Colorectal cancer (CRC) is one of the most lethal malignancies worldwide, and the precise identification of biomarkers from colonic adenoma to cancer is of great significance for preventing the development of adenocarcinoma. Given that existing methods inadequately capture the topological network relationships among genes, this study proposes a graph neural network model based on multifeature learning, named ChebTs, to investigate the correlation between key genes involved in the colorectal "adenoma-cancer" transition. The GSE41657 and GSE31905 datasets from the GEO database were stratified into normal, adenoma, and colorectal cancer groups. Feature encoding was introduced to enhance node features, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of differentially expressed genes (DEGs). A protein-protein interaction (PPI) network was constructed using Cytoscape software, and the aggregated information was embedded into the model for training to generate a list of key genes with corresponding importance scores. An attention pooling mechanism aggregated node-level representations into graph level representations for sample classification, and a two layer fully connected network, following activation and regularization, produced predicted probabilities. Furthermore, we provided interpretable analyses at the gene level using GNNExplainer. The results were validated through virtual knockout techniques. The eight screened genes, Fibronectin 1 (FN1), Claudin 2 (CLDN2), Interleukin-33 (IL-33), Matrix Metallopeptidase 1 (MMP1), Stanniocalcin 2 (STC2), Insulin-Like Growth Factor-Binding Protein 7 (IGFBP7), NADPH Oxidase 4 (NOX4), and Secreted Frizzled Related Protein 1 (SFRP1), were all found to be associated with overall survival (OS) in CRC. In this study, eight molecules closely related to the development of colorectal adenocarcinoma were screened out, and they may be diagnostic biomarkers of colorectal cancer. These genes affect the prognosis of patients by participating in biological processes such as remodeling of extracellular mechanisms, and are of great significance for preventing the carcinogenesis of adenoma.

Pulmonary hypertension (PH) is a progressive vasculopathy characterized by pulmonary vascular remodeling, right ventricular maladaptation, and premature death. Exosomes, an endosome-derived subset of small extracellular vesicles, mediate intercellular communication through the transfer of selected RNAs, proteins, and lipids. In PH, they can modulate endothelial dysfunction, pulmonary artery smooth muscle cell proliferation and phenotypic switching, immune dysregulation, and right ventricular remodeling, implicating them in key aspects of disease pathobiology. These properties have prompted interest in exosomes as minimally invasive biomarkers and as candidates for cell-free therapeutic delivery. However, clinical translation remains at an early stage. Most evidence supporting exosome-based therapeutic strategies in PH is preclinical, whereas current clinical data are largely limited to exploratory biomarker studies. Important gaps also remain in distinguishing exosomes from other extracellular vesicle populations and in addressing methodological standardization, heterogeneity, cargo attribution, biodistribution, dosing, and clinical validation. This review summarizes current evidence on exosome biology in PH and discusses both translational opportunities and the major barriers to clinical implementation.

Chronic obstructive pulmonary disease (COPD) is increasingly recognized as a systemic inflammatory syndrome in which forced expiratory volume in 1 s expressed as a percentage of predicted value (FEV1% predicted) may dissociate from patient-reported symptom burden. This study evaluated an Integrated Inflammatory Profile Score (IIPS) as a potential biological correlate for a more holistic assessment of stable COPD.

Serum concentrations of 11 cytokines were measured in 62 patients with stable COPD. The IIPS was derived using automated linear modeling with forward stepwise selection guided by the corrected Akaike information criterion (AICc). Associations of the IIPS with COPD Assessment Test (CAT) scores were compared with those of FEV1% predicted.

The optimized IIPS, comprising interferon-gamma (IFN-γ), interleukin-2 (IL-2), and interleukin-10 (IL-10), was significantly associated with FEV1% predicted (R² = 0.1867, p = 0.0005) and outperformed the individual cytokines in discriminating spirometric severity (area under the receiver operating characteristic curve (AUC) = 0.7381). For symptom burden, FEV1% predicted showed greater explanatory power for exertional domains, particularly breathlessness (R² = 0.0832) and activities (R² = 0.1280). By contrast, the IIPS showed numerically stronger trend-level associations for sleep (R² = 0.0519, p = 0.0750) and energy (R² = 0.0511, p = 0.0773), although these associations did not reach conventional statistical significance.

The IIPS reflects Global Initiative for Chronic Obstructive Lung Disease (GOLD)-defined physiological severity more effectively than any single cytokine measured in this study. Its associations with patient-reported systemic symptoms were modest and exploratory, suggesting that integrated inflammatory profiling may complement spirometry in characterizing COPD burden but requires external and longitudinal validation before clinical application.

The "DescribinG bUrden of COPD and occurrence of mortaLity in a cohort of Italian Patients" (GULP) study was conducted to investigate the potential of biological treatments for patients with severe chronic obstructive pulmonary disease (COPD). The purpose of this article was to perform a narrative review highlighting the role of type 2 inflammation in the mechanism of disease for COPD, summarizing the efficacy data of the Phase III RCTs that investigated biologics in COPD, and exploring the potential role for biological treatment in this setting with a particular focus on Italian patients with COPD. To this end, we conducted an exploratory analysis of GULP study data to identify patients at high risk of exacerbations, with high symoptom burden and classify them based upon the eosinophilic counts, to provide further insights into the potential use of biologics in this cohort. Out of the 1111 patients of the GULP cohort, we retrieved in total 286 (25.7%) GULP participants with ≥2 moderate or ≥1 severe exacerbation per year. Of them, despite maintenance therapy with dual bronchodilators or triple therapy, a high symptom burden (dyspnea, cough and chronic sputum production) was present in between 51.1 and 74.8%, while patients that presented with eosinophilic COPD accounted for the 7.2% of the patients at risk. This indicates that biological agents, specifically those that target the type 2 inflammatory response in COPD, should be considered in symptomatic patients with COPD and unstable disease, but that careful and pro-active phenotyping through clinical and biomarker evaluation is mandatory. Dupilumab is currently approved in Italy for eosinophilc exacerbating COPD patients, and through its favorable efficacy and safety profile, could represent a valid opportunity for these patients.

The value of exercise cardiovascular magnetic resonance (CMR) has been shown in many clinical scenarios. We have developed a novel MR-compatible exercise device and aim to validate it against the reference standard MR-conventional ergometer. The novel device consisted of two half-pipes fixed to a wooden base, with participants wearing knee-length socks with a 0.5 kg weight in each sock. Increased workload was achieved by increasing the rate of alternating leg flexion and extension in time with a bleep sound of increasing frequency. Twenty subjects (10 healthy volunteers, 10 patients with pulmonary arterial hypertension) performed two CMR-augmented cardiopulmonary exercise tests (CMR/CPET) using the novel exercise device and a conventional ergometer in a randomised order. Comparing peak metrics elicited on both exercise devices, there was a moderate correlation in peak oxygen consumption (VO₂, r = 0.86, p < 0.001), cardiac output (CO, r = 0.73, p < 0.001), stroke volume (SV, r = 0.90, p < 0.001), peak heart rate (HR, r = 0.65, p = 0.002) and peak arteriovenous oxygen content gradient (ΔavO₂, r = 0.71, p < 0.001). However, all metrics were significantly lower with the novel device. Both devices were able to elicit statistically significant differences in VO₂, HR and right ventricular ejection fraction (RVEF) between patients and healthy subjects (p ≤ 0.04). We have created a simple, easy to use and affordable exercise device for the CMR environment. This may encourage greater dissemination of exercise CMR in clinical and research practice.

The COVID-19 pandemic dramatically reduced early-life respiratory viral exposures, yet the long-term respiratory consequences for pandemic-born children remain unclear. We aimed to compare respiratory morbidity at age 4 between children born during versus before the pandemic and assess whether atopic risk modifies this association. This single-center birth cohort follow-up study included children born in February-March 2020 (COVID-19 cohort) and pre-pandemic controls born during the same period in 2019. Respiratory outcomes at age 4 were assessed using a structured telephone questionnaire (The International Study of Wheezing in Infants). The primary outcome was composite respiratory morbidity (CRM), defined as use of a short-acting bronchodilator (SABA), inhaled and/or oral corticosteroids, and/or emergency room visits for respiratory symptoms. Of 588 eligible children, 329 (55.9%) completed follow-up (139 COVID-19, 190 controls); baseline characteristics, including atopy prevalence, were comparable between cohorts. CRM during the fourth year of life was significantly more common in the COVID-19 cohort (39.6% vs. 27.4%, p = 0.02). In stratified analyses, CRM was markedly higher among atopic-risk pandemic-born children (atopic subgroup: n = 47 COVID-19, n = 68 controls) (66% vs. 27.9%, p < 0.001), but not among non-atopic children (26.1% vs. 27%). Multivariable logistic regression confirmed a significant COVID-19 cohort × atopy interaction for CRM (OR 5.04, 95% CI 1.83-13.89, p = 0.002), while neither factor alone was independently associated with the outcome.

 Birth during the COVID-19 pandemic was associated with increased preschool respiratory morbidity, particularly among children with atopic risk. These findings suggest that altered early-life microbial exposures may modify respiratory morbidity trajectories according to host atopic susceptibility.

• Children born during the COVID-19 pandemic had fewer respiratory infections and wheezing episodes in infancy due to reduced viral circulation. • Atopic predisposition is among the strongest risk factors for recurrent wheezing and persistent asthma in preschool children.

• Pandemic-born children show significantly higher composite respiratory morbidity at age 4, but exclusively among those with atopic risk (OR 5.04). • Neither pandemic birth nor atopy alone predicts excess morbidity-only their combination does, suggesting host susceptibility shapes long-term respiratory trajectories.

Influenza, a prevalent disease, significantly threatens public health. Accurately predicting severe influenza occurrences is crucial for developing personalized prevention strategies and treatment plans.

This study aimed to construct a highly interpretable model to assess the risk of severe influenza in hospitalized children, using the SHapley Additive exPlanation (SHAP) method to interpret the Random Forest (RF) model and identify risk factors for severe influenza.

A retrospective cohort study was conducted, collecting hospitalization records of influenza patients from the Electronic Medical Records (EMR) system for model development and performance evaluation. Data were collected within the first 24 h after patient admission and the dataset was randomly divided into two parts: 70% for model training and 30% for model accuracy verification.

A total of 436 eligible influenza patients were included in the study analysis. The RF model demonstrated the highest predictive accuracy among the six models compared, with an area under the receiver-operating-characteristiccurve(AUROC) of 0.88. The decision curve analysis (DCA) indicated that the net benefit of the RF model surpassed that of other machine learning models. According to the importance ranking by the SHAP method, blood glucose was identified as the most significant predictive variable.

The study established a RF model capable of predicting severe influenza occurrence in hospitalized children. The model, interpreted through the SHAP method, assists physicians in providing better treatment plans.

Alveolar macrophages (AMs), as primary immune sentinels in the respiratory tract, are uniquely adapted to tissue-specific demands shaped by local factors and the physiological environment, maintaining airway homeostasis through the clearance of surfactants and particulates. While AMs exhibit remarkable plasticity shaped by dynamic microenvironmental cues, the traditional M1/M2 paradigm fails to adequately capture this complex heterogeneity, and the specific role of AMs in the pathogenesis of pulmonary diseases, as well as AM-centric therapies, remain to be fully elucidated. This review systematically synthesizes the current understanding of AM ontogeny, homeostatic regulation, and their signaling pathways in lung homeostasis and pathologies. Furthermore, we propose a refined conceptual framework categorizing AMs into five distinct functional subtypes to better capture their in vivo plasticity. Then, pathological shifts in the lung microenvironment that drive the pathogenesis of diverse pulmonary disorders, including asthma, chronic obstructive pulmonary disease, acute lung injury, and pulmonary fibrosis, and trigger dysregulated transcriptional and metabolic reprogramming in AMs, are comprehensively discussed. Finally, we explore emerging therapeutic avenues, including metabolic modulation, trained immunity, and autophagy-apoptosis balance restoration. By addressing challenges in precisely regulating AM function, this overview provides a concise yet comprehensive framework for advancing AM-centric therapies in respiratory medicine.

Endovascular therapy is the standard of care for eligible patients with acute ischemic stroke due to LVO. However, nearly half of patients remain functionally dependent even after successful reperfusion. Albumin is a biologically plausible adjunctive cerebroprotective therapy and has shown neuroprotective effects in preclinical studies, but the clinical benefit of albumin combined with EVT remains uncertain.

Albumin for patients with acute large vessel occlusive stroke undergoing endovascular therapy-2 (ARISE-2) is an investigator-initiated, prospective, multicenter, randomised, open-label, controlled trial with blinded endpoint assessment (PROBE design). Eligible participants will be randomly assigned in a 1:1 ratio to receive either EVT plus intravenous 25% albumin at 0.5 g/kg (maximum dose, 37.5 g) initiated within 60 min after randomisation and then repeated once daily on days 2-4, or EVT alone. The initial target sample size is 1192 participants (596 per group). A prespecified adaptive sample size re-estimation will be performed after approximately 50% of participants have completed the 90-day follow-up, allowing the total sample size to be increased, but not reduced, to a maximum of 1788 participants. This trial aims to evaluate the efficacy and safety of albumin administered in combination with EVT in patients with acute ischemic stroke caused by anterior circulation LVO.

The primary outcome is a favourable functional outcome at 90 days, defined as a mRS score of 0-2, or return to the premorbid mRS for participants with a prestroke mRS score of 3-4.

The ARISE-2 trial is designed to evaluate the efficacy and safety of moderate-dose albumin combined with EVT in participants with acute ischemic stroke caused by anterior circulation LVO. The trial will provide evidence on whether albumin can improve functional outcomes as an adjunctive cerebroprotective strategy in patients undergoing reperfusion therapy.

ClinicalTrials.gov: NCT07263308.

Antithrombotic therapy is central to stroke prevention after spontaneous cervical artery dissection (sCAD), but its efficacy may depend on the underlying vascular morphology. We examined the temporal course of vascular changes after sCAD.

Consecutive patients with first-time sCAD underwent prospective serial duplex sonography for up to 10 years. In this retrospective cohort analysis, pathology change was defined as recanalisation of an occluded artery or progression from stenosis to occlusion. New pathology resulting from dissection recurrence was considered if an occlusion occurred in the presence of pre-existing stenosis or vice versa.

In total, 159 patients (83 men; mean age 44.8 years) with first-time sCAD were followed-up starting 1.3 days (median) after symptom onset. Bilateral dissections occurred in 6 patients and recurrent dissections in 26 arteries, resulting in follow-up of 191 arteries (108 internal carotid, 83 vertebral). Recanalisation occurred in 40/80 occluded arteries (cumulative rate 70.4%), whereas progression to occlusion occurred in 9/109 stenosed arteries (10.0%). All changes except one (recanalisation) occurred within 4 months. Recurrent dissection occurred within 6 weeks in 20 patients (14.7%) and between 2 and 7 years in 7 patients (13.7%). Overall, 62 vascular changes or new pathologies were observed in 50 patients (31.4%), with 42 occurring within the first 6 weeks.

Under intensive surveillance, vessel morphology after sCAD is highly dynamic during the early phase, with frequent recanalisation and recurrent dissection. Structured early vascular follow-up may be important if evolving vessel morphology is to guide future individualised antithrombotic treatment strategies.