Pulmonology

Instantaneous Abdominal T2* Mapping via Single-Shot MOLED Under Free-Breathing: A Preliminary Study of Hepatic Glycometabolism Imaging.

PURPOSE: To propose a gradient-echo multiple overlapping-echo detachment (GRE-MOLED) method for rapid abdominal T2* mapping, and to systematically validate its efficacy in non-invasive monitoring of dynamic hepatic glycometabolism. METHODS: The GRE-MOLED sequence was optimized to achieve ultrafast abdominal T2* mapping under free-breathing without respiratory gating, the scan time per slice was on...

Dysfunctional resting state network connectivity predicts postoperative delirium after major surgery.

BACKGROUND: Postoperative delirium is associated with increased morbidity, mortality, future cognitive decline, or dementia. Understanding the neural mechanisms that differentiate individual brain vulnerabilities is critical for future therapeutic development and prevention of postoperative delirium. We investigated the hypothesis that impaired resting state functional connectivity indicates predi...

Hitting the Bull's AI: Artificial Intelligence-derived Imaging Features and their Association with Outcomes in CT-guided Lung Biopsy, a Retrospective Study.

PURPOSE: This study aims to evaluate whether quantitative imaging features analyzed by an artificial intelligence (AI) tool are associated with succes...

Unraveling the molecular mechanisms of air pollution-induced lung adenocarcinoma through machine learning, network toxicology, and molecular docking.

OBJECTIVE: Airborne environmental contaminants are established carcinogens. This investigation elucidates the mechanistic contributions to pulmonary a...

Vulnerabilities of feature clustering in EIT radiomics.

BACKGROUND: We aimed to determine whether unsupervised machine learning was able to discover latent and possibly clinically-relevant clusters, hidden ...

Radiologist- and computer-based chest imaging quantification at 12 months post transplant correlates with baseline lung allograft dysfunction.

BACKGROUND: Baseline lung allograft dysfunction (BLAD), defined as failure to achieve ≥ 80% predicted spirometry after lung transplant, is associated ...

Harnessing artificial intelligence and machine learning to accelerate structure-based drug discovery for tuberculosis.

TNFAIP3, PADI4, and CXCR4 as biomarkers of neutrophil extracellular traps in severe asthma.

Asthma is a heterogeneous condition impacting an estimated 300 million individuals globally. Although inhaled corticosteroids are effective in allevia...

Efficacy and safety of recombinant human bone morphogenetic protein-2 biomaterials in promoting bone regeneration: A systematic review and meta-analysis.

OBJECTIVES: This study aims to systematically evaluate the efficacy and safety of recombinant human bone morphogenetic protein-2 (rhBMP-2) in promotin...

Diagnostic accuracy of ¹⁸F-FDG PET/CT radiomics for non-invasive prediction of PD-L1 expression in non-small cell lung cancer: A systematic review and meta-analysis.

To evaluate the diagnostic performance, methodological quality, and clinical feasibility of ¹⁸F-FDG PET/CT-based radiomics machine learning models for...

Motion Mitigation Techniques for Abdominal and Cardiac MR Imaging.

MRI of the heart and abdominal organs provides unparalleled soft tissue contrast and quantitative biomarkers, yet remains highly susceptible to physio...

A chalcone-rich extract from licorice root inhibits liver steatosis and fibrosis via regulating PPARα and TGFβ signaling.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a major liver disorder, which seriously affects human health globally....

Cardiovascular pulsing-based ultrasound strain imaging with deep learning using paired proximal and distal strain sequences for liver fibrosis detection: a feasibility study.

Accurate assessment of liver fibrosis in the left liver lobe remains clinically challenging due to motion artifacts that compromise the reliability of...

Targeting key proteins of pleural mesothelioma using plumbagin-indole-3-propionic acid ester: Insights from network pharmacology, molecular dynamics simulation and machine learning-based analysis.

This study explores the network pharmacology (NP) and molecular dynamics (MD) simulation analysis of pleural mesothelioma (PM) related enzymes. Throug...

Construction and validation of a machine learning-based risk prediction model for invasive mechanical ventilation in AECOPD patients complicated with respiratory failure.

OBJECTIVE: This study aimed to create and validate a machine learning (ML) model to predict the likelihood of invasive mechanical ventilation (IMV) in...

Factors Impacting the Performance of Deep Learning Detection of Pulmonary Emboli.

OBJECTIVE: AI models are increasingly adopted in clinical practice, yet their generalizability outside controlled validation settings remains unclear....

AI-assisted qualitative analysis of formative feedback to support student-centered learning in physiology.

This study evaluates the effectiveness of large language models (LLMs), specifically Claude Sonnet 4.0 and ChatGPT 4.1, for analyzing formative feedba...

Automotive Health 2.0: Steering Toward Proactive Preventive Care.

Cardiovascular and chronic disease prevention remains limited by episodic, clinic-based assessments that fail to capture physiological changes arising...

Proteomic Profiling of Plasma Extracellular Vesicles Combined with Multivariate Modeling Identified Potential Biomarkers for Distinguishing Benign Pulmonary Nodules from Early-Stage Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer and is difficult to distinguish from benign pulmonary nodules (BPNs), particularl...

Computed Tomography-Pathology Deep Learning Model for the Precise Prediction of Recurrence in Pathological Stage IA Lung Adenocarcinoma.

BACKGROUND: The postoperative prognosis of pathological stage IA lung adenocarcinoma (LUAD) exhibits significant heterogeneity. While the tumor node m...