Pulmonology

Determination of osimertinib concentration in rat plasma and lung/brain tissues.

OBJECTIVES: The aim of this study was to establish an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method for the detection of osimertinib in rat plasma, lung and brain tissues.

Feasibility of Sub-milliSievert Low-dose Computed Tomography with Deep Learning Image Reconstruction in Evaluating Pulmonary Subsolid Nodules: A Prospective Intra-individual Comparison Study.

RATIONALE AND OBJECTIVES: To comprehensively assess the feasibility of low-dose computed tomography (LDCT) using deep learning image reconstruction (DLIR) for evaluating pulmonary subsolid nodules, which are challenging due to their susceptibility to noise.

Diagnosis of Fibrotic Interstitial Lung Diseases Based on the Combination of Label-Free Quantitative Multiphoton Fiber Histology and Machine Learning.

Interstitial lung disease (ILD), characterized by inflammation and fibrosis, often suffers from low diagnostic accuracy and consistency. Traditional h...

Exploratory study of extracellular matrix biomarkers for non-invasive liver fibrosis staging: A machine learning approach with XGBoost and explainable AI.

BACKGROUND: Novel circulating markers for the non-invasive staging of chronic liver disease (CLD) are in high demand. Although underutilized, extracel...

Real-World and Clinical Trial Validation of a Deep Learning Radiomic Biomarker for PD-(L)1 Immune Checkpoint Inhibitor Response in Advanced Non-Small Cell Lung Cancer.

PURPOSE: This study developed and validated a novel deep learning radiomic biomarker to estimate response to immune checkpoint inhibitor (ICI) therapy...

The effects of camel milk in systemic inflammation and oxidative stress of cigarette smoke-induced chronic obstructive pulmonary disease model in rat.

BACKGROUND: The effects of camel milk in inflammation and systemic oxidative stress of cigarette smoke (CS)-induced chronic obstructive pulmonary dise...

A hybrid deep learning model based on signal decomposition and dynamic feature selection for forecasting the influent parameters of wastewater treatment plants.

Accurate prediction of influent parameters such as chemical oxygen demand (COD) and biochemical oxygen demand over five days (BOD) is crucial for opti...

Prediction of stroke-associated hospital-acquired pneumonia: Machine learning approach.

BACKGROUND: Stroke-associated Hospital Acquired Pneumonia (HAP) significantly impacts patient outcomes. This study explores the utility of machine lea...

Evaluation of the mandibular canal and the third mandibular molar relationship by CBCT with a deep learning approach.

OBJECTIVE: The mandibular canal (MC) houses the inferior alveolar nerve. Extraction of the mandibular third molar (MM3) is a common dental surgery, of...

Quantitative fibrosis identifies biliary tract involvement and is associated with outcomes in pediatric autoimmune liver disease.

BACKGROUND: Children with autoimmune liver disease (AILD) may develop fibrosis-related complications necessitating a liver transplant. We hypothesize ...

Comparative Analysis of Generative Pre-Trained Transformer Models in Oncogene-Driven Non-Small Cell Lung Cancer: Introducing the Generative Artificial Intelligence Performance Score.

PURPOSE: Precision oncology in non-small cell lung cancer (NSCLC) relies on biomarker testing for clinical decision making. Despite its importance, ch...

Integrated multi-omics analysis describes immune profiles in ischemic heart failure and identifies PTN as a novel biomarker.

INTRODUCTION: Heart failure is a leading global cause of mortality, with ischemic heart failure (IHF) being a major contributor. IHF is primarily driv...

Development and validation of interpretable machine learning models for postoperative pneumonia prediction.

BACKGROUND: Postoperative pneumonia, a prevalent form of hospital-acquired pneumonia, poses significant risks to patients' prognosis and even their li...

Past, present, and future of electrical impedance tomography and myography for medical applications: a scoping review.

This scoping review summarizes two emerging electrical impedance technologies: electrical impedance myography (EIM) and electrical impedance tomograph...

Machine learning models for quantitatively prediction of toxicity in macrophages induced by metal oxide nanoparticles.

As nanotechnology advances, metal oxide nanoparticles (MeONPs) increasingly come into contact with humans. The inhaled MeONPs cannot be effectively cl...

Hyb_SEnc: An Antituberculosis Peptide Predictor Based on a Hybrid Feature Vector and Stacked Ensemble Learning.

Tuberculosis has plagued mankind since ancient times, and the struggle between humans and tuberculosis continues. Mycobacterium tuberculosis is the le...

DMAMP: A Deep-Learning Model for Detecting Antimicrobial Peptides and Their Multi-Activities.

Due to the broad-spectrum and high-efficiency antibacterial activity, antimicrobial peptides (AMPs) and their functions have been studied in the field...

Utility of a Large Language Model for Extraction of Clinical Findings from Healthcare Data following Lung Ablation: A Feasibility Study.

To assess the feasibility of utilizing a large language model (LLM) in extracting clinically relevant information from healthcare data in patients who...

Integrating microbial profiling and machine learning for inference of drowning sites: a forensic investigation in the Northwest River.

Drowning incidents present significant challenges for forensic investigators in determining the exact site of occurrence. Traditional forensic methods...

Comparative study of DCNN and image processing based classification of chest X-rays for identification of COVID-19 patients using fine-tuning.

The conventional detection of COVID-19 by evaluating the CT scan images is tiresome, often experiences high inter-observer variability and uncertainty...