Skin Manifestations Following COVID-19 Vaccination: A Multicenter Case Series Study in Korea

Background: During the coronavirus disease 2019 (COVID-19) pandemic, large-scale vaccinations have been performed worldwide without sufficient verification of safety profiles. So far, little is known about skin manifestations following COVID-19 vaccination in Korean patients. Objective: We investigated the epidemiological and clinical characteristics of patients who had skin manifestations following COVID-19 vaccination in Korea. Methods: We retrospectively reviewed me data of 123 patients that presented with skin manifestations within 1 month after COVID-19 vaccination from two tertiary referral hospitals in Korea. The types of COVID-19 vaccinations administered to the patients, demographics, comorbidities, and clinical course of the patients were obtained from the data. Statistical analyses of the extracted data were performed using Microsoft Excel. Results: Skin manifestations following COVID-19 vaccination were mostly observed in patients in their 40s (23.6%), according to our data. Urticarial eruption was the most common manifestation, followed by macular rash (17.1%) and papulosquamous eruption (17.1%). Notably, 70% of the patients showed delayed reactions. More than half of the patients showed a good prognosis, and their symptoms were relieved with conservative treatment, including corti-costeroids and antihistamines, even after additional vaccination. Conclusion: We statistically analyzed the prevalence and characteristics of skin manifestations after COVID-19 vaccination. Urticarial eruptions are the most common skin manifestations associated with the COVID-19 vacci¬nation. We believe that this real-world retrospective study will provide valuable information for doctors who treat patients with skin manifestations after COVID-19 vaccination by providing real-world experience in Korea. (Korean J Dermatol 2022;60(9):576~584). © 2022 Korean Dermatological Association. All rights reserved.

Exploring international customers' luxury service experiences during the COVID-19 pandemic

During the COVID-19 pandemic and the associated strain on consumption activities, demand for luxury hotels has risen steadily as a safe way to enjoy leisure services. This study aims to examine the consequences of congruence and identification. An online survey generating 315 datasets from international customers of luxury hotel services is conducted. Partial least squares structural equation modeling is used to test the hypotheses. The results demonstrate that perceived brand authenticity by customers influences both revisit and word-of-mouth intentions via engagement. Also, brand congruence leads to customer engagement with luxury hotel services and word-of-mouth intention. In contrast, no direct impact of congruence on revisit intention was found. Results regarding the effects of brand authenticity and congruence demonstrate that luxury service brands have the potential to influence customers' patronage behaviors through different approaches. The findings confirm the importance of engagement with brands in the luxury service setting. © The Author(s) 2023.

Qualitative Evaluations of Obstetric Transport Experiences During the COVID-19 Pandemic

INTRODUCTION: The COVID-19 pandemic drastically affected the emergency obstetric transport experience on a multidimensional level. The objective of this study is to understand how the pandemic has affected the physical and emotional health of these patients prior to, during, and after their transport to the University of New Mexico Hospital (UNMH). METHODS: Participants transported for emergency obstetric indications were recruited at UNMH. Patient informed consent, demographic questionnaires, interviews, and surveys were obtained and graded on a 5- point Likert scale. These were delivered in person, via telephone, or via Zoom software and securely recorded. English interviews were transcribed via Otter.ai, and interviews conducted in Spanish were manually translated. Institutional review board approval was obtained. RESULTS: Preliminary results consisted of 10 participants. A majority (90%) felt that they had a good experience with the transport service and 70% of the individuals felt that proper protocols were put in place to protect them from a COVID-19 exposure. However, 40% of respondents had concerns about their safety during the transport process and 50% had concerns about their baby's safety. Additionally, 60% of participants felt that COVID-19 overall had a negative effect on their transport experience. CONCLUSION: A significant portion of participants felt that the COVID-19 pandemic had a negative effect on their obstetric transport experience. Understanding the needs of these patients during the COVID-19 pandemic is important given the safety-restrictions that have been in place. This highlights the need for studies identifying barriers for patients during the transport process to improve the patient experience.

The Restorative Power of Nostalgia: Thwarting Loneliness by Raising Happiness During the COVID-19 Pandemic

Lockdowns during the COVID-19 pandemic increased the risk for loneliness. We tested whether nostalgia counteracts loneliness via rises in happiness. We conducted surveys in China (N = 1,546), the United States (N = 1,572), and the United Kingdom (N = 603). Although feeling lonely was associated with unhappiness, it was also associated with nostalgia, which in turn conduced to increased happiness. We complemented these findings with three experiments testing MTurk workers (Study 4, N = 209;Study 5, N = 196;Study 6, N = 190), where we manipulated nostalgia and assessed happiness. Nostalgia increased happiness immediately after the manipulation (Studies 4–6) and, following an induction booster, up to 2 days later (Studies 4–5). Nostalgia is a psychological resource that can be harnessed to raise happiness and help combat loneliness. © The Author(s) 2021.

COVID-19 in Korea: Success Based on Past Failure

This paper analyzes how the Republic of Korea (Korea) halted the massive transmission of COVID-19 in just two months. The quarantine was achieved successfully without any need for a national lockdown because, simply, Korean citizens actively followed quarantine guidelines. During the 2015 Middle East Respiratory Syndrome (MERS) outbreak, the country recognized the importance of an early response in the form of a systematic approach and adopted the necessary procedures. Comparing the spread of COVID-19 among different countries, Korea demonstrated several distinct characteristics. First, the duration of the coronavirus crisis was relatively short, and Korea was able to flatten the coronavirus curve in a brief period. Second, Korea blocked expanded transmission of the virus without implementing a national lockdown. Third, the coronavirus pandemic did not lead to economic panic. Korea, which had developed an institutional response to infectious diseases prior to COVID-19, used a strategy of balancing quarantine measures with economic policies. The paper summarizes the specific measures Korea implemented to overcome COVID-19, and discusses the sustainability of the economy after overcoming the virus.

Chest X-ray Outlier Detection Model using Dimension Reduction and Edge Detection

With the advancement of Artificial Intelligence technology, the development of various applied software and studies are actively conducted on detection, classification, and prediction through interdisciplinary convergence and integration. Among them, medical AI has been drawing huge interest and popularity in Computer-Aided Diagnosis, which collects human body signals to predict abnormal symptoms of health, and diagnoses diseases through medical images such as X-ray and CT. Since X-ray and CT in medicine use high-resolution images, they require high specification equipment and huge energy consumption due to high computation in learning and recognition, incurring huge costs to create an environment for operation. Thus, this paper proposes a chest X-ray outlier detection model using dimension reduction and edge detection to solve these issues. The proposed method scans an X-ray image using a window of a certain size, conducts difference imaging of adjacent segment-images, and extracts the edge information in a binary format through the AND operation. To convert the extracted edge, which is visual information, into a series of lines, it is computed in convolution with the detection filter that has a coefficient of 2n and the lines are divided into 16 types. By counting the converted data, a one-dimensional 16-size array per one segment-image is produced, and this reduced data is used as an input to the RNN-based learning model. In addition, the study conducted various experiments based on the COVID-chest X-ray dataset to evaluate the performance of the proposed model. According to the experiment results, the LFA-RNN showed the highest accuracy at 97.5% in the learning calculated through learning, followed by CRNN 96.1%, VGG 96.6%, AlexNet 94.1%, Conv1D 79.4%, and DNN 78.9%. In addition, LFA-RNN showed the lowest loss at about 0.0357. CCBY