Effect of monovalent COVID-19 vaccines on viral interference between SARS-CoV-2 and several DNA viruses in patients with long-COVID syndrome

Abstract

Epstein–Barr virus (EBV) reactivation may be involved in long-COVID symptoms, but reactivation of other viruses as a factor has received less attention. Here we evaluated the reactivation of parvovirus-B19 and several members of the Herpesviridae family (DNA viruses) in patients with long-COVID syndrome. We hypothesized that monovalent COVID-19 vaccines inhibit viral interference between SARS-CoV-2 and several DNA viruses in patients with long-COVID syndrome, thereby reducing clinical symptoms. Clinical and laboratory data for 252 consecutive patients with PCR-verified past SARS-CoV-2 infection and long-COVID syndrome (155 vaccinated and 97 non-vaccinated) were recorded during April 2021–May 2022 (median 243 days post-COVID-19 infection). DNA virus–related IgG and IgM titers were compared between vaccinated and non-vaccinated long-COVID patients and with age- and sex-matched non-infected, unvaccinated (pan-negative for spike-antibody) controls. Vaccination with monovalent COVID-19 vaccines was associated with significantly less frequent fatigue and multiorgan symptoms (p < 0.001), significantly less cumulative DNA virus–related IgM positivity, significantly lower levels of plasma IgG subfractions 2 and 4, and significantly lower quantitative cytomegalovirus IgG and IgM and EBV IgM titers. These results indicate that anti-SARS-CoV-2 vaccination may interrupt viral cross-talk in patients with long-COVID syndrome (ClinicalTrials.gov Identifier: NCT05398952).

Improvement of Symptoms and Cardiac Magnetic Resonance Abnormalities in Patients with Post-Acute Sequelae of SARS-CoV-2 Cardiovascular Syndrome (PASC-CVS) after Guideline-Oriented Therapy

“…Several viruses, especially the Epstein-Barr virus, can be reactivated during coronavirus infection [36][37][38]. We cannot exclude the role of reactivated concomitant cardiotropic viruses (e.g., herpes virus, cytomegalovirus) causing chronic pericardial effusion and myopericarditis, even without signs of acute viral infections.…”

Impact of Circulating Anti-Spike Protein Antibody Levels on Multi-Organ Long COVID Symptoms

“…Patients were classified into symptom clusters on the basis of the number of involved organs: Class I (mild) with symptoms in three organs, Class II (moderate) with symptoms in four to five organs, and Class III (severe) with symptoms in six or more organs. The clustering method was based on our previous observations, the literature, and the specifications outlined in the international Delphi consensus [ 1 , 3 , 6 , 15 , 16 , 17 ]. Our classification notably aligns with the definitions provided by Ayoubkhani et al, who quantified individual symptoms in a large prospective out-of-hospital study and categorized them into three groups, similar to ours, on the basis of quantitative observations [ 11 ].…”

Identification of Myocardial Infarction (MI) Probability from Imbalanced Medical Survey Data: An Artificial Neural Network (ANN) with Explainable AI (XAI) Insights

“…These strategies have shown great potential in a variety of healthcare applications, including medical picture analysis, disease diagnosis, drug development, and personalized medical care recommendations 18,19,20 . Support Vector Machines, Random Forests, Decision Trees, K-Nearest Neighbors, Gaussian Naïve Bayes, Gradient Boosting, Adaptive Boosting, Random Undersampling Boosting, and Logistic Regression are examples of machine learning algorithms that have proved successful in discovering patterns and predicting outcomes in medical datasets 21,22,23 . Deep learning models, specifically Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), have excelled in image identification, natural language processing, and sequential data analysis in the medical field 24,25 .…”