Allergy & Immunology

Validation of AI-enabled surrogate models in quantitative systems pharmacology: a practical, context-of-use-driven review.

Quantitative systems pharmacology (QSP) models support model-informed drug development but are computationally expensive, limiting workflows requiring many simulations. Artificial intelligence (AI)-enabled surrogates can accelerate these workflows, but require validation aligned with context of use and regulatory expectations. Here, we present an eight-step validation framework covering simulation...

A population-based retrospective machine learning study of COVID-19 severity using integrated clinical and viral genomic data in Jiangsu Province, China.

BACKGROUND: As coronavirus disease 2019 (COVID-19) has transitioned into an endemic phase characterized by sustained transmission and widespread hybrid immunity, understanding region-specific determinants of severe disease remains important for real-world risk stratification and public health planning. METHODS: A retrospective surveillance study was conducted using 5,072 severe acute respiratory s...

AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.

BACKGROUND: Neoantigens-tumor-specific peptides generated by somatic mutations-are central targets of effective anticancer T cell immunity and underpi...

Intraloop neoangiogenesis in an AI-classified scleroderma pattern: recognizing a morphological clue to suspected dermatomyositis.

OBJECTIVES: To describe a diagnostic near miss in AI-assisted nailfold capillaroscopy, illustrating how a correct AI-based classification as an early ...

Macrophage heterogeneity in breast cancer microenvironment and development of a prognostic model: exploring TFF1 as a novel therapeutic target.

BACKGROUNDS: Breast cancer (BRCA) represents the most prevalent malignancy globally, with projections indicating 3.2 million new cases anticipated by ...

Identification of exosome-related genes signature based on bioinformatics and machine learning for prognostic prediction in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is a prevalent malignant tumor with increasing incidence and mortality rates worldwide. Exosomes are secretory ves...

A novel prognostic zinc finger gene model for hepatocellular carcinoma via machine learning.

BACKGROUND: Hepatocellular carcinoma (HCC) is a highly lethal malignancy with poor prognosis, and effective biomarkers for predicting immunotherapy re...

Development and external validation of a dual-stage predictive model for self-reported physician-diagnosed chronic lung disease, asthma, and lung cancer in Chinese adults.

Chronic respiratory diseases represent a leading cause of global mortality, yet robust prediction tools integrating onset risk and long-term prognosis...

An AI-augmented review of childhood atopic dermatitis biomarkers across genetic, immune, microbial, and metabolic domains.

BACKGROUND: Atopic dermatitis (AD) is a prevalent inflammatory skin disease and a major source of disease burden in children. Biomarker studies in chi...

Identifying predictor factors for asthma using machine learning: evidence from the English Longitudinal Study of Ageing.

BACKGROUND: Asthma is a common chronic inflammatory airway disease. Accumulating evidence highlights the roles of demographic, lifestyle, and comorbid...

Exploration of optical nanosensors for environmental and biomedical applications: a comprehensive review.

Optical nanosensors represent a rapidly developing class of analytical tools for biomedical diagnostics and environmental monitoring, offering high se...

Multidimensional analysis of tumour immune microenvironment in HCC: prognostic significance and implications for immunotherapy.

BACKGROUND: The combination of immune checkpoint inhibitors (ICIs) and anti-angiogenic agents represents the standard first-line therapy for patients ...

Exploring potential key genes and mitotic catastrophe-associated correlates in sepsis-induced acute respiratory distress syndrome using transcriptomics and experimental validation.

BACKGROUND: Sepsis-induced acute respiratory distress syndrome (ARDS) is a life-threatening form of acute diffuse lung injury caused by sepsis. Howeve...

New approach methodologies (NAMs) for preclinical and translational evaluation of mRNA-lipid nanoparticle (LNP) therapeutics.

Messenger RNA-lipid nanoparticle (mRNA-LNP) therapeutics have emerged as a versatile drug modality, enabling in vivo protein expression for vaccines, ...

Leveraging multimodal cancer immunotherapy to amplify the efficacy of oncolytic viruses.

Oncolytic viruses (OVs) represent a versatile platform for cancer immunotherapy, capable of selectively infecting and lysing tumor cells while trigger...

AI-driven Approach to Treat Acute Coronary Syndrome: A Review on Integration of AlphaFold in Cardiovascular Genomics.

INTRODUCTION: The constantly evolving nature of diseases has put humanity in a never- ending struggle for survival, adapting to each disease and fight...

Diagnostic Model Construction of Mitochondrial-Mitophagy Related Genes and Their Regulatory Network and Potential Drug Discovery in Childhood Allergic Asthma.

Childhood allergic asthma (CAA) is a chronic airway inflammatory disorder driven by allergens, and its underlying molecular mechanisms as well as prec...

Single-cell and machine learning-based neural regulation signature for prognosis prediction and immunotherapy response in lung adenocarcinoma.

OBJECTIVE: Lung adenocarcinoma (LUAD) molecular heterogeneity limits traditional prognostic models. Given the emerging role of neural regulation (NR) ...

Bioinformatics roadmap for characterizing the gut microbiome to study its interactions and associations with the gut mucosal immune system.

Cataloging the gut microbiome and understanding its interactions and association with the host immune system remains relevant to identify potential tr...

Explainable machine learning to predict immunotherapy outcomes in metastatic renal cell carcinoma - Meet-URO 15-AI study.

Metastatic renal cell carcinoma remains clinically challenging because of heterogeneous outcomes and limited predictive biomarkers for immunotherapy. ...