Cutaneous Adverse Drug Reactions of COVID-19 Vaccines: A Cross-Sectional Study of the Largest Healthcare System in Taiwan

Objectives: Potential cutaneous adverse drug reactions (cADRs) associated with COVID-19 vaccinations are well-known. However, comprehensive evaluation including detailed patient characteristics, vaccine types, signs and symptoms, treatments and outcomes from such cADRs are still lacking in Taiwan. Method(s): A cross-sectional study was conducted from December 2019 to October 2022 to analyze spontaneous ADR reporting data from Taiwan's largest multi-institutional healthcare system. Physicians and pharmacists initially ensured the data quality and completeness of the reported ADR records. Subsequently, we applied descriptive statistics to analyze the patient cohort based on demographic characteristics, administered COVID-19 vaccines, clinical manifestations, and patient management. Result(s): We identified 242 cADRs from 759 reported COVID-19 vaccine-related ADRs, 88.3% of which were judged as "possible" using the Naranjo Scale. The mean age of patients with cADRs was 48.1+/-17.5 years, with the majority (44.2%) of cADRs reported in the 40-64yr old age group. cADRs were more common in women (68.2%) and most of the patients had no history of allergy to vaccines (99.6%). Oxford/AstraZeneca (58.6%) accounted for the most reported brand of COVID-19 vaccines. Patients developed cADRs within 1 to 198 days (median = 5.5 days), and mostly after first-dose vaccination (77.8%). The most frequently reported cADR was rash/eruption (18.7%), followed by itchiness/pruritus (11.7%) and urticaria (9.2%), mainly affecting the lower limbs (23.8%) and upper limbs (22.6%). Medications were prescribed for 65.1% of the cADRs, and signs and symptoms were resolved within 1 to 167 days (median = 7 days) after treatment with oral antihistamines (23.0%), topical corticosteroids (14.6%) or oral corticosteroids (14.4%). Conclusion(s): Our findings provide comprehensive details regarding COVID-19 vaccine-related cADRs in Taiwan. Certain groups, especially women and the middle-aged, who reported a relatively higher rate of cADRs, may benefit from pre-vaccination counseling about the risks of cADRs and the use of appropriate medications.Copyright © 2023

Risk of autoimmune diseases in patients with COVID-19 a retrospective cohort study

Background There are a growing number of case reports of various autoimmune diseases occurring after COVID-19, yet there is no large-scale population-based evidence to support this potential association. This study provides a closer insight into the association between COVID-19 and autoimmune diseases and reveals discrepancies across sex, age, and race of participants.Methods This is a retrospective cohort study based on the TriNetX U.S. Collaborative Network. In the test-negative design, cases were participants with positive polymerase chain reaction (PCR) test results for SARS-CoV-2, while controls were participants who tested negative and were not diagnosed with COVID-19 throughout the follow-up period. Patients with COVID-19 and controls were propensity score-matched (1: 1) for age, sex, race, adverse socioeconomic status, lifestyle-related variables, and comorbidities. The primary endpoint is the incidence of newly recorded autoimmune diseases. Adjusted hazard ratios (aHRs) and 95% confident intervals (CIs) of autoimmune diseases were calculated between propensity score-matched groups with the use of Cox proportional-hazards regression models.Findings Between January 1st, 2020 and December 31st, 2021, 3,814,479 participants were included in the study (888,463 cases and 2,926,016 controls). After matching, the COVID-19 cohort exhibited significantly higher risks of rheumatoid arthritis (aHR:2.98, 95% CI:2.78-3.20), ankylosing spondylitis (aHR:3.21, 95% CI:2.50-4.13), systemic lupus erythematosus (aHR:2.99, 95% CI:2.68-3.34), dermatopolymyositis (aHR:1.96, 95% CI:1.47-2.61), systemic sclerosis (aHR:2.58, 95% CI:2.02-3.28), Sjogren's syndrome (aHR:2.62, 95% CI:2.29-3.00), mixed connective tissue disease (aHR:3.14, 95% CI:2.26-4.36), Behcet's disease (aHR:2.32, 95% CI:1.38-3.89), polymyalgia rheumatica (aHR:2.90, 95% CI:2.36-3.57), vasculitis (aHR:1.96, 95% CI:1.74-2.20), psoriasis (aHR:2.91, 95% CI:2.67-3.17), inflammatory bowel disease (aHR:1.78, 95%CI:1.72-1.84), celiac disease (aHR:2.68, 95% CI:2.51-2.85), type 1 diabetes mellitus (aHR:2.68, 95%CI:2.51-2.85) and mortality (aHR:1.20, 95% CI:1.16-1.24).Interpretation COVID-19 is associated with a different degree of risk for various autoimmune diseases. Given the large sample size and relatively modest effects these findings should be replicated in an independent dataset. Further research is needed to better understand the underlying mechanisms.Funding Kaohsiung Veterans General Hospital (KSVGH111-113).

Network Pharmacology Evaluation of Corona Virus Disease 2019(COVID-19) Prevention and Treatment of Traditional Chinese Medicine

Objective: To analyze the action mechanism of anti-Corona Virus Disease 2019(COVID-19)by Chinese herbal compound and propose a combination of Chinese medicine through network pharmacology and molecular docking. Method(s): Based on the Chinese medicine and Chinese medicine prescription for prevention and treatment of COVID-19, ADME properties(OB>=30%;DL>=0.18)was used for virtual screening;Potentially active molecules in protease Mpro and receptor ACE2 were screened by molecular docking(Binding Scores>4);Through the overlap ratio of active components and key targets, the suggestions on optimizing formula combination were provided. Result(s): 127 Chinese medicinal materials and 885 active components were obtained. The active components close to Lopinavie scores included squalene, shikonin, stigmasterol, etc. The traditional chinese medicinal materials with overlap rate of active molecules>=15% included Ephedrae Herba, Lonicerae Japonicae Flos, Scutellariae Radix, etc. The key Chinese herbal medicines with overlapping rate of key targets>=15% included Glycyrrhizae Radix et Rhizoma, Pinelliae Rhizoma, Curcumae Radix, etc. Conclusion(s): The combinations of Chinese herbal are proposed:(1)Ephedrae Herba, Armeniacae Semen Amarum, Glycyrrhizae Radix et Rhizoma, Curcumae Longae Rhizoma;(2)Lonicerae Japonicae Flos, Scutellariae Radix, Arnebiae Radix, Verbenae Herba;(3)Ephedrae Herba, Pinelliae Rhizoma, Curcumae Radix, Pseudostellariae Radix. Copyright © 2021, Central Station of Chinese Medicinal Materials Information, National Medical Products Administration. All right reserved.

Sevi: Speech-to-Visualization through Neural Machine Translation

Data visualization is a powerful tool for understating information through visual cues. However, allowing novices to create visualization artifacts for what they want to see is not easy, just as not everyone can write SQL queries. Arguably, the most natural way to specify what to visualize is through natural language or speech, similar to our daily search on Google or Apple Siri, leaving to the system the task of reasoning about what to visualize and how. In this demo, we present Sevi an end-to-end data visualization system that acts as a virtual assistant to allow novices to create visualizations through either natural language or speech. Sevi is powered by two main components: Speech2Text which is based on Google Cloud Speech-to-Text Rest API, and Text2VIS, which uses an end-to-end neural machine translation model called ncNet trained using a cross-domain benchmark called nvBench. Both ncNet and nvBench have been developed by us. We will walk the audience through two general domain datasets, one related to COVID-19 and the other on NBA player statistics, to highlight how Sevi enables novices to easily create data visualizations. Because nvBench contains Text2VIS training samples from 105 domains (e.g., sport, college, hospital, etc.), the audience can play with speech or text input with any of these domains.

Mathematical Model Simulation of Detailed Classification of Telemedicine Sensing Data

Medical and health field is a hot application field of wireless sensor networks. How to correctly refine and classify telemedicine sensor data is the research focus in related fields. Therefore, a detailed classification mathematical model simulation of telemedicine sensor data based on multi feature fusion is proposed. On the basis of telemedicine sensor data acquisition, it is preprocessed to reduce the computational overhead of detailed classification. The reliability features of the preprocessed telemedicine sensing data are extracted, the extracted features are fused by the principal component analysis method, and the refined classification model of telemedicine sensing data is constructed based on the principle of machine learning. The fused features are input into the model to complete the refined classification of telemedicine sensing data. The experimental results show that the correct refinement classification rate of the proposed method is more than 90%, the refinement classification accuracy is higher than 98.5%, the convergence speed is good, and the refinement classification time is 4 similar to 12 s, which proves that the correct refinement classification rate and accuracy of the proposed method are high, the classification time is short, and has good application performance.

Clinical Considerations in Osteoporosis Management During the COVID-19 Pandemic

Coronavirus disease of 2019(COVID-19) is a highly contagious viral disease, causing reparatory symptoms, ranging from flu-like symptoms to acute respiratory distress. Since the end of 2019, COVID-19 has posed a tremendous threat to the healthcare systems nationwide. Multiple public health interventions, including mandating social distancing, closing outpatient visits, or postponing elective procedures have been implemented to mitigate the impact on disease transmission and prevent consumption of medical resources. Since the beginning of the pandemic, resources have been shifted away from chronic disease management and prevention. Osteoporosis, a chronic condition, which requires continuous and concerted medical attention to alleviate the long-term consequences such as osteoporotic fractures, morbidities, or mortalities. In this review article, we will discuss the strategies to cope with osteoporosis, especially focusing on pharmaceutical management considerations during the era of COVID-19 pandemic. We will also discuss different drug distribution models when outpatient clinics are not readily available or mandatory social distancing policy is employed. After all, we will propose alternative therapeutic options when the continuity of particular medications cannot be maintained. © 2021 Society of Internal Medicine of Taiwan. All rights reserved.

To mask or not to mask: Epistemic injustice in the COVID-19 Pandemic1

Reluctance to adopt mask-wearing as a preventive measure is widely observed in many Western societies since the beginning of the COVID-19 pandemics. This reluctance toward mask adoption, like any other complex social phenomena, will have multiple causes. Plausible explanations have been identified, including political polarization, skepticism about media reports and the authority of public health agencies, and concerns over liberty, amongst others. In this paper, we propose potential explanations hitherto unnoticed, based on the framework of epistemic injustice. We show how testimonial injustice and hermeneutical injustice may be at work to shape the reluctant mask adoption at both the societal and individual levels. We end by suggesting how overcoming these epistemic injustices can benefit the global community in this challenging situation and in the future. © 2021 Philosophy Documentation Center. All rights reserved.

Multivariate Long and Short Term LSTM-Based Network for Traffic Forecasting under Interference: Experiments during COVID-19

Due to COVID-19, work-from-home policy and travel restrictions were taken to decelerate the virus spreading. While these policies successfully eliminated the transmission of COVID-19, original traffic patterns have been completely disrupted, including considerable reductions in travel time and vehicle miles traveled. The impacted traffic patterns from the unexpected event brings challenges to the U.S. Department of Transportation and transportation planners. With fluctuated traffic conditions, it is difficult for transportation agencies to learn representative traffic patterns from short-term historical data. Therefore, we proposed a multivariate long and short-term LSTM-based model (var LS-LSTM) for network-wide traffic forecasting under interference. We considered multiple spatial and temporal features to evaluate network-wide traffic performance and forecast the influenced travel behaviors. Multi-dimensional spatial-temporal features were fused into long-term and short-term historical matrices and fed into the model, which enabled the model to accommodate intervention from unexpected events. Thorough experiments were conducted using loop detector data in the Greater Seattle Area from 2020 to early 2021 and achieved reliable prediction performance in both robustness as well as accuracy. The proposed model showed competitiveness against other state-of-art algorithms in all experiment time frames, from pre-COVID-19 to COVID-19-relieving period. This study would benefit government agencies and the general public in making sustainable policies and future resilience plans for post-pandemic smart cities. © 2021 IEEE.

A weakly supervised learning method based on attention fusion for COVID-19 segmentation in CT images

Since the end of 2019, the COVID-19, which has swept across the world, has caused serious impacts on public health and economy. Although Reverse Transcription-Polymerase Chain Reaction (RT-PCR) is the gold standard for clinical diagnosis, it is very time-consuming and labor-intensive. At the same time, more and more people have doubted the sensitivity of RT-PCR. Therefore, Computed Tomography (CT) images are used as a substitute for RT-PCR. Powered by the research of the field of artificial intelligence, deep learning, which is a branch of machine learning, has made a great success on medical image segmentation. However, general full supervision methods require pixel-level point-by-point annotations, which is very costly. In this paper, we put forward an image segmentation method based on weakly supervised learning for CT images of COVID-19, which can effectively segment the lung infection area and doesn't require pixel-level labels. Our method is contrasted with another four weakly supervised learning methods in recent years, and the results have been significantly improved.

An Effective Deep Neural Network for Lung Lesions Segmentation From COVID-19 CT Images

Automatic segmentation of lung lesions from COVID-19 computed tomography (CT) images can help to establish a quantitative model for diagnosis and treatment. For this reason, this article provides a new segmentation method to meet the needs of CT images processing under COVID-19 epidemic. The main steps are as follows: First, the proposed region of interest extraction implements patch mechanism strategy to satisfy the applicability of 3-D network and remove irrelevant background. Second, 3-D network is established to extract spatial features, where 3-D attention model promotes network to enhance target area. Then, to improve the convergence of network, a combination loss function is introduced to lead gradient optimization and training direction. Finally, data augmentation and conditional random field are applied to realize data resampling and binary segmentation. This method was assessed with some comparative experiment. By comparison, the proposed method reached the highest performance. Therefore, it has potential clinical applications.