Samsung Biomedical Research Institute

Researchers report the development of ultrahigh refractive index metamaterials which are integrated with a low refractive index polymer producing distributed Bragg reflector (DBR). The highest refractive index in the visible and near-infrared regions was reported. The new technology is applicable to precision semiconductor processes and high-resolution display technology. We all look in the mirror at least once a day to see our reflection. Mirrors are used not only in daily life but also in cutting-edge technologies such as semiconductor processing and high-resolution displays. Recently, a powerful Bragg reflection mirror based on high-index metamaterials has been developed that only reflects desired light. A research team led by Professor Gi-Ra Yi (Department of Chemical Engineering) at POSTECH with the research team led by professors Seok Joon Kwon and Pil Jin Yoo (School of Chemical Engineering) at Sungkyunkwan University have together developed an ultrahigh refractive index metamaterial by closely packing gold nanospheres and a reflector that combines the metamaterial with a polymer. Metamaterials -- with properties that do not exist in nature -- can be designed to have a negative (−) or ultrahigh refractive index. However, metamaterials with a high refractive index still have limitations from designing to manufacturing. To overcome this issue, the research team developed a metamaterial that is uniformly arranged with the 1-nanometer-level gaps (nm, 1 billionth of a meter) by assembling spherical gold nanoparticles. This material, which maximizes light-matter interaction, recorded the highest refractive index in the visible and near-infrared regions. The 2D superstructures exhibited the highest-ever refractive index of 7.8 The distributed Bragg reflector (DBR), which is made by stacking these metamaterials and polymer layers with a low refractive index, strongly reflected specific wavelengths. Furthermore, the research team established the theory of a plasmonic percolation model that can explain the extremely high refractive index. As it theoretically explains the ultrahigh refractive index of metamaterials that could not be explained in previous studies, the development of related research fields is anticipated in the future. This study is also garnering attention from academic circles and industry for its applicability in precise semiconductor processes and high-resolution displays. Recently published in Advanced Materials, the study was supported by the Samsung Research Funding Center and the National Research Foundation of Korea.

New research finds that machine-learning algorithms can help health care staff distinguish the two conditions. Researchers show how algorithms may be effective predictive tools using a few simple variables and routinely available structured clinical information. Acute cholangitis is a potentially life-threatening bacterial infection that often is associated with gallstones. Symptoms include fever, jaundice, right upper quadrant pain, and elevated liver enzymes. While these may seem like distinctive, telltale symptoms, unfortunately, they are similar to those of a much different condition: alcohol-associated hepatitis. This challenges emergency department staff and other health care professionals who need to diagnose and treat patients with liver enzyme abnormalities and systemic inflammatory responses. New Mayo Clinic research finds that machine-learning algorithms can help health care staff distinguish the two conditions. In an article published in Mayo Clinic Proceedings, researchers show how algorithms may be effective predictive tools using a few simple variables and routinely available structured clinical information. "This study was motivated by seeing many medical providers in the emergency department or ICU struggle to distinguish acute cholangitis and alcohol-associated hepatitis, which are very different conditions that can present similarly," says Joseph Ahn, M.D., a third-year gastroenterology and hepatology fellow at Mayo Clinic in Rochester. Dr. Ahn is first author of the study. "We developed and trained machine-learning algorithms to distinguish the two conditions using some of the routinely available lab values that all of these patients should have," Dr. Ahn says. "The machine-learning algorithms demonstrated excellent performances for discriminating the two conditions, with over 93% accuracy." The researchers analyzed electronic health records of 459 patients older than age 18 who were admitted to Mayo Clinic in Rochester between Jan. 1, 2010, and Dec. 31, 2019. The patients were diagnosed with acute cholangitis or alcohol-associated hepatitis. Ten routinely available laboratory values were collected at the time of admission. After removal of patients whose data were incomplete, 260 patients with alcohol-associated hepatitis and 194 with acute cholangitis remained. These data were used to train eight machine-learning algorithms. The researchers also externally validated the results using a cohort of ICU patients who were seen at Beth Israel Deaconess Medical Center in Boston between 2001 and 2012. The algorithms also outperformed physicians who participated in an online survey, which is described in the article. "The study highlights the potential for machine-learning algorithms to assist in clinical decision-making in cases of uncertainty," says Dr. Ahn. "There are many instances of gastroenterologists receiving consults for urgent endoscopic retrograde cholangiopancreatography in patients who initially deny a history of alcohol use but later turn out to have alcohol-associated hepatitis. In some situations, the inability to obtain a reliable history from patients with altered mental status or lack of access to imaging modalities in underserved areas may force providers to make the determination based on a limited amount of objective data." If the machine-learning algorithms can be made easily accessible with an online calculator or smartphone app, they may help health care staff who are urgently presented with an acutely ill patient with abnormal liver enzymes, according to the study. "For patients, this would lead to improved diagnostic accuracy and reduce the number of additional tests or inappropriate ordering of invasive procedures, which may delay the correct diagnosis or subject patients to the risk of unnecessary complications," Dr. Ahn says. The authors are from the Division of Gastroenterology and Hepatology and the Division of Internal Medicine at Mayo Clinic in Rochester, and from the Department of Computer Science at Hanyang University in Seoul, South Korea. Co-author Yung-Kyun Noh was supported in this research by Samsung Research Funding and Incubation Center of Samsung Electronics. The authors report no competing interests.