Pulmonology

Comprehensive analysis of neutrophil extracellular traps-associated inflammatory genes for patients with idiopathic pulmonary fibrosis.

Neutrophil extracellular traps (NETs) facilitate inflammation and epithelial-mesenchymal transition (EMT), promoting the progression of pulmonary fibrosis. Various machine learning methods were used to screen for prognostic genes. Based on prognostic genes, a risk model was constructed to assess their ability for prognosis prediction of idiopathic pulmonary fibrosis (IPF). Mendelian Randomization ...

TB-SERS analyzer: Analysis tool for tuberculosis prediction based on Raman spectroscopy with machine learning and convolutional neural network.

Raman spectroscopy (RS) and surface-enhanced Raman spectroscopy (SERS) are promising technologies that have been applied across various fields, including clinical diagnostics. In the context of tuberculosis (TB) diagnosis, RS/SERS offers significant potential for rapid, non-invasive, and sensitive biomolecular detection. However, no software currently exists that is specifically designed to analyz...

Multi-omics integration and machine learning identify a novel gene signature consisting of CCDC141, CHI3L2, RIMKLB, and PDLIM7 in bronchopulmonary dysplasia associated with neutrophil-driven immune dysregulation.

BACKGROUND: Bronchopulmonary Dysplasia (BPD) is a major complication in preterm infants, lacking effective diagnostic biomarkers to facilitate early d...

Time, Tissue, and Team: Reframing Transbronchial Biopsy in Lung Transplantation as an Integrated Diagnostic System.

Despite remarkable advances in transplant pathology, molecular diagnostics, imaging, and biomarker discovery, uncertainty remains an intrinsic feature...

Explainable Machine-learning Model Based on Multimodal Ultrasound for Non-invasive Detection of Early Renal Fibrosis: A Multicenter Study.

RATIONALE AND OBJECTIVES: To develop and validate an interpretable, multimodal, ultrasound-based machine-learning (ML) model for the non-invasive iden...

Experimental astrochemistry and machine learning unravel the formation of oxygen-bearing organic molecules in extraterrestrial ices.

Oxygen-bearing organic molecules, including aldehydes, alcohols, and peroxides, serve as key precursors to complex organics in extraterrestrial enviro...

A High Granularity Machine Learning Model to Predict Successful Weaning During V-V ECMO.

PURPOSE: Weaning from venovenous extracorporeal membrane oxygenation (V-V ECMO) remains unstandardized due to limited literature, with delayed weaning...

Cross-Scale Design of Low-Loading Ru-Based Catalysts for the Oxygen Evolution Reaction in Proton Exchange Membrane Water Electrolysis.

Proton exchange membrane water electrolysis (PEMWE) is a promising route for green hydrogen production, but the acidic oxygen evolution reaction (OER)...

Open dataset and deep learning model for intelligent diagnosis of neonatal respiratory distress syndrome and aspiration syndrome in newborns.

Neonatal pulmonary diseases such as aspiration syndrome (AS) and respiratory distress syndrome (NRDS) require timely diagnosis, yet manual interpretat...

Multi-omics machine learning-driven investigation of super-enhancers signatures and prognostic biomarkers in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common histological subtype of malignant lung tumors, characterized by high incidence and mortality rates. Supe...

MiracleNet: A Biologically Interpretable Machine Learning Model for Resected Non-small-cell Lung Cancer.

Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Accurate prediction of relapse is notoriously difficult, posing s...

Transforming Pulmonary Arterial Hypertension: Key Milestones and Future Perspectives.

Pulmonary arterial hypertension (PAH) is a rare, progressive disease of the precapillary pulmonary arteries, characterized by fibroproliferative vascu...

Machine learning-based prognostic model and single-cell transcriptomic integration for identifying brain metastasis-associated malignant subpopulations and potential therapeutic targets in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is a highly prevalent and lethal form of lung cancer. Brain metastasis (BrM) is a major cause of mortality in p...

Enhanced Ionic Conductivity at the Solid Electrolyte Interphase of Oxygen-Doped Li6PS5Cl.

Understanding the formation and transport properties of the solid electrolyte interphase (SEI) at the Li metal | solid state electrolyte (SSE) interfa...

Label-Free Prediction of EGFR Mutation Status Using Fluorescence Lifetime Imaging and Deep Learning in Lung Adenocarcinoma.

UNLABELLED: Accurate prediction of epidermal growth factor receptor (EGFR) mutations is essential for guiding targeted therapy in non-small cell lung ...

Deep learning-based respiratory-triggered magnetic resonance cholangiopancreatography: comparison with conventional respiratory-triggered and breath-hold magnetic resonance cholangiopancreatography.

PURPOSE: To evaluate whether deep learning-based respiratory-triggered (DL) 3D magnetic resonance cholangiopancreatography (MRCP) improves acquisition...

Pharmacogenomics in oncology: mutation-targeted therapy and biomarker integration in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is the most common cancer-related cause of death among all countries globally, mostly because of late diagnosis, he...

Predicting Neonatal Respiratory Outcomes Using Machine Learning: A Systematic Review Through a Life Course Lens.

BACKGROUND: Neonatal respiratory outcomes remain leading drivers of neonatal intensive care unit (NICU) morbidity, mortality, and prolonged hospitaliz...

AI-Assisted Chest X-Ray Interpretation in Resource-Limited Settings: LuAna Stepped-Wedge Trial Protocol.

BACKGROUND: Artificial intelligence (AI) has the potential to transform chest radiography interpretation by enhancing diagnostic accuracy, identifying...

Wearable fatigue detection system for wheelchair propulsion: Identifying key muscles via an attention-based LSTM Model.

Fatigue induces adverse short- and long-term health risks in wheelchair users. This study aimed to develop a machine learning approach to predict fati...