Identification of Severe Acute Exacerbations of Chronic Obstructive Pulmonary Disease Subgroups by Machine Learning Implementation in Electronic Health Records.

Rationale: Acute exacerbations of chronic obstructive pulmonary disease (AECOPDs) are heterogeneous. Machine learning (ML) has previously been used to dissect some of the heterogeneity in COPD. The widespread adoption of electronic health records (EHRs) has led to the rapid accumulation of large amounts of patient data as part of routine clinical care.

cGAS Expression is Enhanced in Systemic Sclerosis Associated Interstitial Lung Disease and Stimulates Inflammatory Myofibroblast Activation.

Objective: The lungs of patients with Systemic Sclerosis Associated Interstitial Lung Disease (SSc-ILD) contain inflammatory myofibroblasts arising in association with fibrotic stimuli and perturbed innate immunity. The innate immune DNA binding receptor Cyclic GMP-AMP synthase (cGAS) is implicated in inflammation and fibrosis, but its involvement in SSc-ILD remains unknown. We examined cGAS expression, activity, and therapeutic potential in SSc-ILD using cultured fibroblasts, precision cut lung slices (PCLS), and a well-accepted animal model.

SPLUNC1: a novel marker of cystic fibrosis exacerbations.

Background: Acute pulmonary exacerbations (AE) are episodes of clinical worsening in cystic fibrosis (CF), often precipitated by infection. Timely detection is critical to minimise morbidity and lung function declines associated with acute inflammation during AE. Based on our previous observations that airway protein short palate lung nasal epithelium clone 1 (SPLUNC1) is regulated by inflammatory signals, we investigated the use of SPLUNC1 fluctuations to diagnose and predict AE in CF.