Characteristics of immune response profile in patients with immediate allergic and autoimmune urticarial reactions induced by SARS-CoV-2 vaccines.

Severe allergic reactions following SARS-COV-2 vaccination are generally rare, but the reactions are increasingly reported. Some patients may develop prolonged urticarial reactions following SARS-COV-2 vaccination. Herein, we investigated the risk factors and immune mechanisms for patients with SARS-COV-2 vaccines-induced immediate allergy and chronic urticaria (CU). We prospectively recruited and analyzed 129 patients with SARS-COV-2 vaccine-induced immediate allergic and urticarial reactions as well as 115 SARS-COV-2 vaccines-tolerant individuals from multiple medical centers during 2021-2022. The clinical manifestations included acute urticaria, anaphylaxis, and delayed to chronic urticaria developed after SARS-COV-2 vaccinations. The serum levels of histamine, IL-2, IL-4, IL-6, IL-8, IL-17 A, TARC, and PARC were significantly elevated in allergic patients comparing to tolerant subjects (P-values = 4.5 × 10-5-0.039). Ex vivo basophil revealed that basophils from allergic patients could be significantly activated by SARS-COV-2 vaccine excipients (polyethylene glycol 2000 and polysorbate 80) or spike protein (P-values from 3.5 × 10-4 to 0.043). Further BAT study stimulated by patients' autoserum showed positive in 81.3% of patients with CU induced by SARS-COV-2 vaccination (P = 4.2 × 10-13), and the reactions could be attenuated by anti-IgE antibody. Autoantibodies screening also identified the significantly increased of IgE-anti-IL-24, IgG-anti-FcεRI, IgG-anti-thyroid peroxidase (TPO), and IgG-anti-thyroid-related proteins in SARS-COV-2 vaccines-induced CU patients comparing to SARS-COV-2 vaccines-tolerant controls (P-values = 4.6 × 10-10-0.048). Some patients with SARS-COV-2 vaccines-induced recalcitrant CU patients could be successfully treated with anti-IgE therapy. In conclusion, our results revealed that multiple vaccine components, inflammatory cytokines, and autoreactive IgG/IgE antibodies contribute to SARS-COV-2 vaccine-induced immediate allergic and autoimmune urticarial reactions.

Recommendations and guidelines for the diagnosis and management of Coronavirus Disease-19 (COVID-19) associated bacterial and fungal infections in Taiwan.

Coronavirus disease-19 (COVID-19) is an emerging infectious disease caused by SARS-CoV-2 that has rapidly evolved into a pandemic to cause over 600 million infections and more than 6.6 million deaths up to Nov 25, 2022. COVID-19 carries a high mortality rate in severe cases. Co-infections and secondary infections with other micro-organisms, such as bacterial and fungus, further increases the mortality and complicates the diagnosis and management of COVID-19. The current guideline provides guidance to physicians for the management and treatment of patients with COVID-19 associated bacterial and fungal infections, including COVID-19 associated bacterial infections (CABI), pulmonary aspergillosis (CAPA), candidiasis (CAC) and mucormycosis (CAM). Recommendations were drafted by the 7th Guidelines Recommendations for Evidence-based Antimicrobial agents use Taiwan (GREAT) working group after review of the current evidence, using the grading of recommendations assessment, development, and evaluation (GRADE) methodology. A nationwide expert panel reviewed the recommendations in March 2022, and the guideline was endorsed by the Infectious Diseases Society of Taiwan (IDST). This guideline includes the epidemiology, diagnostic methods and treatment recommendations for COVID-19 associated infections. The aim of this guideline is to provide guidance to physicians who are involved in the medical care for patients with COVID-19 during the ongoing COVID-19 pandemic.

The Effect of Flow Experience on Online Game Addiction during the COVID-19 Pandemic: The Moderating Effect of Activity Passion

Stay-at-home mandates and quarantines related to the coronavirus disease of 2019 (COVID-19) pandemic have led to significantly increased participation in online gaming. However, as players continue to participate in online games, it may also trigger online game addiction. This study aimed to explore the relationship between players' flow experience and online game addiction, and to verify whether differences in the type of passion lead to online game addiction. This study used the structural equation model (SEM) to verify the causal relationship between the constructs and then considered model implications with the fit index measurement standard. After investigating 232 players who are passionate about online games, the analysis results show that the higher the flow experience experienced by online game players, the more likely it is to lead to online gaming addiction. Further verification results show that players' activity passion significantly moderates the relationship between flow experience and online game addiction, and players with obsessive passion are more likely to experience online game addiction than players with harmonious passion. Future work will explore the causes of online game addiction from different perspectives.

Coronavirus disease 2019 (COVID-19) associated bacterial coinfection: Incidence, diagnosis and treatment.

Coronavirus disease 2019 (COVID-19) emerged as a pandemic that spread rapidly around the world, causing nearly 500 billion infections and more than 6 million deaths to date. During the first wave of the pandemic, empirical antibiotics was prescribed in over 70% of hospitalized COVID-19 patients. However, research now shows a low incidence rate of bacterial coinfection in hospitalized COVID-19 patients, between 2.5% and 5.1%. The rate of secondary infections was 3.7% in overall, but can be as high as 41.9% in the intensive care units. Over-prescription of antibiotics to treat COVID-19 patients fueled the ongoing antimicrobial resistance globally. Diagnosis of bacterial coinfection is challenging due to indistinguishable clinical presentations with overlapping lower respiratory tract symptoms such as fever, cough and dyspnea. Other diagnostic methods include conventional culture, diagnostic syndromic testing, serology test and biomarkers. COVID-19 patients with bacterial coinfection or secondary infection have a higher in-hospital mortality and longer length of stay, timely and appropriate antibiotic use aided by accurate diagnosis is crucial to improve patient outcome and prevent antimicrobial resistance.

Fitness Movement Types and Completeness Detection Using a Transfer-Learning-Based Deep Neural Network.

Fitness is important in people's lives. Good fitness habits can improve cardiopulmonary capacity, increase concentration, prevent obesity, and effectively reduce the risk of death. Home fitness does not require large equipment but uses dumbbells, yoga mats, and horizontal bars to complete fitness exercises and can effectively avoid contact with people, so it is deeply loved by people. People who work out at home use social media to obtain fitness knowledge, but learning ability is limited. Incomplete fitness is likely to lead to injury, and a cheap, timely, and accurate fitness detection system can reduce the risk of fitness injuries and can effectively improve people's fitness awareness. In the past, many studies have engaged in the detection of fitness movements, among which the detection of fitness movements based on wearable devices, body nodes, and image deep learning has achieved better performance. However, a wearable device cannot detect a variety of fitness movements, may hinder the exercise of the fitness user, and has a high cost. Both body-node-based and image-deep-learning-based methods have lower costs, but each has some drawbacks. Therefore, this paper used a method based on deep transfer learning to establish a fitness database. After that, a deep neural network was trained to detect the type and completeness of fitness movements. We used Yolov4 and Mediapipe to instantly detect fitness movements and stored the 1D fitness signal of movement to build a database. Finally, MLP was used to classify the 1D signal waveform of fitness. In the performance of the classification of fitness movement types, the mAP was 99.71%, accuracy was 98.56%, precision was 97.9%, recall was 98.56%, and the F1-score was 98.23%, which is quite a high performance. In the performance of fitness movement completeness classification, accuracy was 92.84%, precision was 92.85, recall was 92.84%, and the F1-score was 92.83%. The average FPS in detection was 17.5. Experimental results show that our method achieves higher accuracy compared to other methods.

Real-Time Internet of Medical Things System for Detecting Blood Leakage during Hemodialysis Using a Novel Multiple Concentric Ring Sensor.

Venous needle dislodgement (VND) is a major healthcare safety concern in patients undergoing hemodialysis. Although VND is uncommon, it can be life-threatening. The main objective of this study was to implement a real-time multi-bed monitoring system for VND by combining a novel leakage-detection device and IoMT (Internet of Medical Things) technology. The core of the system, the Acusense IoMT platform, consisted of a novel leakage-detection patch comprised of multiple concentric rings to detect blood leakage and quantify the leaked volume. The performance of the leakage-detection system was evaluated on a prosthetic arm and clinical study. Patients with a high risk of blood leakage were recruited as candidates. The system was set up in a hospital, and the subjects were monitored for 2 months. During the pre-clinical simulation experiment, the system could detect blood leakage volumes from 0.3 to 0.9 mL. During the test of the IoMT system, the overall success rate of tests was 100%, with no lost data packets. A total of 701 dialysis sessions were analyzed, and the accuracy and sensitivity were 99.7% and 90.9%, respectively. Evaluation questionnaires showed that the use of the system after training changed attitudes and reduced worry of the nursing staff. Our results show the feasibility of using a novel detector combined with an IoMT system to automatically monitor multi-bed blood leakage. The innovative concentric-circle design could more precisely control the warning blood-leakage threshold in any direction to achieve clinical cost-effectiveness. The system reduced the load on medical staff and improved patient safety. In the future, it could also be applied to home hemodialysis for telemedicine during the era of COVID-19.