Review of Image-Processing-Based Technology for Structural Health Monitoring of Civil Infrastructures.

The continuous monitoring of civil infrastructures is crucial for ensuring public safety and extending the lifespan of structures. In recent years, image-processing-based technologies have emerged as powerful tools for the structural health monitoring (SHM) of civil infrastructures. This review provides a comprehensive overview of the advancements, applications, and challenges associated with image processing in the field of SHM. The discussion encompasses various imaging techniques such as satellite imagery, Light Detection and Ranging (LiDAR), optical cameras, and other non-destructive testing methods. Key topics include the use of image processing for damage detection, crack identification, deformation monitoring, and overall structural assessment. This review explores the integration of artificial intelligence and machine learning techniques with image processing for enhanced automation and accuracy in SHM. By consolidating the current state of image-processing-based technology for SHM, this review aims to show the full potential of image-based approaches for researchers, engineers, and professionals involved in civil engineering, SHM, image processing, and related fields.

Development of human pluripotent stem cell-derived hepatic organoids as an alternative model for drug safety assessment.

Human in vitro hepatic models that faithfully recapitulate liver function are essential for successful basic and translational research. A limitation of current in vitro models, which are extensively used for drug discovery and toxicity testing, is the loss of drug metabolic function due to the low expression and activity of cytochrome P450 (CYP450) enzymes. Here, we aimed to generate human pluripotent stem cell-derived hepatic organoids (hHOs) with a high drug metabolic ability. We established a two-step protocol to produce hHOs from human pluripotent stem cells for long-term expansion and drug testing. Fully differentiated hHOs had multicellular composition and exhibited cellular polarity and hepatobiliary structures. They also displayed remarkable CYP450 activity and recapitulated the metabolic clearance, CYP450-mediated drug toxicity, and metabolism. Furthermore, hHOs successfully modeled Wilson's disease in terms of Cu metabolism, drug responses, and diagnostic marker expression and secretion. In conclusion, hHOs exhibit high capacity for drug testing and disease modeling. Hence, this hepatic model system provides an advanced tool for studying hepatic drug metabolism and diseases.

COVID-19 International Collaborative Research by the Health Insurance Review and Assessment Service Using Its Nationwide Real-world Data: Database, Outcomes, and Implications.

This article aims to introduce the inception and operation of the COVID-19 International Collaborative Research Project, the world's first coronavirus disease 2019 (COVID-19) open data project for research, along with its dataset and research method, and to discuss relevant considerations for collaborative research using nationwide real-world data (RWD). COVID-19 has spread across the world since early 2020, becoming a serious global health threat to life, safety, and social and economic activities. However, insufficient RWD from patients was available to help clinicians efficiently diagnose and treat patients with COVID-19, or to provide necessary information to the government for policy-making. Countries that saw a rapid surge of infections had to focus on leveraging medical professionals to treat patients, and the circumstances made it even more difficult to promptly use COVID-19 RWD. Against this backdrop, the Health Insurance Review and Assessment Service (HIRA) of Korea decided to open its COVID-19 RWD collected through Korea's universal health insurance program, under the title of the COVID-19 International Collaborative Research Project. The dataset, consisting of 476 508 claim statements from 234 427 patients (7590 confirmed cases) and 18 691 318 claim statements of the same patients for the previous 3 years, was established and hosted on HIRA's in-house server. Researchers who applied to participate in the project uploaded analysis code on the platform prepared by HIRA, and HIRA conducted the analysis and provided outcome values. As of November 2020, analyses have been completed for 129 research projects, which have been published or are in the process of being published in prestigious journals.

Building Linked Big Data for Stroke in Korea: Linkage of Stroke Registry and National Health Insurance Claims Data.

BACKGROUND: Linkage of public healthcare data is useful in stroke research because patients may visit different sectors of the health system before, during, and after stroke. Therefore, we aimed to establish high-quality big data on stroke in Korea by linking acute stroke registry and national health claim databases. METHODS: Acute stroke patients (n = 65,311) with claim data suitable for linkage were included in the Clinical Research Center for Stroke (CRCS) registry during 2006-2014. We linked the CRCS registry with national health claim databases in the Health Insurance Review and Assessment Service (HIRA). Linkage was performed using 6 common variables: birth date, gender, provider identification, receiving year and number, and statement serial number in the benefit claim statement. For matched records, linkage accuracy was evaluated using differences between hospital visiting date in the CRCS registry and the commencement date for health insurance care in HIRA. RESULTS: Of 65,311 CRCS cases, 64,634 were matched to HIRA cases (match rate, 99.0%). The proportion of true matches was 94.4% (n = 61,017) in the matched data. Among true matches (mean age 66.4 years; men 58.4%), the median National Institutes of Health Stroke Scale score was 3 (interquartile range 1-7). When comparing baseline characteristics between true matches and false matches, no substantial difference was observed for any variable. CONCLUSION: We could establish big data on stroke by linking CRCS registry and HIRA records, using claims data without personal identifiers. We plan to conduct national stroke research and improve stroke care using the linked big database.