A systematic review of the use of CytoSorb© Hemoadsorption in patients undergoing veno-venous ECMO support for severe acute respiratory distress syndrome

Objectives: Acute respiratory distress syndrome (ARDS) often results in high mortality and morbidity. Hemoadsorption therapy, such as CytoSorb©, is being increasingly used to target the underlying hyperinflammation that occurs with ARDS. This review aims to evaluate the available data on the use of CytoSorb in combination with veno-venous extracorporeal membrane oxygenation (V-V ECMO) in severe ARDS cases, and to assess its effects on inflammatory, laboratory, and clinical parameters, as well as on patient outcomes. Method(s): A systematic literature review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and MetaAnalyses (PRISMA) statement. Whenever possible, an analysis of changes in relevant biomarkers and clinical parameters was performed. Result(s): CytoSorb© therapy was associated with significant reductions in circulating levels of C-reactive protein and interleukin-6 (p = 0.039 and p = 0.049, respectively), as well as an increase in PaO2/FiO2 levels (p = 0.028). There was also a trend towards reduced norepinephrine dosage (p = 0.067). Mortality rates in patients treated with CytoSorb©tended to be lower than in the control groups, but these studies had high heterogeneity and low power. In an exploratory analysis of 90-day mortality in COVID19 patients receiving V-V ECMO, the therapy was associated with a reduced risk of death. Conclusion(s): Overall, the reviewed data suggests that CytoSorb© therapy can effectively reduce inflammation and potentially improve survival in ARDS patients treated with V-V ECMO. Therefore, early initiation of CytoSorb ©in conjunction with ECMO may offer a promising approach to enhance lung rest and promote recovery in patients with severe ARDS. A randomised trial is warranted to confirm our findings.

Genetic algorithm application for the prediction of potential SARS-CoV-2 new variant of concern

The COVID-19 pandemic motivated an intense debate over high transmissibility and unavailability of effective vaccine to cover all existent variants, and also has raised critical questions, such as concerns about new mutations and genetic recombination that could lead to novel variants of concerns. The density of mutation observed in the different residue indices of spike protein sequence, may correlate to the speed of virus distribution. Therefore, predicting an accurate determination of mutation rates is essential to comprehend this virus evolution and assess the risk of emergent infectious disease. The current study predicts the mutations that may be cause of new variants of concerns using a genetic algorithm approach. In this regard, we mutated randomly the wild-type sequence of SARS-CoV-2 spike protein to generate first 100 different sequences (initial population) that were modelled individually and used to evaluate their discrete optimized protein energy score. After applying cross-over and breeding 200 new generations, one of the sequences with the lowest discrete optimized protein energy score was identified and chosen for a further analysis to realize whether this sequence is potential for being the next variant of concern. © 2022 IEEE.

A multi-step and multi-scale bioinformatic approach to investigate potential source of cross-reactive immunity against SARS-CoV-2 UK variant

Technological advances in fields such as computer modelling and simulation are playing a fundamental role, during the COVID-19 pandemic, that has been afflicting us for 2 years now. These methodologies may help in the development, assessment and monitoring for better prevention, diagnosis, treatment and generation of specific therapeutic strategies. In this perspective, in silico platforms are emerging thanks to their ability to predict the efficacy and safety of new therapies and vaccines. Here, our integrated bioinformatics pipeline for evaluating the elicited response of human immune system against every pathogen is applied to predict the cross-reactive immunity induced by pre-existing vaccinations against SARS-CoV-2 UK variants. © 2021 IEEE.

PEAK: A Clever Python Tool for Exploratory, Regression, and Classification Data. A Case Study for COVID-19

Researchers often face the need to collect, explore, correlate, analyze, and classify different data sources to discover unknown relationships while performing basic steps of pattern recognition and regression analysis with classification. PEAK is a Python tool designed to make easier all of these the basic steps of pattern recognition, allowing less experienced users to reduce the time required for analysing data and promoting the discovery of unknown relationships between different data. As a working example, we applied PEAK to a specific case study dealing with a well-defined dataset representing a cohort of COVID-19 10000 digital twins with different immunological characteristics. PEAK is a freely available open-source software. It runs on all platforms that support Python3. The user manual and source code are accessible following this link: https://github.com/Pex2892/PEAK. © 2021, Springer Nature Switzerland AG.

Computational modeling ofimmunologic response to immune checkpoint inhibitors inCOVID-19 patients with and without cancer

Cancer patients have an increased risk of severe COVID-19 infection due to the suppression of the immune systemand the development of cytokine release syndrome (CRS) that favor respiratory syndromes and interstitialpneumonia. However, substantial differences exist between patients treated with chemotherapy and patients treated with immune checkpoint inhibitors (ICIs), for which the risk of COVID-19 infection and the immunologic and cytokineprofile in case of infection have not yet been well characterized. The administration of ICIs for the treatment ofsevere COVID-19 infection has been recently suggested. However, no conclusive data have been generated on thismatter. To recognize the therapeutic potential of ICIs administration in COVID-19 patients with or without cancer, theUniversal Immune System Simulator (UISS) prediction model was used to simulate the immunologic response ofCOVID-19 patients after ICIs administration. Briefly, UISS represents an appropriate computational modelinginfrastructure able to simulate the dynamics of every single entity of the immune system after a stimulus or atherapeutic intervention by using an agent-based methodology. Therefore, the UISS platform, already used for theprediction of the efficacy of specific SARS-CoV-2 candidate vaccines, was here adopted to characterize theimmunologic behavior in both COVID-19 and cancer patients and to predict the effects of ICIs in these patients. Thecomputational results allowed us to identify key inflammatory and immune-related factors responsible for severerespiratory syndromes in COVID-19 infected patients with and without cancer. UISS results suggest that theadministration of ICIs modulates the immune system and the inflammatory status in both groups of patients withCOVID-19 infection, reducing the risk of severe symptoms. Although the results of the present study are still undervalidation in peripheral blood samples obtained from COVID-19 patients and from cancer patients after two cycles oftreatment with ICIs, we can speculate that ICIs may be a good therapeutic approach for the treatment of COVID-19severe respiratory syndrome even with a concomitant cancer diagnosis. If this is the case, the lower expressionlevels of inflammatory biomarkers can result in the drop-down of the viral load, assessed by droplet digital PCR inCOVID-19 patients.