Identification of potentially effective drugs for metabolic dysfunction-associated steatotic liver disease against liver cirrhosis: In-silico drug repositioning-based retrospective cohort study.

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a major risk factor for liver cirrhosis, yet effective prevention or treatment strategies remain limited. To address this, we utilized a signature-based in silico drug repositioning approach to identify potential therapeutics for MASLD that may reduce the risk of cirrhosis.

Methods: We analyzed gene expression datasets to identify differentially expressed genes (DEGs) in MASLD and matched them to candidate drugs using L1000CDS2.

Necessity, accessibility and acquisition cost of unstructured medical data for South Korean medical device companies.

Objective: This research aimed to identify the needs and priorities concerning structured and unstructured medical data of medical device companies engaged in developing medical software or hardware.

Method: The study was conducted between 23 March and 26 March 2023 and involved a survey of 76 managers, each of whom represented a single Korean medical device company.

Result: Companies focused on novel medical devices and software expressed higher demand for unstructured data than for structured data.

Increased risk of colorectal adenoma and benign colorectal polyp associated with infection: a systematic review and meta-analysis.

Background: Although the association between () infection and the risk of colorectal adenomas (CRAs) is suggested, specific analysis of the histological subtype of CRA is limited. The aim of the study was to conduct a meta-analysis on the risk of histological classifications of CRA as benign colorectal polyp (BCP), CRA, and advanced CRA to investigate the effects of .

Methods: A comprehensive literature searches of the PubMed, Embase, and Cochrane databases through January 2024.

Patient Survival Prediction by Analyzing Pathological Images of Patients After Liver Transplantation.

Predicting whether a patient will develop cancer using nuclear features on pathological images is important for decision making regarding patient treatment after liver transplantation or hepatectomy. Unlike manual segmentation to extract nuclei parts from pathology images, we performed the entire process of predicting patient survival automatically. In addition, we established a method to correctly predict survival even in cases where the amount of data is small.

Diagnosis of Chronic Kidney Disease Using Retinal Imaging and Urine Dipstick Data: Multimodal Deep Learning Approach.

Background: Chronic kidney disease (CKD) is a prevalent condition with significant global health implications. Early detection and management are critical to prevent disease progression and complications. Deep learning (DL) models using retinal images have emerged as potential noninvasive screening tools for CKD, though their performance may be limited, especially in identifying individuals with proteinuria and in specific subgroups.

Poorer outcomes following COVID-19 infection for patients with depression: A cohort analysis in South Korea.

Introduction: It remains unknown whether patients with pre-existing depressive conditions are at high risk of severe COVID-19. Therefore, this study aims to investigate the association between patients with pre-existing depressive conditions and severe COVID-19.

Method: This study is part of the Korea Disease Control and Prevention Agency-COVID19-National Health Insurance Service cohort study of an ongoing large-scale health screening survey of adults 18 years and older residing in South Korea.

Development of a deep learning model for predicting recurrence of hepatocellular carcinoma after liver transplantation.

Background: Liver transplantation (LT) is one of the main curative treatments for hepatocellular carcinoma (HCC). Milan criteria has long been applied to candidate LT patients with HCC. However, the application of Milan criteria failed to precisely predict patients at risk of recurrence.

Medical data market platforms and medical data trade demands of medical device companies.

Objective: The significance of big data is increasingly acknowledged across all sectors, including medicine. Moreover, the trend of data trading is on the rise, particularly in exchanging other data for medical data to rejuvenate the medical industry. This study aimed to discern the facilitating factors of healthcare data trade.

Empowering Healthcare through Comprehensive Informatics Education: The Status and Future of Biomedical and Health Informatics Education.

Objectives: Education in biomedical and health informatics is essential for managing complex healthcare systems, bridging the gap between healthcare and information technology, and adapting to the digital requirements of the healthcare industry. This review presents the current status of biomedical and health informatics education domestically and internationally and proposes recommendations for future development.

Methods: We analyzed evidence from reports and papers to explore global trends and international and domestic examples of education.

A blockchain-based healthcare data marketplace: prototype and demonstration.

Objectives: This study aimed to develop healthcare data marketplace using blockchain-based B2C model that ensures the transaction of healthcare data among individuals, companies, and marketplaces.

Materials And Methods: We designed an architecture for the healthcare data marketplace using blockchain. A healthcare data marketplace was developed using Panacea, MySQL 8.

Association between short- and medium-term exposure to air pollutants and depressive episode using comprehensive air quality index among the population in South Korea.

Background: Currently, air pollution is suggested as a risk factor for depressive episodes. Our study aimed to consider multiple air pollutants simultaneously, and continuously evaluate air pollutants using comprehensive air quality index (CAI) with depressive episode risk.

Methods: Using a nationally representative sample survey from South Korea between 2014 and 2020, 20,796 participants who underwent health examination and Patient Depression Questionnaire-9 were included in the study.

Machine Learning Models for Low Back Pain Detection and Factor Identification: Insights From a 6-Year Nationwide Survey.

This study aimed to enhance performance, identify additional predictors, and improve the interpretability of biopsychosocial machine learning models for low back pain (LBP). Using survey data from a 6-year nationwide study involving 17,609 adults aged ≥50 years (Korea National Health and Nutrition Examination Survey), we explored 119 factors to detect LBP in individuals who reported experiencing LBP for at least 30 days within the previous 3 months. Our primary model, model 1, employed eXtreme Gradient Boosting (XGBoost) and selected primary factors (PFs) based on their feature importance scores.

Moderate-to-Vigorous Physical Activity Changes with the Risk of SARS-CoV-2 Infection: A Nested Case-Control Study.

We identified the association of changes in moderate-to-vigorous physical activity (MVPA) with SARS-CoV-2 infection. From 6,396,500 patients, we performed a nested case-control study who participated in both biennial check-ups. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) were calculated using multivariable logistic regression.

Predicted Body Composition Against COVID-19: A Potential Digital Health Strategy.

This study focused on the associations between predicted lean body mass index (LBMI), appendicular skeletal muscle mass index (ASMI), and body fat mass index (BFMI) with the 2019 coronavirus disease (COVID-19). A nationwide population-based non-underweight cohort of 2,037,714 participants underwent two consecutive biennial health screening examinations, with changes in predicted body composition indices estimated using a multivariable-adjusted logistic regression model. Increased LBMI and ASMI were associated with a lower COVID-19 risk among men who became obese.

COVID-19-related cardiovascular disease risk due to weight gain: a nationwide cohort study.

Background: Cardiovascular disease (CVD) is a significant contributor to morbidity and mortality worldwide, with CVD and post-acute COVID-19 associated CVD increasing. It remains unknown whether COVID-19 patients with weight gain are at a high risk for CVD events. Therefore, the primary objective of this study is to investigate the association between weight control and the risk of CVD following COVID-19.

Association of weight changes with SARS-CoV-2 infection and severe COVID-19 outcomes: A nationwide retrospective cohort study.

Background: Obesity is a risk factor for COVID-19. However, it is unknown whether weight changes can alter this risk. We investigated the association of weight changes with SARS-CoV-2 infection and acute severe COVID-19 outcomes occurring within two months of the infection.

Risk of Subsequent Primary Cancer in Thyroid Cancer Survivors: A Nationwide Population-Based Study.

Despite obtaining a good prognosis and long life expectancy, survivors of thyroid cancer can nevertheless develop subsequent primary cancer (SPC). We investigated the risk and types of SPC in patients with thyroid cancer and compared them with subjects without thyroid cancer history (controls). We conducted a nationwide, population-based, retrospective cohort study based on the Korean National Health Insurance Database.

Non-Alcoholic Fatty Liver Disease, Atherosclerosis, and Cardiovascular Disease in Asia.

The prevalence of non-alcoholic fatty liver disease (NAFLD) is estimated to increase to over half of the adult population by 2040 globally. Since the final diagnosis of NAFLD is made by a liver biopsy, several non-invasive approaches have been developed and validated to define NAFLD and evaluate NAFLD-associated diseases. Presently, NAFLD has been identified as an important and independent risk factor for developing several extrahepatic diseases, including atherosclerosis, cardiovascular disease (CVD), diabetes, and dementia.

Inverse association between changes in systolic and diastolic blood pressure and risk of depression: A nationally representative cohort study.

Background: Depression is one of complex mental disorders with diverse etiological factors but the association between blood pressure (BP) and depression is unknown. We aimed to investigate the association between changes in BP (systolic and diastolic) and incident depression.

Methods: From the National Health Insurance Service-Health Screening Cohort (NHIS-HEALS), 224,192 participants who underwent biennial health screenings from period I (2004-05) and II (2006-07) were included in the study.