Early biochemical analysis of COVID-19 patients helps severity prediction.

COVID-19 pandemic has put the protocols and the capacity of our Hospitals to the test. The management of severe patients admitted to the Intensive Care Units has been a challenge for all health systems. To assist in this challenge, various models have been proposed to predict mortality and severity, however, there is no clear consensus for their use. In this work, we took advantage of data obtained from routine blood tests performed on all individuals on the first day of hospitalization. These data has been obtained by standardized cost-effective technique available in all the hospitals. We have analyzed the results of 1082 patients with COVID19 and using artificial intelligence we have generated a predictive model based on data from the first days of admission that predicts the risk of developing severe disease with an AUC = 0.78 and an F1-score = 0.69. Our results show the importance of immature granulocytes and their ratio with Lymphocytes in the disease and present an algorithm based on 5 parameters to identify a severe course. This work highlights the importance of studying routine analytical variables in the early stages of hospital admission and the benefits of applying AI to identify patients who may develop severe disease.

Immune cell population and cytokine profiling suggest age dependent differences in the response to SARS-CoV-2 infection

Aging population is at higher risk of developing severe COVID-19, including hospitalization and death. In this work, to further understand the relationship between host age-related factors, immunosenescence/exhaustion of the immune system and the response to the virus, we characterized immune cell and cytokine responses in 58 COVID-19 patients admitted to the hospital and 40 healthy controls of different age ranges. Lymphocyte populations and inflammatory profiles were studied in blood samples, using different panels of multicolor flow cytometry. As expected, our analysis reveals differences at both the cellular and cytokine level in COVID-19 patients. Interestingly, when the age range analysis was carried out, the immunological response to the infection was found to differ with age, being especially affected in the group of 30–39 years. In this age range, an increased exhausted T cell response and a decrease of naïve T helper lymphocytes was found in patients, as well as a reduced concentration of the proinflammatory TNF, IL-1β and IL-8 cytokines. Besides, the correlation between age and the study variables was evaluated, and multiple cell types and interleukins were found to correlate with donor age. Notably, the correlations of T helper naïve and effector memory cells, T helper 1–17 cells, TNF, IL-10, IL-1β, IL-8, among others, showed differences between healthy controls and COVID-19 patients. Our findings, in the context of other previous studies, suggest that aging affects the behavior of the immune system in COVID-19 patients. They suggest that young individuals are able to mount an initial response to SARS-CoV-2, but some of them present an accelerated exhaustion of the cell response and an insufficient inflammatory response, resulting in a moderate to severe COVID-19. On the other hand, in older patients there is a smaller immune cell response to the virus, reflected in fewer differences in immune populations between COVID-19 patients and controls. Nevertheless, old patients show more evidence of an inflammatory phenotype, suggesting that the underlying inflammation associated with their age is exacerbated by the SARS-CoV-2 infection.

O group is a protective factor for COVID19 in Basque population.

ABO blood groups have recently been related to COVID19 infection. In the present work, we performed this analysis using data from 412 COVID19 patients and 17796 blood donors, all of them from Gipuzkoa, a region in Northern Spain. The results obtained confirmed this relation, in addition to showing a clear importance of group O as a protective factor in COVID19 disease, with an OR = 0.59 (CI95% 0.481-0.7177, p<0.0001) while A, B and AB are risk factors. ABO blood groups are slightly differently distributed in the populations and therefore these results should be replicated in the specific areas with a proper control population.

Open Access of COVID-19-related publications in the first quarter of 2020: a preliminary study based in PubMed.

Background: The COVID-19 outbreak has made funders, researchers and publishers agree to have research publications, as well as other research outputs, such as data, become openly available. In this extraordinary research context of the SARS CoV-2 pandemic, publishers are announcing that their coronavirus-related articles will be made immediately accessible in appropriate open repositories, like PubMed Central, agreeing upon funders' and researchers' instigation. Methods: This work uses Unpaywall, OpenRefine and PubMed to analyse the level of openness of articles about COVID-19, published during the first quarter of 2020. It also analyses Open Access (OA) articles published about previous coronavirus (SARS CoV-1 and MERS CoV) as a means of comparison. Results: A total of 5,611 COVID-19-related articles were analysed from PubMed. This is a much higher amount for a period of 4 months compared to those found for SARS CoV-1 and MERS during the first year of their first outbreaks (335 and 116 articles, respectively).  Regarding the levels of openness, 88.8% of the SARS CoV-2 papers are freely available; similar rates were found for the other coronaviruses. Deeper analysis showed that (i) 67.4% of articles belong to an undefined Bronze category; (ii) 76.4% of all OA papers don't carry any license, followed by 10.4% which display restricted licensing. These patterns were found to be repeated in the three most frequent publishers: Elsevier, Springer and Wiley. Conclusions: Our results suggest that, although scientific production is much higher than during previous epidemics and is open, there is a caveat to this opening, characterized by the absence of fundamental elements and values ​​on which Open Science is based, such as licensing.