An RNA-seq Based Machine Learning Approach Identifies Latent Tuberculosis Patients With an Active Tuberculosis Profile.

A better understanding of the response against Tuberculosis (TB) infection is required to accurately identify the individuals with an active or a latent TB infection (LTBI) and also those LTBI patients at higher risk of developing active TB. In this work, we have used the information obtained from studying the gene expression profile of active TB patients and their infected -LTBI- or uninfected -NoTBI- contacts, recruited in Spain and Mozambique, to build a class-prediction model that identifies individuals with a TB infection profile. Following this approach, we have identified several genes and metabolic pathways that provide important information of the immune mechanisms triggered against TB infection. As a novelty of our work, a combination of this class-prediction model and the direct measurement of different immunological parameters, was used to identify a subset of LTBI contacts (called TB-like) whose transcriptional and immunological profiles are suggestive of infection with a higher probability of developing active TB. Validation of this novel approach to identifying LTBI individuals with the highest risk of active TB disease merits further longitudinal studies on larger cohorts in TB endemic areas

Rapid development of proteomic applications with the AIBench framework

In this paper we present two case studies of Proteomics applications development using the AIBench framework, a Java desktop application framework mainly focused in scientific software development. The applications presented in this work are Decision Peptide- Driven, for rapid and accurate protein quantification, and Bacterial Identification, for Tuberculosis biomarker search and diagnosis. Both tools work with mass spectrometry data, specifically with MALDI-TOF spectra, minimizing the time required to process and analyze the experimental data

Derivación y validación de una puntuación de riesgo de ingreso en la Unidad de Cuidados Intensivos para pacientes con COVID-19.

This work aims to identify and validate a risk scale for admission to intensive care units (ICU) in hospitalized patients with coronavirus disease 2019 (COVID-19). We created a derivation rule and a validation rule for ICU admission using data from a national registry of a cohort of patients with confirmed SARS-CoV-2 infection who were admitted between March and August 2020 (n = 16,298). We analyzed the available demographic, clinical, radiological, and laboratory variables recorded at hospital admission. We evaluated the performance of the risk score by estimating the area under the receiver operating characteristic curve (AUROC). Using the β coefficients of the regression model, we developed a score (0 to 100 points) associated with ICU admission. The mean age of the patients was 67 years; 57% were men. A total of 1,420 (8.7%) patients were admitted to the ICU. The variables independently associated with ICU admission were age, dyspnea, Charlson Comorbidity Index score, neutrophil-to-lymphocyte ratio, lactate dehydrogenase levels, and presence of diffuse infiltrates on a chest X-ray. The model showed an AUROC of 0.780 (CI: 0.763-0.797) in the derivation cohort and an AUROC of 0.734 (CI: 0.708-0.761) in the validation cohort. A score of greater than 75 points was associated with a more than 30% probability of ICU admission while a score of less than 50 points reduced the likelihood of ICU admission to 15%. A simple prediction score was a useful tool for forecasting the probability of ICU admission with a high degree of precision