Diagnostic Accuracy of Lipid Transfer Proteins (LTPs) Specific IgE Assay in Food Allergy: A Systematic Review.

The aim of this systematic review was to evaluate the diagnostic accuracy of molecular-based LTPs serum sIgE for the diagnosis of food allergies in patients with suspected allergy to one of the LTPs-containing foods. Cohort, prospective or retrospective cross-sectional studies were considered for inclusion in this review. Oral food challenge (both open and double-blind placebo-controlled) was the reference standard for the diagnosis.

APDB: a database on air pollutant characterization and similarity prediction.

The World Health Organization estimates that 9 out of 10 people worldwide breathe air containing high levels of pollutants. Long-term and chronic exposure to high concentrations of air pollutants is associated with deleterious effects on vital organs, including increased inflammation in the lungs, oxidative stress in the heart and disruption of the blood-brain barrier. For this reason, in an effort to find an association between exposure to pollutants and the toxicological effects observable on human health, an online resource collecting and characterizing in detail pollutant molecules could be helpful to investigate their properties and mechanisms of action.

KUALA: a machine learning-driven framework for kinase inhibitors repositioning.

The family of protein kinases comprises more than 500 genes involved in numerous functions. Hence, their physiological dysfunction has paved the way toward drug discovery for cancer, cardiovascular, and inflammatory diseases. As a matter of fact, Kinase binding sites high similarity has a double role.

Exploring Machine Learning Techniques to Predict the Response to Omalizumab in Chronic Spontaneous Urticaria.

Background: Omalizumab is the best treatment for patients with chronic spontaneous urticaria (CSU). Machine learning (ML) approaches can be used to predict response to therapy and the effectiveness of a treatment. No studies are available on the use of ML techniques to predict the response to Omalizumab in CSU.

Adipose stem cell niche reprograms the colorectal cancer stem cell metastatic machinery.

Obesity is a strong risk factor for cancer progression, posing obesity-related cancer as one of the leading causes of death. Nevertheless, the molecular mechanisms that endow cancer cells with metastatic properties in patients affected by obesity remain unexplored.Here, we show that IL-6 and HGF, secreted by tumor neighboring visceral adipose stromal cells (V-ASCs), expand the metastatic colorectal (CR) cancer cell compartment (CD44v6 + ), which in turn secretes neurotrophins such as NGF and NT-3, and recruits adipose stem cells within tumor mass.