Allergy & Immunology

Artificial intelligence in pediatric allergy research.

UNLABELLED: Atopic dermatitis, food allergy, allergic rhinitis, and asthma are among the most common...

Detection of Patient-Level Immunotherapy-Related Adverse Events (irAEs) from Clinical Narratives of Electronic Health Records: A High-Sensitivity Artificial Intelligence Model.

PURPOSE: We developed an artificial intelligence (AI) model to detect immunotherapy -related adverse...

Geometric deep learning improves generalizability of MHC-bound peptide predictions.

The interaction between peptides and major histocompatibility complex (MHC) molecules is pivotal in ...

Comparison of the composition, immunological activity and anti-fatigue effects of different parts in sika deer antler.

BACKGROUND: Sika deer (, 1838) antler is a highly esteemed tonic renowned for its abundant assortmen...

Optimizing the management of immune-related adverse events and survival in patients with thoracic cancer receiving immunotherapy through artificial intelligence (electronic patient-reported outcomes): The IMPATHI study.

INTRODUCTION: The lung cancer continues to be the primary cause of cancer-related deaths, despite si...

Targeting liver cancer stem cells: the prognostic significance of MRPL17 in immunotherapy response.

BACKGROUND: Liver hepatocellular carcinoma (LIHC) ranks as the foremost cause of cancer-related deat...

The establishment of PD-1 inhibitor treatment prognosis model based on dynamic changes of peripheral blood indexes in patients with advanced lung squamous cell carcinoma.

BACKGROUND: Unlike patients with lung adenocarcinoma, patients with lung squamous cell carcinoma (LU...

Serum Vitamin D Profiles of Children with Asthma in Southwest Saudi: A Comparative Cross-Sectional Study.

BACKGROUND: Evidence suggests a strong association between vitamin D status and asthma, with individ...

Potential shared mechanisms in atopic dermatitis and type 2 diabetes identified via transcriptomic and machine learning approaches.

Although atopic dermatitis (AD) and type 2 diabetes mellitus (T2DM) may appear clinically and pathop...

Investigation of the efficiency of different reprocessing methods on disposable laryngeal masks contaminated with HBV DNA.

BACKGROUND: The use of laryngeal masks (LM) has increased widely today, both in anesthesia and in em...

Analysis of HIV infection among voluntary blood donors based on HIV ELISA and nucleic acid detection.

OBJECTIVE: To analyze the epidemiological characteristics of human immunodeficiency virus (HIV) infe...

Functional MRGPRX2 expression on peripheral blood-derived human mast cells increases at low seeding density and is suppressed by interleukin-9 and fetal bovine serum.

Primary human mast cells (MC) obtained through culturing of blood-derived MC progenitors are the pre...

Multi-omics analysis and experiments uncover the function of cancer stemness in ovarian cancer and establish a machine learning-based model for predicting immunotherapy responses.

BACKGROUND: The heterogeneity of cancer makes it challenging to predict its response to immunotherap...

Integrating machine learning, bioinformatics and experimental verification to identify a novel prognostic marker associated with tumor immune microenvironment in head and neck squamous carcinoma.

Head and neck squamous carcinoma (HNSC), characterized by a high degree of malignancy, develops in c...

Integrating necroptosis into pan-cancer immunotherapy: a new era of personalized treatment.

INTRODUCTION: Necroptosis has emerged as a promising biomarker for predicting immunotherapy response...

Editorial: Systems immunology to advance vaccine development.

VaxOptiML: leveraging machine learning for accurate prediction of MHC-I and II epitopes for optimized cancer immunotherapy.

Cancer immunotherapy hinges on accurate epitope prediction for advancing vaccine development. VaxOpt...

Milk ladder: Who? When? How? Where? with the lowest risk of reaction.

The milk ladder (ML) approach, which is the gradual reintroduction of the milk allergen from the lea...

Integrated explainable machine learning and multi-omics analysis for survival prediction in cancer with immunotherapy response.

To demonstrate the efficacy of machine learning models in predicting mortality in melanoma cancer, w...

Harnessing machine learning and multi-omics to explore tumor evolutionary characteristics and the role of AMOTL1 in prostate cancer.

Although recent advancements have shed light on the crucial role of coordinated evolution among cell...

Machine learning based on multiplatform tests assists in subtype classification of mature B-cell neoplasms.

Mature B-cell neoplasms (MBNs) are clonal proliferative diseases encompassing over 40 subtypes. The ...