Guimin Zhang

Objective: This study aims to compare image feature identification and diagnostic performance between artificial intelligence‐aided image‐enhanced endoscopy (using the Storz Professional Image Enhancement System, SPIES) and conventional white‐light endoscopy (WLE) for sinonasal inverted papilloma (SIP). Methods: A total of 163 patients were investigated using Storz Professional Image Enhancement System (SPIES) endoscopy (SPIES Spectral A[SA]/Spectral B[SB]) and WLE. From each patient, one or two images (only in the presence of bilateral lesions) were obtained, and a large number of potential features were extracted from WLE, SA, and SB. Independent t‐tests and LASSO methods were used to feature selection. Machine learning (ML) models were trained and evaluated for each channel of images. Results: The Random Forest (RF) algorithm on SPIES SB mode achieved superior performance: accuracy = 0.86, sensitivity = 0.89, specificity = 0.83, AUC = 0.86, and Kappa = 0.71. SB significantly outperformed SA (accuracy = 0.77) and WLE (accuracy=0.75). ROC analysis confirmed SB's diagnostic superiority (area under curve [AUC]: SB = 0.86 vs. SA = 0.77, WLE = 0.75). Conclusions: SPIES SB mode demonstrated the highest diagnostic accuracy and AUC for SIP identification, highlighting its potential as a superior imaging modality. SPIES endoscopy exhibits superior diagnostic accuracy and predictive performance compared to conventional WLE in the diagnosis of SIP. However, the single‐center design and limited sample size necessitate validation through multicenter studies with larger cohorts.

Allergic rhinitis (AR) is a common inflammatory disorder characterized by dysregulated immune responses. Exosomes, as extracellular vesicles carrying diverse functional RNAs, play essential roles in intercellular communication. However, the profiles and regulatory roles of circulating exosomal RNAs in AR remain largely unknown. This study aimed to characterize serum exosomal RNAs in AR patients and explore their potential association in immune regulation. Serum-derived exosomes from AR patients and healthy controls were analyzed using small RNA and long non-coding RNA sequencing. Four exosomal microRNAs-hsa-miR-186-5p, hsa-miR-184, hsa-miR-454-3p, and hsa-miR-4446-3p-were significantly downregulated in AR, and their differential expression was further validated by RT-qPCR. To explore their potential downstream effects, integrated analysis of public mRNA transcriptomic datasets and miRNA target prediction identified CADM1 as a putative target of hsa-miR-186-5p. Single-cell RNA sequencing analysis further revealed that CADM1 was highly expressed specifically in plasma cells from patients with seasonal allergic rhinitis. Functional enrichment analysis demonstrated that plasma cells with high CADM1 expression showed enrichment in immune-related signaling pathways, suggesting a possible link between the exosomal hsa-miR-186-5p-CADM1 axis and plasma cell function in AR. These findings suggest that the exosomal hsa-miR-186-5p-CADM1 axis may be associated with plasma cell-mediated immune responses in allergic rhinitis. Circulating exosomal microRNAs may serve as non-invasive biomarkers and provide a foundation for further investigation into allergic inflammation.

Background: Chronic rhinosinusitis with nasal polyps (CRSwNP) comorbid with asthma (CRSwA) represents a severe “unified airway” phenotype, yet the metabolic mechanisms linking upper and lower airway inflammation remain unclear. This study aimed to identify shared metabolic signatures connecting local pathology with systemic circulation by comparing the metabolic profiles of nasal polyp tissue and serum. Methods: We performed an integrated analysis using non-targeted metabolomics and transcriptomics on paired nasal polyp tissue and serum samples from 22 CRSwA patients and 40 non-asthmatic CRSwNP patients to identify differential metabolites and explore their association with the immune microenvironment. Results: CRSwA patients exhibited distinct metabolic signatures dominated by lipids and their derivatives in both tissue and serum. An analysis of the metabolome shared between compartments revealed a weak but significant positive correlation in metabolic fold changes, suggesting a subtle systemic link to the local inflammation. This shared metabolic profile was strongly associated with a local Th2-polarized immune microenvironment. This shared profile was strongly associated with a local Th2-polarized immune microenvironment, where key metabolites (e.g., Resolvin D2, Lipoxin A4) correlated significantly with the abundance of M2 macrophages and eosinophils. Furthermore, a logistic regression model based on serum metabolites effectively distinguished CRSwA from non-asthmatic CRSwNP (AUC = 0.8322). Conclusion: Our study reveals a highly conserved “metabolic-immune axis” that connects local tissue inflammation with systemic circulation, positioning metabolic dysregulation as a central hub in the unified airway disease model for CRSwA. These findings offer new perspectives for developing serum-based diagnostic markers and metabolically-targeted therapies for this challenging clinical condition.

Objectives: Increasing evidence indicates that the local microbiome can be used to predict host disease states. However, constructing models that obtain better results with fewer features is still challenging. Methods: In this study, we established a nasal microbiome database consisting of 132 chronic rhinosinusitis patients, 27 nasal inverted papilloma patients, and 45 control patients. 16S rRNA gene sequencing was used to identify the species and abundance of bacteria in each sample, and a nasal microbiome database was generated after low-abundance bacteria were eliminated. The correlation data network of different groups of bacteria was constructed by calculating the correlation coefficient among bacterial genera, and the correlation parameters of the network were calculated based on graph theory. Through the development and application of a machine learning framework to optimize the screening process, combined with microbiome relationship network parameters based on graph theory, basic bacteria with high contributions to classification prediction were selected for the prediction of nasal diseases. Results: We found that patients with nasal disease have a specific nasal microbiome signature and identified Moraxella, Prevotella, and Rothia as keystone genera that are markers of nasal disease; these markers can be interpreted as key control routes through graph theory analysis of the microbiota. With this strategy, we were able to characterize microbial community changes in nasal disease patients, which could reveal the potential role of the nasal microbiome in nasal disease. Conclusion: This study can provide a reference for the formulation of disease prevention and control policies. Our framework can be applied to other diseases to identify keystone genera that influence disease states and can be used to predict disease states.

Purpose A growing body of evidence has elucidated that the gut microbiota has a crucial impact on the pathophysiological process of atopic diseases. Eosinophilic chronic rhinosinusitis with nasal polyps (eCRSwNP) is a local atopic disease of the systemic immune response. Alterations in the gut microbiome in eCRSwNP patients remain largely undefined. Methods 16S rRNA gene sequencing was performed in a cross-sectional study of 17 eCRSwNP patients, 9 noneCRSwNP patients and 13 healthy controls, and gut microbiota DNA sequencing between each pair of groups was compared using bioinformatic methods. Results Compared with that of healthy controls, the gut microbiomes of eCRSwNP patients were characterised by a distinct overall microbial composition. However, no significant differences were found in the alpha diversity of the gut microbiota between patients and healthy controls. The distinct differences in microbial composition were significantly correlated with the severity of disease. At the genus level, the abundance of Faecalibacterium positively correlated with Lund-Mackay CT scores. Similarly, the abundance of Turicibacter positively correlated with the percentage of tissue eosinophils. Conclusions We found alterations in the gut microbiome in eCRSwNP patients, and the alterations in the gut microbiome were correlated with the severity of disease.