Purpose: Whether celiac plexus neurolysis (CPN) retains opioid-sparing efficacy in palliative care patients receiving modern extended-release opioids remains uncertain after a recent randomized trial that reported no benefit. We describe fluoroscopy-guided CPN in such patients. Methods: Thirteen consecutive palliative care patients with unresectable or metastatic upper abdominal cancer (seven pancreatic, five hepatobiliary, one gastric) on strong opioid therapy underwent posterior fluoroscopy-guided CPN with absolute ethanol after a diagnostic lidocaine block. Daily oral morphine equivalent (OME) and Numeric Rating Scale (NRS) pain scores were recorded at baseline and 1, 2, and 4 weeks. Pain response was defined a priori as ≥30% NRS reduction. Results: Median age was 67 years, baseline median OME 86.5 mg/day (range, 15~215), and median NRS 6. Median OME decreased to 72, 60, and 57.5 mg/day at 1, 2, and 4 weeks (median reductions 46.5%, 26.7%, 43.5%;all P≤0.002), and median NRS improved to 3, 4, and 4 (all P≤0.001). A pain responsewas achieved in 8/13 (61.5%) at 1 week, 7/13 (53.8%) at 2 weeks, and 5/12 (41.7%) at 4weeks. Opioid-related adverse events declined numerically (nausea, 7/13→3/12; constipation, 4→2; somnolence, 2→0; all McNemar P>0.10). No procedural complication occurred(0/13). Conclusion CPN was followed by reduction in opioid use and pain scores, withresolution of opioid-related symptoms and no complications. Although limited by the smallsample size, these findings suggest a potential role for CPN in palliative care patients receiving extended-release opioids and warrant prospective investigation.

The pathogenesis of endotoxin-induced acute lung injury is fundamentally driven by dysregulated innate immune responses, where macrophage-mediated cytokine surges and subsequent signaling cascades trigger neutrophil infiltration and tissue damage.This study investigated whether curcumin modulates inflammatory signaling pathways and attenuates lung injury in experimental endotoxemia. Using endotoxin-stimulated murine macrophages and a mouse model of intratracheal lipopolysaccharide challenge, inflammatory cytokine production, mitogen-activated protein kinase activation, pulmonary edema, neutrophil accumulation, histopathologic injury, and short-term survival were assessed. Curcumin suppressed endotoxin-induced tumor necrosis factor- production and selectively inhibited ERK1/2 and JNK phosphorylation without affecting p38 signaling in macrophages. In vivo, curcumin reduced pulmonary cytokine levels, neutrophil infiltration, lung edema, and histologic injury, and was associated with improved survival following severe endotoxin exposure. These findings indicate that curcumin attenuates acute lung injury by selectively modulating intracellular inflammatory signaling pathways, supporting the concept that targeted inhibition of specific kinase cascades may mitigate inflammatory lung damage without broad immune suppression.

Background: The rapid advancement of artificial intelligence (AI) in healthcare necessitates comprehensive and standardized reporting guidelines to ensure transparency, reproducibility, and ethical applications in clinical research. Existing reporting standards are limited by their focus on specific study designs. We aimed to develop a comprehensive set of guidelines and a checklist for reporting studies that develop and utilize AI models in healthcare, covering all essential components of AI research regardless of the study design. Methods: Two experts in statistics from the Statistical Round of the Korean Journal of Anesthesiology developed these guidelines and checklist. The key elements essential for AI model reporting were identified and organized into structured sections, including study design, data preparation, model training and evaluation, ethical considerations, and clinical implementation. Iterative reviews and feedback from clinicians and researchers were used to finalize the guidelines and checklist. Results: These guidelines provide a detailed description of each item on the checklist, ensuring comprehensive reporting of AI model research. Full details regarding the AI model specifications and data-handling processes are provided. Conclusions These guidelines and checklist are meant to serve as valuable tools for researchers, addressing key aspects of AI reporting, and thereby supporting the reliability, accountability, and ethical use of AI in healthcare research.

Background: and Purpose: Amyloid-beta (Aβ) plaques are key in Alzheimer’s disease (AD), with Aβ positron emission tomography imaging enabling non-invasive quantification.To address regional Aβ deposition, we developed regional Centiloid scales (rdcCL) and commercialized them through the computed tomography (CT)-based BeauBrain Amylo platform, eliminating the need for three-dimensional T1 magnetic resonance imaging (MRI). Objective: We aimed to establish robust regional Aβ cutoffs using the commercialized BeauBrain Amylo platform and to explore the prevalence of subgroups defined by global, regional, and striatal Aβ cutoffs across cognitive stages. Methods: We included 2,428 individuals recruited from the Korea-Registries to Overcome Dementia and Accelerate Dementia Research project. We calculated regional Aβ cutoffs using Gaussian Mixture Modeling. Participants were classified into subgroups based on global, regional, and striatal Aβ positivity across cognitive stages (cognitively unimpaired [CU], mild cognitive impairment, and dementia of the Alzheimer’s type). Results: MRI-based and CT-based global Aβ cutoffs were highly comparable and consistent with previously reported Centiloid values. Regional cutoffs revealed both similarities and differences between MRI- and CT-based methods, reflecting modality-specific segmentation processes. Subgroups such as global(−)regional(+) were more frequent in non-dementia stages, while global(+)striatal(−) was primarily observed in CU individuals. Conclusions Our study established robust regional Aβ cutoffs using a CT-based rdcCL method and demonstrated its clinical utility in classifying amyloid subgroups across cognitive stages. These findings highlight the importance of regional Aβ quantification in understanding amyloid pathology and its implications for biomarker-guided diagnosis and treatment in AD.

Background: and Purpose: Brain atrophy, characterized by sulcal widening and ventricular enlargement, is a hallmark of neurodegenerative diseases such as Alzheimer’s disease. Visual assessments are subjective and variable, while automated methods struggle with subtle intensity differences and standardization, highlighting limitations in both approaches. This study aimed to develop and evaluate a novel method focusing on cerebrospinal fluid (CSF) regions by assessing segmentation accuracy, detecting stage-specific atrophy patterns, and testing generalizability to unstandardized datasets. Methods: We utilized T1-weighted magnetic resonance imaging data from 3,315 participants from Samsung Medical Center and 1,439 participants from other hospitals. Segmentation accuracy was evaluated using the Dice similarity coefficient (DSC), and W-scores were calculated for each region of interest (ROI) to assess stage-specific atrophy patterns. Results: The segmentation demonstrated high accuracy, with average DSC values exceeding 0.9 for ventricular and hippocampal regions and above 0.8 for cortical regions. Significant differences in W-scores were observed across cognitive stages (cognitively unimpaired, mild cognitive impairment, dementia of Alzheimer’s type) for all ROIs (all, p<0.05). Similar trends were observed in the images from other hospitals, confirming the algorithm’s generalizability to datasets without prior standardization. Conclusions This study demonstrates the robustness and clinical applicability of a novel CSF-focused segmentation method for assessing brain atrophy. The method provides a scalable and objective framework for evaluating structural changes across cognitive stages and holds potential for broader application in neurodegenerative disease research and clinical practice.

The common data model (CDM) has found widespread application in healthcare studies, but its utilization in cancer research has been limited. This article describes the development and implementation strategy for Cancer Clinical Library Databases (CCLDs), which are standardized cancer-specific databases established under the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) project by the Korean Ministry of Health and Welfare. Fifteen leading hospitals and fourteen academic associations in Korea are engaged in constructing CCLDs for 10 primary cancer types. For each cancer type-specific CCLD, cancer data experts determine key clinical data items essential for cancer research, standardize these items across cancer types, and create a standardized schema. Comprehensive clinical records covering diagnosis, treatment, and outcomes, with annual updates, are collected for each cancer patient in the target population, and quality control is based on six-sigma standards. To protect patient privacy, CCLDs follow stringent data security guidelines by pseudonymizing personal identification information and operating within a closed analysis environment. Researchers can apply for access to CCLD data through the K-CURE portal, which is subject to Institutional Review Board and Data Review Board approval. The CCLD is considered a pioneering standardized cancer-specific database, significantly representing Korea’s cancer data. It is expected to overcome limitations of previous CDMs and provide a valuable resource for multicenter cancer research in Korea.

Background: and Purpose: Amyloid-beta (Aβ) plaques are key in Alzheimer’s disease (AD), with Aβ positron emission tomography imaging enabling non-invasive quantification.To address regional Aβ deposition, we developed regional Centiloid scales (rdcCL) and commercialized them through the computed tomography (CT)-based BeauBrain Amylo platform, eliminating the need for three-dimensional T1 magnetic resonance imaging (MRI). Objective: We aimed to establish robust regional Aβ cutoffs using the commercialized BeauBrain Amylo platform and to explore the prevalence of subgroups defined by global, regional, and striatal Aβ cutoffs across cognitive stages. Methods: We included 2,428 individuals recruited from the Korea-Registries to Overcome Dementia and Accelerate Dementia Research project. We calculated regional Aβ cutoffs using Gaussian Mixture Modeling. Participants were classified into subgroups based on global, regional, and striatal Aβ positivity across cognitive stages (cognitively unimpaired [CU], mild cognitive impairment, and dementia of the Alzheimer’s type). Results: MRI-based and CT-based global Aβ cutoffs were highly comparable and consistent with previously reported Centiloid values. Regional cutoffs revealed both similarities and differences between MRI- and CT-based methods, reflecting modality-specific segmentation processes. Subgroups such as global(−)regional(+) were more frequent in non-dementia stages, while global(+)striatal(−) was primarily observed in CU individuals. Conclusions Our study established robust regional Aβ cutoffs using a CT-based rdcCL method and demonstrated its clinical utility in classifying amyloid subgroups across cognitive stages. These findings highlight the importance of regional Aβ quantification in understanding amyloid pathology and its implications for biomarker-guided diagnosis and treatment in AD.

Background: and Purpose: Brain atrophy, characterized by sulcal widening and ventricular enlargement, is a hallmark of neurodegenerative diseases such as Alzheimer’s disease. Visual assessments are subjective and variable, while automated methods struggle with subtle intensity differences and standardization, highlighting limitations in both approaches. This study aimed to develop and evaluate a novel method focusing on cerebrospinal fluid (CSF) regions by assessing segmentation accuracy, detecting stage-specific atrophy patterns, and testing generalizability to unstandardized datasets. Methods: We utilized T1-weighted magnetic resonance imaging data from 3,315 participants from Samsung Medical Center and 1,439 participants from other hospitals. Segmentation accuracy was evaluated using the Dice similarity coefficient (DSC), and W-scores were calculated for each region of interest (ROI) to assess stage-specific atrophy patterns. Results: The segmentation demonstrated high accuracy, with average DSC values exceeding 0.9 for ventricular and hippocampal regions and above 0.8 for cortical regions. Significant differences in W-scores were observed across cognitive stages (cognitively unimpaired, mild cognitive impairment, dementia of Alzheimer’s type) for all ROIs (all, p<0.05). Similar trends were observed in the images from other hospitals, confirming the algorithm’s generalizability to datasets without prior standardization. Conclusions This study demonstrates the robustness and clinical applicability of a novel CSF-focused segmentation method for assessing brain atrophy. The method provides a scalable and objective framework for evaluating structural changes across cognitive stages and holds potential for broader application in neurodegenerative disease research and clinical practice.

Acute facial nerve palsy, particularly Bell’s palsy, is a common neurological disorder with an annual incidence of 20-30 cases per 100,000 individuals. It is characterized by sudden or gradual facial muscle palsy and is caused by viral reactivation, inflammation, or ischemia of the facial nerve. Prognosis varies widely, depending on the severity of nerve damage and timeliness of the intervention. Steroid therapy remains the cornerstone of Bell’s palsy treatment because it reduces inflammation and facilitates recovery. Early administration, preferably within 72 hours of symptom onset, considerably improves outcomes. However, the efficacy of combination therapy remains controversial. Current guidelines recommend oral steroids as the primary treatment for Bell’s palsy and suggest the selective use of antiviral agents in severe cases or when viral involvement is strongly suspected. For severe facial palsy, such as Ramsay Hunt syndrome or varicella-zoster virus-induced cases, combination therapy may improve outcomes and reduce sequelae; however, high-quality evidence is limited. Steroid therapy is the main treatment of Bell’s palsy and antiviral therapy can be added in severe cases to improve prognosis. Additional research is required to develop standardized guidelines, concerning the use of antiviral therapies in conjunction with steroids.