Coronavirus disease is caused by a virus that is the cause of a potentially fatal disease worldwide. Coronavirus is a pathogen that primarily affects the human respiratory system. Coronavirus 2019 (COVID-19) has been named WHO since February 11, 2020. The fi rst cases of COVID-19 were reported in December 2019. In January 2020, COVID-19 infection was identifi ed in hospitalized patients in Wuhan, China. We analyze the role of neutrophil-lymphocyte ratio (NLR) in viral infection with special emphasize on novel corona virus disease-COVID-19. NLR may be used for early detection and may refl ect progression to the more severe illness leading to SARS-CoV-2. In the mini review we investigate the use of NLR as a surrogate marker for diagnosis and stratifi cation of COVID-19. Clinical symptoms such as pneumonia, acute respiratory distress syndrome, acute heart damage have led to death. In some cases, multiple infl ammations have been observed. Treatment with interferon inhalation showed no clinical effect and the condition worsened instead (Tab. 5, Fig. 1, Ref. 18).

Disclosure of potential conflicts of interest (COIs) is used by biomedical journals to guarantee credibility and transparency of the scientific process. Conflict of interest disclosure, however, is not systematically nor consistently dealt with by journals. Recent joint editorial efforts paved the way towards the implementation of uniform vehicles for COI disclosure. This paper provides a comprehensive editorial perspective on classical COI-related issues. New insights into the current COI policies and practices among European Society of Cardiology National Cardiovascular Journals, as derived from a cross-sectional survey using a standardized questionnaire, are discussed.

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In our study, the reversion of hypertension and LVH was not dependent on the restoration of NO-synthase activity. Moreover, LV fibrosis and aortic remodelling seem to be more resistant to conditions resulting in regression of LVH. Preserved level of fibrosis in the initial period of LVH regression might result in loss of structural homogeneity and possible functional alterations of the LV.

The race to make the dream of artifi cial intelligence a reality comes parallel with the increasing struggle of health care systems to cope with information overload and translational pressure. It is clear that a shift in the way data is generated requires a shift in the way they are processed. This is where AI comes with great promises to solve the problem of volume versus applicability of information in science. In medicine, AI is showing exponential progress in the fi elds of predictive analysis and image recognition. These promises however, come with an intricate package of ethico-social, scientifi c and economic implications, towards which a reductionist approach leads to distorted and dramatic predictions. All this, in a time when the growing pressure on healthcare systems towards defensive medicine begs the question of the true need for AI for good medical practice. This article examines the concept and achievements of AI and attempts to offer a complex view on the realistic expectations from it in medicine, in the context of current practice (Ref. 38).