Risk of myocarditis after three doses of COVID-19 mRNA vaccines in the United States, 2020-2022: A self-controlled case series study.

AIM: Myocarditis is a recognized safety concern following COVID-19 mRNA vaccination. However, there is limited research quantifying the risk associated with the third dose or comparing the risk between the three doses. The US Vaccine Adverse Event Reporting System (VAERS) is a passive surveillance system that monitors rare adverse events after US-licensed vaccination. However, studies analyzing VAERS data have often faced criticism for underreporting cases and lacking a control group to assess the increase in baseline risk. METHODS: The temporal association between myocarditis onset and COVID-19 vaccination was studied. To overcome limitations, a novel modified self-controlled case series method was employed, explicitly modeling the case reporting process in VAERS data. RESULTS: We found an increased risk of myocarditis during the 1- to 3-day period following the second and third doses of both the BNT162b2 vaccine and the mRNA-1273 vaccine. Following the second dose, the relative incidence (RI) was 4.89 (95% confidence interval (CI), 2.39-10.08) for the BNT162b2 vaccine and 2.86 (95% CI: 1.18-7.03) for the mRNA-1273 vaccine. Similarly, following the third dose, the RI was 9.04 (95% CI: 2.79-40.99) for the BNT162b2 vaccine and 4.71 (95% CI: 1.42-19.09) for the mRNA-1273 vaccine. No significant increase in risk was observed during other periods. Notably, our analysis also identified a similar increased risk of myocarditis among individuals aged below 30. CONCLUSIONS: These findings raise safety concerns regarding COVID-19 mRNA vaccines, provide insights into the quantification of myocarditis risk at different postvaccination periods, and offer a novel approach to interpreting passive surveillance system data.

Adverse effect signature extraction and prediction for drugs treating COVID-19.

Given the considerable cost of drug discovery, drug repurposing is becoming attractive as it can effectively shorten the development timeline and reduce the development cost. However, most existing drug-repurposing methods omitted the heterogeneous health conditions of different COVID-19 patients. In this study, we evaluated the adverse effect (AE) profiles of 106 COVID-19 drugs. We extracted four AE signatures to characterize the AE distribution of 106 COVID-19 drugs by non-negative matrix factorization (NMF). By integrating the information from four distinct databases (AE, bioassay, chemical structure, and gene expression information), we predicted the AE profiles of 91 drugs with inadequate AE feedback. For each of the drug clusters, discriminant genes accounting for mechanisms of different AE signatures were identified by sparse linear discriminant analysis. Our findings can be divided into three parts. First, drugs abundant with AE-signature 1 (for example, remdesivir) should be taken with caution for patients with poor liver, renal, or cardiac functions, where the functional genes accumulate in the RHO GTPases Activate NADPH Oxidases pathway. Second, drugs featuring AE-signature 2 (for example, hydroxychloroquine) are unsuitable for patients with vascular disorders, with relevant genes enriched in signal transduction pathways. Third, drugs characterized by AE signatures 3 and 4 have relatively mild AEs. Our study showed that NMF and network-based frameworks contribute to more precise drug recommendations.

Venous Thromboembolism following Two Doses of COVID-19 mRNA Vaccines in the US Population, 2020-2022.

The COVID-19 mRNA vaccine is one of the most effective strategies used to fight against COVID-19. Recently, venous thromboembolism (VTE) events after COVID-19 mRNA vaccination have been reported in various research. Such a concern may hamper the ongoing COVID-19 vaccination campaign. Based on the US Vaccine Adverse Event Reporting System data, this modified self-controlled case series study investigated the association of COVID-19 mRNA vaccination with VTE events among US adults. We found the VTE incidence rate in the recommended dose interval does not change significantly after receiving COVID-19 mRNA vaccines. This conclusion still holds if the analysis is stratified by age and gender. The VTE onset may not be significantly associated with COVID-19 mRNA vaccination.

How to organise travel restrictions in the new future: lessons from the COVID-19 response in Hong Kong and Singapore.

It has been nearly 2 years since the first case of COVID-19 was reported. Governments worldwide have introduced numerous non-pharmaceutical interventions (NPIs) to combat this disease. Many of these NPIs were designed in response to initial outbreaks but are unsustainable in the long term. Governments are exploring how to adjust their current NPIs to resume normal activities while effectively protecting their population. As one of the most controversial NPIs, the implementation of travel restrictions varies across regions. Some governments have abandoned their previous travel restrictions because of the induced costs to society and on the economy. Other areas, including Hong Kong (Special Administrative Region of China) and Singapore, continue employing these NPIs as a long-term disease prevention tactic. However, the multidimensional impacts of travel restrictions require careful consideration of how to apply restrictions more appropriately. We have proposed an adapted framework to examine Hong Kong and Singapore's travel restrictions. We aimed to study these two regions' experiences in balancing disease control efforts with easing the burden on lives and livelihoods. Based on the experiences of Hong Kong and Singapore, we have outlined six policy recommendations to serve as the cornerstone for future research and policy practices.

Environmental Features Recognition for Lower Limb Prostheses Toward Predictive Walking.

This paper aims to present a robust environmental features recognition system (EFRS) for lower limb prosthesis, which can assist the control of prosthesis by predicting the locomotion modes of amputees and estimating environmental features in the following steps. A depth sensor and an inertial measurement unit are combined to stabilize the point cloud of environments. Subsequently, the 2D point cloud is extracted from origin 3D point cloud and is classified through a neural network. Environmental features, including slope of road, width, and height of stair, were also estimated via the 2D point cloud. Finally, the EFRS is evaluated through classifying and recognizing five kinds of common environments in simulation, indoor experiments, and outdoor experiments by six healthy subjects and three transfemoral amputees, and databases of five healthy subjects and three amputees are used to validate without training. The classification accuracy of five kinds of common environments reach up to 99.3% and 98.5% for the amputees in the indoor and outdoor experiments, respectively. The locomotion modes are predicted at least 0.6 s before the switch of actual locomotion modes. Most estimation errors of indoor and outdoor environments features are lower than 5% and 10%, respectively. The overall process of EFRS takes less than 0.023 s. The promising results demonstrate the robustness and the potential application of the presented EFRS to help the control of lower limb prostheses.