Allergy

Rapid visualization of PD-L1 expression level in glioblastoma immune microenvironment via machine learning cascade-based Raman histopathology.

INTRODUCTION: Combination immunotherapy holds promise for improving survival in responsive glioblastoma (GBM) patients. Programmed death-ligand 1 (PD-L1) expression in immune microenvironment (IME) is the most important predictive biomarker for immunotherapy. Due to the heterogeneous distribution of PD-L1, post-operative histopathology fails to accurately capture its expression in residual tumors,...

Prediction of the number of asthma patients using environmental factors based on deep learning algorithms.

BACKGROUND: Air pollution, weather, pollen, and influenza are typical aggravating factors for asthma. Previous studies have identified risk factors using regression-based and ensemble models. However, studies that consider complex relationships and interactions among these factors have yet to be conducted. Although deep learning algorithms can address this problem, further research on modeling and...

Phosphorylated p38 MAP kinase expression by leucocytes is increased in allergic humans and associated with IgE responses.

Mitogen-activated protein kinases (MAPK) activate cascades that regulate cell proliferation, differentiation and death. Phosphorylated (phos-)p38 MAPK...

Transfer learning for a foundational chemistry model.

Data-driven chemistry has garnered much interest concurrent with improvements in hardware and the development of new machine learning models. However,...

Hot topics in allergen immunotherapy, 2023: Current status and future perspective.

The importance of allergen immunotherapy (AIT) is multifaceted, encompassing both clinical and quality-of-life improvements and cost-effectiveness in ...

Development and Validation of a Machine Learning-Based Model Using CT Radiomics for Predicting Immune Checkpoint Inhibitor-related Pneumonitis in Patients With NSCLC Receiving Anti-PD1 Immunotherapy: A Multicenter Retrospective CaseControl Study.

RATIONALE AND OBJECTIVES: This study aimed to develop and evaluate a radiomics-based model combined with clinical and qualitative radiological (semant...

A deep learning LSTM-based approach for forecasting annual pollen curves: Olea and Urticaceae pollen types as a case study.

Airborne pollen can trigger allergic rhinitis and other respiratory diseases in the synthesised population, which makes it one of the most relevant bi...

A Novel Machine-Learning Framework Based on a Hierarchy of Dispute Models for the Identification of Fish Species Using Multi-Mode Spectroscopy.

Seafood mislabeling rates of approximately 20% have been reported globally. Traditional methods for fish species identification, such as DNA analysis ...

Reducing Gadolinium Contrast With Artificial Intelligence.

Gadolinium contrast is an important agent in magnetic resonance imaging (MRI), particularly in neuroimaging where it can help identify blood-brain bar...

Untargeted metabolomic profiling in children identifies novel pathways in asthma and atopy.

BACKGROUND: Asthma and other atopic disorders can present with varying clinical phenotypes marked by differential metabolomic manifestations and enric...

The water-soluble subfraction from Artemisia argyi alleviates LPS-induced inflammatory responses via multiple pathways and targets in vitro and in vivo.

ETHNOPHARMACOLOGICAL RELEVANCE: As a traditional Chinese medicine, Artemisia argyi has been used medicinally and eaten for more than 2000 years in Chi...

Artificial Intelligence Chatbot Shows Multiple Inaccuracies When Responding to Questions About Eosinophilic Esophagitis.

Anaphylactic Shock to Intravenous Indocyanine Green During a Robotic Right Colectomy.

Intravenous indocyanine green (IV ICG) is regarded as a safe immunofluorescence agent used to assess bowel perfusion prior to creating bowel anastomos...

ChatGPT's performance in dentistry and allergyimmunology assessments: a comparative study.

Large language models (LLMs) such as ChatGPT have potential applications in healthcare, including dentistry. Priming, the practice of providing LLMs w...

Predicting the therapeutic efficacy of AIT for asthma using clinical characteristics, serum allergen detection metrics, and machine learning techniques.

Bronchial asthma is a prevalent non-communicable disease among children. The study collected clinical data from 390 children aged 4-17 years with asth...

A robust deep learning workflow to predict CD8 + T-cell epitopes.

BACKGROUND: T-cells play a crucial role in the adaptive immune system by triggering responses against cancer cells and pathogens, while maintaining to...

Demonstration of Convolutional Neural Networks to Determine Patch Test Reactivity.

Convolutional neural networks (CNNs) have the potential to assist allergists and dermatologists in the analysis of patch tests. Such models can help ...

COVID-19 diagnosis using clinical markers and multiple explainable artificial intelligence approaches: A case study from Ecuador.

The COVID-19 pandemic erupted at the beginning of 2020 and proved fatal, causing many casualties worldwide. Immediate and precise screening of affecte...

Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry.

Pollen and tracheophyte spores are ubiquitous environmental indicators at local and global scales. Palynology is typically performed manually by micro...

Groundwork for AI: Enforcing a benchmark for neoantigen prediction in personalized cancer immunotherapy.

This article expands on recent studies of machine learning or artificial intelligence (AI) algorithms that crucially depend on benchmark datasets, oft...