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ObjectivesTo evaluate the diagnostic performance of attenuation imaging (ATI) in the detection of hepatic steatosis compared with a histopathology gold standard.MethodsWe prospectively enrolled 108 consecutive patients (35 males; median age, 54.0 years) who underwent percutaneous liver biopsy for evaluation of diffuse liver disease between January 2018 and November 2018 in a tertiary academic center. Grayscale ultrasound examination with ATI was performed just before biopsy, and an attenuation coefficient (AC) was obtained from each patient. The degree of hepatic steatosis, fibrosis stage, and necroinflammatory activity were assessed on histopathologic examination. The significant factor associated with the AC was found by a linear regression analysis, and the diagnostic performance of the AC for the classification into each hepatic steatosis stage was evaluated by receiver operating characteristic (ROC) analysis.ResultsThe distribution of hepatic steatosis grade on histopathology was 53/11/22/16/6 for none/mild (< 10%)/mild (≥ 10%)/moderate/severe steatosis, respectively. The area under the ROC curve, sensitivity, specificity, and optimal cutoff AC value for detection of hepatic steatosis ranged from 0.843–0.926, 74.5–100.0%, 77.4–82.8%, and 0.635–0.745, respectively. Multivariate analysis revealed that the degree of steatosis was the only significant determinant factor for the AC.ConclusionsThe AC from ATI provided good diagnostic performance in detecting the varying degrees of hepatic steatosis. The degree of steatosis was the only significant factor affecting the AC, whereas fibrosis and inflammation were not.Key Points• Attenuation imaging (ATI) is based on two-dimensional grayscale ultrasound images that can incorporate into routine ultrasound examinations with less than 2 min of acquisition time.• ATI provided good diagnostic performance in detecting the varying degrees of hepatic steatosis with an area under the ROC curves ranging from 0.843 to 0.926, and there was no technical failure in this study indicating high applicability of this technique.• The degree of hepatic steatosis was the only significant factor affecting the result of ATI examination.

This study investigated the adult density, timing of migration, and biological characteristics of Pacific herring ( Clupea pallasii ) entering Jinhae Bay, South Korea, in winter, a major spawning ground for the species. A wideband autonomous transceiver (WBAT) was deployed from November 2022 to March 2023, and standardized gillnet surveys and environmental DNA (eDNA) were conducted concurrently. WBAT monitoring showed that C. pallasii school signals appeared predominantly from mid-January to mid-February, coinciding with the period during which herring were highly dominant in gillnet catches. Assessment of female reproductive maturity indicated that most individuals were ripe or spent, confirming that this period represented the peak spawning season. eDNA concentrations exhibited a sharp peak in mid-January in both surface and bottom waters and then declined steadily thereafter. Taken together, these results indicate that entry into the spawning ground begins in late-December, peaks in mid-to-late January, and declines toward late-February. By integrating acoustic, catch, and eDNA datasets, this study provides a comprehensive assessment of the timing, density, and spawning ecology of C . pallasii , offering evidence-based guidance for future resource management and spawning-ground protection in Jinhae Bay.

Deep learning (DL) models are widely adopted in biomedical imaging, where image segmentation is increasingly recognized as a quantitative tool for extracting clinically meaningful information. However, model performance critically depends on dataset size and training configuration, including model capacity. Traditional sample size estimation methods are inadequate for DL due to its reliance on high-dimensional data and its nonlinear learning behavior. To address this gap, we propose a DL-specific framework to estimate the minimal dataset size required for stable segmentation performance. We validate this framework across two distinct clinical tasks: colorectal polyp segmentation from 2D endoscopic images (Kvasir-SEG) and glioma segmentation from 3D brain MRIs (BraTS 2020). We trained residual U-Nets—a simple, yet foundational architecture—across 200 configurations for Kvasir-SEG and 40 configurations for BraTS 2020, varying data subsets (2%–100% for the 2D task and 5%–100% for the 3D task). In both tasks, performance metrics such as the Dice Similarity Coefficient (DSC) consistently improved with increasing data and depth, but gains invariably plateaued beyond approximately 80% data usage. The best configuration for polyp segmentation (6 layers, 100% data) achieved a DSC of 0.86, while the best for brain tumor segmentation reached a DSC of 0.79. Critically, we introduce a surrogate modeling pipeline using Long Short-Term Memory (LSTM) networks to predict these performance curves. A simple uni-directional LSTM model accurately forecasted the final DSC, accurately forecasting the final DSC with low mean absolute error across both tasks. These findings demonstrate that segmentation performance can be reliably estimated with lightweight models, suggesting that collecting a moderate amount of high-quality data is often sufficient for developing clinically viable DL models. Our framework provides a practical, empirical method for optimizing resource allocation in medical AI development.

Background Despite the integral role of cephalometric analysis in orthodontics, there have been limitations regarding the reliability, accuracy, etc. of cephalometric landmarks tracing. Attempts on developing automatic plotting systems have continuously been made but they are insufficient for clinical applications due to low reliability of specific landmarks. In this study, we aimed to develop a novel framework for locating cephalometric landmarks with confidence regions using Bayesian Convolutional Neural Networks (BCNN). Methods We have trained our model with the dataset from the ISBI 2015 grand challenge in dental X-ray image analysis. The overall algorithm consisted of a region of interest (ROI) extraction of landmarks and landmarks estimation considering uncertainty. Prediction data produced from the Bayesian model has been dealt with post-processing methods with respect to pixel probabilities and uncertainties. Results Our framework showed a mean landmark error (LE) of 1.53 ± 1.74 mm and achieved a successful detection rate (SDR) of 82.11, 92.28 and 95.95%, respectively, in the 2, 3, and 4 mm range. Especially, the most erroneous point in preceding studies, Gonion, reduced nearly halves of its error compared to the others. Additionally, our results demonstrated significantly higher performance in identifying anatomical abnormalities. By providing confidence regions (95%) that consider uncertainty, our framework can provide clinical convenience and contribute to making better decisions. Conclusion Our framework provides cephalometric landmarks and their confidence regions, which could be used as a computer-aided diagnosis tool and education.

The relationship between nonalcoholic fatty liver disease and incident metabolic syndrome in metabolically healthy subjects is unknown. We aimed to investigate whether nonalcoholic fatty liver disease is a predictor of future metabolic syndrome in metabolically healthy subjects. Subjects who underwent health evaluation at least twice between 2009 and 2015 from the National Health Insurance Service-National Sample Cohort in South Korea were included. Patients without obesity who had no metabolic syndrome components were finally analyzed (n = 28,880). The definition of nonalcoholic fatty liver disease was based on both the hepatic steatosis and fatty liver indices. The incidence of metabolic syndrome, prediabetes/type 2 diabetes, hypertension, and dyslipidemia was compared between the subjects with and without nonalcoholic fatty liver disease. The presence of nonalcoholic fatty liver disease was associated with a higher risk of incident metabolic syndrome, prediabetes/type 2 diabetes, hypertension, and dyslipidemia in the entire cohort (metabolic syndrome: adjusted hazard ratio, 2.10; 95% confidence interval, 1.18-3.71; prediabetes/type 2 diabetes: adjusted hazard ratio, 1.42; 95% confidence interval, 1.06-1.90; hypertension: adjusted hazard ratio, 2.36; 95% confidence interval, 1.35-4.12; dyslipidemia: adjusted hazard ratio, 1.49; 95% confidence interval, 1.07-2.06). A similar finding was observed in the age-, sex-, smoking status-, and body mass index-based 1:5 propensity score-matched cohort of 1,092 subjects (metabolic syndrome: adjusted hazard ratio, 3.56; 95% confidence interval, 1.79-7.07; prediabetes/type 2 diabetes: adjusted hazard ratio, 1.97; 95% confidence interval, 1.04-3.73; hypertension: adjusted hazard ratio, 2.57; 95% confidence interval, 1.35-4.88; dyslipidemia: adjusted hazard ratio, 1.61; 95% confidence interval, 1.12-2.32). Nonalcoholic fatty liver disease is an early predictor of metabolic dysfunction even in metabolically healthy populations.

Clinical practice guidelines are important for guiding the management of specific diseases by medical practitioners, trainees, and nurses. In some cases, the guidelines are utilized as a reference for health policymakers in controlling diseases with a large public impact. With this in mind, practice guidelines for the management of chronic hepatitis B (CHB) have been developed in the United States, Europe, and Asian-Pacific regions to suggest the best-fit recommendations for each social and medical circumstance. Recently, the Korean Association for the Study of the Liver published a revised version of its clinical practice guidelines for the management of CHB. The guidelines included updated information based on newly available antiviral agents, the most recent opinion on the initiation and cessation of treatment, and updates for the management of drug resistance, partial virological response, and side effects. Additionally, CHB management in specific situations was comprehensively revised. This review compares the similarities and differences among the various practice guidelines to identify unmet needs and improve future recommendations.

Hepatocellular carcinoma harbors numerous genomic and epigenomic aberrations of DNA copy numbers and DNA methylation. Transcriptomic deregulation by these aberrations plays key driver roles in heterogeneous progression of cancers. Here, we profile DNA copy numbers, DNA methylation, and messenger RNA expression levels from 64 cases of hepatocellular carcinoma specimens. We find that the frequencies of the aberrancies of the DNA copy-number-correlated (CNVcor) expression genes and the methylation-correlated expression (METcor) genes are co-regulated significantly. Multi-omics integration of the CNVcor and METcor genes reveal three prognostic subtypes of hepatocellular carcinoma, which can be validated by an independent data. The most aggressive subtype expressing stemness genes has frequent BAP1 mutations, implying its pivotal role in the aggressive tumor progression. In conclusion, our integrative analysis of genomic and epigenomic regulation provides new insights on the multi-layered pathobiology of hepatocellular carcinoma, which might be helpful in developing precision management for hepatocellular carcinoma patients. Hepatocellular carcinoma is known to harbour numerous genomic and epigenomic aberrations, driving transcriptomic deregulation. Here, the authors integrate genomic, epigenomic, and expression data to reveal three prognostic subtypes, providing insight to the pathobiology of hepatocellular carcinoma.

Carbon nanotubes (CNTs) have garnered significant interest in the field of nanotechnology owing to their unique structure and exceptional properties. These materials find applications across a diverse array of fields, including electronics, environmental science, energy, and biotechnology. CNTs serve as potent reinforcing agents in polymer composites; even minimal additions can significantly improve the mechanical, electrical, and thermal properties of polymers. With the growing demand for polymer composites across various industries, there is an anticipation for CNT/polymer composites to evolve in increasingly diverse directions. This paper reviews recent advancements in the manufacturing techniques of various CNT/polymer composites and discusses the enhancements in their mechanical, electrical, and thermal properties. Furthermore, it explores the potential applications of these composites.

This study was conducted to evaluate the outcomes of multi-channel switching RFA using a separable cluster electrode in patients with HCC. From November 2011 to July 2013, 79 patients with 98 HCCs < 5 cm were enrolled and treated with RFA using a multi-channel switching radiofrequency system and a separable cluster electrode under the guidance of a real-time fusion imaging system. The primary and secondary endpoints were the 3-year local tumor progression (LTP) rate and recurrence-free survival (RFS) rate, respectively. For post hoc analyses, LTP, RFS, and major complication rates were retrospectively compared with a historical control group treated with RFA using the same radiofrequency system but with multiple internally-cooled electrodes. The technique success rate of the 98 tumors was 100%. Cumulative 1-year, 2-year, and 3-year LTP rates were 3.4%, 6.9%, and 12.4%, respectively. For patient-level data, cumulative 1-year, 2-year, and 3-year RFS rates were 83.9%, 68.6%, and 45.4%, respectively. On post hoc analyses, none of the baseline characteristics showed a significant difference between the separable cluster electrode and multiple internally-cooled electrodes group. Cumulative LTP and RFS rates of the two groups also showed no significant difference (p = 0.401 and p = 0.881, respectively). Finally, major complication rates of the separable cluster electrode group (5.0%, 4/79) and multiple internally-cooled electrodes group (5.9%, 4/74) were also comparable (p = 1.000). Switching monopolar RFA using a separable cluster electrode is a feasible and efficient technique for the treatment of HCCs smaller than 5 cm, providing comparable local tumor control to multiple internally-cooled electrodes. ClinicalTrials.gov NCT02745483.