Allergy & Immunology

Artificial intelligence and machine learning in respiratory medicine.

: The application of artificial intelligence (AI) and machine learning (ML) in medicine and in particular in respiratory medicine is an increasingly relevant topic.: We aimed to identify and describe the studies published on the use of AI and ML in the field of respiratory diseases. The string '(((pulmonary) OR respiratory)) AND ((artificial intelligence) OR machine learning)' was used in PubMed a...

Explainable decision support through the learning and visualization of preferences from a formal ontology of antibiotic treatments.

The aim of eXplainable Artificial Intelligence (XAI) is to design intelligent systems that can explain their predictions or recommendations to humans. Such systems are particularly desirable for therapeutic decision support, because physicians need to understand rcommendations to have confidence in their application and to adapt them if required, e.g. in case of patient contraindication. We propos...

Regular cold water swimming during winter time affects resting hematological parameters and serum erythropoietin.

Recreational winter swimming in cold sea water evokes body responses to regularly repeated cold water immersion. However, the understanding of adaptiv...

A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules.

High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resu...

Characterizing Protein-Ligand Binding Using Atomistic Simulation and Machine Learning: Application to Drug Resistance in HIV-1 Protease.

Over the past several decades, atomistic simulations of biomolecules, whether carried out using molecular dynamics or Monte Carlo techniques, have pro...

Multifactorial Deep Learning Reveals Pan-Cancer Genomic Tumor Clusters with Distinct Immunogenomic Landscape and Response to Immunotherapy.

PURPOSE: Tumor genomic features have been of particular interest because of their potential impact on the tumor immune microenvironment and response t...

pVACtools: A Computational Toolkit to Identify and Visualize Cancer Neoantigens.

Identification of neoantigens is a critical step in predicting response to checkpoint blockade therapy and design of personalized cancer vaccines. Thi...

A modern approach to identifying and characterizing child asthma and wheeze phenotypes based on clinical data.

'Asthma' is a complex disease that encapsulates a heterogeneous group of phenotypes and endotypes. Research to understand these phenotypes has previou...

Machine Learning Characterization of COPD Subtypes: Insights From the COPDGene Study.

COPD is a heterogeneous syndrome. Many COPD subtypes have been proposed, but there is not yet consensus on how many COPD subtypes there are and how th...

Vitamin D and Serum Immunoglobulin E Levels in Allergic Rhinitis: A Case-control Study from Pakistan.

Introduction Allergic rhinitis (AR) is the most common non-infectious rhinitis and is associated with sneezing, cough, and flu-like symptoms. The exac...

Membranous Nephropathy with Rapid Progression.

We report a 49-year-old man with microscopic hematuria, subnephrotic proteinuria, and rapidly progressive renal failure. His biopsy had features of Ph...

Artificial intelligence approaches using natural language processing to advance EHR-based clinical research.

The wide adoption of electronic health record systems in health care generates big real-world data that open new venues to conduct clinical research. ...

The doctor will see you now: How machine learning and artificial intelligence can extend our understanding and treatment of asthma.

High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets.

Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins can be used to predict patient res...

Identification of Catecholamine Neurotransmitters Using a Fluorescent Electronic Tongue.

Catecholamine neurotransmitters, specifically, dopamine (DA), epinephrine (EP), and norepinephrine (NE), are known as substantial indicators of variou...

Machine learning for the detection of early immunological markers as predictors of multi-organ dysfunction.

The immune response to major trauma has been analysed mainly within post-hospital admission settings where the inflammatory response is already underw...

Absolute quantitation of high abundant Fc-glycopeptides from human serum IgG-1.

Absolute quantitation of IgG-1 Fc-glycosylation, which is crucial for the clinical practice of glyco-biomarkers and quality control of biopharmaceutic...

In vitro selection of DNA aptamers and their integration in a competitive voltammetric biosensor for azlocillin determination in waste water.

The uncontrolled usage of veterinary antibiotics has led to their widespread pollution in waterways and milk products. Potential impact of antibiotic ...

Classifying T cell activity in autofluorescence intensity images with convolutional neural networks.

The importance of T cells in immunotherapy has motivated developing technologies to improve therapeutic efficacy. One objective is assessing antigen-i...

Efficient expression of EpEX in the cytoplasm of using thioredoxin fusion protein.

Recombinant epithelial cell adhesion molecule extracellular domain (EpEX) has a high potential as a candidate for passive and active immunotherapy as ...