Descriptive analysis of the incidence rate of postacute COVID-19 syndrome in the Republic of Korea Army.

Background: This study aimed to identify the incidence rate of post-coronavirus disease-2019 (COVID-19) conditions in the Republic of Korea (ROK) Army and to investigate the trend of the incidence rate according to changes in dominant variants. Method(s): We used the results of a 19-item, self-completed survey of those who had recovered from COVID-19 in the ROK Army between March 24, 2020, and April 30, 2022. We used both descriptive and multiple logistic regression analyses to identify factors associated with the incidence rate of post-COVID-19 conditions. Result(s): Among the total of 48,623 COVID-19 episodes in the ROK Army, the overall incidence rate of post-COVID-19 conditions was 32.9%. Based on the survey, the incidence of cough was the highest at 15.4%, followed by fatigue (15.1%) and sputum (13.8%). The delta variant had the highest incidence rate of post-COVID-19 conditions at 50.7%, whereas the omicron variant had the lowest at 19.7%. Concerning the type of post-COVID-19 condition, the neuropsychiatric symptoms had the highest incidence at 27.4% when the delta variant was dominant, and the respiratory symptoms were highest at 37.3% when the omicron variant was dominant. In the case of smell and taste symptoms, the incidence rate was high at 21.1% only when the delta variant was predominant. Conclusion(s): The overall incidence rate of post-COVID-19 conditions in the ROK Army was 32.9%. When the delta variant was dominant, the overall incidence as well as the proportion of neuropsychiatric symptoms were high. However, as the omicron variant became dominant, the overall incidence decreased, but the proportion of respiratory symptoms increased.Copyright © Korean Medical Association.

Descriptive analysis of the incidence rate of postacute COVID-19 syndrome in the Republic of Korea Army

Background: This study aimed to identify the incidence rate of post-coronavirus disease-2019 (COVID-19) conditions in the Republic of Korea (ROK) Army and to investigate the trend of the incidence rate according to changes in dominant variants. Method(s): We used the results of a 19-item, self-completed survey of those who had recovered from COVID-19 in the ROK Army between March 24, 2020, and April 30, 2022. We used both descriptive and multiple logistic regression analyses to identify factors associated with the incidence rate of post-COVID-19 conditions. Result(s): Among the total of 48,623 COVID-19 episodes in the ROK Army, the overall incidence rate of post-COVID-19 conditions was 32.9%. Based on the survey, the incidence of cough was the highest at 15.4%, followed by fatigue (15.1%) and sputum (13.8%). The delta variant had the highest incidence rate of post-COVID-19 conditions at 50.7%, whereas the omicron variant had the lowest at 19.7%. Concerning the type of post-COVID-19 condition, the neuropsychiatric symptoms had the highest incidence at 27.4% when the delta variant was dominant, and the respiratory symptoms were highest at 37.3% when the omicron variant was dominant. In the case of smell and taste symptoms, the incidence rate was high at 21.1% only when the delta variant was predominant. Conclusion(s): The overall incidence rate of post-COVID-19 conditions in the ROK Army was 32.9%. When the delta variant was dominant, the overall incidence as well as the proportion of neuropsychiatric symptoms were high. However, as the omicron variant became dominant, the overall incidence decreased, but the proportion of respiratory symptoms increased.Copyright © Korean Medical Association.

Descriptive analysis of the incidence rate of post-acute COVID-19 syndrome in the Republic of Korea Army

Background: This study aimed to identify the incidence rate of post-coronavirus disease-2019 (COVID-19) conditions in the Republic of Korea (ROK) Army and to investigate the trend of the incidence rate according to changes in dominant variants.Methods: We used the results of a 19-item, self-completed survey of those who had recovered from COVID-19 in the ROK Army between March 24, 2020, and April 30, 2022. We used both descriptive and multiple logistic regression analyses to identify factors associated with the incidence rate of post-COVID-19 conditions.Results: Among the total of 48,623 COVID-19 episodes in the ROK Army, the overall incidence rate of postCOVID-19 conditions was 32.9%. Based on the survey, the incidence of cough was the highest at 15.4%, followed by fatigue (15.1%) and sputum (13.8%). The delta variant had the highest incidence rate of postCOVID-19 conditions at 50.7%, whereas the omicron variant had the lowest at 19.7%. Concerning the type of post-COVID-19 condition, the neuropsychiatric symptoms had the highest incidence at 27.4% when the delta variant was dominant, and the respiratory symptoms were highest at 37.3% when the omicron variant was dominant. In the case of smell and taste symptoms, the incidence rate was high at 21.1% only when the delta variant was predominant.Conclusion: The overall incidence rate of post-COVID-19 conditions in the ROK Army was 32.9%. When the delta variant was dominant, the overall incidence as well as the proportion of neuropsychiatric symptoms were high. However, as the omicron variant became dominant, the overall incidence decreased, but the proportion of respiratory symptoms increased.

VQC-COVID-NET: Vector Quantization Contrastive Learning for Covid-19 Image Base Classification

Today, the COVID-19 epidemic has become extremely widespread. The first step in combating COVID-19 is identifying cases of infection. Real-time reverse transcriptase polymerase chain reaction is the most common method for identifying COVID (RT-PCR). This method, however, has been compromised by a time-consuming, laborious, and complex manual process. In addition to the RT-PCR test, screening computed tomography scan (CT) or X-ray images may be used to identify positive COVID-19 results, which could aid in the detection of COVID-19. Because of the continuing increase in new infections, the development of automated techniques for COVID-19 detection utilizing CT images is in high demand. This will aid in clinical diagnosis and alleviate the arduous task of image interpretation. Aggregating instances from various medical systems is highly advantageous for enlarging datasets for the development of machine learning techniques and the acquisition of robust, generalizable models. This study proposes a novel method for addressing distinct feature normalization in latent space due to cross-site domain shift in order to accurately execute COVID-19 identification using heterogeneous datasets with distribution disagreement. We propose using vector quantization to enhance the domain invariance of semantic embeddings in order to enhance classification performance on each dataset. We use two large, publicly accessible COVID-19 diagnostic CT scan datasets to develop and validate our proposed model. The experimental results demonstrate that our proposed method routinely outperforms state-of-the-art techniques on testing datasets. Public access to the implementation of our proposed method is available at https://github.com/khaclinh/VQC-COVID-NET. © 2022 IEEE.

Clinical characteristics and treatments of multi-system inflammatory syndrome in children: a systematic review.

OBJECTIVE: Multisystem inflammatory syndrome in children (MIS-C) can occur in association with coronavirus disease 2019 (COVID-19). It is not easy to differentiate MIS-C from severe COVID-19 or Kawasaki disease based on symptoms. The aim of this study was to describe the clinical and laboratory characteristics of MIS-C. PATIENTS AND METHODS: We searched PubMed/Medline for case series and reports of MIS-C published until June 20, 2020. From a total of nine articles involving 45 cases, various clinical and laboratory data were extracted. Each target case was evaluated by using different diagnostic criteria. RESULTS: The average age at onset of MIS-C was 8.6 years. In 80% of cases, the age of patients ranged from 5 to 15 years. Fever (100%) and shock (82%) were the most common presenting symptoms. Sixty percent of cases met the diagnostic criteria for typical or atypical Kawasaki disease. Biomarkers indicative of inflammation, coagulopathy, or cardiac injury were characteristically elevated as follows: ferritin (mean: 1,061 ng/mL), CRP (217 mg/L), ESR (69 mm/hr), IL-6 (214.8 pg/mL), TNFα (63.4 pg/mL), D-dimer (3,220 ng/mL), PT (15.5 s), troponin I (1,006 ng/L), and BNP (12,150 pg/mL). Intravenous immunoglobulin was administered in all target cases, and inotropic agents were commonly used as well. No case of death was observed. CONCLUSIONS: This study demonstrated that MIS-C is a serious condition that presents with fever, rash, as well as cardiovascular and gastrointestinal symptoms. Although it is challenging to differentiate MIS-C from Kawasaki disease or severe COVID-19, initiation of appropriate treatments through early diagnosis is warranted.

Taking a Break from On-field Soccer Activity May be Beneficial to Collegiate Women Soccer Players

Introduction: A typical season for Canadian collegiate women soccer players consists of a pre-season training camp, in-season competitive games, and in-season practices. Over the course of a single season, players are exposed to a high number of subconcussive head impacts (1), which have been hypothesized to lead to cumulative brain trauma (2). Due to the COVID-19 pandemic, collegiate athletes in Canada did not have an inperson pre-season and off-season training was conducted virtually. Players experienced extended time off from the field and thus their training was less focused on soccer-specific training and more focused on fitness retention. The purpose of this study was to investigate the COVID-19 effect of playing time restrictions on brain function (using electroencephalography (EEG) and N-back-2), sleep, anxiety, and depression. Methods: Ten female collegiate soccer players were recruited. EEG, N-Back-2, Pittsburgh Sleep Quality Index (PSQI) and Sleep Condition Indicator (SCI), and the Hospital Anxiety and Depression Scale (HADS) were collected during both the 2019 and 2020 seasons (1-3). Average spectral power density was calculated across five frequency bands. Accuracy and reaction time were evaluated for the N-Back-2. PSQI, SCI and HADS scores were evaluated. Results: When comparing the end of the 2019 season and the beginning of the 2020 season, power analysis of the resting state EEG demonstrated a decrease in power spectral density across the delta, theta, and alpha frequency bands. There was a 9% increase in accuracy and a 3% decrease in reaction time using the N-Back-2. There was no change in sleep quality using the PSQI but a 6% change in sleep condition using the SCI. There was a 25% decrease in depression and a 19% decrease in anxiety. Discussion: This study is unique in our ability to access a significant time off in between two collegiate soccer seasons. Our pilot data suggests that there is an association between brain function, sleep and anxiety and depression after a stoppage in playing time. Varsity athletes are used to playing consistently over the course of their collegiate season, even playing through the summer. Additionally, on-filed practices and games expose players to a high number of subconcussive impacts. This pilot study sheds light on the potential benefit of providing collegiate athletes time off from on-field activities in between their collegiate seasons to fully recover from the strains of the season (4). Additionally, a switch in focus from on-field work to fitness-based activities may help alleviate athlete burnout and may boost overall stressors experienced by high-performance athletes (5,6). This is a preliminary pilot study and data analysis is ongoing. Further analyses will be conducted to better understand these relationships.

How to Remember COVID-19 in the Post-pandemic Era A Virtual Class Environment as Conformist Interactive Art

In the unprecedented era of the COVID-19 pandemic, people lost a lot of face-to-face interactions in society. They have grown accustomed to virtual meetings via Internet services. People look at their computer displays to take part in most of their events. After the vaccinations and our immune system help them return to their normal life, how do they remember the virtual events? How to Remember COVID-19 in the Post-pandemic Era represents a virtual class environment in the pandemic and provides visitors with an experiential space as a theme park ride. This project focuses on indirect interactions with people in the virtual world and makes them rethink the visual-centric and restricted interface for virtual events. © 2021 Association for Computing Machinery.