The understanding of the impact of radiation quality in early and late responses of biological targets to ionizing radiation exposure necessarily grounds on the results of mechanistic studies starting from physical interactions. This is particularly true when, already at the physical stage, the radiation field is mixed, as it is the case for neutron exposure. Neutron Relative Biological Effectiveness (RBE) is energy dependent, maximal for energies ~1 MeV, varying significantly among different experiments. The aim of this work is to shed light on neutron biological effectiveness as a function of field characteristics, with a comprehensive modeling approach: this brings together transport calculations of neutrons through matter (with the code PHITS) and the predictive power of the biophysical track structure code PARTRAC in terms of DNA damage evaluation. Two different energy dependent neutron RBE models are proposed: the first is phenomenological and based only on the characterization of linear energy transfer on a microscopic scale; the second is purely ab-initio and based on the induction of complex DNA damage. Results for the two models are compared and found in good qualitative agreement with current standards for radiation protection factors, which are agreed upon on the basis of RBE data.

Recent advancements in bidimensional nanoparticles production such as Graphene (G) and Graphene oxide (GO) have the potential to meet the need for highly functional personal protective equipment (PPE) against SARS-CoV-2 infection. The ability of G and GO to interact with microorganisms provides an opportunity to develop engineered textiles for use in PPE and limit the spread of COVID-19. PPE in current use in high-risk settings for COVID transmission provide only a physical barrier that decreases infection likelihood and does not inactivate the virus. Here, we show that virus pre-incubation with soluble GO inhibits SARS-CoV-2 infection of VERO cells. Furthermore, when G/GO functionalized polyurethane or cotton were in contact SARS-CoV-2, the infectivity of the fabric was nearly completely inhibited. The findings presented here constitute an important innovative nanomaterial-based strategy to significantly increase PPE efficacy in protection against the SARS-CoV-2 virus that may implement water filtration, air purification, and diagnostics methods.

The Envelope (E) protein of SARS-CoV-2 is the most enigmatic protein among the four structural ones. Most of its current knowledge is based on the direct comparison to the SARS E protein, initially mistakenly undervalued and subsequently proved to be a key factor in the ER-Golgi localization and in tight junction disruption. We compared the genomic sequences of E protein of SARS-CoV-2, SARS-CoV and the closely related genomes of bats and pangolins obtained from the GISAID and GenBank databases. When compared to the known SARS E protein, we observed a significant difference in amino acid sequence in the C-terminal end of SARS-CoV-2 E protein. Subsequently, in silico modelling analyses of E proteins conformation and docking provide evidences of a strengthened binding of SARS-CoV-2 E protein with the tight junction-associated PALS1 protein. Based on our computational evidences and on data related to SARS-CoV, we believe that SARS-CoV-2 E protein interferes more stably with PALS1 leading to an enhanced epithelial barrier disruption, amplifying the inflammatory processes, and promoting tissue remodelling. These findings raise a warning on the underestimated role of the E protein in the pathogenic mechanism and open the route to detailed experimental investigations.

Envelope protein of coronaviruses is a structural protein existing in both monomeric and homopentameric form. It has been related to a multitude of roles including virus infection, replication, dissemination and immune response stimulation. In the present study, we employed an immunoinformatic approach to investigate the major immunogenic domains of the SARS-CoV-2 envelope protein and map them among the homologue proteins of coronaviruses with tropism for animal species that are closely interrelated with the human beings population all over the world. Also, when not available, we predicted the envelope protein structural folding and mapped SARS-CoV-2 epitopes. Envelope sequences alignment provides evidence of high sequence homology for some of the investigated virus specimens; while the structural mapping of epitopes resulted in the interesting maintenance of the structural folding and epitope sequence localization also in the envelope proteins scoring a lower alignment score. In line with the One-Health approach, our evidences provide a molecular structural rationale for a potential role of taxonomically related coronaviruses in conferring protection from SARS-CoV-2 infection and identifying potential candidates for the development of diagnostic tools and prophylactic-oriented strategies.

The advent of Precision Medicine has globally revolutionized the approach of translational research suggesting a patient-centric vision with therapeutic choices driven by the identification of specific predictive biomarkers of response to avoid ineffective therapies and reduce adverse effects. The spread of “multi-omics” analysis and the use of sensors, together with the ability to acquire clinical, behavioral, and environmental information on a large scale, will allow the digitization of the state of health or disease of each person, and the creation of a global health management system capable of generating real-time knowledge and new opportunities for prevention and therapy in the individual person (high-definition medicine). Real world data-based translational applications represent a promising alternative to the traditional evidence-based medicine (EBM) approaches that are based on the use of randomized clinical trials to test the selected hypothesis. Multi-modality data integration is necessary for example in precision oncology where an Avatar interface allows several simulations in order to define the best therapeutic scheme for each cancer patient.