Background/Objectives: Obstructive sleep apnea (OSA) is a common disorder characterized by intermittent hypoxia and sleep fragmentation, assessed using the Apnea–Hypopnea Index (AHI). Systemic inflammation is central to OSA progression, and the systemic inflammatory response index (SIRI) has emerged as a potential biomarker for inflammatory diseases. This study investigates the relationship between SIRI and OSA severity while comparing other inflammatory markers. Methods: A retrospective study was conducted among 150 OSA patients at a tertiary care hospital. Based on AHI, patients were categorized into mild, moderate, and severe OSA groups. Blood parameters, including neutrophil, monocyte, and lymphocyte counts, were analyzed, and inflammatory indices (SIRI, NLR, PLR) were calculated. Correlation, ROCs, and regression analyses assessed associations between inflammatory markers and OSA severity. Results: SIRI demonstrated an excellent predictive ability for severe OSA with an AUC of 0.960 (cut-off: 1.105; sensitivity: 92.2%; specificity: 91.4%). The STOP-BANG score alone had lower discriminatory power (AUC: 0.737), but combining it with SIRI improved accuracy (AUC: 0.983). The best performance was observed when SIRI, STOP-BANG, PLR, and CRP were combined, yielding an AUC of 1.00, indicating perfect discrimination. Conclusions: SIRI shows strong predictive value for identifying severe OSA, underscoring its utility as a simple, cost-effective biomarker to aid early recognition and referral, particularly in primary care and resource-limited settings.

Background: Chronic Obstructive Pulmonary Disease (COPD) remains a leading cause of morbidity and mortality worldwide, particularly in low- and middle-income countries (LMICs), where both tobacco and biomass smoke exposure are major risk factors. While spirometry is the diagnostic gold standard, reliable non-invasive biomarkers are needed for early detection and disease monitoring. The soluble receptor for advanced glycation end-products (sRAGE), a circulating decoy receptor with anti-inflammatory activity, has shown potential in this context. Methods: In this prospective, exposure-stratified, cross-sectional study, 150 adults were enrolled into four groups of 25 each—tobacco-smoke COPD, male tobacco-exposed controls, biomass-smoke COPD, and female biomass-exposed controls—along with 50 healthy controls (25 males, 25 females). Participants underwent clinical evaluation, spirometry, and serum sRAGE quantification (ELISA). Systemic inflammation was assessed using neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR). Correlation and receiver operating characteristic (ROC) analyses determined diagnostic performance. Results: Serum sRAGE levels were significantly lower in tobacco-induced (545 ng/mL) and biomass-induced COPD (540 ng/mL) versus controls (1207–1462 ng/mL; p < 0.001). sRAGE correlated positively with FEV₁, FVC, and FEV₁/FVC (r = 0.54–0.75, p < 0.001), and negatively with CAT, mMRC, and SGRQ-C. ROC analysis showed excellent discrimination (AUC = 0.990; 94% sensitivity; 96% specificity at 946 ng/mL cutoff). Conclusions: Serum sRAGE is a robust, non-invasive biomarker for COPD diagnosis and severity assessment across exposure phenotypes. Its integration into clinical practice may enhance early detection and risk stratification, particularly in LMICs.

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.