Allergy

ABCC2 as a novel therapeutic target in lung adenocarcinoma: a machine learning-driven discovery linking ammonia metabolism to prognosis and drug resistance.

BACKGROUND: Ammonia, long regarded as a metabolic waste product, has recently been recognized as a pivotal oncometabolite in the tumor microenvironment, contributing to cancer progression and immune evasion. However, its prognostic value and therapeutic relevance in lung adenocarcinoma (LUAD) remain insufficiently characterized. METHODS: Transcriptomic data from multiple LUAD cohorts were obtained...

AI-MIRACLE: Artificial Intelligence and MultIpaRAmetric MRI Predict CLinical OutcomEs to Neoadjuvant Immunotherapy in Patients with Muscle-invasive Bladder Cancer Undergoing Radical Cystectomy.

BACKGROUND AND OBJECTIVE: Neoadjuvant immune-checkpoint inhibitors (ICIs) in muscle-invasive bladder cancer (MIBC) were tested in patient's ineligible for cisplatin-based chemotherapy. The PURE-01 trial (NCT02736266) evaluated three courses of pembrolizumab before radical cystectomy (RC). We developed AI-MIRACLE, an international study assessing artificial intelligence (AI) and multiparametric mag...

Artificial intelligence in cancer immunotherapy: current trends in predicting response and personalizing treatment.

Artificial intelligence (AI) can transform cancer immunotherapy by enabling more accurate prediction of treatment responses, the discovery of specific...

Artificial intelligence and transforming cancer care.

Artificial Intelligence (AI) is reshaping oncology by addressing key limitations in traditional cancer care and enabling data-driven, personalized app...

RAS pathway activity subtypes identified by machine learning define prognostic and immune microenvironment characteristics in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD), the predominant histological subtype of non-small cell lung cancer, remains a leading cause of cancer-related ...

Machine learning-driven drug repurposing for GPR17: activity prediction via graph neural networks and multistage computational validation.

To identify novel GPR17-targeting ligands with potential relevance to multiple sclerosis (MS) therapy, we developed an integrated computational workfl...

Deep learning and radiomics models in patients with advanced non-small cell lung cancer treated with immunotherapy combined with stereotactic radiotherapy.

Quantitative imaging is an emerging field that may allow prediction of oncological outcomes. We investigate whether radiomics and deep learning can pr...

Nanomedicine-based cancer immunotherapy: translational barriers, mechanistic strategies, and future perspectives.

Nanomedicine-based cancer immunotherapy integrates nanotechnology with immune modulation, representing a promising strategy to improve both the effica...

Advances in immune checkpoint inhibitor therapy for microsatellite instability-high (MSI-H) and mismatch repair-deficient (dMMR) colorectal cancer: Insights from the tumor immune microenvironment.

This review systematically analyzes the relationship between the immune microenvironment characteristics of microsatellite instability-high (MSI-H) or...

Decoding lactylation heterogeneity in glioblastoma: machine learning identifies G6PC3 as a prognostic target.

BACKGROUND: Glioblastoma (GBM) is the most common malignant glioma in adults. It has an extremely poor prognosis, highlighting an urgent need for new ...

Beyond target lesions: Prognostic value of longitudinal AI-derived whole-body [1⁸F]FDG PET/CT metrics in metastatic melanoma.

PURPOSE: To investigate the prognostic value of an artificial intelligence (AI)-based semi-automated tool for longitudinal whole-body quantification o...

Enterotype-specific microbial biomarkers of immune checkpoint inhibitor response revealed by large-scale integrated metagenomic analysis.

The gut microbiota appears to play a critical role in modulating antitumor immune responses and influencing the efficacy of cancer immunotherapy drugs...

Cancer immunotherapy: mechanisms, clinical applications, resistance and future directions.

Cancer immunotherapy has revolutionised oncology by utilising immune-mediated mechanisms to achieve durable anti-tumour responses and long-term clinic...

Detection of maxillary sinusitis of endodontic origin in cone-beam CT images using deep learning algorithms.

Cone-Beam Computed Tomography (CBCT) scans were retrospectively collected and examined to acquire a balanced dataset representative of Normal Maxillar...

Multi-epitope tick vaccines: From computational design to field deployment, immunoinformatics approaches, validation challenges, and translational pathways.

Ticks are major vectors of pathogens affecting both humans and animals, yet effective and sustainable control strategies remain limited. Conventional ...

Tobacco rattle virus triggers local defense and systemically coordinates taxoid biosynthesis in Taxus baccata revealed by integrated metabolic analysis.

The clinical demand for paclitaxel necessitates novel strategies to enhance its production. While recent breakthroughs have elucidated the complete bi...

A High-Resolution Multifocal RGB Pollen Grain Image Dataset for Deep Learning Computer Vision Tasks from Biobío Region, Chile.

PollenBB16 is an RGB pollen image dataset of Chilean flora with pixel-accurate instance segmentation masks, whose annotation was fully verified by an ...

In silico discovery of novel small-molecule PD-L1 inhibitors through a multi-stage computational workflow integrating machine learning and molecular dynamics.

Cancer immunotherapy targeting the PD-1/PD-L1 pathway has transformed modern oncology; however, developing small-molecule inhibitors as viable alterna...

Predicting response to neoadjuvant chemotherapy combined with immunotherapy in gastric cancer based on habitat imaging and peritumoral radiomics: a two-center study.

BACKGROUND: Predicting pathological response to neoadjuvant chemotherapy combined with immunotherapy (NACI) in locally advanced gastric cancer (LAGC) ...

Identification of Lipid Metabolism-Related Gene GM2A as a Potential Biomarker in Atopic Dermatitis by Combining Weighted Gene Co-Expression Network Analysis and Machine Learning.

Lipid metabolism is abnormal in patients with atopic dermatitis (AD). This study aimed to screen lipid metabolism-related gene (LMRG) in AD, providing...