Pulmonology

Identification of potential diagnostic markers and molecular mechanisms of asthma and ulcerative colitis based on bioinformatics and machine learning.

BACKGROUNDS: Asthma and ulcerative colitis (UC) are chronic inflammatory diseases linked through the "gut-lung axis," but their shared mechanisms remain unclear. This study aims to identify common biomarkers and pathways between asthma and UC using bioinformatics.

Relationships between vitamin C intake and COPD assessed by machine learning approaches from the NHANES (2017-2023).

BACKGROUND: This research aims to explore the possible link between Vitamin C Intake (VCI) and the incidence of Chronic Obstructive Pulmonary Disease (COPD) in Americans aged over 20.

YOLOv8 framework for COVID-19 and pneumonia detection using synthetic image augmentation.

OBJECTIVE: Early and accurate detection of COVID-19 and pneumonia through medical imaging is critical for effective patient management. This study aim...

Home spirometry telemonitoring in pediatric patients with asthma: a mixed study.

BACKGROUND: To evaluate the feasibility and practicality of home spirometry telemonitoring for pediatric patients with asthma, including both motivato...

Deployable machine learning-based decision support system for tracheostomy in acute burn patients.

BACKGROUND: Airway obstruction is a common emergency in acute burns with high mortality. Tracheostomy is the most effective method to keep patency of ...

TMEM132A: a novel susceptibility gene for lung adenocarcinoma combined with venous thromboembolism identified through comprehensive bioinformatic analysis.

BACKGROUND: Mounting evidence indicates that lung adenocarcinoma (LUAD) patients are at elevated risk for venous thromboembolism (VTE), presenting a m...

Association of early enoxaparin prophylactic anticoagulation with ICU mortality in critically ill patients with chronic obstructive pulmonary disease: a machine learning-based retrospective cohort study.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a major contributor to global morbidity and mortality, particularly during acute exacerbat...

Single-cell transcriptomics and machine learning unveil ferroptosis features in tumor-associated macrophages: Prognostic model and therapeutic strategies for lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is a major cause of cancer-related mortality worldwide. Tumor-associated macrophages (TAMs) play a crucial role...

Multimodal deep learning model for prediction of prognosis in central nervous system inflammation.

Inflammatory diseases of the CNS impose a substantial disease burden, necessitating prompt and appropriate prognosis prediction. We developed a multim...

Emerging biomaterials and bio-nano interfaces in pulmonary hypertension therapy: transformative strategies for personalized treatment.

Pulmonary hypertension (PH) is still an aggressive and progressive illness with vascular remodeling and right heart failure despite the therapeutic ad...

Development of an immune-related gene signature applying Ridge method for improving immunotherapy responses and clinical outcomes in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is a major cause of cancer mortality. Considering the critical role of tumor infiltrating lymphocytes in effect...

Predicting Superaverage Length of Stay in COPD Patients with Hypercapnic Respiratory Failure Using Machine Learning.

OBJECTIVE: The purpose of this study was to develop and validate machine learning models that can predict superaverage length of stay in hypercapnic-t...

Development and validation of a predictive model combining radiomics and deep learning features for spread through air spaces in stage T1 non-small cell lung cancer: a multicenter study.

PURPOSE: The goal of this paper is to compare the effectiveness of three deep learning models (2D, 3D, and 2.5D), three radiomics models(INTRA, Peri2m...

Recent progress in tuberculosis diagnosis: insights into blood-based biomarkers and emerging technologies.

Tuberculosis (TB) remains a global health challenge, with timely and accurate diagnosis being critical for effective disease management and control. R...

Identification of regulatory cell death-related genes during MASH progression using bioinformatics analysis and machine learning strategies.

BACKGROUND: Metabolic dysfunction-associated steatohepatitis (MASH) is becoming increasingly prevalent. Regulated cell death (RCD) has emerged as a si...

Interpretable noninvasive diagnosis of tuberculous pleural effusion using LGBM and SHAP: development and clinical application of a machine learning model.

BACKGROUND: Tuberculous pleural effusion (TPE) is a prevalent tuberculosis complication, with diagnosis presenting considerable challenges. Timely and...

Exploring shared pathogenic mechanisms and biomarkers in hepatic fibrosis and inflammatory bowel disease through bioinformatics and machine learning.

BACKGROUND: The coexistence of hepatic fibrosis (HF) and inflammatory bowel disease (IBD) represents a significant clinical concern due to their poorl...

Identifying Common Diagnostic Biomarkers and Therapeutic Targets between COPD and Sepsis: A Bioinformatics and Machine Learning Approach.

BACKGROUND: Evidence suggests a bidirectional association between chronic obstructive pulmonary disease (COPD) and sepsis, but the underlying mechanis...

XLLC-Net: A lightweight and explainable CNN for accurate lung cancer classification using histopathological images.

Lung cancer imaging plays a crucial role in early diagnosis and treatment, where machine learning and deep learning have significantly advanced the ac...

Identification of hub gene for the pathogenic mechanism and diagnosis of MASLD by enhanced bioinformatics analysis and machine learning.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a heterogeneous disease caused by multiple etiologies. It is characterized by exce...