Pulmonology

Building an Extracorporeal Membrane Oxygenation Digital Twin Using High-Resolution Patient Data: An artificial intelligence model for virtual reality simulation.

OBJECTIVES: Extracorporeal membrane oxygenation (ECMO) is a life-saving therapy for severe cardiopulmonary failure, but structured training remains constrained by costs, logistics, and the absence of validated high-fidelity simulators. This study aimed to develop an ECMO digital twin capable of supporting training in virtual reality (VR). METHODS: We integrated high-frequency ECMO machine data wit...

Deep learning-based estimation of lung collapse in electrical impedance tomography: A simulation and phantom study.

Inhomogeneity of air volume distribution in the lungs of mechanically ventilated patients can lead to lung collapse and overdistention, increasing the risk of ventilator-induced lung injury. While Electrical Impedance Tomography (EIT) enables bedside monitoring of regional ventilation, traditional Degree of Lung Collapse (DoLC) assessment using the Global Inhomogeneity (GI) index is limited by lun...

Assessing mortality risk in pulmonary tuberculosis and severe malnutrition: development of the IIR marker via artificial intelligence.

Pneumonia detection from enhanced chest X-Ray images based on Double SGAN model.

Medical imaging plays a crucial role in clinical diagnosis, however, deep learning models often struggle with imbalanced datasets, which has a negativ...

Artificial Intelligence Could Predict Chest Tube Drainage Necessity for Spontaneous Pneumothorax.

Artificial intelligence (AI) is increasingly utilized in the medical field, primarily for diagnostic purposes. Although AI has demonstrated efficacy i...

Predicting cardiopulmonary exercise testing outcomes in congenital heart disease through multimodal data integration and geometric learning.

Cardiopulmonary exercise testing (CPET) provides a comprehensive assessment of functional capacity by measuring key physiological variables including ...

Development and Validation of a Frailty Risk Prediction Model for Preoperative Non-Small-Cell Lung Cancer Patients: A Cross-Sectional Study.

PURPOSE: Frailty is increasingly recognized as a predictor of poor surgical outcomes, yet its preoperative assessment in patients with non-small-cell ...

Bulk and single-cell transcriptomics for identification of potential diagnostic biomarkers associated with pulmonary arterial hypertension and integrated stress response and experimental validation.

BACKGROUND: Pulmonary arterial hypertension (PAH) is a lethal vascular disorder characterized by irreversible remodeling of the pulmonary arteries, bu...

Prediction of Symptomatic Radiation Pneumonitis in Lung Cancer Patients: A Radiomics and Dosiomics Machine Learning Approach Using the Prospective Multicenter RTOG 0617 and REQUITE Trials.

PURPOSE: Radiation-induced pneumonitis (RP) is a side effect after thoracic radiation therapy (RT). The ability to predict RP would facilitate treatme...

A multiscale theory framework predicts size- and ordering-dependent activity in Pt-Cu oxygen reduction reaction catalysts.

Optimizing the oxygen reduction reaction (ORR) performance of Pt-based alloy nanocatalysts requires a quantitative understanding of how catalytic acti...

Microbial community biomarkers can forecast methane production in full-scale anaerobic digesters.

Methane production from wastewater sludge via anaerobic digestion is a complex process and a disturbance in any one of the microbial stages can lead t...

In Situ Transmission Electron Microscopy Visualization of Electric-Field-Induced Phase Transitions at the Morphotropic Phase Boundary in Hf0.5Zr0.5O2.

Understanding electric-field-induced phase transitions is crucial for optimizing the ferroelectric and antiferroelectric properties of hafnium zirconi...

Carbon dot nanoparticles as multifunctional fluorescent probes: from tunable synthesis to biomedical applications.

Carbon dots (CDs) have emerged as highly promising fluorescent nanomaterials due to their exceptional optical properties, biocompatibility, and facile...

Multi-modal machine learning and molecular modelling reveal structurally diverse inhibitors of Mycobacterium tuberculosis protein tyrosine phosphatase B.

Protein tyrosine phosphatase B (PtpB) is a virulence-associated phosphotyrosine phosphatase secreted by Mycobacterium tuberculosis (Mtb), known to dis...

Unraveling Scaling Relationships in Dual-Atom Catalysts with Electronic Descriptors: A Machine Learning Investigation for OER/ORR Activity.

Dual-atom catalysts (DACs) have emerged as a new frontier in heterogeneous catalysis, offering improved stability and superior performance in key elec...

Deep Learning-Based Auto-Navigation for Free-Breathing Golden-Angle Radial MRI.

PURPOSE: To develop a deep learning-based auto-navigation technique for free-breathing golden-angle radial MRI named RANGR (Respiratory Auto-Navigator...

Interpretable machine learning and deep neural networks for ICU admission prediction in paediatric respiratory patients.

Development and multicenter validation of a predictive model for malignant pleural effusion recurrence.

Early prediction of malignant pleural effusion (MPE) recurrence within 3 months is essential for optimizing management in lung cancer patients. This s...

Systemic immunometabolic profiling classifies cisplatin sensitivity states using interpretable machine learning.

Cisplatin resistance limits the effectiveness of platinum-based chemotherapy for lung adenocarcinoma, yet practical systemic diagnostics for cisplatin...

Spironolactone and Fibrosis in Heart Failure Risk: Machine Learning Analysis of HOMAGE Trial Plasma Proteomics.

In the HOMAGE (Heart Omics in AGEing) trial, spironolactone reduced serum concentrations of procollagen Type I C-terminal propeptide (PICP), a fibrosi...