Allergy

Development, validation, and user-centric evaluation of an interpretable machine learning decision support tool for the preoperative prediction of mild bleeding disorders (MBD-Check): a prospective diagnostic prediction study.

BACKGROUND: Mild bleeding disorders are the most common inherited bleeding disorders, often leading to perioperative haemorrhages. Preoperative screening for mild bleeding disorders remains challenging due to the limitations of existing screening tools, resulting in a substantial proportion of patients being referred for preoperative investigations. The aim of this study was to develop, externally...

Atopic dermatitis.

Atopic dermatitis (AD) is the most common inflammatory skin disease and carries the highest disability-adjusted life-years burden, ranking 15th among all non-fatal diseases globally. It is characterized by intensely itchy skin and is associated with multiple comorbidities, such as food allergy, asthma, allergic rhinitis and eosinophilic oesophagitis, which are mainly driven by type 2 immune respon...

Impact of nanocarrier mediated drug delivery systems: a deep dive into therapeutics and clinical management of breast cancer.

Breast cancer (BC) is the most frequently diagnosed malignancy and the leading cause of cancer-related death among women worldwide. The therapeutic li...

An integrated machine learning and mendelian randomization approach identifies SERPING1 as a prognostic biomarker associated with CD8 + T-cell infiltration in DLBCL.

BACKGROUND: SERPING1, which encodes the C1 inhibitor (C1-INH) of the complement system, and plays a key regulator in regulating inflammatory responses...

How to quickly determine whether patients with chronic cough need corticosteroid treatment--construction of a predictive model for corticosteroid-responsive cough in chronic cough.

Patients with chronic cough need to undergo a wide range of tests and rely on empirical medication to determine the underlying cause. Corticosteroid-r...

ViGNet: A clinical data-supported deep learning approach for NSCLC immunotherapy response prediction in digital pathology.

Histopathology is the cornerstone of oncology diagnosis, while whole-slide images (WSIs) enable the transition to digital, quantitative pathology. Lev...

Prospective germline exome and machine learning-based risk score identify predictive and prognostic biomarkers of immunotherapy outcomes in advanced non-small cell lung cancer.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape of advanced non-small cell lung cancer (NSCLC). However, a su...

Nanomedicine in precision treatment of gastric cancer: current clinical landscape.

Gastric cancer (GC) remains a leading cause of cancer-related mortality worldwide, with therapeutic efficacy often hindered by late-stage diagnosis, c...

Spatial omics of immunity: Mapping cellular landscapes in tissue microenvironments.

The tumor microenvironment (TME) is composed of diverse heterogeneous components and plays a crucial role in immune cell infiltration, immune evasion,...

Landscape of T-cell bispecific antibodies in cancer therapy: therapeutic strategies, challenges and future prospection.

T-cell bispecific antibodies (TCBs), a specialized subclass of bispecific antibodies (BsAbs), are engineered to simultaneously engage T cells and tumo...

Ferroptosis in cancer: integrating multi-omics, AI-driven biomarkers, and translational challenges.

Ferroptosis, an iron-dependent regulated cell death driven by lipid peroxidation, has emerged as a potential target in cancers resistant to apoptosis ...

Targeting Type 2 and Non-type 2 Asthma: Emerging Biologics and Personalized Strategies.

PURPOSE OF REVIEW: Asthma continues to pose a serious global health issue affecting billions of people and causing significant morbidity. It is immuno...

AI-Based Quantitative Nasal Cytology for Predicting Eosinophilic Chronic Rhinosinusitis With Nasal Polyps: A Pilot Study.

Supervised deep learning with gene functional annotation for cell classification.

Gene-by-gene differential expression analysis is a widely used supervised approach for interpreting single-cell RNA-sequencing (scRNA-seq) data. Howev...

AI for predicting exacerbations in KIDs with asthma (AIRE-KIDS).

Recurrent acute care visits are a common yet preventable outcome for many children with asthma. Machine learning (ML) applied to electronic medical re...

From Theory to Practice: Which Biomarkers Are Ready for Predicting Response in Advanced HCC?

Systemic therapies for advanced hepatocellular carcinoma (HCC) have expanded considerably with the advent of tyrosine kinase inhibitors, immune checkp...

Combining bioinformatics and machine learning to analyze and validate sepsis-related cell senescence genes and potential drugs.

Sepsis is a life-threatening organ malfunction induced by the host's abnormal reaction to infection. Sepsis can induce cellular senescence, thereby ex...

Automatic framework for PD-L1 expression evaluation in Latino patients with non-small cell lung cancer.

Programmed death ligand-1 (PD-L1) expression is a key biomarker for identifying non-small cell lung cancer (NSCLC) patients eligible for immunotherapy...

Neoadjuvant Immunotherapy in Locally Advanced Head and Neck Squamous Cell Carcinoma: From Oncologic Outcomes to Response-Adaptive Treatment De-escalation.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) are associated with poor 5-year survival and substantial treatment-related morbidity. Neoadj...

Machine learning-based screening model for Tic disorders comorbid with attention-deficit/hyperactivity disorder in children and SHAP value interpretation: a retrospective observational study.

Tic disorders (TD) are common neurodevelopmental conditions in children and adolescents, characterized primarily by motor and vocal tics, with heterog...