Allergy & Immunology

The role of endothelial cell-related gene COL1A1 in prostate cancer diagnosis and immunotherapy: insights from machine learning and single-cell analysis.

BACKGROUND: Endothelial cells are integral components of the tumor microenvironment and play a multifaceted role in tumor immunotherapy. Targeting endothelial cells and related signaling pathways can improve the effectiveness of immunotherapy by normalizing tumor blood vessels and promoting immune cell infiltration. However, to date, there have been no comprehensive studies analyzing the role of e...

Machine learning-derived asthma and allergy trajectories in children: a systematic review and meta-analysis.

INTRODUCTION: Numerous studies have characterised trajectories of asthma and allergy in children using machine learning, but with different techniques and mixed findings. The present work aimed to summarise the evidence and critically appraise the methodology.

Multimodal data deep learning method for predicting symptomatic pneumonitis caused by lung cancer radiotherapy combined with immunotherapy.

OBJECTIVES: The pairing of immunotherapy and radiotherapy in the treatment of locally advanced nonsmall cell lung cancer (NSCLC) has shown promise. By...

Integration of single-cell transcriptomics and bulk transcriptomics to explore prognostic and immunotherapeutic characteristics of nucleotide metabolism in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is a highly aggressive tumor with one of the highest morbidity and mortality rates in the world. Nucleotide met...

Hepatoid adenocarcinoma of the stomach with ideal response to neoadjuvant chemo-immunotherapy: a case report.

Hepatoid adenocarcinoma of the stomach (HAS) is a rare subtype of gastric cancer characterized by histological features resembling hepatocellular carc...

Understanding the role of the gut microbiome in solid tumor responses to immune checkpoint inhibitors for personalized therapeutic strategies: a review.

Immunotherapy, especially immune checkpoint inhibitor (ICI) therapy, has yielded remarkable outcomes for some patients with solid cancers, but others ...

Integrating single cell analysis and machine learning methods reveals stem cell-related gene S100A10 as an important target for prediction of liver cancer diagnosis and immunotherapy.

BACKGROUND: Hepatocellular carcinoma (LIHC) poses a significant health challenge worldwide, primarily due to late-stage diagnosis and the limited effe...

Predictive modeling of consecutive intravenous immunoglobulin treatment resistance in Kawasaki disease: A nationwide study.

Kawasaki disease (KD) is a leading cause of acquired heart disease in children, often resulting in coronary artery complications such as dilation, ane...

Meibomian gland alterations in allergic conjunctivitis: insights from a novel quantitative analysis algorithm.

PURPOSE: To investigate the changes in meibomian gland (MG) structure in allergic conjunctivitis (AC) patients using an intelligent quantitative analy...

Integrating bioinformatics and machine learning to identify AhR-related gene signatures for prognosis and tumor microenvironment modulation in melanoma.

BACKGROUND: The Aryl Hydrocarbon Receptor (AhR) pathway significantly influences immune cell regulation, impacting the effectiveness of immunotherapy ...

Bioequivalence study of fluticasone propionate nebuliser suspensions in healthy Chinese subjects.

BACKGROUND: Fluticasone propionate is a synthetic trifluoro-substituted glucocorticoid, a highly selective glucocorticoid receptor agonist. Fluticason...

Multiparametric MRI along with machine learning predicts prognosis and treatment response in pediatric low-grade glioma.

Pediatric low-grade gliomas (pLGGs) exhibit heterogeneous prognoses and variable responses to treatment, leading to tumor progression and adverse outc...

Predicting autoimmune thyroiditis in primary Sjogren's syndrome patients using a random forest classifier: a retrospective study.

BACKGROUND: Primary Sjogren's syndrome (pSS) and autoimmune thyroiditis (AIT) share overlapping genetic and immunological profiles. This retrospective...

Identification of key gene signatures for predicting chemo-immunotherapy efficacy in extensive-stage small-cell lung cancer using machine learning.

OBJECTIVES: The lack of definitive biomarkers presents a significant challenge for chemo-immunotherapy in extensive-stage small-cell lung cancer (ES-S...

Identification of a gene score related to antigen processing and presentation machinery for predicting prognosis in head and neck squamous cell carcinoma and its potential implications for immunotherapy.

BACKGROUND: Despite its crucial role in immune surveillance and cell survival of tumors, the significance of MHC antigen processing and presentation m...

Exploring machine learning tools in a retrospective case-study of patients with metastatic non-small cell lung cancer treated with first-line immunotherapy: A feasibility single-centre experience.

BACKGROUND: Artificial intelligence (AI) models are emerging as promising tools to identify predictive features among data coming from health records....

A machine learning-based immune response signature to facilitate prognosis prediction in patients with endometrial cancer.

Endometrial cancer is the most prevalent form of gynecologic malignancy, with a significant surge in incidence among youngsters. Although the advent o...

Multiomics integration and machine learning reveal prognostic programmed cell death signatures in gastric cancer.

Gastric cancer (GC) is characterized by notable heterogeneity and the impact of molecular subtypes on treatment and prognosis. The role of programmed ...

Integrating immune multi-omics and machine learning to improve prognosis, immune landscape, and sensitivity to first- and second-line treatments for head and neck squamous cell carcinoma.

In recent years, immune checkpoint inhibitors (ICIs) has emerged as a fundamental component of the standard treatment regimen for patients with head a...

Predicting candidate biomarkers for COVID-19 associated with leukemia in children.

Since the COVID-19 pandemic, a significant number of pediatric leukemia patients have shown to have also contracted COVID-19 several weeks or months p...