Pulmonology

Nonlinear meteorological controls on coastal air quality along the Indian East Coast: Insights from a multi-city interpretable machine learning framework.

This study investigates the role of meteorological variability in shaping ambient air quality across five coastal cities along the Indian East Coast (Haldia, Balasore, Visakhapatnam, Chennai, and Puducherry). Despite their shared coastal setting, the cities exhibit pronounced meteorological heterogeneity, leading to a clear latitudinal gradient in air pollution, with northern cities (Haldia and Ba...

Precision Management of Autoimmune Ocular Complications: Th17 Mechanisms and Therapeutic Innovations.

Precision management of ocular complications in systemic autoimmune diseases, such as Sjögren's syndrome (SS), systemic lupus erythematosus (SLE), Behçet's disease, and thyroid-associated ophthalmopathy (TAO), requires integration of immune mechanisms with clinical translation. These disorders manifest as dry eye, uveitis, retinal vasculitis, optic neuritis, and orbital fibroinflammatory disease, ...

RAS pathway activity subtypes identified by machine learning define prognostic and immune microenvironment characteristics in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD), the predominant histological subtype of non-small cell lung cancer, remains a leading cause of cancer-related ...

Deep learning and radiomics models in patients with advanced non-small cell lung cancer treated with immunotherapy combined with stereotactic radiotherapy.

Quantitative imaging is an emerging field that may allow prediction of oncological outcomes. We investigate whether radiomics and deep learning can pr...

Development and Validation of an Integrated Artificial Intelligence-Powered Clinical Decision Support Application for Cardiac Critical Care: Combining Coagulation Management, Blood Gas Analysis, Mechanical Ventilation, Hemodynamic Assessment, and Extracorporeal Membrane Oxygenation.

This study describes the development and preliminary validation of an integrated clinical decision support application (Cardiac Critical Care Advisor ...

Machine-learning-model based tool for screening bone metastases from lung cancer patients in primary care practice.

PURPOSE: This study aimed to use the machine-learning methods to predict bone metastasis (BM) in patients with lung cancer. METHODS: This study includ...

Machine-learning algorithms identifies sTREM1 has a key biomarker for outcome prediction in critically ill.

INTRODUCTION: Prognostic assessment in critically ill patients traditionally relies on severity scores or single biomarkers, each with limited ability...

Reassessing the Heterogeneous Effects of Greenspace on Air Pollution Conditioned on Seasons, Vegetation Types, and Street Structures.

Urban greenspace is generally assumed to mitigate air pollution. However, clear large-scale real-world evidence remains limited, particularly regardin...

Predicting antifolate resistance in the unculturable fungal pathogen Pneumocystis jirovecii.

Pneumocystis jirovecii is an opportunistic fungal pathogen responsible for Pneumocystis pneumonia (PCP) in immunocompromised patients. Antifolate drug...

Integrating causality with temporal fusion transformer for interpretable and probabilistic forecasting of riverine dissolved oxygen.

Accurate, interpretable, and uncertainty-aware riverine dissolved oxygen forecasting is essential for aquatic ecosystem management. Challenges include...

Interpretable machine learning model using peripheral blood for non-invasive detection of moderate-to-severe myelofibrosis in JAK2 V617F-positive MPNs: A multicentre pilot proof-of-concept study.

Progression to moderate-to-severe myelofibrosis (MF) in JAK2 V617F-positive myeloproliferative neoplasms (MPNs) is often clinically silent, and bone m...

The solute carrier transporter SLCO5A1 as a novel therapeutic target for the treatment of myocardial infarction-induced heart failure.

Heart failure (HF) following myocardial infarction (MI) remains a major threat to health worldwide. While transcriptomics has revealed numerous genes ...

The evolution of scientific knowledge in childhood asthma over time.

Paediatric asthma management has undergone a significant transformation from rudimentary assessments in the early 20th century to sophisticated diagno...

Current understanding and future directions in severe asthma through artificial intelligence-integrated multi-omic approaches.

Severe asthma remains a challenging, heterogeneous condition, despite significant advances in therapeutic strategies. A subset of patients continues t...

Artificial Intelligence in Kidney Transplant Pathology: Current Evidence, Limitations, and Relevance to the Banff Framework.

The Banff Classification, established in 1991, provides a global standard for diagnosing and grading kidney transplant pathology, evolving through reg...

Lessons from the first U.S. school districts with real-time indoor environmental quality (IEQ) sensors in classrooms: benefits, barriers and opportunities.

With aging buildings, rising temperatures, and limited budgets, school districts in the United States need effective strategies to address classroom e...

Multi-dimensional risk prediction and genetic architecture of aspiration pneumonia: a population-based analysis.

BACKGROUND: Aspiration pneumonia represents a significant but understudied cause of morbidity and mortality, particularly among aging populations with...

Integrative analysis and experimental validation of dioxin-interacting genes reveal diagnostic and prognostic biomarkers in lung adenocarcinoma.

Dioxins are persistent environmental pollutants and key components of the human exposome with established carcinogenic potential. As airborne toxicant...

Local control and dose selection for lung cancer brain metastases treated with radiosurgery: an artificial intelligence model.

Clinical and radiological outcomes after Stereotactic radiosurgery (SRS) for lung cancer brain metastases are heterogeneous, and prescription dose sel...

Semi-automatic individual tooth segmentation from CBCT images using anatomy-guided deep neural networks and watershed transform.

Individual tooth segmentation in cone beam computed tomography (CBCT) scan plays a crucial role in quantitatively analyzing oral diseases. However, th...