Analysis of transcriptomic responses to SARS-CoV-2 reveals plausible defective pathways responsible for increased susceptibility to infection and complications and helps to develop fast-track repositioning of drugs against COVID-19.

Background: To understand the transcriptomic response to SARS-CoV-2 infection, is of the utmost importance to design diagnostic tools predicting the severity of the infection.

Methods: We have performed a deep sampling analysis of the viral transcriptomic data oriented towards drug repositioning. Using different samplers, the basic principle of this methodology the biological invariance, which means that the pathways altered by the disease, should be independent on the algorithm used to unravel them.

Robust Sampling of Defective Pathways in Alzheimer's Disease. Implications in Drug Repositioning.

We present the analysis of the defective genetic pathways of the Late-Onset Alzheimer's Disease (LOAD) compared to the Mild Cognitive Impairment (MCI) and Healthy Controls (HC) using different sampling methodologies. These algorithms sample the uncertainty space that is intrinsic to any kind of highly underdetermined phenotype prediction problem, by looking for the minimum-scale signatures (header genes) corresponding to different random holdouts. The biological pathways can be identified performing posterior analysis of these signatures established via cross-validation holdouts and plugging the set of most frequently sampled genes into different ontological platforms.

Analysis of defective pathways and drug repositioning in Multiple Sclerosis via machine learning approaches.

Background: Although some studies show that there could be a genetic predisposition to develop Multiple Sclerosis (MS), attempts to find genetic signatures related to MS diagnosis and development are extremely rare.

Method: We carried out a retrospective analysis of two different microarray datasets, using machine learning techniques to understand the defective pathways involved in this disease. We have modeled two data sets that are publicly accessible.

Direct Methane Conversion on Zn Nano-Particles with Methane Jet Generated by Spark Discharge.

Methane direct conversion on nano-catalysts using methane jet generated by spark discharges was carried out. In particular, the effect of the spark discharge on the nano-catalyst was investigated by SEM analysis. In addition, the effect of the supply pressure on the methane conversion was studied.