Pulmonology

A hybrid framework integrating serum biochemical markers and FTIR spectroscopy with machine learning for early cancer screening.

Early detection of cancer remains a key challenge in clinical diagnosis. This study explored two serum sample-based early cancer screening methods: one based on biochemical parameters and the other based on infrared spectroscopy data, and combined the two methods with machine learning techniques. By statistically analyzing 20 serum biochemical indices from healthy individuals, lung cancer patients...

HeptaTB Dx: a diagnostic model leveraging cuproptosis-ferroptosis crosstalk for distinguishing latent from active tuberculosis.

UNLABELLED: Distinguishing latent tuberculosis infection (LTBI) from active tuberculosis (ATB) remains challenging. The roles of cuproptosis-ferroptosis crosstalk in TB immunopathology and diagnostic potential are unexplored. Transcriptomic data from Gene Expression Omnibus data sets (GSE37250/GSE28623) were analyzed to identify cuproptosis-/ferroptosis-related differentially expressed genes. Bioi...

Amentoflavone from Ginkgo biloba inhibits EMT-driven lung cancer metastasis by targeting TGFBR2: Integrative network pharmacology, machine learning, and experimental validation.

ETHNOPHARMACOLOGICAL RELEVANCE: In non-small cell lung cancer (NSCLC), fatal outcomes predominantly result from metastatic dissemination, highlighting...

Identification of Individualised Aspiration Mechanisms in Post-Stroke Dysphagia: A VFSS-Based Interpretable Machine Learning Approach.

BACKGROUND: Post-stroke dysphagia (PSD) significantly increases the risk of aspiration-related pneumonia. While prior studies have linked abnormal tem...

Template-guided reconstruction of pulmonary segments with neural implicit functions.

High-quality 3D reconstruction of pulmonary segments plays a crucial role in segmentectomy and surgical planning for the treatment of lung cancer. Due...

Integrated multi-omics profiling of wooden breast reveals energy metabolism dysfunction and RHOA/ROCK pathway activation.

Wooden breast (WB) is a prevalent meat quality defect in broilers, characterized by muscle hardness, and reduced functionality. To investigate its mol...

Next-generation multicenter studies: using artificial intelligence to automatically process unstructured health records of patients with lung cancer across multiple institutions.

BACKGROUND: Manual abstraction of real-world data (RWD) from unstructured health records (HRs) remains resource intensive, error prone, and highly var...

Optimising Sleep Stage Detection Using a Minimal Non-EEG Physiological Signal Set and Deep Learning.

Automatic sleep stage classification is essential for enabling non-invasive, at-home monitoring. However, current methods often rely on electroencepha...

Clinical practice, barriers to implementation, and priorities for equitable access of Stereotactic Body Radiation Therapy: An analysis of the global status by the ESTRO SBRT Focus Group.

BACKGROUND: Stereotactic Body Radiation Therapy (SBRT) has become an established treatment for several primary and metastatic malignancies; however, c...

Integrative bioinformatics analysis reveals that microplastics promote chronic rhinosinusitis with nasal polyps through NOXO1-mediated oxidative stress and myoepithelial cell reprogramming.

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a complex inflammatory disease whose environmental pathogenic factors remain unclear. Microplasti...

Synergistic impact of insulin resistance and hepatic fibrosis on cardiovascular events in patients with coronary artery disease: A machine learning subtyping and interaction analysis.

BACKGROUND AND AIMS: Insulin resistance (IR) and hepatic fibrosis are significant yet underexplored synergistic risk factors for cardiovascular events...

Quantification differences between supine and prone CT in interstitial lung disease.

PURPOSE: To evaluate the measurement variability and reproducibility of prone imaging with quantitative computed tomography (QCT) compared with supine...

Automated interstitial lung abnormalities detection at CT: external validation and potential recognition of traction bronchiectasis/bronchiolectasis.

PURPOSE: An artificial intelligence (AI) system for detecting interstitial lung abnormalities (ILA) was previously developed but requires external val...

Biosecurity practices useful for porcine reproductive and respiratory syndrome control and eradication on commercial swine farms using machine learning models.

It is well known that porcine reproductive and respiratory syndrome (PRRS) decreases herd productivity and leads to economic loss, and it is believed ...

Toxicophore-informed machine learning integrating Tox21 assay readouts for organ system-specific carcinogenicity prediction.

Accurate identification of carcinogenic hazards is essential for public health protection, yet traditional animal-based assays are time-consuming, exp...

A Simple Laboratory-based Machine Learning Model Accurately Predicts Advanced Liver Fibrosis in Metabolic Dysfunction-associated Steatotic Liver Disease Patients.

BACKGROUND/AIM: Metabolic dysfunction-associated steatotic liver disease (MASLD) is often silent and progressive, affecting nearly one-fourth of the g...

Quantitative CT of emphysema, wall thickness and mucus plugs in alpha-1-antitrypsin deficiency: relationship to clinical outcomes.

OBJECTIVES: Alpha-1-antitrypsin deficiency (AATD) is a rare genetic disorder leading to chronic obstructive pulmonary disease (COPD). Emphysema is the...

Artificial intelligence in quantitative chest imaging analysis for occupational lung disease: appraisal of its current status.

PURPOSE OF REVIEW: The application of mathematical algorithms for detecting lung abnormalities has been a challenge for decades. Occupational lung dis...

Integrative metabolomics and machine learning reveal diagnostic biomarkers for gelsenicine intoxication.

ETHNOPHARMACOLOGICAL RELEVANCE: Gelsemium elegans Benth. (G. elegans) is a highly toxic medicinal plant traditionally used to treat pain and inflammat...

Establishment of CT diagnostic reference levels (DRLs) in Tokyo.

OBJECTIVE: Diagnostic reference levels (DRLs) are essential for optimizing radiation dose in CT examinations. However, current DRLs may not reflect th...