Herpesvirus infections and post-COVID-19 manifestations: a pilot observational study.

The global spread of SARS-CoV-2 points to unrivaled mutational variation of the virus, contributing to a variety of post-COVID sequelae in immunocompromised subjects and high mortality. Numerous studies have reported the reactivation of "sluggish" herpes virus infections in COVID-19, which exaggerate the course of the disease and complicate with lasting post-COVID manifestations CMV, EBV, HHV6). This study aimed to describe clinical and laboratory features of post-COVID manifestations accompanied by the reactivation of herpes virus infections (CMV, EBV, HHV6). 88 patients were recruited for this study, including subjects with reactivation of herpes viruses, 68 (72.3%) (main group) and 20 (27.7%) subjects without detectable DNA of herpesviruses (control group): 46 (52.3%) female and 42 (47.7%) male; median age was 41.4 ± 6.7 years. Patients with post-COVID manifestations presented with reactivation of EBV in 42.6%, HHV6 in 25.0%, and EBV plus HHV6 in 32.4%. Compared with controls, patients with herpes virus infections presented with more frequent slight fever temperature, headache, psycho-neurological disorders, pulmonary abnormalities and myalgia (p < 0.01), activation of liver enzymes, elevated CRP and D-dimer, and suppressed cellular immune response (p ≤ 0.05). Preliminary results indicate a likely involvement of reactivated herpes virus infections, primarily EBV infections in severe COVID-19 and the formation of the post-COVID syndrome. Patients with the post-COVID syndrome and reactivation of EBV and HHV6 infections are at high risk of developing various pathologies, including rheumatologic diseases.

A new hybrid ensemble machine-learning model for severity risk assessment and post-COVID prediction system.

Starting from December 2019, the COVID-19 pandemic has globally strained medical resources and caused significant mortality. It is commonly recognized that the severity of SARS-CoV-2 disease depends on both the comorbidity and the state of the patient's immune system, which is reflected in several biomarkers. The development of early diagnosis and disease severity prediction methods can reduce the burden on the health care system and increase the effectiveness of treatment and rehabilitation of patients with severe cases. This study aims to develop and validate an ensemble machine-learning model based on clinical and immunological features for severity risk assessment and post-COVID rehabilitation duration for SARS-CoV-2 patients. The dataset consisting of 35 features and 122 instances was collected from Lviv regional rehabilitation center. The dataset contains age, gender, weight, height, BMI, CAT, 6-minute walking test, pulse, external respiration function, oxygen saturation, and 15 immunological markers used to predict the relationship between disease duration and biomarkers using the machine learning approach. The predictions are assessed through an area under the receiver-operating curve, classification accuracy, precision, recall, and F1 score performance metrics. A new hybrid ensemble feature selection model for a post-COVID prediction system is proposed as an automatic feature cut-off rank identifier. A three-layer high accuracy stacking ensemble classification model for intelligent analysis of short medical datasets is presented. Together with weak predictors, the associative rules allowed improving the classification quality. The proposed ensemble allows using a random forest model as an aggregator for weak repressors' results generalization. The performance of the three-layer stacking ensemble classification model (AUC 0.978; CA 0.920; F1 score 0.921; precision 0.924; recall 0.920) was higher than five machine learning models, viz. tree algorithm with forward pruning; Naïve Bayes classifier; support vector machine with RBF kernel; logistic regression, and a calibrated learner with sigmoid function and decision threshold optimization. Aging-related biomarkers, viz. CD3+, CD4+, CD8+, CD22+ were examined to predict post-COVID rehabilitation duration. The best accuracy was reached in the case of the support vector machine with the linear kernel (MAPE = 0.0787) and random forest classifier (RMSE = 1.822). The proposed three-layer stacking ensemble classification model predicted SARS-CoV-2 disease severity based on the cytokines and physiological biomarkers. The results point out that changes in studied biomarkers associated with the severity of the disease can be used to monitor the severity and forecast the rehabilitation duration.