Pulmonology

Single-cell and spatial transcriptomic profiling of POU5F1 in Lung Adenocarcinoma: Dynamics, spatial niche, and prognosis via multi-algorithm ML.

BACKGROUND: POU5F1 (OCT4), a core regulator of pluripotency, plays an important role in tumor stemness and immune microenvironment remodeling, yet its systematic function and mechanisms in lung adenocarcinoma (LUAD) remain incompletely elucidated. METHODS: This study integrated genetic causality inference, single‑cell and spatial transcriptomics, RNA velocity analysis, and multi‑algorithm machine ...

Automated Delineation of Couinaud Segments on CT for Future Liver Remnant Volumetry.

Purpose To develop a deep learning model that automatically delineates the eight liver Couinaud segments and the spleen on CT for future liver remnant (FLR) volumetry. Materials and Methods In this retrospective study (January 2001 and October 2025), eight liver Couinaud segments and the spleen were manually labeled on CT scans of patients from Institution-A and the public Medical Segmentation Dec...

Hyperglycemia-driven metabolic memory signaling destabilizes METTL1 to trigger inflammatory hypertrophy and fibrosis in diabetic cardiomyopathy.

BACKGROUND: Diabetic cardiomyopathy (DCM) occurs in the context of coronary artery disease or pressure overload heart disease, characterized by altera...

From Fitness to Cognition: Machine-Learning Prediction of Cognitive Performance Using Physiological Parameters in Healthy Adults.

INTRODUCTION: Current cognitive tasks are not suitable for frequent monitoring of cognitive function in healthy adults. Increasing evidence suggests t...

AI and Internet of Things for Chronic Obstructive Pulmonary Disease Remote Monitoring: Systematic Review of Exacerbation Prediction and Key Physiological Variables.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide, with frequent exacerbations of COPD ...

Testing Near-Field Radio-Frequency Sensors to Predict Respiratory Distress in Patients Living With Dementia: A Pilot Feasibility and Acceptability Study.

BackgroundPeople living with dementia (PLWD) with advanced illness are prone to respiratory distress yet often cannot self-report dyspnea, delaying re...

CT quantification of interstitial lung abnormalities: a prospective comparison between supine and prone positions.

OBJECTIVE: To prospectively evaluate positional variability in quantitative CT (qCT)-derived measurements of interstitial lung abnormality (ILA) using...

Beyond NIHSS and neuroimaging: an interpretable gradient boosting model for predicting in-hospital mortality in ICU patients with acute ischemic stroke.

This study aimed to develop an interpretable machine learning model for predicting in-hospital mortality among acute ischemic stroke (AIS) patients ad...

Establishing a competence model in discriminating Mycobacterium species and Mycobacterium abscessus subspecies by using surface-enhanced Raman spectroscopy.

Surface-enhanced Raman spectroscopy (SERS) is a powerful laser-based technique with high sensitivity and rapid detection. The present study aimed to e...

Use of Machine-Learning Models to Identify Clinical Features Associated With A Future Clinical Worsening Event in Patients With Pulmonary Arterial Hypertension.

Clinical worsening events are increasingly recognized as a meaningful outcome in pulmonary arterial hypertension (PAH). We applied machine-learning mo...

Publisher Correction: Machine Learning Models for Predicting Significant Liver Fibrosis in Patients with Severe Obesity and Nonalcoholic Fatty Liver Disease.

Combining generative large language models and pretrained language models for predicting acute respiratory distress syndrome progression.

Acute Respiratory Distress Syndrome (ARDS) is a life-threatening condition in which early diagnosis and timely intervention are crucial. Recent resear...

Multi-Objective Catalyst Discovery in High-Entropy Alloy Composition Space: The Role of Noble Metals on the Pareto Front for Oxygen Reduction Reaction.

Discovering new materials for electrocatalytic energy conversion reactions is a key step toward energy sustainability. However, for catalysts to be vi...

Prognostic significance of right ventricular myocardial features derived from echocardiography using a deep learning framework in pulmonary arterial hypertension.

BACKGROUND: The function and myocardial characteristics of the right ventricle (RV) are linked to RV dysfunction and prognosis in pulmonary arterial h...

Artificial intelligence-assisted peri-operative echocardiography: a multicentre observational study.

INTRODUCTION: Point-of-care transthoracic echocardiography performed by anaesthetists can influence peri-operative management but is constrained by ti...

Comprehensive evaluation of cross cancer generalization in histopathology segmentation models across 21 tumor types.

BACKGROUND: Artificial intelligence has significantly advanced computational pathology by enabling high-resolution, clinical-grade tumor segmentation ...

Deep learning model development and clinical validation for radiographic surrogate markers of implant esthetic risk.

Reliable risk assessment for implant-supported restorations in the esthetic zone is critical yet challenging due to complex anatomical variations and ...

The Application of Machine Learning in the Development of Co-Amorphous Dry Powder Inhalation.

Besides improving drug solubility and stability, co-amorphous systems (COAMS) have recently been reported to enhance the pulmonary delivery efficiency...

Machine learning-based prediction of ventilator therapeutic pressure for optimized CPAP titration.

BACKGROUND: Accurate prediction of therapeutic pressure for Continuous Positive Airway Pressure (CPAP) therapy is essential for effective treatment of...

Early identification of high-risk individuals for mortality after lung transplantation: A retrospective cohort study with topological feature engineering.

Lung transplantation remains the only definitive treatment for end-stage respiratory failure; however, it has substantial post-operative mortality ris...