Background and Objectives: Thin-section CT (TSCT) is currently the most sensitive imaging modality for detecting bronchiectasis. However, conventional TSCT or HRCT may overlook subtle lung involvement such as alveolar and interstitial changes. Artificial Intelligence (AI)-based analysis offers the potential to identify novel information on lung parenchymal involvement that is not easily detectable with traditional imaging techniques. This study aimed to assess lung involvement in patients with bronchiectasis using the Bronchiectasis Radiologically Indexed CT Score (BRICS) and AI-based quantitative lung texture analysis software (IMBIO, Version 2.2.0). Methods: A cross-sectional study was conducted on 45 subjects diagnosed with bronchiectasis. The BRICS severity score was used to classify the severity of bronchiectasis into four categories: Mild, Moderate, Severe, and tractional bronchiectasis. Lung texture mapping using the IMBIO AI software tool was performed to identify abnormal lung textures, specifically focusing on detecting alveolar and interstitial involvement. Results: Based on the Bronchiectasis Radiologically Indexed CT Score (BRICS), the severity of bronchiectasis was classified as Mild in 4 (8.9%) participants, Moderate in 14 (31.1%), Severe in 11 (24.4%), and tractional in 16 (35.6%). AI-based lung texture analysis using IMBIO identified significant alveolar and interstitial abnormalities, offering insights beyond conventional HRCT findings. This study revealed trends in lung hyperlucency, ground-glass opacity, reticular changes, and honeycombing across severity levels, with advanced disease stages showing more pronounced structural and vascular alterations. Elevated pulmonary vascular volume (PVV) was noted in cases with higher BRICSs, suggesting increased vascular remodeling in severe and tractional types. Conclusions: AI-based lung texture analysis provides valuable insights into lung parenchymal involvement in bronchiectasis that may not be detectable through conventional HRCT. Identifying significant alveolar and interstitial abnormalities underscores the potential impact of AI on improving the understanding of disease pathology and disease progression, and guiding future therapeutic strategies.

Background: Ferroptosis, a regulated form of cell death characterized by iron-dependent lipid peroxidation, has been implicated in the pathogenesis of asthma. The ferroptosis markers PEBP1 and 15-LO-1 are increasingly recognized as potential biomarkers for asthma. This study investigates the association of these markers with asthma and its severity to evaluate their diagnostic potential. Methods: This cross-sectional study included 45 asthmatic patients and 45 healthy controls. Serum phosphatidylethanolamine-binding protein 1 (PEBP1) and 15-lipoxygenase-1 (15-LO-1) levels were measured using ELISA. Spirometric parameters (FEV₁, FEV₁/FVC ratio, and PEFR) were recorded. A multivariate regression assessed associations between ferroptosis markers and asthma severity. A generalized linear model (GLM) analyzed the relationship between biomarkers (PEBP1 and 15-LO-1) and lung function parameters. A receiver operating characteristic (ROC) analysis evaluated the discriminative capacity of PEBP1 and 15-LO-1. Results: PEBP1 and 15-LO-1 levels were significantly associated with asthma. The multivariate analysis revealed that low PEBP1 levels were strongly associated with asthma and severe asthma (p < 0.001). While elevated 15-LO-1 levels were associated with asthma (p < 0.001), they did not correlate with severity. The ROC analysis demonstrated excellent discriminative capacity for PEBP1 (AUC 0.962, cutoff 1509.8 pg/mL) and 15-LO-1 (AUC 0.895, cutoff 144.8 pg/mL). Lower PEBP1 and higher 15-LO-1 levels were associated with reduced lung function, and lower FEV₁, FEV₁/FVC, and PEF. Older age and female gender were associated with severe asthma. Conclusions: PEBP1 and 15-LO-1 are promising biomarkers for asthma, with PEBP1 showing strong correlations with asthma severity. These findings highlight the potential role of ferroptosis markers in asthma and underscore the need for further longitudinal studies to explore these markers’ clinical utility in personalized asthma management.