in patients with ankylosing spondylitis (AS). Methods: We retrospectively analyzed the claims records of patients diagnosed with AS in Korea’s Health Insurance Review and Assessment Service Database between 2016 and 2019. The Assessment of Spondyloarthritis International Society nonsteroidal anti-inflammatory drugs (NSAIDs) intake score was used to calculate the NSAID dosage over 1 year after AS diagnosis. The incidence rate of interstitial kidney disease was calculated as the number of events per 1,000 person-years. The risks associated with sex, age, Charlson Comorbidity Index, comorbidities, and concomitant medications were assessed using the Cox proportional hazards model, with results presented as hazard ratios (HRs) and 95% confidence intervals (CIs). Results: In total, 11,749 patients with AS were included in this study. Of these, 79 patients had interstitial nephritis, with an incidence rate of 2.50 per 1,000 person-years. In multivariable analysis, female sex (HR, 2.44; 95% CI, 1.56~3.83), hypertension (HR, 2.08; 95% CI, 1.15~3.76), and renal failure (HR, 3.70; 95% CI, 1.30~10.55) showed significant associations. However, NSAID use in the first year after AS diagnosis was not associated with interstitial nephritis occurrence. Conclusion The incidence of interstitial nephritis in patients with AS was 2.50 per 1,000 person-years, with female sex and comorbidities identified as significant risk factors. However, NSAID use during the first year after AS diagnosis was not associated with interstitial nephritis development.

Background/Aims: Various expanded criteria (EC) for liver transplantation (LT) in patients with hepatocellular carcinoma (HCC) have been proposed to avoid the narrow nature of the Milan criteria (MC). To investigate which EC predicts more favorable outcomes in terms of overall survival (OS) and recurrence-free survival (RFS), we conducted a network meta-analysis (NMA). Methods: A database search was conducted on PubMed, Embase, and the Cochrane Library, to identify studies comparing OS and RFS between patients within the MC and those exceeding the MC but within the EC. Hazard ratios (HRs) were pooled using a random-effects NMA and validated in an in-house cohort of 1,008 LT recipients. Results: Among 22,466 articles identified, 35 studies with 45 pairwise comparisons were included in the NMA along with 8 different EC. The University of California San Francisco (HR, 1.43; 95% CI, 1.19–1.71), Up-to-Seven (HR, 1.50; 95% CI, 1.15–1.97), and Hangzhou criteria (HR, 1.69; 95% CI, 1.11–2.57) showed inferior OS to the MC. The MC ranked highest for both OS and RFS, followed by Metroticket 2.0 for OS and the Asan criteria for RFS. In the validation cohort, both Metroticket 2.0 and AFP model yielded more favorable HCC-specific mortality than other EC. Conclusions Several EC, of which those of Metroticket 2.0 were the best, yielded comparable outcomes to the MC. AFP-based EC such as Metroticket 2.0 and AFP model appeared to be useful in both the NMA and the validation cohort, suggesting a potential role in identifying selected low-risk patients beyond the MC.

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: Dutasteride, a 5-alpha reductase inhibitor, is prescribed for male androgenetic alopecia (AGA) in Korea and Japan. Despite its efficacy, its use is limited by its long half-life, potent dihydrotestosterone suppression, and adverse effects. Objective: To investigate the efficacy and safety of 0.2 mg dutasteride for male AGA. Methods: Patients with male AGA were randomized to receive 0.2 mg dutasteride, placebo, or 0.5 mg dutasteride (2:2:1) once daily for 24 weeks. Safety and efficacy endpoints were assessed. Results: Overall, 139 men were analyzed. At week 24, the change in hair count within the target area at the vertex from baseline was significantly higher in the 0.2 mg dutasteride group than in the placebo group (21.53 vs. 5.96, p=0.0072). Dutasteride (0.2 mg) treatment led to greater hair growth improvement, as assessed by investigators at week 24 (p=0.0096) and an independent panel at weeks 12 and 24 (p=0.0306, p=0.0001). For all efficacy endpoints, 0.2 mg dutasteride was as effective as 0.5 mg dutasteride. The incidence of adverse events was low and not statistically different between the 0.2 mg dutasteride and placebo groups. The limitation of this study is the limited number of participants. Conclusion Low-dose (0.2 mg) dutasteride for male AGA showed significant efficacy and favorable safety profile.Trial Registration: ClinicalTrials.gov Identifier: NCT04825561

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.

While advances in cancer treatment have led to improved survival rates, cancer survivors are at a significant risk of developing atherosclerotic cardiovascular disease (ASCVD).This review examines the risk, diagnosis, and prevention of ASCVD in this population.Cancer survivors, especially those diagnosed with certain types, face a significantly higher risk of developing ASCVD than the general population. We introduce the “triad model” to explain this increased risk of ASCVD among cancer patients. This model includes three interconnected components: common catalysts, cancer influence, and treatment impact.The factors contributing to this model are the shared risk factors between cancer and ASCVD, such as smoking, obesity, and systemic inflammation; the direct effects of cancer on cardiovascular health through chronic systemic inflammation and endothelial damage;and the significant effects of anticancer treatments, including chemotherapy and radiation, which can worsen cardiovascular complications and hasten the progression of ASCVD.Furthermore, cancer survivors are at a higher risk of developing and dying from ASCVD, highlighting the necessity for tailored guidelines and strategies for ASCVD prevention and management in this population. The review explores the utility of diagnostic tools, such as coronary artery calcium scoring, in predicting and managing ASCVD risk. It also emphasizes the importance of prevention strategies that include regular cardiovascular monitoring and lifestyle modifications. Finally, the relationship between cancer survival and cardiovascular health highlights the importance of integrated and comprehensive care approaches.Continued research, the development of prediction models, and specific preventative strategies are essential to improve cancer survivors’ overall health outcomes.