Pulmonology

Early prediction of non-invasive ventilation outcome using the TabPFN machine learning model: a multi-centre validation study.

Application of machine learning models for zooplankton abundance prediction in ponds of Southeastern Coastal Regions in Bangladesh.

Zooplankton abundance prediction in surface water bodies is crucial because they reflect ecosystem health and have role in aquatic food webs and nutrient cycling. This study examined the applicability of machine learning algorithms to estimate zooplankton abundance in ponds using water quality parameters. Data on zooplankton abundance and water quality parameters were collected monthly. Multiple l...

Genome sequencing is critical for forecasting outcomes following congenital cardiac surgery.

While exome and whole genome sequencing have transformed medicine by elucidating the genetic underpinnings of both rare and common complex disorders, ...

Inflammatory, fibrotic and endothelial biomarker profiles in COVID-19 patients during and following hospitalization.

Survivors of severe COVID-19 often suffer from long-term respiratory issues, but the molecular drivers of this damage remain unclear. This study explo...

Application progress of machine learning in patient-ventilator asynchrony during mechanical ventilation: a systematic review.

INTRODUCTION: Patient-ventilator asynchrony (PVA) is a common and harmful complication during mechanical ventilation, often requiring labor-intensive ...

Machine learning identifies lipid-associated genes and constructs diagnostic and prognostic models for idiopathic pulmonary fibrosis.

OBJECTIVE: Emerging evidence suggests a potential relationship between lipid metabolism and idiopathic pulmonary fibrosis (IPF). This study aimed to i...

Automated detection of air trapping from mechanical ventilation waveform through interpretable dual-channel 1D convolutional neural network.

. Air trapping is a major symptom of respiratory diseases like chronic obstructive pulmonary disease and asthma, and has always been a significant pro...

Advancing Tuberculosis Screening: The Clinical Potential of Artificial Intelligence-Generated Radiology Reports for Chest Radiographs.

Detection of focal impaired awareness seizures using a biometric shirt.

OBJECTIVE: In recent years, seizure detection using wearable technology has gained significant attention in research. Most studies, however, have focu...

Predicting time-to-first cancer diagnosis across multiple cancer types.

Cancer causes over 10 million deaths annually worldwide, with 40.5% of Americans expected to be diagnosed in their lifetime. Early detection is critic...

Machine learning for synchronous bone metastasis risk prediction in high grade lung neuroendocrine carcinoma.

Bone metastasis (BM) is common in high-grade lung neuroendocrine tumors (NETs). This study aimed to use multiple machine learning algorithms to explor...

Anoikis-related biomarkers PARP1 and SDCBP as diagnostic and therapeutic targets for asthma.

This study aims to explore the association between anoikis-related genes (ARGs) and asthma. The dataset GSE143303 for asthma were sourced from the GEO...

Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detection.

Artificial intelligence models using digital histopathology slides stained with hematoxylin and eosin offer promising, tissue-preserving diagnostic to...

The association of life's essential 8 with prevalence of chronic respiratory diseases in adults: insights from NHANES 2007-2018.

OBJECTIVE: Chronic respiratory diseases (CRDs) and cardiovascular diseases (CVD) share common risk factors and frequently co-occur, leading to poorer ...

A clinicians' guide to non-invasive ventilation for exacerbated chronic obstructive pulmonary disease.

INTRODUCTION: Chronic Obstructive Pulmonary Disease (COPD) is a debilitating condition marked by persistent airflow limitation, leading to increased m...

Contemporary Concise Review 2024: New Techniques in Interventional Pulmonology.

Navigational bronchoscopy and cone beam computed tomography (CBCT) guided bronchoscopy show comparable yields to percutaneous transthoracic needle bio...

Deep Learning-Based Classification of NSCLC-Derived Extracellular Vesicles Using AFM Nanomechanical Signatures.

Nonsmall cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality, with liquid biopsy emerging as a promising tool for noninvasive...

Development of a Machine Learning Algorithm-Based Predictive Model for Physical Activity Levels in Lung Cancer Survivors: A Cross-Sectional Study.

AIMS: To investigate the physical activity levels of lung cancer survivors, analyse the influencing factors, and construct a predictive model for the ...

Electronic-Nose Technology for Lung Cancer Detection: A Non-Invasive Diagnostic Revolution.

BACKGROUND: Lung cancer (LC) remains a leading cause of cancer-related mortality worldwide, primarily due to late-stage diagnosis and the absence of e...

Predicting SARS-CoV-2-specific CD4 and CD8 T-cell responses elicited by inactivated vaccines in healthy adults using machine learning models.

The ongoing evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants highlights the importance of monitoring immune response...