Allergy

Data augmentation in dermatology image recognition using machine learning.

BACKGROUND: Each year in the United States, over 80 million people are affected by acne, atopic dermatitis, rosacea, psoriasis, and impetigo. Artificial intelligence and machine learning could prove to be a good tool for assisting in the diagnosis of dermatological conditions. The objective of this study was to evaluate the use of data augmentation in machine learning image recognition of five der...

Capturing the differences between humoral immunity in the normal and tumor environments from repertoire-seq of B-cell receptors using supervised machine learning.

BACKGROUND: The recent success of immunotherapy in treating tumors has attracted increasing interest in research related to the adaptive immune system in the tumor microenvironment. Recent advances in next-generation sequencing technology enabled the sequencing of whole T-cell receptors (TCRs) and B-cell receptors (BCRs)/immunoglobulins (Igs) in the tumor microenvironment. Since BCRs/Igs in tumor ...

Level of neo-epitope predecessor and mutation type determine T cell activation of MHC binding peptides.

BACKGROUND: Targeting epitopes derived from neo-antigens (or "neo-epitopes") represents a promising immunotherapy approach with limited off-target eff...

Do no harm: Natural language processing of social media supports safety of aseptic allergen immunotherapy procedures.

Quantitative Prediction of the Landscape of T Cell Epitope Immunogenicity in Sequence Space.

Immunodominant T cell epitopes preferentially targeted in multiple individuals are the critical element of successful vaccines and targeted immunother...

ELM-MHC: An Improved MHC Identification Method with Extreme Learning Machine Algorithm.

The major histocompatibility complex (MHC) is a term for all gene groups of a major histocompatibility antigen. It binds to peptide chains derived fro...

Joint reconstruction and classification of tumor cells and cell interactions in melanoma tissue sections with synthesized training data.

PURPOSE: Cancers are almost always diagnosed by morphologic features in tissue sections. In this context, machine learning tools provide new opportuni...

Application of adaptive-network-based fuzzy inference systems to the parameter optimization of a biochemical rule-based model.

In this study, the binding of allergens to antibody-receptor complexes was investigated. This process is important in understanding the allergic respo...

Skin Sensitization Testing-What's Next?

There is an increasing demand for alternative in vitro methods to replace animal testing, and, to succeed, new methods are required to be at least as ...

Sex Differences in Nociceptin/Orphanin FQ Peptide Receptor-Mediated Pain and Anxiety Symptoms in a Preclinical Model of Post-traumatic Stress Disorder.

Nociceptin/Orphanin FQ (N/OFQ) is a neuropeptide that modulates pain transmission, learning/memory, stress, anxiety, and fear responses via activation...

Deep learning for DNase I hypersensitive sites identification.

BACKGROUND: The DNase I hypersensitive sites (DHSs) are associated with the cis-regulatory DNA elements. An efficient method of identifying DHSs can e...

Ceftaroline Fosamil as an Alternative for a Severe Methicillin-resistant Staphylococcus aureus Infection: A Case Report.

Bacteremia secondary to methicillin-resistant Staphylococcus aureus (MRSA) is a dreaded medical condition that is not only associated with a significa...

Leveraging Multilayered "Omics" Data for Atopic Dermatitis: A Road Map to Precision Medicine.

Atopic dermatitis (AD) is a complex multifactorial inflammatory skin disease that affects ~280 million people worldwide. About 85% of AD cases begin i...

Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography.

OBJECTIVES: To apply a deep-learning system for diagnosis of maxillary sinusitis on panoramic radiography, and to clarify its diagnostic performance.

Demographic, Clinical, and Allergic Characteristics of Children with Eosinophilic Esophagitis in Isfahan, Iran.

Eosinophilic esophagitis (EoE) is a chronic immune-mediated disease isolated to the esophagus Food allergy is thought to play an important role in the...

Nasal cytology with deep learning techniques.

BACKGROUND: In recent years, cytological observations in the Rhinology field are being increasingly utilized. This development has taken place over th...

Dynamic changes in specific anti-L-asparaginase antibodies generation during acute lymphoblastic leukemia treatment.

BACKGROUND: L-asparaginase (L-asp) remains one of the key components of acute lymphoblastic leukemia therapy. Immune reactions to the drug are associa...

Specific and sensitive ELISA for measurement of IgE-binding variations of milk allergen β-lactoglobulin in processed foods.

Immunochemical detection of food allergens is usually based on the use of polyclonal or monoclonal immunoglobulins G (IgG) antibodies. However, due to...

Precision immunoprofiling by image analysis and artificial intelligence.

Clinical success of immunotherapy is driving the need for new prognostic and predictive assays to inform patient selection and stratification. This re...

Machine learning to identify pairwise interactions between specific IgE antibodies and their association with asthma: A cross-sectional analysis within a population-based birth cohort.

BACKGROUND: The relationship between allergic sensitisation and asthma is complex; the data about the strength of this association are conflicting. We...