Purpose: This study aimed to identify predictive factors affecting adolescents’ subjective happiness using data from the 2023 Korea Youth Risk Behavior Survey. A random forest model was applied to determine the strongest predictive factors, and its predictive performance was compared with traditional regression models. Methods: Responses from a total of 44,320 students from grades 7 to 12 were analyzed. Data pre-processing involved handling missing values and selecting variables to construct an optimal dataset. The random forest model was employed for prediction, and SHAP (Shapley Additive Explanations) analysis was used to assess variable importance. Results: The random forest model demonstrated a stable predictive performance, with an R2 of .37. Mental and physical health factors were found to significantly affect subjective happiness. Adolescents’ subjective happiness was most strongly influenced by perceived stress, perceived health, experiences of loneliness, generalized anxiety disorder, suicidal ideation, economic status, fatigue recovery from sleep, and academic performance. Conclusion This study highlights the utility of machine learning in identifying factors influencing adolescents’ subjective happiness, addressing limitations of traditional regression approaches. These findings underscore the need for multidimensional interventions to improve mental and physical health, reduce stress and loneliness, and provide integrated support from schools and communities to enhance adolescents’ subjective happiness.

Background/Aims: Direct-acting antivirals (DAAs) effectively eradicate hepatitis C virus. This study investigated whether metabolic dysfunction influences the likelihood of fibrosis regression after DAA treatment in patients with chronic hepatitis C (CHC). Methods: This multicenter, retrospective study included 8,819 patients diagnosed with CHC who were treated with DAAs and achieved a sustained virological response (SVR) between January 2014 and December 2022. Fibrosis regression was defined as a 20% reduction in noninvasive surrogates for liver fibrosis, such as liver stiffness (LS) measured by vibration-controlled transient elastography (VCTE) and the fibrosis-4 (FIB-4) score. Hypercholesterolemia (h-TC) was defined as >200 mg/dL. Results: The median age of the study population was 59.6 years, with a predominance of male patients (n=4,713, 57.3%). Genotypes 1, 2, and others were confirmed in 3,872 (46.2%), 3,487 (41.6%), and 1,024 (12.2%) patients, respectively. Diabetes mellitus (DM) was present in 1,442 (17.2%) patients and the median LS was 7.50 kPa (interquartile range, 5.30–12.50). Multivariate analysis revealed that the presence of DM and pre-DAA h-TC were independently associated with a decreased probability of fibrosis regression by VCTE. Additionally, pre-DAA h-TC was independently associated with a decreased probability of fibrosis regression by the FIB-4. Conclusions Metabolic dysfunction has an unfavorable influence on fibrosis regression in patients with CHC who achieve SVR after DAA treatment.

It is difficult to determine a cause of bile duct stricture and dilatation. Eosinophilic cholangitis, a rare benign condition, may be one cause of bile duct stricture and dilatation. It can be evaluated using various methods of histopathology, radiographs, endoscopy, and hematologic findings. Treatment generally involves steroid therapy which can lead to improvement. This case report will discuss eosinophilic cholangitis, emphasizing that while it can easily be overlooked but should be considered in differential diagnoses.

It is difficult to determine a cause of bile duct stricture and dilatation. Eosinophilic cholangitis, a rare benign condition, may be one cause of bile duct stricture and dilatation. It can be evaluated using various methods of histopathology, radiographs, endoscopy, and hematologic findings. Treatment generally involves steroid therapy which can lead to improvement. This case report will discuss eosinophilic cholangitis, emphasizing that while it can easily be overlooked but should be considered in differential diagnoses.

This paper analyzes the regulatory frameworks for artificial intelligence/machine learning AI/ML-enabled medical devices in the European Union (EU), the United States (US), and the Republic of Korea, with a focus on applications in spine surgery. The aim is to provide guidance for developers and researchers navigating regulatory pathways. A review of current literature, regulatory documents, and legislative frameworks was conducted. Key differences in regulatory bodies, risk classification, submission requirements, and approval pathways for AI/ML medical devices were examined in the EU, US, and Republic of Korea. The EU AI Act (2024) establishes a risk-based framework, requiring regulatory review based on device risk, with high-risk devices subject to stricter oversight. The US applies a more flexible approach, allowing multiple submission pathways and incorporating a focus on continuous learning. The Republic of Korea emphasizes possibilities of streamlined approval and with growing use of real-world data to support validation. Developers must ensure regulatory alignment early in the development process, focusing on key aspects like dataset quality, transparency, and continuous monitoring. Across all regions, the need for technical documentation, quality management systems, and bias mitigation are essential for approval. Developers are encouraged to adopt adaptable strategies to comply with evolving regulatory standards, ensuring models remain transparent, fair, and reliable. The EU’s comprehensive AI Act enforces stricter oversight, while the US and Korea offer more flexible pathways. Developers of spine surgery AI/ML devices must tailor development strategies to align with regional regulations, emphasizing transparent development, quality assurance, and postmarket monitoring to ensure approval success.

Purpose: 11C-Methionine PET/CT is a promising method for detecting parathyroid lesions in patients with primary hyperparathyroidism (PHPT). We aimed to determine the diagnostic ability and correlation of digital 11C-Methionine PET/CT for parathyroid lesions in patients with PHPT, particularly in cases where standard imaging methods yielded inconclusive results. Methods: This retrospective analysis was conducted on patients diagnosed with PHPT who underwent digital 11C-Methionine PET/CT imaging because of ambiguous results on standard imaging work-up ( 99m Tc-MIBI parathyroid scan and/or neck ultrasonography). Quantitative 11C-Methionine PET/CT parameters, including maximum standardized uptake value (SUVmax), mean SUV (SUVmean), peak SUV (SUVpeak), parathyroid methionine volume (PMV), and whole methionine uptake(WMU: PMV multiplied by SUVmean) were calculated with various thresholds, and their correlations with biochemical andpathologic parameters were investigated. Results: This study included 22 consecutive patients (10 men and 12 women) with a median age of 64.0 years. The lesion detection rate and sensitivity of digital 11C-Methionine PET/CT were 81.8% (18/22) and 100.0% (18/18), respectively.Quantitative analysis revealed that serum PTH (r = 0.490, P = 0.039) and serum calcium (r = 0.583, P = 0.011) were signifi-cantly correlated with PMV50%. Conclusion Digital 11C-Methionine PET/CT offers good performance in the detection of parathyroid lesions in PHPT patients with inconclusive standard imaging work-up. The volume parameter of PMV50% significantly correlated biochemi-cal parameters and can serve as a complementary diagnostic tool.