Microbial dysbiosis and lack of SCFA production in a Spanish cohort of patients with multiple sclerosis

Background: Multiple sclerosis (MS) is a chronic, demyelinating, and immune-mediated disease of the central nervous system caused by a combination of genetic, epigenetic, and environmental factors. The incidence of MS has increased in the past several decades, suggesting changes in the environmental risk factors. Much effort has been made in the description of the gut microbiota in MS; however, little is known about the dysbiosis on its function. The microbiota produces thousands of biologically active substances among which are notable the short-chain fatty acid (SCFA) excretion. Objectives: Analyze the interaction between microbiota, SCFAs, diet, and MS. Methods: 16S, nutritional questionnaires, and SCFAS quantification have been recovered from MS patients and controls. Results: Our results revealed an increment in the phylum Proteobacteria, especially the family Enterobacteriaceae, a lack in total SCFA excretion, and an altered profile of SCFAs in a Spanish cohort of MS patients. These alterations are more evident in patients with higher disability. Conclusions: The abundance of Proteobacteria and acetate and the low excretion of total SCFAs, especially butyrate, are common characteristics of MS patients, and besides, both are associated with a worse prognosis of the disease.This work was supported by the Spanish Network of Multiple Sclerosis (REEM) under the grant (BIOD19-021) and by Basque government projects (2018111038 and 2019111013)

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