Adherence to inhaled therapy remains a persistent challenge in asthma management and continues to compromise disease control in routine clinical practice. Although data from Greece are limited, international evidence indicates that a substantial proportion of patients do not use their inhalers consistently or as prescribed. Suboptimal adherence is associated with avoidable exacerbations, loss of lung function, increased healthcare utilization, and inappropriate escalation of therapy, including early introduction of biologic agents. This narrative review examines recent evidence on behavioral, treatment-related, and healthcare-system factors influencing adherence to inhaled therapy in asthma, with particular attention to the Greek healthcare environment. Findings from clinical trials, meta-analyses, and real-world studies published between 2018 and 2025 are synthesized to identify determinants of adherence, methods of assessment, and strategies that support sustained treatment engagement. Across studies, adherence rates typically range between 30% and 60%, with major barriers including limited understanding of asthma as a chronic condition, concerns about inhaled corticosteroids, complex dosing regimens, and persistent inhaler technique errors. These challenges are often compounded by system-level constraints such as brief consultations and fragmented follow-up. Simplified treatment strategies, particularly once-daily inhaled corticosteroid/long-acting β₂-agonist regimens and single maintenance and reliever therapy approaches, are consistently associated with more regular medication use. Pharmacist-led interventions improve inhaler technique and daily adherence, while digital inhaler technologies support early identification of non-adherence, although their implementation in Greece remains limited. Improving adherence requires coordinated, patient-centered care that integrates behavioral support, appropriate inhaler selection, simplified regimens, and targeted use of digital monitoring within existing healthcare pathways.

Microvesicles (MVs) are extracellular vesicles released from many cell types under physiological and pathological conditions, influencing viral transmission, immune regulation, and inflammation. This exploratory pilot study characterized and compared plasma MV profiles in patients infected with human immunodeficiency virus (HIV), hepatitis B virus (HBV), and hepatitis C virus (HCV). Plasma samples (n = 125; HIV: 25, HBV: 50, HCV: 50) were analyzed using nanoparticle tracking analysis (NanoSight NS300) to assess MV size and concentration, classifying them as small (300 nm). Patients with HBV exhibited significantly larger mean MV size compared with both patients with HIV (131.5 ± 14.6 nm vs. 113.1 ± 14.0 nm, p < 0.0001) and HCV (131.5 ± 14.6 nm vs. 118.0 ± 18.5 nm, p = 0.0002). HCV infection showed higher concentrations of large MVs than HIV (p = 0.0022), while total and small MV levels did not differ. No sex-related differences were detected. Distinct MV size distributions appear linked to chronic viral infections, with HBV and HCV showing greater alterations than HIV. MVs may serve as potential biomarkers reflecting infection-associated biological processes; however, mechanistic, or functional roles were not assessed in this study and will require dedicated future investigations in larger controlled studies.

Background: COPD remains a leading cause of global morbidity and mortality, with acute exacerbations driving disease progression and healthcare utilisation. Artificial intelligence (AI) offers new opportunities to predict exacerbation risk by integrating multimodal data such as electronic health records (EHRs), spirometry and wearable sensor inputs. Methods: This systematic review, conducted in accordance with PRISMA 2020 guidelines and registered in PROSPERO (CRD420251165476), evaluated AI-based models developed for COPD exacerbation prediction using combined data modalities. Results: Comprehensive searches of PubMed, Embase and Google Scholar identified 859 records, of which five studies published between 2021 and 2025 met inclusion criteria. Study designs ranged from prospective monitoring cohorts to EHR-based and hybrid datasets. Models applied diverse approaches including random forests, gradient boosting, convolutional neural networks and ensemble learning frameworks. Reported discriminative performance was moderate to high, with area under the curve values between 0.73 and 0.92 and accuracies up to 0.92. Most of these performance metrics were derived from internal validation, with limited external testing, which restricts assumptions about generalisability. Sensitivity reached 0.94 in wearable-driven models, while only one study reported formal calibration assessment. Conclusions: Despite encouraging performance, methodological heterogeneity, limited external validation and incomplete reporting of preprocessing and explainability methods restrict clinical translation. Current evidence supports the potential of multimodal AI to enhance early detection of COPD exacerbations, but future research must prioritise transparent reporting, external validation and integration into real-world care pathways.

Background: Chronic obstructive pulmonary disease (COPD) is a major cause of hospital admission, particularly in older adults, yet scalable approaches to quantify and predict its burden using routine data are limited. Aims: To assess whether age-stratified COPD admissions can be predicted using national hospital data and to identify demographic and clinical proxies of high-risk burden. Methods: Hospital Admitted Patient Care Activity data from England (2020-2025) were analysed. COPD admissions (ICD-10: J44.0-J44.9) were stratified by age, sex and admission type. A high-risk index was defined as the mean proportion aged ≥65 years and emergency admission share. Machine learning models were trained to predict age-specific admissions, with the most recent year used for testing. Results: COPD admissions were predominantly emergency (>98%) and concentrated in older populations (74-79% aged ≥65 years). Random Forest showed high predictive performance (R²=0.97), outperforming linear regression (R²=0.73). Higher burden was observed among females across most subtypes. erjor;12/suppl_19/51/F1 F1 F1 erjor;12/suppl_19/51/F2 F2 F2 Conclusions: COPD hospital burden can be predicted using simple demographic and administrative data. Age structure and emergency admissions drive risk, supporting scalable health plans.

Background: Accurate interpretation of hepatitis B virus (HBV) polymerase sequences is essential for identifying antiviral resistance, particularly for high-genetic-barrier agents such as entecavir. Current resistance interpretation relies largely on deterministic rule-based systems that do not quantify uncertainty and are difficult to evaluate across independent datasets. We aimed to develop and externally validate a transparent probabilistic framework for reconstructing a predefined entecavir resistance pathway from HBV polymerase sequences. Methods: HBV polymerase sequences were retrieved from the NCBI GenBank database and curated through translation, quality control, and deduplication to create the development dataset. Reverse transcriptase (RT) positions were indexed using motif-anchored numbering based on the YMDD-family motif. A genotypic proxy for the entecavir resistance pathway was defined by lamivudine-associated background substitutions combined with entecavir-associated RT substitutions. A logistic regression model with probability calibration was trained and internally validated using prespecified performance metrics and thresholds. External validation was performed on an independent HBVdb dataset with preprocessing, model parameters, and thresholds frozen prior to evaluation. Results: The development dataset comprised 1174 unique polymerase sequences, of which 268 met the resistance pathway definition. Internal validation demonstrated perfect discrimination, consistent with the deterministic genotypic definition of the outcome. External validation on 11,513 independent HBVdb sequences demonstrated reproducible performance across repositories despite a markedly lower prevalence of the resistance pathway (2.2%), with preserved discrimination and stable threshold-based performance. Conclusions: This study presents a transparent and externally validated machine learning framework for probabilistic identification of the entecavir resistance pathway in HBV. The approach provides a transparent and reproducible probabilistic formalization of an established genotypic resistance definition and may serve as a methodological framework for standardized sequence-based resistance interpretation.

Background: Respiratory allergic diseases are highly prevalent and often underdiagnosed, requiring diagnostic approaches that reflect clinically relevant allergic responses. Conventional methods, including skin prick tests (SPTs), serum-specific immunoglobulin E (sIgE), and component-resolved diagnostics (CRD), primarily assess sensitization and may not consistently correlate with clinical reactivity. Methods: Eighty participants (50 patients with respiratory allergy and 30 controls) underwent SPTs, sIgE, CRD, nasal allergen provocation testing (NAPT), and conjunctival allergen provocation testing (CAPT). Diagnostic indices were calculated. Multivariable logistic regression identified predictors of positive provocation tests. Model performance was assessed using receiver operating characteristic (ROC) curves with bootstrap-derived confidence intervals, calibration analysis (locally weighted scatterplot smoothing), and decision curve analysis (DCA). Results: CAPT demonstrated higher sensitivity than NAPT across all reference standards (91.3-94.9% vs 75.0-86.1%). In contrast, NAPT showed superior discriminative performance, diagnostic accuracy, and model calibration. Polysensitization and asthma were the strongest predictors of positive NAPT responses. ROC analysis showed good discrimination, particularly for Olea europaea (AUC 0.875). DCA indicated greater net clinical benefit for the NAPT-based model across relevant threshold probabilities. Agreement analysis suggested that provocation tests capture aspects of clinical reactivity not fully reflected by sensitization-based diagnostics. Conclusions: CAPT demonstrates high sensitivity, whereas NAPT provides greater clinical discrimination and decision-making value, particularly in complex allergic phenotypes. NAPT may offer clinically actionable information in patients with discordant or polysensitized profiles, supporting allergen selection and optimization of immunotherapy strategies.