OBJECTIVE: The purpose of this study was to investigate the effects of neck myoclonus (NM) on sleep quality and daytime sleepiness in patients with narcolepsy (NT) and to further explore possible underlying mechanisms.
METHODS: We included 72 patients with narcolepsy type 1 (NT1), 34 patients with narcolepsy type 2 (NT2) and 33 healthy controls. Patients underwent questionnaires, lumbar puncture procedure, polysomnography, and multiple sleep latency test (MSLT). Healthy controls underwent polysomnography and questionnaires. Orexin-A levels in the cerebrospinal fluid (CSF) were analyzed by radioimmunoassay. Three catecholamines, including dopamine, norepinephrine and epinephrine, in the CSF were measured by high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS).
RESULTS: Both the NT1 and NT2 groups displayed a higher level of NM incidence rate and index compared to the control group in PSG. NT1 displayed greater MSLT REM--NM incidence rate and index than NT2. NM were often associated with arousal or awakening and body movements, which had a prominent influence on sleep quality in both narcoleptic patients and controls. There was a positive correlation between the NM index and the Pittsburgh Sleep Quality Index (PSQI), Stanford Sleepiness Scale (SSS) and Ullanlinna Narcolepsy Scale (UNS) scores in NT1 patients. In MSLT of NT1 patients, REM-NM index were positively correlated with the CSF dopamine levels, and there were elevated dopamine levels but reduced orexin-A levels in patients with REM-NM.
CONCLUSIONS: NM incidence rate and index were high in patients with narcolepsy, which had a huge effect on sleep quality and aggravated daytime sleepiness. NM should be considered pathological and viewed as a new sleep-related movement disorder. Orexin-A and dopamine may be involved in the development of NM.

BACKGROUND: The study seeks to assess serum neurofilament light chain (NfL) levels in paediatric narcolepsy-diagnosed patients. Moreover, it aims to explore the correlation between NfL levels and the severity of narcolepsy symptoms, sleep quality, and manifestations of anxiety and depression.
METHODS: This retrospective analysis included 98 paediatric narcolepsy cases and 100 controls matched for age and gender. The study focused on comparing serum NfL levels across these groups. Severity of EDS in patients was measured with the Epworth Sleepiness Scale (ESS). Moreover, the Pittsburgh Sleep Quality Index (PSQI), Hamilton Depression Rating Scale-24 (HAMD-24), and Hamilton Anxiety Scale-14 (HAMA-14) were used to assess narcolepsy symptoms, sleep quality, and psychological conditions.
RESULTS: Patients with paediatric narcolepsy had significantly higher serum NfL levels than controls (P < 0.05). Additionally, a positive correlation was found between serum NfL levels and ESS scores (P < 0.001). An independent link between serum NfL and paediatric narcolepsy was established via multiple logistic regression (OR = 0.943, 95 % CI = 0.921-0.993, P = 0.004). Moreover, serum NfL\'s diagnostic precision for paediatric narcolepsy was evident from the ROC curve area of 0.938 (95 % CI: 0.86-0.99, P < 0.001).
CONCLUSIONS: The study implies a positive correlation between increased serum NfL levels and the severity of paediatric narcolepsy. Nevertheless, the causative link between serum NfL levels and paediatric narcolepsy remains uncertain, highlighting the need for larger sample sizes and well-structured cohort studies to offer more definitive.

Narcolepsy type 1 (NT1) is a unique central sleepiness disorder that affects individuals with excessive daytime sleepiness (EDS), cataplexy, sleep paralysis, and hypnagogic hallucinations. The etiology and pathogenesis of NT1 remains unclear, although some viral infections are thought to be related to NT1. This paper reports an unusual case of late-onset NT1 with human immunodeficiency virus (HIV) infection and antiretroviral therapy for five years. The relationship between HIV infection, immune, Immune reconstitution inflammatory syndrome (IRIS) and NT1 should be further investigated, as excessive daytime sleepiness is more common in HIV-infected patients than in the general population.

BACKGROUND: Symptoms of depression and anxiety are common complications of narcolepsy. Earlier studies have shown that narcolepsy type 1 (NT1) is an autoimmune inflammatory disease and symptoms of depression and anxiety are closely related to fluctuations in inflammatory cytokines. The objective of the current research was to investigate the potential correlation between cytokines and symptoms of depression and anxiety in patients with NT1.
METHODS: We collected demographic and clinical data and information on cytokine levels from 50 patients with NT1 and used Self-Rating Depression Scale (SDS) and Self-Rating Anxiety Scale (SAS) to assess the severity of depression and anxiety symptoms. Patients with SDS scores ≥ 53 points were defined as depressive narcolepsy type 1 (D-NT1) and those with SDS scores < 53 points as non-depressive narcolepsy type 1 (ND-NT1). Patients with SAS scores ≥ 50 points were defined as anxious narcolepsy type 1 (A-NT1) and those with SAS scores < 50 points as non-anxious narcolepsy type 1 (NA-NT1). A binary logistic regression model was employed to identify the influencing factors of depressive and anxiety symptoms.
RESULTS: Levels of IL-10 (p = 0.02), IL-4 (p = 0.049) and disease duration (p = 0.049) were decreased, while SAS scores (p < 0.001) and total sleep duration (p = 0.03) were increased in D-NT1 relative to ND-NT1 patients. A-NT1 patients had higher SDS scores (p < 0.001) compared to NA-NT1 patients. Binary logistic regression analysis revealed associations of longer disease duration (OR=0.83; 95 % CI: 0.70-0.97) and increased IL-10 (OR=0.40; 95 % CI: 0.17-0.90) with reduced risk of depression and worsening anxiety (SAS score; OR=1.17; 95 % CI: 1.06-1.30) with increased risk of depression in patients with NT1. Consistently, worsening depression (SDS score; OR=1.22; 95 % CI: 1.07-1.39) was correlated with increased risk of anxiety in the NT1 group.
CONCLUSIONS: Our finding that higher IL-10 levels correlate with a lower risk of depression in NT1 patients provides a reference for further exploration of the pathophysiological mechanisms of depressive symptoms in NT1 patients.

BACKGROUND: Numerous risk factors in paediatric narcolepsy may predispose them to obstructive sleep apnea (OSA). The concurrent presence of OSA in these patients might lead to underdiagnosing narcolepsy. This research investigates the prevalence and potential causality between OSA and paediatric narcolepsy.
METHODS: A case-control study coupled with a two-sample Mendelian randomization (MR) analysis was employed to explore the prevalence and causal link between paediatric narcolepsy and OSA risk.
RESULTS: The case-control study revealed that paediatric narcolepsy patients are at an increased risk of OSA, with an Odds ratio (OR) of 4.87 (95% CI: 2.20-10.71; P < 0.001). The inverse-variance weighted (IVW) model further suggests a potential causal link between narcolepsy and OSA (IVW OR: 4.671, 95% CI: 1.925-11.290; P < 0.001). Additionally, sensitivity analysis confirmed these findings\' reliability.
CONCLUSIONS: The findings highlight an elevated prevalence and genetic susceptibility to OSA among paediatric narcolepsy patients, underscoring the necessity for clinical screening of OSA. Continued research is essential to clarify the pathogenic mechanisms and develop potential treatments.

Objective: To establish a prediction model for the identifying of cataplexy facial features based on clinical shooting videos by using a deep learning image recognition network ResNet-18. Methods: A cross-sectional study. Twenty-five narcolepsy type 1 patients who were first diagnosed and never received treatment and 25 healthy controls recruited by advertisement in the Second Affiliated Hospital of Nanchang University from 2020 to 2023.After image preprocessing, a total of 1 180 images were obtained, including 583 cataplexy faces and 597 normal faces.90% were selected as the training set and validation set, and then expanded the data by 5 times.80% of the expanded data set was extracted as the training set and 20% as the validation set, that is, the number of the training set was (583+597)×0.9×0.8×5=4 248, the number of the validation set was (583+597)×0.9×0.2×5=1 062. The data sets for training and validation were used train parameters to establish the model and were trained through the five-fold cross-validation method, to establish the ResNet-18 cataplexy face recognition model via transfer learning.10% (118 images) of the original non-amplified images were extracted as the test set. The test set data did not participate in data enhancement and model training, and was only used to evaluate the final performance of the model. Finally, ResNet-18 was compared with VGG-16, ResNet-34 and Inception V3 deep learning models, and the receiver operating characteristic curve was used to evaluate the value of ResNet-18 image recognition network in cataplexy face recognition. Results: Among 25 patients with narcolepsy type 1, 15 were males and 10 were females, aged [M (Q1, Q3)] of 14.0(11.0, 20.5) years.Among 25 healthy controls, 14 were males and 11 were females, with a median age of 16.0(14.4, 23.0) years.The overall accuracy of ResNet-18 image recognition network in the test set was 90.9%, the sensitivity was 96.4% and the specificity was 85.2%. The area under the ROC curve was 0.99(95%CI:0.96-1.00). The ResNet-18 model parameter amount was 11.69 M, the floating point operation amount was 1 824.03 M, and the single image recognition time was 5.9 ms. Conclusions: The cataplexy face prediction model built based on the deep learning image recognition network ResNet-18 has a high accuracy in identifying cataplexy faces.
目的： 应用深度学习图像识别网络ResNet-18，基于临床拍摄视频，建立猝倒面容预测模型。 方法： 本研究为横断面研究，收集2020至2023年在南昌大学第二附属医院首诊未经治疗的1型发作性睡病患者25例及健康对照25名，采集的图像预处理后，共获得1 180张图片，其中583张猝倒面容，597张正常面容。从中抽取90%作为训练集与验证集，随后数据扩增5倍，扩充后的数据集抽取80%作为训练集，20%作为验证集，即训练集数量为（583+597）×0.9×0.8×5=4 248，验证集数量为（583+597）×0.9×0.2×5=1 062，训练集与验证集用于训练参数建立模型，并通过五折交叉验证法进行训练，构建采用迁移学习方式的ResNet-18猝倒面容识别模型。原未扩增前图像抽取10%（118张）作为测试集，测试集数据不参与数据增强和模型训练，仅用于测试模型最终效果。最后将ResNet-18与VGG-16、ResNet-34和Inception V3深度学习模型进行比较，用受试者工作特征曲线评估ResNet-18图像识别网络在猝倒面容识别中的价值。 结果： 25例1型发作性睡病患者中，男15例，女10例，年龄［M（Q1，Q3）］为14.0（11.0，20.5）岁；25名健康对照者中，男14名，女11名，年龄16.0（14.4，23.0）岁。ResNet-18图像识别网络在测试集中的总体准确率为90.9%，灵敏度为96.4%，特异度为85.2%，受试者工作特征曲线下面积为0.99（95%CI：0.96~1.00）。ResNet-18模型参数量为11.69 M，浮点运算量为1 824.03 M，单张图片识别时间为5.9 ms。 结论： 基于深度学习图像识别网络ResNet-18构建的猝倒面容预测模型在猝倒面容的识别上有较高的准确率。.

The COVID-19 pandemic may have a significant impact on patients with narcolepsy, yet a long-term follow-up study is currently lacking. This study aims to investigate changes in symptom severity and the quality of life of patients with narcolepsy during and after the pandemic. Patients with type 1 or type 2 narcolepsy (NT1, NT2) were retrospectively recruited and prospectively followed from 2020 to 2023. They received evaluations including the Epworth Sleepiness Scale (ESS), the visual analog scale (VAS) for hypersomnolence, the VAS for cataplexy, the Short-form 36 Health Survey questionnaire (SF-36), and a sleep diary. We compared the differences between the pre-lockdown, the lockdown, the post-lockdown, and the post-pandemic periods by repeated measures ANOVA or the Friedman test, with the Bonferroni test for post hoc analysis. A total of 100 patients completed the 4-year study (mean age, 24.06 ± 7.00 years; 55% male). We observed significant differences in the ESS (p = 0.037), total nighttime sleep (p = 0.03), total sleep time (p = 0.035), and sleep efficiency (p = 0.035) during the study period. There was also significantly worse physical role functioning in the post-pandemic period (p = 0.014). In particular, the NT1 group had significantly decreased VAS-C scores (p < 0.001) but experienced worse physical role functioning in the post-pandemic period (p = 0.009). Patients with narcolepsy continue to face challenges after the pandemic. A more flexible lifestyle with an adequate sleep time may be beneficial, and medication adherence should be emphasized.

Orexin in cerebrospinal fluid (CSF) is a neuropeptide synthesized by a cluster of neurons in the lateral hypothalamus. It mainly functions to maintain arousal, regulate feeding, and participate in reward mechanisms. Radioimmunoassay (RIA) and enzyme-linked immunosorbent assay (ELISA) can detect CSF orexin. At present, RIA is widely used but is limited by various conditions, which is not conducive to its widespread development. We aimed to determine whether ELISA can replace RIA in detecting orexin in CSF. We investigated the results of 20 patients with central disorders of hypersomnolence, including 11 with narcolepsy type 1, 2 with narcolepsy type 2, 5 with idiopathic hypersomnia, and 2 with other causes of somnolence. RIA and ELISA were used to detect CSF orexin, and P values <.05 were considered to be significant. In the narcolepsy and non-narcolepsy type 1 groups, there was no correlation between the RIA and ELISA results (P > .05). In the narcolepsy type 1 group, the ELISA and RIA results were significantly different (P < .05), but this was not observed in the non-narcolepsy type 1 group (P > .05). The accuracy of ELISA to detect CSF orexin was lower than that of RIA (P < .05). ELISA cannot replace RIA in the measurement of CSF orexin, and RIA is recommended as the first choice when narcolepsy is suspected.

UNASSIGNED: This study aimed to evaluate nocturnal sleep structure and anxiety, depression, and fatigue in patients with narcolepsy type 1 (NT1).
UNASSIGNED: Thirty NT1 patients and thirty-five healthy controls were enrolled and evaluated using the Epworth sleepiness scale (ESS), Generalized Anxiety Disorder-7, Patient Health Questionnaire-9, Fatigue Severity Scale (FSS), polysomnography, multiple sleep latency test, and brain function state monitoring. Statistical analyses were performed using SPSS Statistics for Windows, version 23.0. Benjamini-Hochberg correction was performed to control the false discovery rate.
UNASSIGNED: Apart from typical clinical manifestations, patients with NT1 are prone to comorbidities such as nocturnal sleep disorders, anxiety, depression, and fatigue. Compared with the control group, patients with NT1 exhibited abnormal sleep structure, including increased total sleep time (P adj=0.007), decreased sleep efficiency (P adj=0.002), shortening of sleep onset latency (P adj<0.001), elevated wake after sleep onset (P adj=0.002), increased N1% (P adj=0.006), and reduced N2%, N3%, and REM% (P adj=0.007, P adj<0.001, P adj=0.013). Thirty-seven percent of patients had moderate to severe obstructive sleep apnea-hypopnea syndrome. And sixty percent of patients were complicated with REM sleep without atonia. Patients with NT1 displayed increased anxiety propensity (P adj<0.001), and increased brain fatigue (P adj=0.020) in brain function state monitoring. FSS scores were positively correlated with brain fatigue (P adj<0.001) and mean sleep latency was inversely correlated with FSS scores and brain fatigue (P adj=0.013, P adj=0.029). Additionally, ESS scores and brain fatigue decreased after 3 months of therapy (P=0.012, P=0.030).
UNASSIGNED: NT1 patients had abnormal nocturnal sleep structures, who showed increased anxiety, depression, and fatigue. Excessive daytime sleepiness and fatigue improved after 3 months of treatment with methylphenidate hydrochloride prolonged-release tablets in combination with venlafaxine.

UNASSIGNED: Excessive daytime sleepiness (EDS) forms a prevalent symptom of obstructive sleep apnea (OSA) and narcolepsy type 1 (NT1), while the latter might always be overlooked. Machine learning (ML) models can enable the early detection of these conditions, which has never been applied for diagnosis of NT1.
UNASSIGNED: The study aimed to develop ML prediction models to help non-sleep specialist clinicians identify high probability of comorbid NT1 in patients with OSA early.
UNASSIGNED: Totally, clinical features of 246 patients with OSA in three sleep centers were collected and analyzed for the development of nine ML models. LASSO regression was used for feature selection. Various metrics such as the area under the receiver operating curve (AUC), calibration curve, and decision curve analysis (DCA) were employed to evaluate and compare the performance of these ML models. Model interpretability was demonstrated by Shapley Additive explanations (SHAP).
UNASSIGNED: Based on the analysis of AUC, DCA, and calibration curves, the Gradient Boosting Machine (GBM) model demonstrated superior performance compared to other machine learning (ML) models. The top five features used in the GBM model, ranked by feature importance, were age of onset, total limb movements index, sleep latency, non-REM (Rapid Eye Movement) sleep stage 2 and severity of OSA.
UNASSIGNED: The study yielded a simple and feasible screening ML-based model for the early identification of NT1 in patients with OSA, which warrants further verification in more extensive clinical practices.