Research on IgG4-related disease (IgG4-RD), an autoimmune condition recognized to be a unique disease entity only two decades ago, has processed from describing patients' symptoms and signs to summarizing its critical pathological features, and further to investigating key pathogenic mechanisms. Challenges in gaining a better understanding of the disease, however, stem from its relative rarity-potentially attributed to underrecognition-and the absence of ideal experimental animal models. Recently, with the development of various high-throughput techniques, "omics" studies at different levels (particularly the single-cell omics) have shown promise in providing detailed molecular features of IgG4-RD. While, the application of omics approaches in IgG4-RD is still at an early stage. In this paper, we review the current progress of omics research in IgG4-RD and discuss the value of machine learning methods in analyzing the data with high dimensionality.
Humans ; Immunoglobulin G4-Related Disease/metabolism* ; Immunoglobulin G/metabolism* ; Machine Learning ; Animals ; Proteomics/methods*

Humans ; Lung Transplantation/adverse effects* ; Risk Factors

Background: Lessons learned from the Household Humidifier Disinfectant Tragedy (HHDT) in Korea, which poisoned thousands of citizens over a period of years, necessitated an examination of national poison prevention and surveillance systems. The objectives of this study are to identify essential changes needed in chemical poisoning prevention regulations and surveillance systems for effective poison control by comparing recent trends in international poison control center (PCC) operations, and to delineate the critical elements for establishing a state-of-the-art poison control surveillance system in Korea based on recent advances in PCCs with toxicovigilance. Methods: A comprehensive review of Korea’s regulatory and surveillance systems for chemical health hazards, with a focus on household products under the HHDT, was conducted. A review of toxicovigilance systems in major countries shows that creating an effective national PCC requires key elements: a centralized database of toxic substances and poisoning cases, mandatory or voluntary reporting of poisoning cases, real-time alerts, collaboration among health organizations, and targeted follow-up of poisoned individuals. Results: Significant deficiencies in Korea’s legislation, toxicological data management, and poisoning surveillance systems, explained the inadequate response of the Korean government to the HHDT for nearly 17 years until the end of 2011. Based on a review of PCC toxicovigilance systems in major countries, a national framework with five core components is recommended for establishing a modern comprehensive Korea PCC system with toxicovigilance capacity. The core components include establishment of a centralized database of toxic substances information and clinical poisoning cases, implementation of mandatory or permissive reporting of poisoning cases, real-time alert mechanisms, collaborative systems among health-related organizations, and clinical follow-up of poisoned sub-groups. Conclusion A rationale and framework for a state-of-the-art national Korean PCC with toxicovigilance is justified and offered. This proposed system could assist neighboring countries in establishing their own sophisticated, globally integrated PCC networks.

Objective: The Montreal Cognitive Assessment (MoCA) is recommended for general cognitive evaluation in Parkinson’s disease (PD) patients. However, age- and education-adjusted cutoffs specifically for PD have not been developed or systematically validated across PD cohorts with diverse education levels. Methods: In this retrospective analysis, we utilized data from 1,293 Korean patients with PD whose cognitive diagnoses were determined through comprehensive neuropsychological assessments. Age- and education-adjusted cutoffs were formulated based on 1,202 patients with PD. To identify the optimal machine learning model, clinical parameters and MoCA domain scores from 416 patients with PD were used. Comparative analyses between machine learning methods and different cutoff criteria were conducted on an additional 91 consecutive patients with PD. Results: The cutoffs for cognitive impairment decrease with increasing age within the same education level. Similarly, lower education levels within the same age group correspond to lower cutoffs. For individuals aged 60–80 years, cutoffs were set as follows: 25 or 24 years for those with more than 12 years of education, 23 or 22 years for 10–12 years, and 21 or 20 years for 7–9 years. Comparisons between age- and education-adjusted cutoffs and the machine learning method showed comparable accuracies. The cutoff method resulted in a higher sensitivity (0.8627), whereas machine learning yielded higher specificity (0.8250). Conclusion Both the age- and education-adjusted cutoff methods and machine learning methods demonstrated high effectiveness in detecting cognitive impairment in PD patients. This study highlights the necessity of tailored cutoffs and suggests the potential of machine learning to improve cognitive assessment in PD patients.

Humans ; Sarcoma, Clear Cell/pathology* ; Melanoma/pathology* ; Skin Neoplasms/pathology* ; Diagnostic Errors ; Soft Tissue Neoplasms ; Melanoma, Cutaneous Malignant

Humans ; Carcinoma, Hepatocellular/surgery* ; Liver Neoplasms/pathology* ; Cholangiocarcinoma/pathology* ; Bile Ducts, Intrahepatic/pathology* ; Bile Duct Neoplasms/pathology*

Humans ; Thyroid Neoplasms/surgery* ; Thyroid Cancer, Papillary