Pulmonology

Development of a Hybrid Algorithm of Claims Data and EMRs with NLP for Lung Cancer Identification.

The use of real-world data, which encompassing administrative claims and electronic medical records, has gained significance in clinical research. Although administrative claims data are widely used, they often lack the clinical specificity and diagnostic reliability. To overcome this, we developed a hybrid "NLP-complemented" algorithm. This approach uses natural language processing (NLP) to integ...

Timely, AQL-Driven Clinical Cohort Identification in openEHR Infrastructures.

Timely cohort identification remains a challenge in hospitals due to fragmented systems, delayed coding, and limited IT capacity. We present CohortMailer, a lightweight tool that automates extraction of patient cohorts from openEHR repositories using Archetype Query Language (AQL) and delivers filtered case lists via scheduled email. Deployed across two sites of the University Hospital Schleswig-H...

Predicting Lung Function from Voice Recordings Using Machine Learning: An Optimised Approach.

Spirometry is a crucial diagnostic test for pulmonary diseases, where measures including Forced Expiratory Volume in one second (FEV1) have diagnostic...

Deep learning multi-omics integration identifies new molecular subtypes of lung cancer.

Cancer is a heterogeneous disease, with numerous subtypes differing in molecular profiles, risk factors, clinical outcomes, and tumor locations. Lung ...

Single-cell N4-acetylcytidine regulatory landscape identifies ARIH1 as a ferroptosis-suppressive oncogenic driver in lung adenocarcinoma.

BACKGROUND: N4-acetylcytidine (ac4C) is an emerging post-transcriptional RNA modification implicated in cancer biology, yet its cellular distribution,...

Hyperbaric oxygen in the artificial intelligence era: integration and innovation.

Hyperbaric oxygen therapy is established for decompression illness, carbon monoxide poisoning, radiation-induced tissue injury, and diabetic foot ulce...

Artificial Intelligence Algorithm to Monitor Inspiratory Muscle Effort and Patient-Ventilator Dyssynchrony During Mechanical Ventilation.

OBJECTIVE: Current methods for estimating inspiratory muscle pressure (Pmus) during mechanical ventilation are either invasive or dependent on occlusi...

Advances in the application of finite element analysis in the diagnosis and treatment of cleft lip and palate and alveolar cleft.

Cleft lip and palate (CLP) and alveolar cleft are common congenital craniofacial anomalies, with postoperative maxillary hypoplasia and secondary defo...

Few-Shot Lung Cancer Classification via Electronic Nose Using Large Language Models: A Multicentre Prospective Study.

BACKGROUND AND OBJECTIVE: Electronic Nose (eNose) breathprints are promising non-invasive lung cancer diagnostic tools, but cross-site validation and ...

Enhancing respiratory virus surveillance among hospitalised children: a machine learning-based predictive model.

BACKGROUND: Viral respiratory tract infections (vRTIs) are a leading cause of paediatric hospitalisation and healthcare utilisation. Existing syndromi...

[Automated analysis of mandibular movements for the screening of obstructive sleep apnea].

INTRODUCTION: Obstructive sleep apnea syndrome (OSAS) is a highly prevalent condition, particularly among at-risk populations such as patients with ob...

Objective-function-guided automated VMAT planning reduces OAR dose, low-dose exposure, and inter-planner variability in breast radiotherapy.

This study presents and evaluates an automated volumetric modulated arc therapy (VMAT) planning framework for breast cancer based on objective functio...

Machine learning modeling of vegetation and limited two dimensional urban morphology effects on land surface temperature in Osaka using open data.

Urban heat islands (UHI) significantly elevate land surface temperatures (LST) in high-density subtropical cities like Osaka, Japan, exacerbating ener...

Identifying and validating ITGB2 and HNRNPAB as diagnostic biomarkers in chronic obstructive pulmonary disease using bioinformatics and Integrated Machine Learning Methods.

BACKGROUND AND AIM: COPD is a common respiratory disease characterized by progressive airflow restriction that severely affects patients' quality of l...

TGFb signaling instructs a conserved fibrosis-associated cell state marked by LRRC15.

Fibroblasts are key potentiators of chronic disease pathophysiology. Despite their established roles in promoting pathological inflammation and tissue...

Prognostic Significance of Baseline 18F-FDG PET/CT Parameters in Combination with an Artificial Intelligence-Based Pleural Effusion Segmentation Model for Malignant Pleural Effusion.

OBJECTIVES: We aimed to use an artificial intelligence (AI)-based pleural effusion segmentation model on baseline 18F-FDG positron emission tomography...

Mechanistic Insights into BDE-153-Induced Idiopathic Pulmonary Fibrosis through Network Toxicology, Machine Learning, and Cellular Validation.

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease with limited therapeutic options. Polybrominated diphenyl ethers (PBDEs...

Multi-dimensional analysis revealing the mechanism of T-2 toxin driving liver fibrosis: integration of network toxicology, machine learning, experimental verification, and molecular docking.

BACKGROUND: T-2 toxin is a highly toxic mycotoxin commonly present in food and the environment, with accumulating evidence supporting its hepatotoxic ...

An Overlooked Formulation Variable: Solvent Reshapes DNA Delivery.

Nonviral polymeric vectors offer a tunable platform for nucleic acid delivery, yet formulation variables beyond polymer structure remain underexplored...

ToxiGuard: an AOP-guided mechanistically interpretable framework for multi-organ toxicity prediction.

Accurate prediction of organ-specific toxicity with mechanistic interpretability remains a central challenge in chemical safety assessment and transla...