Pulmonology

Classification of industrial chemicals for respiratory chemosensory irritation using the TRPV1-expressing neuronal SH-SY5Y cell model and machine learning.

Artificial intelligence in idiopathic pulmonary fibrosis: advances, challenges and future directions.

Idiopathic pulmonary fibrosis (IPF) is a progressive disease of unknown aetiology, characterised by a radiological and/or morphological pattern of usual interstitial pneumonia. Its diagnosis is challenging, and disease progression is often variable and unpredictable. In recent years the introduction of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) models, ...

Assessing in-hospital mortality risk in ICU lung cancer patients using machine learning: An analysis based on the MIMIC-IV database.

BACKGROUND: Patients with advanced lung cancer admitted to the intensive care unit (ICU) face a substantially elevated risk of in-hospital mortality. ...

A screening strategy based on machine learning for diagnostic biomarkers in small cell lung cancer.

Small cell lung cancer (SCLC) is the most aggressive subtype with high mortality rates due to the lack of specific diagnostic biomarkers to delay the ...

Exploring the impact of operating parameters and catalyst design on propane catalytic oxidation: a machine learning perspective.

Propane is a common pollutant in the petroleum industry. In this study, machine learning coupled with SHAP (SHapley Additive exPlanations) was employe...

Machine Learning Model Predicts Recurrent Clostridioides difficile Infection in Patients With Inflammatory Bowel Disease (Recur CDI-IBD).

INTRODUCTION: Clostridioides difficile infection (CDI) present a significant challenge in patients with inflammatory bowel disease (IBD), with high re...

Machine learning-driven prediction model for successful weaning of patients from mechanical ventilation in ICU.

BACKGROUND: Mechanical ventilation is a critical life support technology in the intensive care unit. However, the weaning process remains complex, mak...

Comparison of conventional 512-matrix CT images with Swin2SR-based 2048-matrix images in the visualization and diagnosis of lung nodules.

PURPOSE: Clear visualization and diagnosis of lung nodules depend on the spatial resolution of CT images. Transformer-based generative neural networks...

Quantitative assessment of neonatal health using dried blood spot metabolite profiles and deep learning.

Neonatal prematurity leads to considerable morbidity and mortality, partly because of acquired conditions such as bronchopulmonary dysplasia (BPD), in...

Investigation on the Molecular Mechanism of ORF7a Suppressing BST-2 Antiviral Activity through Structural Modeling of Their Protein-Protein Interface.

In the present work, a structural model for the protein-protein interface of bone marrow stromal cell antigen 2 (BST-2) and severe acute respiratory s...

Deep radiomics for prognostic prediction in locally advanced non-small cell lung cancer by leveraging OmicsMap-based image representation.

Patients with locally advanced non-small cell lung cancer (LA-NSCLC) exhibit heterogeneous prognoses despite receiving standard treatments, highlighti...

Ligand-based machine learning models to classify active compounds for prostaglandin EP2 receptor.

Prostaglandin receptors are pharmacologically validated targets with implications in several medical indications, including glaucoma, cardiac cyanotic...

CT-based Prediction of Visceral Pleural Invasion in Lung Adenocarcinoma ≤ 3 cm: Enhancing Deep Learning Specificity by Waiving Chest Wall Information.

BACKGROUND: Accurate preoperative prediction of visceral pleural invasion (VPI) in lung adenocarcinoma is essential for guiding surgical decision-maki...

Multidimensional analysis of clinical pharmacist intervention impact on hospital length of stay in pulmonary tuberculosis: a random forest-driven retrospective study.

Machine learning using entropy-based texture features from MRI to differentiate histological subtypes of non-small cell lung cancer identified as metabolically active on PET/MRI.

Texture analysis is a foundational approach in imaging studies and demonstrates excellent diagnostic performance, with radiomic analysis being the mos...

Atypical Histology in Pulmonary Biopsies Obtained Using Robotic-Assisted Bronchoscopy: Natural History and Clinical Practice.

BACKGROUND: A recent consensus statement recommends that atypical cells are considered nondiagnostic specimens in the calculation of diagnostic yield....

LungSurg: A Generative AI System for Segmentation and Phase Classification in Thoracoscopic Lobectomy.

The integration of artificial intelligence (AI) into surgical practices is advancing towards greater intelligence and precision. This study assesses t...

Noncanonical genetic markers resolve the pre-GOE emergence of aerobic bacteria in Earth's history.

The transition from anaerobic to aerobic life was a pivotal adaptation in Earth's history, yet the timing and genomic drivers remain poorly resolved. ...

Performance validation of a closed loop fully automated AI model for lung nodule stratification in screening cases.

BACKGROUND: Several limitations hinder the effectiveness of human-based lung cancer screening (LCS): high false-positive rates leading to unnecessary ...

Validation of histopathology-based deep learning algorithms for detection of actionable non-small cell lung cancer biomarkers.

Non-small cell lung cancer (NSCLC) patient management relies on molecular analysis to determine eligibility for targeted therapy. Furthermore, neoadju...