COPD

DFT and machine learning investigation of Au/Pt-decorated SnS₂ monolayers for asthma and COPD diagnosis.

Asthma and Chronic Obstructive Pulmonary Disease (COPD) are among the most prevalent chronic respiratory diseases worldwide, affecting hundreds of millions of people and contributing significantly to global morbidity and mortality. This work introduces a novel Au/Pt-SnS₂ heterostructure for exhaled NO₂ detection, representing the new study to explore its role in lung disease diagnostics. It demons...

Securing Safety and Quality in AI-generated Patient Education: A Nurse-led Methodological Framework Integrating Kolcaba's Comfort Theory.

Generative artificial intelligence offers personalized patient education, yet clinical inaccuracy and lack of theoretical grounding threaten health care safety. This study validates a "nurse-led" AI protocol for generating safe, theory-guided digital education materials based on Kolcaba's Comfort Theory. A methodological design established a three-stage iterative prompt engineering process (initia...

Intraoperative technological advances and new frontiers in precision glioma surgery.

BACKGROUND: Diffuse gliomas remain among the most surgically challenging tumors, characterized by their infiltrative nature, proximity to eloquent bra...

Heat transfer analysis of non-Newtonian hybrid nanofluid flow over a stretching surface using an artificial neural network.

The present study investigates the role of Gold-Fe2O3-Fe3O4 nanoparticles mixed in blood with magnetohydrodynamics Casson fluid flow through a porous ...

Deep learning-based super-resolution ultrashort echo time MRI for pulmonary nodule detection and longitudinal assessment in emphysema.

PURPOSE: This study aimed to evaluate the performance of deep learning-based super-resolution ultrashort echo time magnetic resonance imaging (SR-UTE ...

Body composition's effect on the bone-vascular axis of osteoporosis discovered in AI-based CT analysis of COPD patients.

OBJECTIVES: This study aimed to investigate the effect of body composition on the inverse relationship between vertebral bone density (T12 BMD) and to...

Development and validation of machine learning models for obstructive sleep apnea risk stratification in high-risk pregnant women.

BACKGROUND: Obstructive sleep apnea (OSA) affects approximately 15% of pregnancies and is associated with adverse maternal and fetal outcomes. Althoug...

Machine learning-based MPO and Lp-PLA2 profiling reveals asthma-predominant inflammatory signature in asthma-COPD overlap.

BACKGROUND: Asthma-COPD overlap (ACO) remains poorly characterized at the molecular level, leading to diagnostic uncertainty and suboptimal treatment....

Advancements in wearable technology for heart failure patients.

PURPOSE OF REVIEW: While pharmacologic and device therapies have improved heart failure care in recent decades, age-adjusted rehospitalization rates r...

Diagnostic challenges in re-emerging rickettsioses: why current tools fall short.

SUMMARYRickettsial diseases, encompassing scrub typhus, spotted fever group rickettsioses, and typhus group rickettsioses, represent a significant and...

An attention-infused deep convolutional paradigm for multi-label classification of thoracic pathologies in chest radiographs.

This research proposes an empirical benchmarking study of an attention-infused deep convolutional framework for multi-label thoracic pathology classif...

Ambient AI in primary care: an exploratory mixed methods survey of UK general practitioners.

OBJECTIVE: To examine UK general practitioners' (GPs) adoption of ambient artificial intelligence (AI) scribes and to assess user-reported error rates...

Full-Spectrum Analysis With Machine Learning for Quantitative Assessment of Lateral Flow Immunoassays: A Platform Approach.

Lateral flow immunoassays (LFIAs) provide rapid point-of-care results but lack quantitative capabilities. This study presents a platform technology in...

How artificial intelligence could improve the diagnosis and management of COPD: a perspective from GOLD.

Harnessing Machine Learning and Electronic Health Record Data to Improve Asthma Management.

PURPOSE OF REVIEW: The review examines the application of machine learning (ML) and large language models (LLMs) to asthma management. We sought to id...

Effects of Telerehabilitation Based on Motion Recognition Technology on Exercise Endurance of Patients With Non-Small Cell Lung Cancer After Surgery: Single-Center, Prospective, Open-Label, Randomized Controlled Trial.

BACKGROUND: Non-small cell lung cancer (NSCLC) accounts for approximately 85% of primary pulmonary neoplasms. Complete surgical removal remains the co...

Neural Representation Learning for Compact and Efficient Modeling of Monte Carlo Phase Space Data.

PURPOSE: Monte Carlo (MC) simulations provide gold standard dose calculations in radiation therapy but generate large phase space (PHSP) files that li...

Machine learning models to predict ICU admission of patients with COPD and depression: A retrospective cohort study.

This study aimed to develop and validate a machine learning (ML)-based predictive model to identify risk factors associated with intensive care unit (...

Development and validation of a multimodal artificial intelligence-based model for predicting post-prostatectomy treatment outcomes from baseline biparametric prostate magnetic resonance imaging.

PURPOSE: Prostate cancer (PCa) is the second most common cancer and cause of cancer deaths among American men. Existing risk prediction methods have l...

Benzo(a)pyrene aggregating lung inflammation via inducing IL1R1 expression in asthma: Insights from network toxicology, single-cell transcriptomics, and Mendelian randomization.

OBJECTIVE: Exposure to benzo(a)pyrene (BaP) negatively affects lung inflammation in patients with asthma. However, there is a lack of systematic resea...