Background: The role of memory B cells and their subgroups in allergic rhinitis (AR) and allergen immunotherapy (AIT) remains unclear. This study aimed to investigate the characteristics of memory B cells in the circulation of patients with AR and those undergoing AIT, as well as their clinical significance. Methods: This study involved a cohort comprising 32 healthy control subjects, 39 individuals diagnosed with AR, and 31 AR patients who had received AIT for over one year. Visual analog scale (VAS) scores were used for symptom assessment, and the serum concentrations of immunoglobulins and cytokines were quantified. This study evaluated alterations in the proportions of peripheral blood memory B cells and their subpopulations, plasma cells, and various T-cell subsets across the three participant groups. Results: The proportion of IgA+CD27- class-switched memory B cells in the AR group significantly decreased compared to the control group, but significantly increased following AIT (P < 0.05). In AR patients, circulating IgA+CD27- class-switched memory B cells were significantly positively correlated with Treg cells, IL-10, and IL-4 and significantly negatively correlated with IFN-γ, total IgE, sIgE, and VAS scores (P < 0.05). After AIT, the number of circulating IgA+CD27- class-switched memory B cells in AR patients was significantly positively correlated with the number of Treg cells and IL-10 and significantly negatively correlated with the VAS score (P < 0.05). Conclusion: The IgA+CD27- class-switched memory cell subset in human peripheral blood may serve as a potential biomarker for evaluating AR symptoms and treatment efficacy. Its mechanism may be associated with interactions between T and B cells.

Nasopharyngeal carcinoma (NPC) is a popular malignant tumor of the head and neck which is endemic in the world, more than 75% of the NPC patients suffer from locoregionally advanced nasopharyngeal carcinoma (LA-NPC). The survival quality of these patients depends on the reliable prediction of NPC stages III and IVa. In this paper, we propose a two-stage framework to produce the classification probabilities for predicting NPC stages III and IVa. The preprocessing of MR images enhance the quality of images for further analysis. In stage one transfer learning is used to improve the classification effectiveness and the efficiency of CNN models training with limited images. Then in stage two the output of these models are aggregates using soft voting to boost the final prediction. The experimental results show the preprocessing is quite effective, the performance of transfer learning models perform better than the basic CNN model, and our ensemble model outperforms the single model as well as traditional methods, including the TNM staging system and the Radiomics method. Finally, the prediction accuracy boosted by the framework is, respectively,0.81, indicating that our method achieves the SOTA effectiveness for LA-NPC stage prediction. In addition, the heatmaps generated with Class Activation Map technique illustrate the interpretability of the CNN models, and show their capability of assisting clinicians in medical diagnosis and follow-up treatment by producing discriminative regions related to NPC in the MR images.Graphic