目的：探討慢性非侵襲性真菌性鼻竇炎(NIFS)的治療要點。方法：分析11 例NIFS 患者，全部施行鼻內鏡手術，術后定期隨訪。結果：術后隨訪2年，全部治愈。結論：以鼻內鏡手術治療為主的綜合治療，療效好，復發率低。
To address the challenges of spatiotemporal feature heterogeneity, insufficient utilization of frequency band information, and weak cross-subject generalization in electroencephalogram (EEG)-based emotion recognition, this paper proposes a hierarchical spatiotemporal feature learning architecture named spatio-temporal mamba (ST-Mamba) based on state space models. Firstly, the proposed conv-spatio-temporal (CST) dual-branch collaborative module integrates the local feature extraction capability of convolutional neural network (CNN) with the global modeling ability of state space models. Through adaptive weighted fusion, it effectively mitigates the issue of inadequate modeling of inter-channel relationships in EEG signals. Secondly, the designed multi-band spatio-temporal feature pyramid (MBSTP) module adaptively weights features from different frequency bands via a frequency-band attention mechanism, while capturing spatial topological dependencies across brain regions through a hierarchical fusion strategy. Additionally, a data augmentation framework efficiently enhances the model’s cross-subject generalization by applying augmentations in the frequency, temporal, and spatial domains. The proposed model achieves average accuracies of 95.56% and 84.47% on the Shanghai Jiao Tong University emotion EEG dataset (SEED), version III (SEED-III) and version IV (SEED-IV), respectively. Experiments demonstrate that the state space model effectively alleviates the over-smoothing issue in deep networks, offering a novel solution to spatiotemporal heterogeneity and cross-subject generalization challenges in EEG-based emotion recognition.