Modeling COVID-19 Vaccine Adverse Effects with a Visualized Knowledge Graph Database.

In this study, we utilized ontology and machine learning methods to analyze the current results on vaccine adverse events. With the VAERS (Vaccine Adverse Event Reporting System) Database, the side effects of COVID-19 vaccines are summarized, and a relational/graph database was implemented for further applications and analysis. The adverse effects of COVID-19 vaccines up to March 2022 were utilized in the study. With the built network of the adverse effects of COVID-19 vaccines, the API can help provide a visualized interface for patients, healthcare providers and healthcare officers to quickly find the information of a certain patient and the potential relationships of side effects of a certain vaccine. In the meantime, the model was further applied to predict the key feature symptoms that contribute to hospitalization and treatment following receipt of a COVID-19 vaccine and the performance was evaluated with a confusion matrix method. Overall, our study built a user-friendly visualized interface of the side effects of vaccines and provided insight on potential adverse effects with ontology and machine learning approaches. The interface and methods can be expanded to all FDA (Food and Drug Administration)-approved vaccines.

Soluble uric acid inhibits ß2 integrin-mediated neutrophil recruitment in innate immunity.

Neutrophils are key players during host defense and sterile inflammation. Neutrophil dysfunction is a characteristic feature of the acquired immunodeficiency during kidney disease. We speculated that the impaired renal clearance of the intrinsic purine metabolite soluble uric acid (sUA) may account for neutrophil dysfunction. Indeed, hyperuricemia (HU, serum UA of 9-12 mg/dL) related or unrelated to kidney dysfunction significantly diminished neutrophil adhesion and extravasation in mice with crystal- and coronavirus-related sterile inflammation using intravital microscopy and an air pouch model. This impaired neutrophil recruitment was partially reversible by depleting UA with rasburicase. We validated these findings in vitro using either neutrophils or serum from patients with kidney dysfunction-related HU with or without UA depletion, which partially normalized the defective migration of neutrophils. Mechanistically, sUA impaired ß2 integrin activity and internalization/recycling by regulating intracellular pH and cytoskeletal dynamics, physiological processes that are known to alter the migratory and phagocytic capability of neutrophils. This effect was fully reversible by blocking intracellular uptake of sUA via urate transporters. In contrast, sUA had no effect on neutrophil extracellular trap formation in neutrophils from healthy subjects or patients with kidney dysfunction. Our results identify an unexpected immunoregulatory role of the intrinsic purine metabolite sUA, which contrasts the well-known immunostimulatory effects of crystalline UA. Specifically targeting UA may help to overcome certain forms of immunodeficiency, for example in kidney dysfunction, but may enhance sterile forms of inflammation.

Deep learning guided optimization of human antibody against SARS-CoV-2 variants with broad neutralization.

SignificanceSARS-CoV-2 continues to evolve through emerging variants, more frequently observed with higher transmissibility. Despite the wide application of vaccines and antibodies, the selection pressure on the Spike protein may lead to further evolution of variants that include mutations that can evade immune response. To catch up with the virus's evolution, we introduced a deep learning approach to redesign the complementarity-determining regions (CDRs) to target multiple virus variants and obtained an antibody that broadly neutralizes SARS-CoV-2 variants.

Novel Criteria for When and How to Exit a COVID-19 Pandemic Lockdown.

In the first month of 2020, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a novel coronavirus spreading quickly via human-to-human transmission, caused the coronavirus disease 2019 (COVID-19) pandemic. Italy installed a successful nationwide lockdown to mitigate the exponential increase of case numbers, as the basic reproduction number R0 reached 1 within 4 weeks. But is R0 really the relevant criterion as to whether or not community spreading is under control? In most parts of the world, testing largely focused on symptomatic cases, and we thus hypothesized that the true number of infected cases and relative testing capacity are better determinants to guide lockdown exit strategies. We employed the SEIR model to estimate the numbers of undocumented cases. As expected, the estimated numbers of all cases largely exceeded the reported ones in all Italian regions. Next, we used the numbers of reported and estimated cases per million of population and compared it with the respective numbers of tests. In Lombardy, as the most affected region, testing capacity per reported new case seemed between two and eight most of the time, but testing capacity per estimated new cases never reached four up to April 30. In contrast, Veneto's testing capacity per reported and estimated new cases were much less discrepant and were between four and 16 most of the time. As per April 30 also Marche, Lazio and other Italian regions arrived close to 16 ratio of test capacity per new estimated infection. Thus, the criterion to exit a lockdown should be decided at the level of the regions, based on the local testing capacity that should reach 16 times the estimated true number of newly infected cases as predicted.