Allergy & Immunology

Therapy resistance mechanisms in hematological malignancies.

Hematologic malignancies are model diseases for understanding neoplastic transformation and serve as prototypes for developing effective therapies. Indeed, the concept of systemic cancer therapy originated in hematologic malignancies and has guided the development of chemotherapy, cellular therapies, immunotherapy and modern precision oncology. Despite significant advances in the treatment of leuk...

Integration of reference data from different Rapid-E devices supports automatic pollen detection in more locations.

Pollen is the most common cause of seasonal allergies, affecting over 33 % of the European population, even when considering only grasses. Informing the population and clinicians in real-time about the actual presence of pollen in the atmosphere is essential to reduce its harmful health and economic impact. Thus, there is a growing network of automatic particle analysers, and the reproducibility a...

Deep learning reveals cuproptosis features assist in predict prognosis and guide immunotherapy in lung adenocarcinoma.

BACKGROUND: Cuproptosis is a recently found non-apoptotic cell death type that holds promise as an emerging therapeutic modality in lung adenocarcinom...

Artificial Intelligence in Allergy and Immunology: Comparing Risk Prediction Models to Help Screen Inborn Errors of Immunity.

BACKGROUND: Inborn errors of immunity (IEI) are underdiagnosed disorders, leading to increased morbimortality and expenses for healthcare system.

AsthmaKGxE: An asthma-environment interaction knowledge graph leveraging public databases and scientific literature.

MOTIVATION: Asthma is a complex heterogeneous disease resulting from intricate interactions between genetic and non-genetic factors related to environ...

Neurochemical Concentration Prediction Using Deep Learning vs Principal Component Regression in Fast Scan Cyclic Voltammetry: A Comparison Study.

Neurotransmitters, such as dopamine and serotonin, are responsible for mediating a wide array of neurologic functions, from memory to motivation. From...

Machine Learning Approaches to TCR Repertoire Analysis.

Sparked by the development of genome sequencing technology, the quantity and quality of data handled in immunological research have been changing dram...

Applicability analysis of immunotherapy for lung cancer patients based on deep learning.

According to global and Chinese cancer statistics, lung cancer is the second most common cancer globally with the highest mortality rate and a severe ...

Evaluating the Performance of Inclusive Growth Based on the BP Neural Network and Machine Learning Approach.

In this paper, we use the panel data of 281 cities in China from 2005 to 2020 for capturing the factors driving urban inclusive growth (IG). In doing ...

HDI Highlighter, The First Intelligent Tool to Screen the Literature on Herb-Drug Interactions.

Herbal food supplements are commonly used and can be an important part of patient self-care. Like all other bio-active and therapeutic products, they ...

A three-methylation-driven gene-based deep learning model for tuberculosis diagnosis in patients with and without human immunodeficiency virus co-infection.

Improved diagnostic tests for tuberculosis (TB) among people with human immunodeficiency virus (HIV) are urgently required. We hypothesized that methy...

BepFAMN: A Method for Linear B-Cell Epitope Predictions Based on Fuzzy-ARTMAP Artificial Neural Network.

The public health system is extremely dependent on the use of vaccines to immunize the population from a series of infectious and dangerous diseases, ...

A Deep Learning Model for Accurate Diagnosis of Infection Using Antibody Repertoires.

The adaptive immune receptor repertoire consists of the entire set of an individual's BCRs and TCRs and is believed to contain a record of prior immun...

Deep Learning for Automatic Upper Airway Obstruction Detection by Analysis of Flow-Volume Curve.

BACKGROUND: Due to the similar symptoms of upper airway obstruction to asthma, misdiagnosis is common. Spirometry is a cost-effective screening test f...

Covariate adjustment of spirometric and smoking phenotypes: The potential of neural network models.

To increase power and minimize bias in statistical analyses, quantitative outcomes are often adjusted for precision and confounding variables using st...

DeepNC: a framework for drug-target interaction prediction with graph neural networks.

The exploration of drug-target interactions (DTI) is an essential stage in the drug development pipeline. Thanks to the assistance of computational mo...

S. aureus and IgE-mediated diseases: pilot or copilot? A narrative review.

INTRODUCTION: is a major opportunistic pathogen that has been implicated in the pathogenesis of several chronic inflammatory diseases including bronc...

Vaccine Therapy in Non-Small Cell Lung Cancer.

Immunotherapy using immune checkpoint modulators has revolutionized the oncology field, emerging as a new standard of care for multiple indications, i...

Environmental and clinical data utility in pediatric asthma exacerbation risk prediction models.

BACKGROUND: Asthma exacerbations are triggered by a variety of clinical and environmental factors, but their relative impacts on exacerbation risk are...

Machine learning model for classification of predominantly allergic and non-allergic asthma among preschool children with asthma hospitalization.

OBJECTIVE: Asthma is the most frequent chronic airway illness in preschool children and is difficult to diagnose due to the disease's heterogeneity. T...