Interactive description to enhance accessibility and experience of deaf and hard-of-hearing individuals in museums.

Text descriptions in museums provide detailed and rich information about artifacts that broadens museum visitors' knowledge and enriches their experience. However, since deaf and hard-of-hearing (DHH) individuals have low literacy compared to hearing people and communicate through sign language, museum descriptions are considerably limited in delivering a stimulating and informative environment for understanding and enjoying exhibits. To improve DHH individuals' museum experience, we investigated the potential of three interactive description prototypes: active-linked, graph-based, and chatbot-based. A comparative study with 20 DHH participants confirmed that our interaction-based prototypes improve information accessibility and provide an enhanced experience compared to conventional museum descriptions. Most participants preferred the graph-based prototype, while post-interviews suggested that each prototype has potential benefits and limitations according to DHH individuals' particular literacy skills and preferences. Text descriptions can be enlivened for DHH visitors by adding a simple interaction functionality, e.g., clicking, which can lead to a better museum experience.

Use of Proton Pump Inhibitors vs Histamine 2 Receptor Antagonists for the Risk of Gastric Cancer: Population-Based Cohort Study.

INTRODUCTION: Proton pump inhibitors (PPIs) are commonly prescribed medications. Long-term use of PPIs has been suspected to have a provocative effect on gastric cancer. This study was to determine the association between PPI vs histamine 2 receptor antagonist (H2RA) use and the risk of gastric cancer in a region where the risk of this malignancy is high. METHODS: A population-based cohort study using the Korean National Health Insurance Services Database. The participants with first prescription of PPIs and H2RA with normal esophagogastroduodenoscopy finding from 2004 through 2015 were collected. Among them, 50% of participants were systematic stratified randomly sampled. There were 122,118 users of PPIs or H2RAs who use medication more than cumulative defined daily dose of 180 days. The users were followed up from long-term use threshold until gastric cancer, death from non-gastric cancer cause, gastric surgery, or study end (December 2017). RESULTS: After calculating propensity score weights, we included 39,799 PPI and 38,967 H2RA users. Among the new PPI and H2RA users, we identified 411 cases of incident gastric cancer from 182,643 person-years of follow-up observation and 397 cases from 178,846 person-years of follow-up observation, respectively. Compared with H2RA users, PPI users did not experience significantly different gastric cancer incidence (adjusted hazard ratio, 1.01; 95% confidence interval, 0.88-1.16; P = 0.89). Sensitivity analyses confirmed that gastric cancer incidence did not differ between PPI and H2RA users. DISCUSSION: In this large study, long-term treatment with PPIs vs H2RAs did not show higher risk of gastric cancer even in a high-risk region.

Artificial Intelligence-Based Diagnostic Support System for Patent Ductus Arteriosus in Premature Infants.

Background: Patent ductus arteriosus (PDA) is a prevalent congenital heart defect in premature infants, associated with significant morbidity and mortality. Accurate and timely diagnosis of PDA is crucial, given the vulnerability of this population. Methods: We introduce an artificial intelligence (AI)-based PDA diagnostic support system designed to assist medical professionals in diagnosing PDA in premature infants. This study utilized electronic health record (EHR) data from 409 premature infants spanning a decade at Severance Children's Hospital. Our system integrates a data viewer, data analyzer, and AI-based diagnosis supporter, facilitating comprehensive data presentation, analysis, and early symptom detection. Results: The system's performance was evaluated through diagnostic tests involving medical professionals. This early detection model achieved an accuracy rate of up to 84%, enabling detection up to 3.3 days in advance. In diagnostic tests, medical professionals using the system with the AI-based diagnosis supporter outperformed those using the system without the supporter. Conclusions: Our AI-based PDA diagnostic support system offers a comprehensive solution for medical professionals to accurately diagnose PDA in a timely manner in premature infants. The collaborative integration of medical expertise and technological innovation demonstrated in this study underscores the potential of AI-driven tools in advancing neonatal diagnosis and care.

Developing a national surveillance system for stroke and acute myocardial infarction using claims data in the Republic of Korea: a retrospective study.

BACKGROUND: Limited information is available concerning the epidemiology of stroke and acute myocardial infarction (AMI) in the Republic of Korea. This study aimed to develop a national surveillance system to monitor the incidence of stroke and AMI using national claims data. METHODS: We developed and validated identification algorithms for stroke and AMI using claims data. This validation involved a 2-stage stratified sampling method with a review of medical records for sampled cases. The weighted positive predictive value (PPV) and negative predictive value (NPV) were calculated based on the sampling structure and the corresponding sampling rates. Incident cases and the incidence rates of stroke and AMI in the Republic of Korea were estimated by applying the algorithms and weighted PPV and NPV to the 2018 National Health Insurance Service claims data. RESULTS: In total, 2,200 cases (1,086 stroke cases and 1,114 AMI cases) were sampled from the 2018 claims database. The sensitivity and specificity of the algorithms were 94.3% and 88.6% for stroke and 97.9% and 90.1% for AMI, respectively. The estimated number of cases, including recurrent events, was 150,837 for stroke and 40,529 for AMI in 2018. The age- and sex-standardized incidence rate for stroke and AMI was 180.2 and 46.1 cases per 100,000 person-years, respectively, in 2018. CONCLUSION: This study demonstrates the feasibility of developing a national surveillance system based on claims data and identification algorithms for stroke and AMI to monitor their incidence rates.

Sustainable Antibacterial and Antiviral High-Performance Copper-Coated Filter Produced via Ion Beam Treatment.

With the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), disease prevention has become incredibly important. Consequently, mask and air-purifier use has increased. The filter is the core component of these items. However, most filter materials lack antimicrobial properties. Copper is a sustainable antimicrobial material. When copper is deposited onto the filter's surface, the microorganisms that come into contact with it can be effectively inactivated. In this study, we used an oxygen ion beam with a controlled process temperature to treat filter surfaces with copper. This enabled a strong adhesion of at least 4 N/cm between the copper and the filter fibers without damaging them. Upon exposing the filter to bacteria (Staphylococcus aureus ATCC 6538, Klebsiella pneumoniae ATCC 4352, Escherichia coli ATCC 25922, and Pseudomonas aeruginosa ATCC 27853) for one hour, a >99.99% removal rate was attained; when the filter was exposed to SARS-CoV-2 virus for one hour, it inactivated more than 99%. These beneficial properties minimize the risk of secondary infections, which are significantly more likely to occur when a conventional filter is replaced or removed.

The classification of cancer based on DNA microarray data that uses diverse ensemble genetic programming.

OBJECT: The classification of cancer based on gene expression data is one of the most important procedures in bioinformatics. In order to obtain highly accurate results, ensemble approaches have been applied when classifying DNA microarray data. Diversity is very important in these ensemble approaches, but it is difficult to apply conventional diversity measures when there are only a few training samples available. Key issues that need to be addressed under such circumstances are the development of a new ensemble approach that can enhance the successful classification of these datasets. MATERIALS AND METHODS: An effective ensemble approach that does use diversity in genetic programming is proposed. This diversity is measured by comparing the structure of the classification rules instead of output-based diversity estimating. RESULTS: Experiments performed on common gene expression datasets (such as lymphoma cancer dataset, lung cancer dataset and ovarian cancer dataset) demonstrate the performance of the proposed method in relation to the conventional approaches. CONCLUSION: Diversity measured by comparing the structure of the classification rules obtained by genetic programming is useful to improve the performance of the ensemble classifier.

A novel evolutionary approach to image enhancement filter design: method and applications.

Image enhancement is an important issue in digital image processing. Various approaches have been developed to solve image enhancement problems, but most of them require deep expert knowledge to design appropriate image filters. To automatically design a filter, we propose a novel approach based on the genetic algorithm that optimizes a set of standard filters by determining their types and order. Moreover, the proposed method is able to manage various types of noise factors. We applied the proposed method to local and global image enhancement problems such as impulsive noise reduction, interpolation, and orientation enhancement. In terms of subjective and objective evaluations, the results show the superiority of the proposed method.