Allergy

Machine learning framework develops neutrophil extracellular traps model for clinical outcome and immunotherapy response in lung adenocarcinoma.

Neutrophil extracellular traps (NETs) are novel inflammatory cell death in neutrophils. Emerging studies demonstrated NETs contributed to cancer progression and metastases in multiple ways. This study intends to provide a prognostic NETs signature and therapeutic target for lung adenocarcinoma (LUAD) patients. Consensus cluster analysis performed by 38 reported NET-related genes in TCGA-LUAD cohor...

Exploring the NRF2-TP53 Signaling Network Through Machine Learning and Pan-Cancer Analysis: Identifying Potential targets for Cancer Prognosis Related to Oxidative Stress.

Oxidative stress (OXS) is closely related to tumor prognosis and immune response, while TP53 integrated with NRF2 is closely associated with the regulation of cancer-related OXS. Hence, constructing a TP53-NRF2 integrated OXS signature of pan-cancer is essential in predicting survival prognosis and facilitating cancer drug treatment. The pan-cancer analysis acquired the Cancer Genome Atlas (TCGA) ...

Automated detection and recognition system for chewable food items using advanced deep learning models.

Identifying and recognizing the food on the basis of its eating sounds is a challenging task, as it plays an important role in avoiding allergic foods...

Deep Learning-Based Multi-Class Segmentation of the Paranasal Sinuses of Sinusitis Patients Based on Computed Tomographic Images.

Accurate paranasal sinus segmentation is essential for reducing surgical complications through surgical guidance systems. This study introduces a mult...

Improving the performance of machine learning penicillin adverse drug reaction classification with synthetic data and transfer learning.

BACKGROUND: Machine learning may assist with the identification of potentially inappropriate penicillin allergy labels. Strategies to improve the perf...

Prediction of immunotherapy response in idiopathic membranous nephropathy using deep learning-pathological and clinical factors.

BACKGROUND: Owing to individual heterogeneity, patients with idiopathic membranous nephropathy (IMN) exhibit varying sensitivities to immunotherapy. T...

An integrative machine learning model for the identification of tumor T-cell antigens.

The escalating global incidence of cancer poses significant health challenges, underscoring the need for innovative and more efficacious treatments. C...

Construction of an aerolysin-based multi-epitope vaccine against an machine learning and artificial intelligence-supported approach.

, a gram-negative coccobacillus bacterium, can cause various infections in humans, including septic arthritis, diarrhea (traveler's diarrhea), gastroe...

Synthesis of gadolinium-enhanced glioma images on multisequence magnetic resonance images using contrastive learning.

BACKGROUND: Gadolinium-based contrast agents are commonly used in brain magnetic resonance imaging (MRI), however, they cannot be used by patients wit...

Deep Learning Glioma Grading with the Tumor Microenvironment Analysis Protocol for Comprehensive Learning, Discovering, and Quantifying Microenvironmental Features.

Gliomas are primary brain tumors that arise from neural stem cells, or glial precursors. Diagnosis of glioma is based on histological evaluation of pa...

Residual facial erythema in atopic dermatitis patients treated with dupilumab stratified by machine learning.

BACKGROUND: Persistent facial erythema represents a significant complication in atopic dermatitis (AD) patients undergoing treatment with dupilumab. S...

Machine learning-based identification and characterization of mast cells in eosinophilic esophagitis.

BACKGROUND: Eosinophilic esophagitis (EoE) is diagnosed and monitored using esophageal eosinophil levels; however, EoE also exhibits a marked, underst...

From pixels to patient care: deep learning-enabled pathomics signature offers precise outcome predictions for immunotherapy in esophageal squamous cell cancer.

BACKGROUND: Immunotherapy has significantly improved survival of esophageal squamous cell cancer (ESCC) patients, however the clinical benefit was lim...

A machine-learning exploration of the exposome from preconception in early childhood atopic eczema, rhinitis and wheeze development.

BACKGROUND: Most previous research on the environmental epidemiology of childhood atopic eczema, rhinitis and wheeze is limited in the scope of risk f...

Machine learning developed an intratumor heterogeneity signature for predicting prognosis and immunotherapy benefits in skin cutaneous melanoma.

Intratumor heterogeneity (ITH) is defined as differences in molecular and phenotypic profiles between different tumor cells and immune cells within a ...

Artificial intelligence in immunotherapy PET/SPECT imaging.

OBJECTIVE: Immunotherapy has dramatically altered the therapeutic landscape for oncology, but more research is needed to identify patients who are lik...

A Machine Learning Computational Framework Develops a Multiple Programmed Cell Death Index for Improving Clinical Outcomes in Bladder Cancer.

Comprehensive action patterns of programmed cell death (PCD) in bladder cancer (BLCA) have not yet been thoroughly investigated. Here, we collected 19...

Identification of Hit Compounds Using Artificial Intelligence for the Management of Allergic Diseases.

This study aimed to identify and evaluate drug candidates targeting the kinase inhibitory region of suppressor of cytokine signaling (SOCS) 3 for the ...

Harnessing artificial intelligence for analysing the impacts of nectar and pollen feeding in conservation biological control.

Plant-derived foods, such as nectar and pollen, have garnered substantial research attention due to their potential to support natural enemies of pest...

[Feeling analysis on allergen immunotherapy on using an unsupervised machine learning model].

OBJECTIVE: Analyze feelings about allergen-specific immunotherapy on using the VADER model VADER () model.