Asthma

Update on Non-Biological and RNA-Based Therapeutics in Chronic Inflammatory Diseases: Precision Medicine Through Small Molecules.

In the last decades, critical advancements in research technology and knowledge on disease mechanisms steered therapeutic approaches for chronic inflammatory diseases towards unprecedented target specificity. For allergic and chronic lung diseases, biologic drugs pioneered this goal, acquiring on the way-through the clinical use of monoclonal antibodies-a deeper understanding of how inflammatory a...

Governance Framework for Safe and Ethical Implementation of Artificial Intelligence in Surgery: A Modified-Delphi Consensus.

BACKGROUND: Artificial intelligence (AI)-enabled clinical decision support systems (CDSS) demonstrate performance comparable or superior to human experts in certain tasks. However, their integration into surgical practice faces a significant implementation gap, alongside ethical, privacy, and legal concerns. Clear governance frameworks are needed to guide their responsible adoption in surgery, to ...

Improving the management and outcomes of preschool wheeze: protocol of a prospective multicentre cohort study.

INTRODUCTION: Preschool wheeze and asthma are associated with substantial morbidity and impaired future lung function. Yet, wheeze is unreliably repor...

Predicting the usage of seasonal drugs by utilizing advanced machine learning: An albuterol case study.

Manual forecasting of seasonal medication demand results in inefficiencies and labor burden. With the advancement of machine learning, there is an opp...

Artificial Intelligence-derived Measurements of Myosteatosis from Coronary Artery Calcium CT Scans to Predict COPD: The Multi-Ethnic Study of Atherosclerosis.

Purpose To evaluate the predictive value of myosteatosis as an opportunistic finding in coronary artery calcium (CAC) CT scans for clinically diagnose...

Filling the Gaps in Health Data: Using a Machine Learning Approach to Augment Partially Observed Variables Such as Smoking in Claims Data.

PURPOSE: Missing information is common in real-world claims data, particularly on behavioral confounders, for example, smoking. Often one category of ...

Response to venom immunotherapy: Exploratory retrospective machine learning clustering analysis.

BACKGROUND: Hymenoptera venom immunotherapy is an established treatment for severe allergic reactions, aiming to modulate the immune response and redu...

Expanding role of cell-free DNA for the early diagnosis and monitoring of pulmonary diseases.

Circulating cell-free DNA (cfDNA) has firmly established itself as a cornerstone of liquid biopsy, advancing the noninvasive diagnosis and monitoring ...

Role of WNK4 and FKBP5 in asthma: Insights from bioinformatics analysis, machine learning, and preliminary validation.

BACKGROUND: The complex and heterogeneous molecular mechanisms of asthma are currently unclear, and treatment outcomes are dismal in some patients. Th...

Image-space compensation of inter-crystal scattering in PET using a neural network based filter.

BACKGROUND: In positron emission tomography (PET), gamma photons arriving at the detector ring may undergo one or more Compton scattering events, pote...

Sex disparities in deep learning estimation of ejection fraction from cardiac magnetic resonance imaging.

The advent of artificial intelligence in cardiovascular imaging holds immense potential for earlier diagnoses, precision medicine, and improved diseas...

Reproducing real-world clinical prediction models using the DIVE platform: A comparative validation study across three chronic diseases.

OBJECTIVES: The aim of this analysis is to evaluate the performance and reproducibility of the Python-based Data Insight Validation Engine (DIVE), a m...

Indoor air quality in primary schools: real-time monitoring and predictive modeling of PM10 in Kenitra, Morocco.

The health of children may be adversely influenced by the air quality in schools because they are more sensitive to indoor air pollutants. PM10, which...

Prediction model for cardiovascular mortality risk in asthma patients based on machine learning.

BackgroundAsthma is a common chronic respiratory disease, cardiovascular disease (CVD) mortality constitutes a major public health concern. At present...

Integrating multi-omics and machine learning systematically deciphers cellular heterogeneity and fibrotic regulatory networks in the progression from MASLD to MASH.

The progression from metabolic dysfunction-associated steatotic liver disease (MASLD) to metabolic dysfunction-associated steatohepatitis (MASH) is a ...

Intelligent control for food waste composting using observation-assessment-decision-action cycles: From technical landscape to economic stability analysis.

Intelligent control systems (ICS) based on sensor technology and machine learning (ML) can improve the inefficiency and instability of traditional foo...

TNFAIP3, PADI4, and CXCR4 as biomarkers of neutrophil extracellular traps in severe asthma.

Asthma is a heterogeneous condition impacting an estimated 300 million individuals globally. Although inhaled corticosteroids are effective in allevia...

Construction and validation of a machine learning-based risk prediction model for invasive mechanical ventilation in AECOPD patients complicated with respiratory failure.

OBJECTIVE: This study aimed to create and validate a machine learning (ML) model to predict the likelihood of invasive mechanical ventilation (IMV) in...

Understanding and Addressing Bias in Artificial Intelligence Systems: A Primer for the Emergency Medicine Physician.

Artificial intelligence (AI) tools and technologies are increasingly being integrated into emergency medicine (EM) practice, not only offering potenti...

Pediatric asthma management via integration of a remote spirometry device into an EHR-based artificial intelligence-powered clinical decision support system: A feasibility pragmatic clinical trial.

BACKGROUND: Asthma is the most common chronic disease in children. Suboptimal asthma control is prevalent and causes significant health care costs. El...