Pulmonology

Internally validated machine learning models identify asthma and its phenotypes using multicenter real-world data.

Asthma exhibits significant clinical heterogeneity, necessitating precise phenotyping for personalized treatment. This study aims to develop and internally validate machine learning models for cross-sectional identification and phenotyping of asthma using routinely collected clinical data. Data from the MIMIC-IV and OPCRD databases were integrated, encompassing 3,025 eligible cases (2,180 in asthm...

Applying prior knowledge of regulatory signaling to investigate macrophage cAMP dynamics during Mycobacterium tuberculosis infection.

Mycobacterium tuberculosis (Mtb), the causative agent of Tuberculosis, resides in host lung macrophages and has evolved unique processes to hijack host signaling pathways to facilitate its survival and propagation within macrophages. Notably, Mtb exports cyclic AMP (cAMP), a key regulatory signaling molecule, during infection. As can often be the case, experimental data exploring immune modulation...

Development of an expert-annotated chest X-ray dataset to support AI validation in tuberculosis diagnosis.

OBJECTIVES: To assess inter-rater agreement and diagnostic performance of six United States National Institute for Occupational Safety and Health-cert...

Performance evaluation of five major large language models in tuberculosis Q&A systems: A multidimensional assessment of readability, quality, and reliability.

BACKGROUND: Pulmonary tuberculosis (TB) is a chronic infectious disease that burdens patients and public health systems. Limited reach of traditional ...

A late fusion multi-task learning for respiratory waveform and rate estimation from photoplethysmography.

Continuous respiratory monitoring enables early detection of physiological deterioration, yet conventional capnography remains impractical for prolong...

Machine learning-based analysis of drug resistance mutations in Mycobacterium tuberculosis.

Tuberculosis is a deadly airborne disease caused by Mycobacterium tuberculosis. Drug-resistant tuberculosis presents significant challenges for treatm...

Study on the construction and verification of intraoperative pressure injury risk prediction model for children undergoing cardiac surgery.

BACKGROUND: This study applied nomogram to develop an intraoperative acquired pressure injury (IAPI) risk prediction model for pediatric cardiac surge...

Machine learning approach to analyzing complex care coordination patterns for medically complex children.

BACKGROUND: The number of children with medical complexity (CMC) in Japan has exceeded 20,000, doubling over the past 15 years, forming a significant ...

Decoding the role of phthalate plasticizers (DBP, DOP, and DEHP) in idiopathic pulmonary fibrosis: an integrated network toxicology and machine learning approach.

The molecular mechanism of idiopathic pulmonary fibrosis (IPF) caused by phthalate (PAE) is not well understood, presenting notable clinical and toxic...

Machine learning models predicting extubation success in mechanically ventilated patients: a systematic review and meta-analysis.

BACKGROUND: Optimal timing of extubation in mechanically ventilated patients remains a major challenge in intensive care. Machine learning (ML) models...

Deep Learning-Enabled Robust Regional Lung Function Assessment in Mechanically Ventilated AECOPD Patients Using EIT Parameters.

Reliable quantification of regional lung function via electrical impedance tomography (EIT) in mechanically ventilated AECOPD patients requires identi...

ASTHMA AT THE CROSSROADS: FROM ANTI-TNF-α SETBACKS TO NEXT-GENERATION BIOLOGICS TARGETING TYPE 1 AND TYPE 2 INFLAMMATION.

Severe asthma remains a major unmet clinical challenge due to its marked immunological heterogeneity and the limited efficacy of current therapies in ...

Development and validation of a disease-syndrome integrated assessment model for AECOPD severity: protocol for a nationwide multicentre cross-sectional study.

INTRODUCTION: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) accelerate disease progression and increase mortality. Accurate se...

Construction and validation of an interpretable machine learning model for predicting diabetes risk in COPD patients.

To construct and validate a machine learning (ML) model for predicting diabetes risk in COPD patients, enabling early and personalized intervention. U...

Current progress and future directions of intraoperative frozen section for spread through air spaces in lung cancer.

Spread through air spaces (STAS) is a recently recognized pattern of invasion in lung cancer that is strongly linked to postoperative recurrence and p...

Automated high-resolution computed tomography analysis outperforms visual assessment in predicting interstitial lung disease progression in systemic sclerosis.

OBJECTIVES: Progressive interstitial lung disease (ILD) is the leading cause of mortality in systemic sclerosis (SSc). Early recognition of progressio...

Fostering trust in digital respiratory medicine: the open access ERS CONNECT digital health technology repository.

Access to digital health tools is rapidly expanding, but adoption in respiratory care remains fragmented. The ERS CONNECT Repository provides a truste...

Using artificial intelligence to identify characteristics associated with clinical and economic outcomes in MASH (FOCUS-MASH).

BACKGROUND: Heterogeneity in the disease characteristics of metabolic dysfunction-associated steatohepatitis (MASH) complicates efforts to identify in...

Integrative profiling of senescence-associated cells and genes identifies UGT1A7 as a prognostic and therapeutic target in Lung squamous cell carcinoma.

Lung Squamous Cell Carcinoma (LUSC), a common subtype of non-small cell lung cancer, primarily occurs in elderly individuals and is closely linked to ...

Cardio-respiratory deep learning model to predict hospital admission from emergency department electrocardiogram images and respiration videos: a prospective study.

BACKGROUND: Early prediction of hospital admission at the emergency department (ED) triage can improve patient flow and resource allocation. Most exis...