Microplastics (MPs) have received a lot of attention and have been detected in multiple environmental matrices as a new environmental hazard, but studies on human internal exposure to MPs are limited. Here, we collected lung tissue samples from 12 nonsmoking patients to evaluate the characteristics of MPs in human lung tissues using an Agilent 8700 laser infrared imaging spectrometer and scanning electron microscopy. We detected 108 MPs covering 12 types in the lung tissue samples, with a median concentration of 2.19 particles/g. Most of the MPs (88.89%) were sized between 20 to 100 μm. Polypropylene accounts for 34.26% of the MPs in the lung tissues, followed by polyethylene terephthalate (21.30%) and polystyrene (8.33%). Compared with males and those living far from a major road (≥300 m), females and those living near the main road (<300 m) had higher levels of MPs in lung tissues, which positively correlated with platelet (PLT), thrombocytocrit, fibrinogen (FIB), and negatively related with direct bilirubin (DB). These findings help confirm the presence in the respiratory system and suggest the potential sources and health effects of inhaled MPs.

Background: Airway inflammation is the core pathological process of asthma, with the key inflammatory regulators incompletely defined. Recently, fibroblast growth factor 2 (FGF2) has been reported to be an inflammatory regulator; however, its role in asthma remains elusive. This study aimed to investigate the immunomodulatory role of FGF2 in asthma. Methods: First, FGF2 expression was characterised in clinical asthma samples and the house dust mite (HDM)-induced mouse chronic asthma model. Second, recombinant mouse FGF2 (rm-FGF2) protein was intranasally delivered to determine the effect of FGF2 on airway inflammatory cell infiltration. Third, human airway epithelium-derived A549 cells were stimulated with either HDM or recombinant human interleukin-1β (IL-1β) protein combined with or without recombinant human FGF2. IL-1β-induced IL-6 or IL-8 release levels were determined using enzyme-linked immunosorbent assay, and the involved signalling transduction was explored via Western blotting. Results: Compared with the control groups, the FGF2 protein levels were significantly upregulated in the bronchial epithelium and alveolar areas of clinical asthma samples (6.70 ± 1.79 vs. 16.32 ± 2.40, P = 0.0184; 11.20 ± 2.11 vs. 21.00 ± 3.00, P = 0.033, respectively) and HDM-induced asthmatic mouse lung lysates (1.00 ± 0.15 vs. 5.14 ± 0.42, P < 0.001). Moreover, FGF2 protein abundance was positively correlated with serum total and anti-HDM IgE levels in the HDM-induced chronic asthma model ( R ² = 0.857 and 0.783, P = 0.0008 and 0.0043, respectively). Elevated FGF2 protein was mainly expressed in asthmatic bronchial epithelium and alveolar areas and partly co-localised with infiltrated inflammatory cell populations in HDM-induced asthmatic mice. More importantly, intranasal instillation of rm-FGF2 aggravated airway inflammatory cell infiltration (2.45 ± 0.09 vs. 2.88 ± 0.14, P = 0.0288) and recruited more subepithelial neutrophils after HDM challenge [(110.20 ± 29.43) cells/mm ² vs. (238.10 ± 42.77) cells/mm ² , P = 0.0392] without affecting serum IgE levels and Th2 cytokine transcription. In A549 cells, FGF2 was upregulated through HDM stimulation and promoted IL-1β-induced IL-6 or IL-8 release levels (up to 1.41 ± 0.12- or 1.44 ± 0.14-fold change vs . IL-1β alone groups, P = 0.001 or 0.0344, respectively). The pro-inflammatory effect of FGF2 is likely mediated through the fibroblast growth factor receptor (FGFR)/mitogen-activated protein kinase (MAPK)/nuclear factor kappa B (NF-κB) pathway. Conclusion: Our findings suggest that FGF2 is a potential inflammatory modulator in asthma, which can be induced by HDM and acts through the FGFR/MAPK/NF-κB pathway in the airway epithelial cells.

Background: The incidence of early-stage lung adenocarcinoma (ES-LUAD) is steadily increasing among non-smokers. Previous research has identified dysbiosis in the gut microbiota of patients with lung cancer. However, the local microbial profile of non-smokers with ES-LUAD remains largely unknown. In this study, we systematically characterized the local microbial community and its associated features to enable early intervention. Methods: A prospective collection of ES-LUAD samples (46 cases) and their corresponding normal tissues adjacent to the tumor (41 cases), along with normal lung tissue samples adjacent to pulmonary bullae in patients with spontaneous pneumothorax (42 cases), were subjected to ultra-deep metagenomic sequencing, host transcriptomic sequencing, and proteomic sequencing. The obtained omics data were subjected to both individual and integrated analysis using Spearman correlation coefficients. Results: We concurrently detected the presence of bacteria, fungi, and viruses in the lung tissues. The microbial profile of ES-LUAD exhibited similarities to NAT but demonstrated significant differences from the healthy controls (HCs), characterized by an overall reduction in species diversity. Patients with ES-LUAD exhibited local microbial dysbiosis, suggesting the potential pathogenicity of certain microbial species. Through multi-omics correlations, intricate local crosstalk between the host and local microbial communities was observed. Additionally, we identified a significant positive correlation (rho > 0.6) between Methyloversatilis discipulorum and GOLM1 at both the transcriptional and protein levels using multi-omics data. This correlated axis may be associated with prognosis. Finally, a diagnostic model composed of six bacterial markers successfully achieved precise differentiation between patients with ES-LUAD and HCs. Conclusions: Our study depicts the microbial spectrum in patients with ES-LUAD and provides evidence of alterations in lung microbiota and their interplay with the host, enhancing comprehension of the pathogenic mechanisms that underlie ES-LUAD. The specific model incorporating lung microbiota can serve as a potential diagnostic tool for distinguishing between ES-LUAD and HCs.

Objective: This study developed a multiomics model combining radiomics, pathomics, and temporal imaging to predict major pathological response in patients with locally advanced non-small cell lung cancer (NSCLC) undergoing neoadjuvant immunochemotherapy. Methods: A retrospective, multicenter study was conducted, enrolling 271 patients with stage IB–III NSCLC who received neoadjuvant immunochemotherapy. High-resolution CT images were enhanced using a generative adversarial network-based super-resolution technique. Radiomics features were extracted from multi-sequence CT scans at multiple time points, while pathomics features were derived from whole-slide imaging of surgical specimens. A transformer-based attention mechanism was used to integrate radiomics, pathomics, and temporal imaging data. The model was trained and validated on data from one center and tested on external cohorts. Performance was evaluated using area under the curve (AUC), net reclassification improvement, integrated discrimination improvement, and decision curve analysis. Results: The Trans-Model demonstrated superior predictive performance, achieving an AUC of 0.858 (95% CI 0.783 to 0.933) in the external test cohort. It outperformed Rad-Model (AUC: 0.839) and Patho-Model (AUC: 0.753). The Trans-Model effectively stratified patients by survival outcomes, with major pathological response (MPR)-positive patients exhibiting significantly improved 3-year overall survival (87.3% vs 76.1%, p=0.034) and 5-year progression-free survival (45.8% vs 34.7%, p=0.033) compared with MPR-negative patients. Decision curve analysis confirmed the model’s clinical utility across a wide range of threshold probabilities. Conclusion: The multiomics model, integrating multi-temporal, multi-sequence data with attention-based feature fusion, improves MPR prediction in patients with NSCLC receiving neoadjuvant immunochemotherapy, enabling personalized treatment by identifying responders and optimizing outcomes.