Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by complex neuroimmune interactions. Identifying reliable neuropathological markers and understanding immune cell infiltration in the brain are essential for improving our understanding of AD pathology. We integrated four temporal cortex gene expression datasets from the GEO database (GSE36980, GSE37263, GSE118553, GSE122063). Differentially expressed genes (DEGs) were identified using RobustRankAggreg (RRA) and batch correction. Functional enrichment was analyzed via GO and KEGG, and hub genes were identified through protein-protein interaction networks and comparative intersection analysis. Diagnostic performance was evaluated using ROC curves, and immune cell infiltration was profiled with CIBERSORT, with significant immune subsets identified via Wilcoxon tests and LASSO regression. Analysis revealed 98 robust DEGs, prominently enriched in pathways related to synaptic transmission and neuroactive ligand-receptor interactions. Two hub genes, CRH and GAD2, were identified and validated as being significantly downregulated in AD. ROC analysis affirmed their high discriminatory value (AUC ≥ 0.7), with a combined model demonstrating good performance. Immune infiltration profiling in the AD temporal cortex uncovered significant alterations in six immune cell populations: M2 macrophages, activated dendritic cells, and resting mast cells were increased, while plasma cells, regulatory T cells (Tregs), and activated NK cells were decreased. However, no significant correlation was found between the expression of CRH/GAD2 and these immune cell alterations. CRH and GAD2 are potential neuropathological markers for AD. The distinct immune infiltration patterns observed highlight the involvement of both innate and adaptive immunity in AD pathogenesis, offering new insights for understanding AD pathology and informing future therapeutic strategies. The lack of direct correlation suggests that neuronal gene dysregulation and immune alterations may represent parallel or independently regulated pathological dimensions in AD.

Accurate automatic sleep staging is crucial for diagnosing sleep disorders. However, most existing automatic data-driven sleep staging methods could not perfectly learn the complex knowledge of sleep staging criteria such as the American Academy of Sleep Medicine (AASM) based on the limited labeled data. This paper proposes a novel multimodal and multiscale automatic sleep staging framework, MMS-SleepNet, which explicitly incorporates AASM knowledge. It employs a deep learning multimodal feature extraction module (MMS-FE), embedding expert knowledge to effectively capture multimodal features for each stage and fine-grained EEG features at various frequencies. The module utilizes an attention mechanism to seamlessly fuse extracted multimodal features, significantly enhancing classification accuracy. To further improve the performance of MMS-SleepNet, a contrastive learning module and a data balancing strategy are proposed, addressing class confusion and data imbalance issues in existing models. Specifically, the contrastive classification module (CCM) emphasizes intra-class similarity and inter-class disparity, effectively alleviating class confusion. A simple yet effective data balancing mechanism augments the number of samples for the N1 sleep stage, guaranteeing that the model is trained on amore balanced dataset and proficiently resolves the long-tail distribution problem stemming from class imbalance. Experimental results on two public datasets validate the effectiveness of MMS-SleepNet, achieving a remarkable accuracy of 92.9% on the Sleep-EDF-20 dataset, surpassing other methods. Notably, it attains a 74.1% accuracy in the challenging N1 stage, outperforming other methods by 19.6-49.2%.

The limited availability of sleep data collected by individual institutions poses a significant challenge in developing automatic sleep staging models, especially given that most existing models rely heavily on data-driven approaches. The advent of federated learning has introduced an innovative and reliable paradigm for inter-institutional collaboration, facilitating task knowledge sharing and implicit data augmentation while preserving privacy. However, in traditional federated learning methodologies, local models are typically initialized with the global model at the onset of each iteration. This global-knowledge-centric approach often results in catastrophic forgetting during local training and inadequately adapts to the diversity of local data, compromising the overall generalization performance of the model. In this paper, we introduce a novel re-aggregation strategy for local model initialization that synergizes the global model with historical local models, thereby mitigating the undue influence of global knowledge and preserving local task-specific information. The re-aggregation weights are adaptively determined based on the confidence of the global and historical models on local datasets. Furthermore, to address the inherent ambiguity among sleep stages, a dual prototype-contrastive learning module is proposed, comprising Prototype-Consistency Contrastive (PCC) and Prototype-Distinguishability Contrastive (PDC) components. Specifically, the PCC component is designed to ensure consistency between local prototypes and unbiased prototypes derived from re-aggregation, enhancing intra-class knowledge coherence. The goal of the PDC component is to strengthen the discriminability among various sleep stages, improving inter-class differentiation. Comprehensive experiments conducted on two public datasets demonstrate the effectiveness, superiority, and flexibility of the proposed method.

STUDY OBJECTIVES:To evaluate the efficacy and safety of daridorexant in Chinese patients with insomnia disorder. METHODS:This was a multicenter, randomized, double-blind, parallel-group, placebo-controlled phase III clinical trial in China. Adults aged 18-75 years meeting the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria for insomnia disorder, with a baseline Insomnia Severity Index (ISI) more than equal to 15, were randomly assigned in a 1:1 ratio to receive daridorexant 50 mg or placebo once every evening for 1 month. The primary endpoint was the change from baseline to month 1 in wake time after sleep onset (WASO) assessed by polysomnography (PSG). The secondary endpoints included the change from baseline to month 1 in self-reported total sleep time (sTST) and in latency to persistent sleep (LPS). In addition to the standardized collection of adverse events (AEs), safety data specific to insomnia treatment were assessed as follows: withdrawal symptoms, rebound insomnia, and next-day residual effects. RESULTS:Two hundred and six participants were enrolled and evenly randomized to each group. Compared with the placebo group, the daridorexant group showed a significant reduction in WASO (-15.4 min, p = .0009), a significant increase in sTST (16.8 min, p = .021) and a significant reduction in LPS (geometric mean ratio to placebo 0.7, p = .001) from baseline to month 1. The proportion of participants reporting AEs during double-blind treatment was generally comparable between two groups (21.6 vs. 18.4 percent), with no notable safety signals observed. No withdrawal symptoms, rebound insomnia, and next-day residual effects were observed. CONCLUSIONS:Daridorexant 50 mg significantly improved both objective and self-reported sleep outcomes in Chinese patients with insomnia disorder, with a favorable safety profile. CLINICAL TRIAL:A Study of Daridorexant in Chinese Patients With Insomnia Disorder, https://clinicaltrials.gov/study/NCT06010693, with the number NCT06010693. Statement of Significance Daridorexant has been evaluated and approved for the treatment of insomnia in many countries worldwide, but there is no data of daridorexant in Chinese populations. This study is the first randomized controlled phase III study of daridorexant in Chinese. It verified the efficacy and safety of daridorexant in Chinese people for the first time and fully explained the characteristics of daridorexant in Chinese patients with insomnia. This study provides a basis for the approval of daridorexant for clinical use in China, as well as reference data for clinicians to use the drug in the future.

Temporal lobe epilepsy (TLE) frequently involves an intricate, extensive epileptic frontal-temporal network. This study aimed to investigate the interactions between temporal and frontal regions and the dynamic patterns of the frontal-temporal network in TLE patients with different disease durations. The magnetoencephalography data of 36 postoperative seizure-free patients with long-term follow-up of at least 1 year, and 21 age- and sex-matched healthy subjects were included in this study. Patients were initially divided into LONG-TERM (n = 18, DURATION >10 years) and SHORT-TERM (n = 18, DURATION ≤10 years) groups based on 10-year disease duration. For reliability, supplementary analyses were conducted with alternative cutoffs, creating three groups: 0 < DURATION ≤7 years (n = 11), 7 < DURATION ≤14 years (n = 11), and DURATION >14 years (n = 14). This study examined the intraregional phase-amplitude coupling (PAC) between theta phase and alpha amplitude across the whole brain. The interregional directed phase transfer entropy (dPTE) between frontal and temporal regions in the alpha and theta bands, and the interregional cross-frequency directionality (CFD) between temporal and frontal regions from the theta phase to the alpha amplitude were further computed and compared among groups. Partial correlation analysis was conducted to investigate correlations between intraregional PAC, interregional dPTE connectivity, interregional CFD, and disease duration. Whole-brain intraregional PAC analyses revealed enhanced theta phase-alpha amplitude coupling within the ipsilateral temporal and frontal regions in TLE patients, and the ipsilateral temporal PAC was positively correlated with disease duration (r = 0.38, p <.05). Interregional dPTE analyses demonstrated a gradual increase in frontal-to-temporal connectivity within the alpha band, while the direction of theta-band connectivity reversed from frontal-to-temporal to temporal-to-frontal as the disease duration increased. Interregional CFD analyses revealed that the inhibitory effect of frontal regions on temporal regions gradually increased with prolonged disease duration (r = -0.36, p <.05). This study clarified the intrinsic reciprocal connectivity between temporal and frontal regions with TLE duration. We propose a dynamically reorganized triple-stage network that transitions from balanced networks to constrained networks and further develops into imbalanced networks as the disease duration increases.

STUDY OBJECTIVES:To evaluate the efficacy and safety of Dimdazenil, a novel partial positive allosteric modulator for GABAA receptor in adults with insomnia disorder. METHODS:This was a 2-week, multicenter, randomized, double-blind, placebo-controlled, parallel-group phase III study of Dimdazenil. The primary efficacy outcome was total sleep time (TST) analyzed by polysomnography (PSG) on day 13/14. Latency to persistent sleep (LPS), sleep efficiency (SE), and wake after sleep onset (WASO) were analyzed in the same way by polysomnography (PSG). The other secondary outcomes including the average subjective sleep latency (sSL), subjective TST (sTST), subjective SE (sSE), subjective WASO (sWASO), and subjective number of awakenings (sNAW) were analyzed from sleep diary data, and the insomnia severity index (ISI) was also assessed. Treatment-emergent adverse events (TEAEs) were monitored throughout the study. RESULTS:A total of 546 participants with insomnia (age ≥18 years) were randomized (2:1), received treatment with an oral dose of Dimdazenil (2.5 mg) or placebo, and analyzed. Compared to baseline and placebo, Dimdazenil demonstrated significant improvements in PSG measures, increased TST (71.09, 31.68 minutes, respectively; both p < 0.001), increased SE (13.26%, 5.55%, respectively; both < 0.001), reduced WASO (49.67, 20.16 minutes, respectively; both p < 0.001), and reduced LPS (21.65 minutes, p < 0.001; 6.46 minutes, p = 0.023). Compared to placebo, Dimdazenil also improved key self-reported measures of sTST (18.33 minutes, p < 0.001), sWASO (14.60 minutes, p < 0.001), sSL (4.23 minutes, p < 0.001), sSE (2.97%, p < 0.001), and sNAW (0.29, p < 0.001). Participants treated with Dimdazenil reported a significant improvement in ISI. Dimdazenil was well tolerated. The majority of TEAEs were mild or moderate. There were no clinically relevant treatment-related serious AEs and no deaths. CONCLUSIONS:Dimdazenil of 2.5 mg provided significant benefit on sleep maintenance and sleep onset in individuals with insomnia disorder versus placebo, with a favorable safety profile and was well tolerated. CLINICAL TRIAL INFORMATION:A multicenter, randomized, double-blind phase III clinical study evaluating the efficacy and safety of EVT201 capsules compared to placebo in patients with insomnia disorders (http://www.chinadrugtrials.org), with the number of CTR20201068.

Sleep staging is essential in assessing sleep quality and diagnosing sleep-related disorders, but the lack of labeled data impedes the development of automatic sleep staging models. Generally, institutions rely on semi-supervised approaches to enhance the utilization of their own unlabeled data. However, the task knowledge obtained from a limited amount of labeled data is often insufficient to guide the learning based on large amounts of unlabeled data, which may even lead to catastrophic forgetting and further degrade the performance of most existing methods. In this paper, we propose a novel strategy of building secure collaboration among multiple institutions, to achieve the implicit augmentation of labeled data and expansion of task knowledge for each participating institution by acquiring external knowledge from others. We adopt the Federated Learning (FL) to facilitate secure collaboration and propose a federated semi-supervised sleep staging method based on knowledge sharing, which enables the automatic scoring of sleep stages using only single-channel EEG data. The task knowledge in our method is contained in relationships, which exist naturally among sleep stages and can be extracted from both local labeled and unlabeled data. Furthermore, the knowledge sharing among participating institutions can be achieved by aligning the local relationships to the aggregated global relationships. Additionally, we employ prototype-contrastive learning to enhance the clarity of relationships extracted from labeled data, and propose pseudo-labeling optimization to generate reliable pseudo-labels for subsequent relationship extraction from unlabeled data. Our method is shown to be effective and outperforms compared methods in extensive experiments conducted on two publicly available datasets.

Orthostatic hypotension (OH) is one of the most common symptoms in patients with multiple system atrophy (MSA). Vestibular system plays an important role in blood pressure regulation during orthostatic challenges through vestibular-sympathetic reflex. The current study aimed to investigate the relationship between vestibular function and OH in patients with MSA. Participants with MSA, including 20 with OH (mean age, 57.55 ± 8.44 years; 7 females) and 15 without OH (mean age, 59.00 ± 8.12 years; 2 females) and 18 healthy controls (mean age, 59.03 ± 6.44 years; 8 females) were enrolled. Cervical and ocular vestibular evoked myogenic potentials (cVEMPs and oVEMPs) tests were conducted to evaluate vestibular function. Patients with MSA presented with significantly higher rate of absent cVEMPs (57.1

Insomnia is the most common sleep disorder linked with adverse long-term medical and psychiatric outcomes. Automatic sleep staging plays a crucial role in aiding doctors to diagnose insomnia disorder. Only a few studies have been conducted to develop automatic sleep staging methods for insomniacs, and most of them have utilized transfer learning methods, which involve pre-training models on healthy individuals and then fine-tuning them on insomniacs. Unfortunately, significant differences in feature distribution between the two subject groups impede the transfer performance, highlighting the need to effectively integrate the features of healthy subjects and insomniacs. In this paper, we propose a dual-teacher cross-domain knowledge transfer method based on the feature-based knowledge distillation to improve the performance of sleep staging for insomniacs. Specifically, the insomnia teacher directly learns from insomniacs and feeds the corresponding domain-specific features into the student network, while the health domain teacher guide the student network to learn domain-generic features. During the training process, we adopt the OFD (Overhaul of Feature Distillation) method to build the health domain teacher. We conducted the experiments to validate the proposed method, using the Sleep-EDF database as the source domain and the CAP-Database as the target domain. The results demonstrate that our method surpasses advanced techniques, achieving an average sleep staging accuracy of 80.56% on the CAP-Database. Furthermore, our method exhibits promising performance on the private dataset.

Niemann-Pick disease type C (NPC) is an autosomal recessive hereditary disease in which sphingomyelin and cholesterol are deposited in various organs of the body. The clinical manifestations of NPC include neurologic symptoms and cataplexy; other symptoms related to sleep have seldom been reported. One previous study described various sleep disorders including chronic insomnia, obstructive sleep apnea, restless legs syndrome, and rapid eye movement sleep behavior disorder, thus suggesting that sleep disorders in patients with NPC are more prevalent than previously thought and warrant close attention. Here, we describe sleep disorders in 2 patients with NPC and discuss the clinical characteristics and, for the first time, discuss potential pathogenic mechanisms underlying sleep disorders in such patients.

肝胆外科相关疾病病因及发病机制复杂,临床表现多样,与其他腹部外科疾病的鉴别存在困难,治疗原则具有独特性.肝胆外科的专业性和抽象性,决定了对临床教学更高的要求.近年来,新的教学方法不断被应用于临床教学实践中,其中以问题为基础的教学法(problem-based learning,PBL)和以案例为基础的教学法(case-based learning,CBL)备受关注,PBL教学法的优势主要在于以学生为中心,促进自主学习,在教师的引导下,以问题为导向,通过信息获取、归纳、推理和总结,提高学生解决问题的思维能力.CBL教学法可以为临床医学生提供形象化的情景模式,让学生充分接触案例诊疗过程,增强了临床思维能力.两者的结合可以优势互补,提高学生思辨能力、激发学习主动性并提升临床实践能力,改善了教学的总体满意度.文章旨在对两种教学法在肝胆外科临床教学中的联合应用进展进行综述.

Background:Sleep spindles are a vital sign implying that human beings have entered the second stage of sleep. In addition, they can effectively reflect a person's learning and memory ability, and clinical research has shown that their quantity and density are crucial markers of brain function. The "gold standard" of spindle detection is based on expert experience; however, the detection cost is high, and the detection time is long. Additionally, the accuracy of detection is influenced by subjectivity.Methods:To improve detection accuracy and speed, reduce the cost, and improve efficiency, this paper proposes a layered spindle detection algorithm. The first layer used the Morlet wavelet and RMS method to detect spindles, and the second layer employed an improved k-means algorithm to improve spindle detection efficiency. The fusion algorithm was compared with other spindle detection algorithms to prove its effectiveness.Results:The hierarchical fusion spindle detection algorithm showed good performance stability, and the fluctuation range of detection accuracy was minimal. The average value of precision was 91.6%, at least five percentage points higher than other methods. The average value of recall could reach 89.1%, and the average value of specificity was close to 95%. The mean values of accuracy and F1-score in the subject sample data were 90.4 and 90.3%, respectively. Compared with other methods, the method proposed in this paper achieved significant improvement in terms of precision, recall, specificity, accuracy, and F1-score.Conclusion:A spindle detection method with high steady-state accuracy and fast detection speed is proposed, which combines the Morlet wavelet with window RMS and an improved k-means algorithm. This method provides a powerful tool for the automatic detection of spindles and improves the efficiency of spindle detection. Through simulation experiments, the sampled data were analyzed and verified to prove the feasibility and effectiveness of this method.

Fatal familial insomnia (FFI) is a rare autosomal dominant inherited prion disease characterized by prominent organic sleep-related impairment accompanied by a series of symptoms with strong clinical heterogeneity in a rapid progression. As the most significant clinical feature and the potential diagnostic marker of FFI, progressive sympathetic symptoms were lack of a objective noninvasive quantitative method to detect. From May 2013 to August 2020, nine patients with FFI, eight patients with Creutzfeldt-jakob disease (CJD) and nine normal control (NC) with matched sex and age were recruited in this study. All the participants underwent a nocturnal video-polysomnography included a lead II electrocardiography (ECG). Heart rate variability analysis with successive 5 min (in time domain methods, frequency domain methods and non-linear measurement) was performed during quiet wake before sleep and in the following N2, N3 and REM of the first sleep cycle. In FFI group, the continuous increased heart rate with the relatively decreased amplitude of the oscillations compared with NC and CJD group, revealed the axis of equilibrium shifts towards sympathetic activation. Furthermore, the RMSSD and HF during sleep stage in FFI were significantly lower than in NC group, which reflected the decreased activation of parasympathetic nervous system. The significant abnormal LF/HF ratio and SD1/SD2 ratio in combination with the lower values of correlation dimension D2 in FFI group supported its unbalanced autonomic nervous function. Dysfunctions of autonomic nerve system in patients with FFI may be affected by parasympathetic hypofunction partly except sympathetic activation, and HRV is expected to become a method in diagnosis for FFI.

Spindles differ in density, amplitude, and frequency, and these variations reflect different physiological processes. Sleep disorders are characterized by difficulty in falling asleep and maintaining sleep. In this study, we proposed a new spindle wave detection algorithm, which was more effective compared with traditional detection algorithms such as wavelet algorithm. Besides, we recorded EEG data from 20 subjects with sleep disorders and 10 normal subjects, and then we compared the spindle characteristics of sleep-disordered subjects and normal subjects (those without any sleep disorder) to assess the spindle activity during human sleep. Specifically, we scored 30 subjects on the Pittsburgh Sleep Quality Index and then analyzed the association between their sleep quality scores and spindle characteristics, reflecting the effect of sleep disorders on spindle characteristics. We found a significant correlation between the sleep quality score and spindle density (p = 1.84 × 10−8, p-value <0.05 was considered statistically significant.). We, therefore, concluded that the higher the spindle density, the better the sleep quality. The correlation analysis between the sleep quality score and mean frequency of spindles yielded a p-value of 0.667, suggesting that the spindle frequency and sleep quality score were not significantly correlated. The p-value between the sleep quality score and spindle amplitude was 1.33 × 10−4, indicating that the mean amplitude of the spindle decreases as the score increases, and the mean spindle amplitude is generally slightly higher in the normal population than in the sleep-disordered population. The normal and sleep-disordered groups did not show obvious differences in the number of spindles between symmetric channels C3/C4 and F3/F4. The difference in the density and amplitude of the spindles proposed in this paper can be a reference characteristic for the diagnosis of sleep disorders and provide valuable objective evidence for clinical diagnosis. In summary, our proposed detection method can effectively improve the accuracy of sleep spindle wave detection with stable performance. Meanwhile, our study shows that the spindle density, frequency and amplitude are different between the sleep-disordered and normal populations.

Abnormal discharge (AD) is a discharge mode in electroencephalogram (EEG) with sharp outlines. Lack of related open-access datasets and insufficient annotation hamper the development of AD detection, while cognitive neuroscience, clinical research and pilots’ neural screening demand stable and reliable AD detection methods. An adaptive neuro-fuzzy inference method for AD detection is proposed in this work. First, a probability-density-based (PD) method is proposed to extract lognormal amplitude features from EEG envelopes. Second, the subtractive clustering method (SCM) is modified so that clustering radii can adapt to cluster shapes for each dimension instead of using identical radii for each cluster. The outputs of modified SCM (mSCM), coordinates of cluster centers and adjusting rates for radius components are used to automatically initialize Gaussian membership functions of adaptive-network-based fuzzy inference system (ANFIS). Finally, we conducted multiple experiments to validate mSCM-based ANFIS, comparing it with traditional machine learning classifiers (support vector machines, multi-layer perceptrons, decision trees, and random forests) and the state-of-the-art deep learning-based time-series classification methods InceptionTime and Minirocket, using synthetic data, small-size datasets and a private EEG dataset. Results show that combining PD features with features proposed in previous studies, such as smoothed nonlinear energy operator features and discrete wavelet transform features, achieved higher accuracy (95.13 ± 0.86%) and recall (89.17 ± 3.06%) than other feature combinations. mSCM created more suitable cluster boundaries than SCM on small-size datasets, and its clustering results demonstrated potential in helping interpretation of classification rules built in the ANFIS network. Results of comparison experiments showed that mSCM-based ANFIS produced competitive results in accuracy and recall for AD detection, with lower computational cost, compared to the top 2 results obtained by InceptionTime and Minrocket.

流言:有些人不仅能够记住自己做的梦,还能像讲故事一样给别人复述.对此,有人认为这是大脑在该休息的时候没有休息,说明睡眠质量不好.

睡眠相关进食障碍是一种罕见的,在睡眠期觉醒期间反复出现无意识地进食和饮水的非快速眼动睡眠期异态睡眠,以意识水平降低及行为遗忘为主要临床特征.目前国内鲜有睡眠相关进食障碍的文献报告.本文报告1例重度阻塞性睡眠呼吸暂停的33岁男性患者,夜间反复出现睡眠相关进食障碍合并睡行症等多种异态睡眠,给予持续正压通气治疗后,夜间进食症状消失,日间思睡明显改善.结合文献复习本文提出,睡眠呼吸暂停可以导致睡眠相关进食障碍,以提高临床医师对本病的认识.

流言:生活中的某个场景仿佛在梦里出现过,梦有时也能预示未来. 真相:做梦通常发生在人体睡眠的REM阶段,也就是快速眼动阶段,但梦只能帮助我们梳理记忆,并不能预示未来.

Patients with autoimmune encephalitis (AE) often developed psychiatric features during the disease course. Many studies focused on the psychiatric characteristic in anti-NMDAR encephalitis (NMDAR-E), but anti-LGI1 encephalitis (LGI1-E) had received less attention regarding the analysis of psychiatric features, and no study compared psychiatric characteristic between these two groups. The clinical data of AE patients (62 NMDAR-E and 20 LGI1-E) who developed psychiatric symptoms were analyzed in this study. In NMDAR-E, the most common higher-level feature was “behavior changes” (60/62, 96.8