Allergy

Digital image processing combined with machine learning: A novel approach for bee pollen classification.

The classification of bee pollen is crucial for ensuring product authenticity, quality control, and ...

Corrigendum to: Pollen analysis using multispectral imaging flow cytometry and deep learning.

Integrative analysis of signaling and metabolic pathways, immune infiltration patterns, and machine learning-based diagnostic model construction in major depressive disorder.

Major depressive disorder (MDD) is a multifactorial disorder involving genetic and environmental fac...

Optimizing Immunotherapy: The Synergy of Immune Checkpoint Inhibitors with Artificial Intelligence in Melanoma Treatment.

Immune checkpoint inhibitors (ICIs) have transformed melanoma treatment; however, predicting patient...

Nasopharyngeal cancer screening and immunotherapy efficacy evaluation based on plasma separation combined with label-free SERS technology.

BACKGROUND: In recent years, significant progress has been made in the treatment of nasopharyngeal c...

Sonopermeation combined with stroma normalization enables complete cure using nano-immunotherapy in murine breast tumors.

Nano-immunotherapy shows great promise in improving patient outcomes, as seen in advanced triple-neg...

Designing Clinical Trials for Patients With Rare Cancers: Connecting the Zebras.

The field of rare cancer research is rapidly transforming, marked by significant progress in clinica...

Tumor-educated platelets in lung cancer.

Non-invasive diagnostic monitoring techniques have become essential for treating lung cancer (LC), w...

Integrated multi-omics analysis and machine learning refine molecular subtypes and clinical outcome for hepatocellular carcinoma.

The high morbidity and mortality of hepatocellular carcinoma (HCC) impose a substantial economic bur...

Engineering TCR-controlled fuzzy logic into CAR T cells enhances therapeutic specificity.

Chimeric antigen receptor (CAR) T cell immunotherapy represents a breakthrough in the treatment of h...

Identification of M1 macrophage infiltration-related genes for immunotherapy in Her2-positive breast cancer based on bioinformatics analysis and machine learning.

Over the past several decades, there has been a significant increase in the number of breast cancer ...

Smart MXene-based microrobots for targeted drug delivery and synergistic therapies.

MXenes and their composites exhibit remarkable electrical conductivity, mechanical flexibility, and ...

Integrative Multi-Omics Analysis Reveals Molecular Subtypes of Ovarian Cancer and Constructs Prognostic Models.

Ovarian cancer (OV) remains the most lethal gynecological malignancy. The aim of this study was to i...

Integration of graph neural networks and transcriptomics analysis identify key pathways and gene signature for immunotherapy response and prognosis of skin melanoma.

OBJECTIVE: The assessment of immunotherapy plays a pivotal role in the clinical management of skin m...

Prediction of outpatient visits for allergic rhinitis using an artificial intelligence LSTM model - a study in Eastern China.

BACKGROUND: Allergic rhinitis is a common disease that can affect the health of patients and bring h...

Application and research progress of artificial intelligence in allergic diseases.

Artificial intelligence (AI), as a new technology that can assist or even replace some human functio...

Advancements in artificial intelligence for atopic dermatitis: diagnosis, treatment, and patient management.

Atopic dermatitis (AD) is a common and complex skin disease that significantly affects the quality o...

MedScale-Former: Self-guided multiscale transformer for medical image segmentation.

Accurate medical image segmentation is crucial for enabling automated clinical decision procedures. ...

MIST: An interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis.

Joint analysis of transcriptomic and T cell receptor (TCR) features at single-cell resolution provid...

101 Machine Learning Algorithms for Mining Esophageal Squamous Cell Carcinoma Neoantigen Prognostic Models in Single-Cell Data.

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignant tumors in the dige...

Prediction of PD-L1 expression in NSCLC patients using PET/CT radiomics and prognostic modelling for immunotherapy in PD-L1-positive NSCLC patients.

AIM: To develop a positron emission tomography/computed tomography (PET/CT)-based radiomics model fo...