Tongue Biting Event in Patients with Sleep-Related Facial Mandibular Myoclonus: A Case Series Study.

Background: Sleep-related facial mandibular myoclonus (SRFMM) remains rare in clinical practice. The aim of this study was to provide a comprehensive understanding of the electroclinical manner, therapeutic regimen, and prognosis of SRFMM. Methods: Twenty-three patients who were diagnosed with SRFMM by clinical manifestation, video-electroencephalography (EEG) and electromyography over bilateral masseter and temporalis muscles were enrolled. Clinical and electrophysiological evaluation as well as follow-up information were recorded and analyzed. Results: The cohort involved 4 infants and 19 adults with a mean onset age of 43.5 years for SRFMM, among whom 19 were male. Twenty-one patients complained of tongue injuries and disturbed night-time sleep. SRFMM in 4 patients were ascribed to oral aripiprazole, brainstem ischemia and brain trauma. In 62 SRFMM episodes, 93.5% occurred in NREM sleep and 6.5% in REM sleep, and all events were associated with EEG arousals. In 13 patients with or without clonazepam, the motor events gradually disappeared, and the rest turned to be sporadic. Conclusion: SRFMM is a characteristic parasomnia manifested by tongue biting and accompanying facial mandibular myoclonus, leading to disrupted sleep. Besides adults, infants can also experience SRFMM with spontaneous remission. Most patients respond well to clonazepam, eventually with favorable prognosis.

Machine learning-Based model for prediction of Narcolepsy Type 1 in Patients with Obstructive Sleep Apnea with Excessive Daytime Sleepiness.

Background: 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. Objective: 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. Methods: 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). Results: 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. Conclusion: 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.

28-Day hindlimb unweighting reduces expression of Rho kinase and inhibits its effects in femoral artery of rat

Previous studies have demonstrated inconsistent roles of Rho kinase (ROCK) in the decreased vasoconstriction of rat hindquarter vessels induced by hindlimb unweighting (HU). The present study was designed to determine the unclear role of ROCK in the mediation of HU-induced decreased femoral arterial vasoconstriction. 28-day HU rat was adopted as the animal model. With or without Y-27632, a ROCK inhibitor, isometric force of femoral artery was measured. The expression of ROCK and its effects on downstream targets were also examined. Results showed that (1) HU caused a significant decrease of the phenylephrine (PE)-evoked and potassium chloride (KCl)-evoked femoral arterial vasoconstriction (P < 0.05), confirming the functional findings by previous studies. (2) Inhibition of ROCK with Y-27632 produced an equal reduction of the vasoconstriction in CON and HU. (3) HU significantly decreased ROCK II expression and the effects of ROCK on myosin light-chain phosphatase (MLCP) and MLC (P < 0.05), but increased p65 nuclear translocation (P < 0.05) and inducible nitric oxide synthase (iNOS) expression (P < 0.05). (4) HU significantly (P < 0.05) increased NO production in femoral arteries, with Y-27632 significantly (P < 0.01) amplifying this effect. These findings have revealed that 28-day HU reduced the expression and effects of ROCK on downstream targets both directly (MLCP and MLC) and possibly indirectly (NF-κB/iNOS/NO pathway) related to vasoconstriction in femoral arteries