The Evolution of Clinical and Scientific Evidence-Based Management Guidelines for COVID-19 Disease: A Longitudinal Descriptive Analysis (preprint)

In January 2020, the novel Coronavirus Disease-2019 (COVID-19) epidemic spread to Italy. The ensuing high rates of patients with pulmonary disease due to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infections, overwhelmed the Italian health services. Management of inpatients was based on World Health Organization (WHO) and other public health bodies’ and specialist societies’ clinical, diagnostic and therapeutic protocols developed with very low-quality evidence base at that time. Over time, management guidelines and protocols were progressively modified and adapted based on the evolving first hand clinical management experience, and the evidence, which has slowly accumulated from clinical large cohort studies and clinical trials. As of August 9th, 2020, there have been 250.103 confirmed COVID-19 cases (with 35.203 deaths) reported from Italy. We present chronological evolution of the clinical and scientific evidence-based management guidelines to date, and their influence on the health care workers management of patients with COVID-19 disease.Funding Statement: This research was supported by funds to National Institute for Infectious Diseases ‘Lazzaro Spallanzani’ IRCCS from Line one-Ricerca Corrente ‘Infezioni Emergenti e Riemergenti’ and by Progetto COVID 2020 12371675 both funded by Italian Ministry of Health and from European Commission – Horizon 2020 (EXSCALATE4CoV).Sir Zumla and Prof Ippolito are co-PIs of the Pan-African Network on Emerging and Re-Emerging Infections (PANDORA-ID-NET – https://www.pandora-id.net/) funded by the European and Developing Countries Clinical Trials Partnership. Sir Zumla is in receipt of a National Institutes of Health Research senior investigator award.Declaration of Interests: EN received grants from Gilead science for educational purpose. Al other authors have no conflicts of interest to declareEthics Approval Statement: The authors stated that Ethical approval was not required.

COVID-19: Viral-host interactome analyzed by network based-approach model to study pathogenesis of SARS-CoV-2 infection. (preprint)

BackgroundEpidemiological, virological and pathogenetic characteristics of SARS-CoV-2 infection are under evaluation. A better understanding of the pathophysiology associated with COVID-19 is crucial to improve treatment modalities and to develop effective prevention strategies. Transcriptomic and proteomic data on the host response against SARS-CoV-2 still have anecdotic character; currently available data from other coronavirus infections are therefore a key source of information. MethodsWe investigated selected molecular aspects of three human coronavirus (HCoV) infections, namely SARS-CoV, MERS-CoV and HCoV-229E, through a network based-approach. A functional analysis of HCoV-host interactome was carried out in order to provide a theoretic host-pathogen interaction model for HCoV infections and in order to translate the results in prediction for SARS-CoV-2 pathogenesis. The 3D model of S-glycoprotein of SARS-CoV-2 was compared to the structure of the corresponding SARS-CoV, HCoV-229E and MERS-CoV S-glycoprotein. SARS-CoV, MERS-CoV, HCoV-229E and the host interactome were inferred through published protein-protein interactions (PPI) as well as gene co-expression, triggered by HCoV S-glycoprotein in host cells. ResultsAlthough the amino acid sequences of the S-glycoprotein were found to be different between the various HCoV, the structures showed high similarity, but the best 3D structural overlap shared by SARS-CoV and SARS-CoV-2, consistent with the shared ACE2 predicted receptor. The host interactome, linked to the S-glycoprotein of SARS-CoV and MERS-CoV, mainly highlighted innate immunity pathway components, such as Toll Like receptors, cytokines and chemokines. ConclusionsIn this paper, we developed a network-based model with the aim to define molecular aspects of pathogenic phenotypes in HCoV infections. The resulting pattern may facilitate the process of structure-guided pharmaceutical and diagnostic research with the prospect to identify potential new biological targets.