Allergy

The use of advanced machine learning to predict outcomes after atezolizumab plus bevacizumab for advanced hepatocellular carcinoma: a retrospective cohort study.

BACKGROUND: Combination immune checkpoint inhibitors are recommended as first-line therapy for advanced hepatocellular carcinoma. However, only a third of patients respond to treatment, and improved approaches to predict response are required. Using baseline clinical data, we aimed to use advanced machine learning models to predict overall survival and progression-free survival in patients with ad...

Association of Circulating T Cell and Tumor Microenvironment Profiles with Immune Checkpoint Blockade Outcome in Sarcoma.

PURPOSE: Immune checkpoint blockade (ICB) benefits only a subset of sarcoma patients. Biomarkers of response and resistance are needed to help guide patient selection. PATIENTS AND METHODS: We analyzed peripheral blood and tumor samples from sarcoma patients treated on five ICB-based clinical trials. Baseline peripheral blood mononuclear cells (PBMCs) underwent 11-color flow cytometry to define T ...

epiGPTope: A Machine Learning-Based Epitope Generator and Classifier.

Epitopes are short antigenic peptide sequences that are recognized by antibodies or immune cell receptors. These are central to the development of imm...

Projected typical allergic diseases prevalence under changing environments based on multiple machine learning models.

Predictive modeling of molecular activity underlying physical cell-cell interactions.

Interactions between cells are central to tissue organization and function in health and disease. Labeling immune partnerships by sortagging intercell...

Atopic Dermatitis in Denmark: Regional Variation.

Using national registers, this study investigates regional variations in the number of individuals with and treatment of atopic dermatitis (AD) in Den...

Integrative bioinformatics and experiments identify RIBC2 as a key regulator in the esophageal cancer.

Early detection of esophageal cancer (EC) remains a major challenge due to the limited understanding of its initial molecular alterations. Therefore, ...

Proteomics-driven innovations in plant-based foods: Current advances, emerging technologies, and future perspectives.

Despite rapid market growth, plant-based foods such as meat analogs, plant-based milk, yogurt alternatives, and fermented products still fall short of...

Development and validation of a machine learning stratified prediction model for early warning of anti-tuberculosis drug-induced liver injury risk based on real-world data: a retrospective cohort study.

OBJECTIVE: This study aimed to develop and validate stratified machine learning models for early prediction of anti-tuberculosis drug-induced liver in...

Integrative analysis of immune and microbial subtypes predicts immunotherapy response in stomach adenocarcinoma.

The tumor immune microenvironment and intratumoral microbiota play critical roles in cancer progression and immunotherapy response, yet their integrat...

[Imaging of eosinophilic pneumonia : Differential diagnosis in radiological practice].

CLINICAL/METHODICAL ISSUE: Eosinophilic pneumonias are rare inflammatory lung diseases with heterogeneous clinical presentation and variable computer ...

Machine Learning Model Development and Validation in Individuals With Suggestive Hypersensitivity to Nonsteroidal Anti-inflammatory Drugs.

BACKGROUND AND OBJECTIVES: Nonsteroidal anti-inflammatory drugs (NSAIDs) are the main triggers of drug hypersensitivity reactions (HSRs). NSAID-induce...

Bio-Inspired Smart Elastin-Like Polypeptides (ELPs) for Precision Drug Delivery: Molecular Strategies, Thermal Responsiveness, and Translational Advances.

Elastin-like polypeptides (ELPs), inspired by the natural elasticity of human elastin, are rapidly evolving as next-generation platforms for precision...

Integrating Spatial Proteogenomics in Cancer Research.

BACKGROUND: Spatial proteogenomics marks a paradigm shift in oncology by integrating molecular analysis with spatial information from both spatial pro...

Update on Non-Biological and RNA-Based Therapeutics in Chronic Inflammatory Diseases: Precision Medicine Through Small Molecules.

In the last decades, critical advancements in research technology and knowledge on disease mechanisms steered therapeutic approaches for chronic infla...

Xanthine oxidase inhibitors for gout: Applications and novel drug development.

Hyperuricemia is a well-established direct trigger of gouty arthritis. Xanthine oxidase (XO), the rate-limiting enzyme in uric acid production, repres...

Multi-omics profiling of chronic immune-mediated skin diseases: SKINERGY protocol and strategic evaluation.

BACKGROUND: The Dutch flagship project Next Generation ImmunoDermatology (NGID) aims to profile five chronic immune-mediated inflammatory skin disease...

CT-based radiomics for predicting PD-L1 expression status in non-small cell lung cancer using a hybrid machine learning model.

PURPOSE: Programmed cell death ligand-1 (PD-L1) is a key prognostic and predictive biomarker for immunotherapy in non-small cell lung cancer (NSCLC). ...

Stochasticity in cancer immunotherapy stems from rare but functionally critical Spark T cells.

Cancer immunotherapies trigger highly variable responses in patients and in genetically identical mouse models. To assess the intrinsic stochasticity ...

Learning the Principles of T Cell Antigen Discernment.

T cells are central to the adaptive immune response, capable of detecting pathogenic antigens while ignoring healthy tissues with remarkable specifici...