Pulmonology

Plasma proteomics improves thrombosis prediction in patients with cancer and identifies targetable IL-17-driven endothelial activation.

Thrombosis remains a major cause of morbidity and mortality in patients with cancer. Existing risk models fail to reliably predict venous thromboembolism (VTE), underscoring the need for more accurate predictive models. In this study, we conducted a high-throughput proteomic analysis of 1105 plasma proteins in peripheral blood samples from patients with newly diagnosed lung or gastric cancer who w...

RetiMap: Automated Retinal Vascular Measures Link Microvascular Structure to Metabolic Health and Predict Cardiovascular Risk.

Fundus imaging enables noninvasive, high-resolution visualization of the retinal microvasculature. Advances in artificial intelligence (AI) now allow for extraction of quantitative vascular metrics from retinal images, offering new opportunities for identifying systemic health biomarkers. This study sought to characterize retinal microvascular features in a large healthy population and assess thei...

Modelling the fate of chemicals of emerging concern in wastewater treatment: From mechanistic models to hybrid data driven approaches.

Chemicals of emerging concern (CECs) pose major challenges for wastewater treatment plants because they are difficult to biodegrade, persist in the en...

Integrated toxicological analysis links air pollution-associated acute respiratory distress syndrome signatures to benzo[a]pyrene-induced acute lung injury.

Acute respiratory distress syndrome (ARDS) is associated with high mortality, and increasing evidence suggests that air pollution may contribute to it...

Development and validation of artificial intelligence-assisted volumetric response criteria in pleural mesothelioma (ARTIMES): a retrospective, multicohort, multicentre study.

BACKGROUND: Response evaluation in pleural mesothelioma is challenging because its crescent growth pattern is poorly captured by diameter-based criter...

Post-Pandemic Forecasting of Pediatric Acute Respiratory Infections with Deep Learning: A Multi-Pathogen, Multi-Time-Horizon Study.

BACKGROUND: Acute respiratory infections (ARIs) remain a major cause of morbidity and hospitalization in children worldwide. In the post-COVID-19 era,...

Repeatability of AI-quantified incidental findings on lung cancer screening CT scans in the NELSON trial.

OBJECTIVES: Computed tomography (CT) scans for lung cancer screening provide the opportunity of quantifying incidental findings. We evaluated the repe...

Based on a radiomics-clinical nomogram to predict the therapeutic effect of pulmonary cryptococcosis pneumonia.

We aimed to develop a radiomics-clinical nomogram to predict the therapeutic effect of PC pneumonia. A total of 255 PC pneumonia patients (165 cases w...

Histology-guided 3D virtual staining of microCT-imaged lung tissue via deep learning.

Histologically stained tissue sections are considered the gold standard for studying microscopic anatomy and diagnosing disease in clinical practice. ...

Artificial intelligence and the future of telemonitoring in home mechanical ventilation.

Artificial intelligence for telemonitoring in home mechanical ventilation https://bit.ly/4q9XxCa.

The role of artificial intelligence in predicting COPD exacerbations using multimodal data: a systematic review.

BACKGROUND: COPD remains a leading cause of global morbidity and mortality, with acute exacerbations driving disease progression and healthcare utilis...

LXNet: A lightweight CNN for lung disease classification from Chest X-ray with XAI-based interpretability.

The diagnosis of lung diseases such as pneumonia and tuberculosis remains a major global health challenge, especially in resource-limited regions. Art...

Radiomics using contrast-enhanced T1-weighted imaging and clinical features for predicting response to EGFR-TKIs in EGFR-mutated non-small cell lung cancer patients with brain metastases.

OBJECTIVES: To construct and validate a model based on clinical characteristics and magnetic resonance imaging (MRI) radiomics to predict 1-year effic...

Machine learning model based on clinical and imaging features for predicting fungal infections in children with leukemia.

BACKGROUND: Due to immunosuppression, mucosal barrier injury, and prolonged neutropenia resulting from both the disease and chemotherapy, along with t...

Predicting oxygen levels in microbial habitats using a metagenome-based approach.

Oxygen is a primary driver of the distribution and activity of microbial life. Since oxygen levels are often difficult to measure in situ, one potenti...

Advances in artificial intelligence for the evaluation of mitral regurgitation.

PURPOSE OF REVIEW: The diagnostic evaluation of mitral regurgitation (MR) is complex, time-intensive, and prone to significant interobserver variabili...

Machine learning to identify novel bleeding and residual thromboembolic risks in patients on anticoagulation.

Balancing thromboembolic prevention against bleeding risk remains a key challenge during oral anticoagulant (OAC) therapy. CHA₂DS₂-VASc cannot predict...

AI-Driven Preoperative Chest Radiograph Analysis for Prognostic Stratification in Surgically Resected Pathological Stage 1 Non-Small Cell Lung Cancer.

The purpose of the study is to investigate the potential of artificial intelligence (AI)-driven analysis of preoperative chest radiograph (CXR) for pr...

The potential utility of in-silico approach in identifying phytochemicals against various targets for the management of lung cancer.

Research on novel treatment approaches is crucial for lung cancer, because it is one of the most common and aggressive malignancies with high mortalit...

AMS-HD: Hyperdimensional Computing for Real-Time and Energy-Efficient Acute Mountain Sickness Detection.

OBJECTIVE: Acute mountain sickness (AMS) is the most prevalent altitude illness, affecting unacclimatized individuals ascending above 2,500 m and pote...