Multiomic Integration Analysis for Monitoring Severe Asthma Treated With Mepolizumab or Omalizumab.

Rationale: Biologics are becoming increasingly important in the management of severe asthma. However, little is known about the systemic immunometabolic consequences of Th2 response blockage.

Objectives: To provide a better immunometabolic understanding of the effects of mepolizumab and omalizumab treatments by identifying potential biomarkers for monitoring.

Macrophages are activated toward phagocytic lymphoma cell clearance by pentose phosphate pathway inhibition.

Macrophages in the B cell lymphoma microenvironment represent a functional node in progression and therapeutic response. We assessed metabolic regulation of macrophages in the context of therapeutic antibody-mediated phagocytosis. Pentose phosphate pathway (PPP) inhibition induces increased phagocytic lymphoma cell clearance by macrophages in vitro, in primary human chronic lymphocytic leukemia (CLL) patient co-cultures, and in mouse models.

Assessing the Impact of New Technologies on Managing Chronic Respiratory Diseases.

Chronic respiratory diseases (CRDs), including asthma and chronic obstructive pulmonary disease (COPD), represent significant global health challenges, contributing to substantial morbidity and mortality. As the prevalence of CRDs continues to rise, particularly in low-income countries, there is a pressing need for more efficient and personalized approaches to diagnosis and treatment. This article explores the impact of emerging technologies, particularly artificial intelligence (AI), on the management of CRDs.

Predictive risk models for COVID-19 patients using the multi-thresholding meta-algorithm.

This study aims to develop a Machine Learning model to assess the risks faced by COVID-19 patients in a hospital setting, focusing specifically on predicting the complications leading to Intensive Care Unit (ICU) admission or mortality, which are minority classes compared to the majority class of discharged patients. We operate within a multiclass framework comprising three distinct classes, and address the challenge of dataset imbalance, a common source of model bias. To effectively manage this, we introduce the Multi-Thresholding meta-algorithm (MTh), an innovative output-level methodology that extends traditional thresholding from binary to multiclass classification.