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Beyond structural and chemical barriers to pathogens, the immune system has two fundamental lines of defense: innate immunity and adaptive immunity. Innate immunity is the first immunological mechanism for fighting against an intruding pathogen. It is a rapid immune response, initiated within minutes or hours after aggression, that has no immunologic memory. Adaptive immunity, on the other hand, is antigen-dependent and antigen-specific; it has the capacity for memory, which enables the host to mount a more rapid and efficient immune response upon subsequent exposure to the antigen. There is a great deal of synergy between the adaptive immune system and its innate counterpart, and defects in either system can provoke illness or disease, such as inappropriate inflammation, autoimmune diseases, immunodeficiency disorders and hypersensitivity reactions. This article provides a practical overview of innate and adaptive immunity, and describes how these host defense mechanisms are involved in both heath and illness.

Background Chronic obstructive pulmonary disease (COPD) is characterized by progressive lung function decline, commonly measured by forced expiratory volume in one second (FEV 1 ). Uncovering the genetic basis of FEV 1 decline is essential for understanding COPD pathophysiology and for developing therapies. We hypothesized that gene expression patterns in inflammatory pathways are associated with FEV 1 decline. Methods We analyzed whole blood RNA-sequencing data from the 5 ( n  = 4,147) and 10 year visits ( n  = 435) in the COPDGene Study. Gene expression was assessed in three analyses: cross-sectional associations with FEV 1 at two separate time points, association between year 5 gene expression and FEV 1 changes from year 5–10, and longitudinal changes in both gene expression and FEV 1 . A gene signature derived from the 5-year visit was linked to FEV 1 decline across three intervals (baseline to 5 years, 5 to 10 years, and baseline to 10 years) and tested for validation in the ECLIPSE study. Results Distinct gene sets emerged in the three analyses (Cross-sectional: 961 genes; FEV 1 Change: 179; Longitudinal: 532). Only two genes ( NOV and AC009404.2 ) overlapped across all analyses, while unique genes (e.g., MMP9 , IL1RL1 , and CHI3L1 ) were context-specific. Pathway analysis of genes from the longitudinal analysis highlighted oxidative stress and immune processes. A 20-gene signature was derived, including 17 genes positively and three negatively associated with FEV 1 . These signatures were significantly associated with FEV 1 -related traits in COPDGene and ECLIPSE. Conclusions These findings reveal molecular markers of FEV 1 decline, offering insights into COPD pathophysiology and potential therapeutic targets.

Background Chronic obstructive pulmonary disease (COPD) remains an underestimated and underdiagnosed condition due to low disease awareness. Generative Artificial Intelligence (AI) chatbots are convenient and accessible sources of medical information, but evaluation of the quality of answers provided by patient-generated questions about COPD has not been performed to date. Objective To assess and compare accuracy, comprehensiveness, understandability and reliability of different AI chatbots in response to patient-generated questions on the clinical management of COPD. Methods A cross-sectional study was conducted in collaboration with the European Respiratory Society (ERS), the European Lung Foundation (ELF), and the ERS CONNECT Clinical Research Collaboration (CRC). Fifteen real questions formulated by ELF COPD patient representatives were divided into three difficulty tiers (easy, medium, difficult) and submitted to ChatGPT (version 3.5), Bard, and Copilot. Experts assessed accuracy and comprehensiveness on a 0–10 scale; patients assessed understandability using the same scale. Reliability was assessed by two investigators. Reviewers were blinded to which AI system generated the answers, and only those who completed all evaluations were included in the analysis. Results ChatGPT responses were the most reliable (14/15), followed by Copilot (12/15) and Bard (11/15). ChatGPT scored higher for accuracy (8.0 [7.0 – 9.0]) and comprehensiveness (8.0 [6.8 – 9.0]) than Bard (6.0 [5.0 – 8.0] and 6.0 [5.0 – 7.0]) and Copilot (6.0 [5.0 – 7.3] and 6.0 [5.0 – 8.0]) (both P  < 0.001). Understandability was similar across all software (ChatGPT: 8.0 [8.0–10.0]; Bard: 9.0 [8.0–10.0]; Copilot: 9.0 [8.0–10.0]) ( P  = 0.53). No significant effect was detected according to the difficulty of the question. Conclusion Our findings suggest that AI chatbots, particularly ChatGPT, can provide accurate, comprehensive and understandable answers to patients’ questions.

Background Flexible bronchoscopy (FB) is recommended for pediatric Mycoplasma pneumoniae pneumonia (MPP) with persistent consolidation or atelectasis, though substantial heterogeneity in treatment effects exists. This study aimed to develop a causal forest-based predictive model to identify pediatric MPP patients most likely to benefit from FB. Methods This retrospective two-center study enrolled pediatric MPP patients in derivation ( n  = 753) and validation ( n  = 139) cohorts. Clinical, laboratory, and AI-quantified computed tomography (CT) data were analyzed. Individual treatment effects (ITEs) were estimated using causal forest algorithms. FB-beneficial subgroups were defined using receiver operating characteristic (ROC) analysis of ITEs, with the varying treatment effect across the subgroups validated via multivariable linear regression. Subgroup characteristics, feature importance, and heatmap-based feature interactions were also analyzed. Results FB treatment significantly reduced total fever duration in identified FB-beneficial subgroups in both derivation (β = − 1.16, p  < 0.001) and validation (β = − 0.68, p  = 0.04) cohorts. These beneficial subgroups exhibited significantly higher consolidation/atelectasis volume (CAV), pneumonia attenuation (PA), and consolidation-to-pneumonia ratio (CAR) compared to non-beneficial groups (all p  < 0.001). Heatmap analyses confirmed that increased CAV combined with elevated PA or lymphocyte counts could improve FB efficacy. Conclusions This study developed and validated an individualized prediction model to identify pediatric MPP patients most likely to benefit from FB treatment. Our model may serve as a tool to support clinicians in optimizing FB utilization, potentially reducing unnecessary interventions and associated risks. An accessible online tool of this model facilitates practical clinical implementation.