目的 研究利多卡因對海馬的神經毒性是否會對大鼠空間學習記憶能力產生影響，并探討大鼠空間學習能力的變化與海馬CA3區錐體細胞數目的相關性。 方法 將成年Wistar雄性大鼠隨機分為基礎值組（n=7）和利多卡因驚厥組（n=40）。基礎值組大鼠靜脈給予生理鹽水后使用Y迷宮測定大鼠的空間學習能力。利多卡因驚厥組大鼠尾靜脈持續輸注利多卡因造成驚厥，待大鼠恢復正常運動以后放入鼠籠重新飼養。并于驚厥后第1、3、5、7天從中隨機抓取大鼠測試其空間學習能力以及組織學改變。根據對應天數將利多卡因驚厥組的40只大鼠隨機細分為Day-1、Day-3、Day-5、Day-7亞組，每亞組10只。所有大鼠在測定空間學習能力之后立即處死，取出大腦并做石蠟包埋，冠狀面切片后進行組織學檢測，顯微鏡下評估海馬CA3區錐體細胞狀態。 結果 ① 基礎值組和Day-1、Day-3、Day-5、Day-7亞組大鼠的Y迷宮穿梭次數分別為（25.2 ± 3.7）、（27.1 ± 8.1）、（36.9 ± 9.9）、（38.7 ± 10.6）、（40.6 ± 16.3）次，除Day-1亞組與基礎值組比較差異無統計學意義（P＞0.05）外，其余各亞組與基礎值組差異均有統計學意義（P＜0.05）；② 與基礎值組單位面積（10.3 ± 4.5）個（異常錐體）細胞比較，利多卡因驚厥組大鼠海馬CA3區異常錐體細胞數增加，Day-1、Day-3、Day-5、Day-7亞組計數值分別為13.0 ± 7.2、15.6 ± 5.0、19.6 ± 8.1、18.1 ± 5.1，且與大鼠Y迷宮穿梭次數呈正相關（r=0.711，P＜0.05）。 結論 利多卡因引起的驚厥使成年大鼠海馬依賴性空間學習能力下降，利多卡因的神經毒性引起的海馬異常錐體細胞增多可能是造成這一現象的一種原因。
Aiming at the deficiencies of insufficient cross-domain generalization and poor rhythm sensitivity in atrial fibrillation (AF) detection from multi-lead electrocardiogram (ECG) signals, this paper proposes a novel AF detection algorithm based on multi-scale patch attention fusion. The method segmented ECG signals into overlapping temporal fragments of different scales to capture local waveform details and long-range rhythm patterns respectively; it fused cross-scale feature information through a multi-scale attention mechanism to strengthen the model’s ability to perceive local and global rhythm features, and introduced the self-attention mechanism of Transformer to capture long-range rhythm correlations among fragments, thus realizing in-depth mining of ECG features. The algorithm was validated on the public CinC2021 dataset and the self-constructed clinical Clin-ECG dataset. Experimental results showed that the algorithm achieved an accuracy of 94.6% and 92.7% on the two datasets, with F1 scores reaching 0.945 and 0.923, respectively. Compared with baseline models such as ECG-ResNet and CNN-BiLSTM, the proposed algorithm exhibited higher accuracy and better cross-dataset generalization ability, providing an effective method for the automatic detection of AF from multi-lead ECG signals.
Accurate neurological outcome assessment after cardiac arrest is critical for clinical diagnosis and treatment. Existing electroencephalogram (EEG) prediction models suffer from high computational complexity, redundant full-channel data, and insufficient fusion of time-frequency domain features. This study proposes a lightweight deep learning model, WaveConv-SR-RepVGG, based on adaptive time-frequency feature fusion and reparameterized convolution. Taking the reparameterized visual convolutional network (RepVGG) as the baseline framework, the model introduces a shrinkage (SR) mechanism into the time-domain branch to suppress interference from low signal-to-noise ratio EEG signals. A multi-scale wavelet convolution (MS-Conv) module is constructed to extract multi-scale spectral features, and time-frequency features are fused to form joint representations for prognostic classification. Experiments were conducted on the PhysioNet 2023 dataset. Under the 18-channel setting, the model achieved an accuracy of 81.7%, an F1 score of 86.1%, and an AUC of 0.847. Under the 4-channel setting, the accuracy reached 81.2%, the F1 score reached 86.7%, and the AUC reached 0.877, with overall performance outperforming various mainstream deep learning algorithms. The proposed model enables neurological outcome prediction for patients after cardiac arrest and maintains stable and excellent classification performance with fewer EEG acquisition channels.