Sleep Stages Research Articles

Effects and mechanisms of electroacupuncture on fear extinction and sleep phase in single prolonged stress mice

To observe the effects of electroacupuncture (EA) on fear extinction and sleep phase in single prolonged stress (SPS) mice, and explore its mechanism in view of the expression of relevant synaptic proteins. Thirty-two C57BL/6J male mice were randomly divided into a control group, a model group, an EA group and a paroxetine (PRX) group, with 8 mice in each one. Modified SPS method was used to establish PTSD model in the model group, the EA group and the PRX group. Seven days after modeling completion, in the EA group, the intervention was delivered at "Baihui" (GV 20) and bilateral "Zusanli" (ST 36), with disperse-dense wave, 3 Hz/15 Hz in frequency and 1 mA in current intensity, for 30 min. In the PRX group, paroxetine solution (2.5 g/L) was administered intragastrically (10 mg/kg). The intervention was given once daily and for consecutive 10 days in the above two groups. The fear conditioning task and the elevated plus-maze test were adopted to evaluate the fear extinction and anxiety of the mice in each group. Using Medusa electroencephalogram (EEG) and electromyography (EMG) recording system from rats and mice, the sleep phase was determined in the mice. With Western blot method adopted, the protein expression of the postsynaptic density protein 95 (PSD95), activity-regulated cytoskeleton-associated protein (ARC), brain-derived neurotrophic factor (BDNF), N-methyl-D-aspartic acid receptor 2A (GluN2A), N-methyl-D-aspartic acid receptor 2B (GluN2B) and alpha-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid receptor 1 (GluA1) in the hippocampus was detected in the mice. Compared with the control group, the freezing time for the fear re-exposure in 3 min to 15 min and the fear extinction in 0 min to 3 min were prolonged (P<0.05), the fear extinction index decreased (P<0.05), and the open arm time (OT) of the elevated plus-maze was shortened (P<0.05) in the model group. When compared with the model group, in the EA group and the PRX group, the freezing time for the fear re-exposure in 3 min to 6 min and 12 min to 15 min, as well as the fear extinction in 0 min to 3 min was shortened (P<0.05), the fear extinction index increased (P<0.05); the OT in elevated plus-maze was longer in the mice of the EA group (P<0.05). The period of wake (Wake) was prolonged (P<0.05), the non-rapid eye movement period (NREM) and the total sleep time (Sleep) were reduced in the model group (P<0.05) in comparison with the control group. Compared with the model group, the Wake was declined (P<0.05), and the NREM and Sleep increased in the EA group and the PRX group (P<0.05). When compared with the control group, the protein expression of PSD95, ARC, BDNF, GluN2A and GluA1 in the hippocampus decreased (P<0.05), and that of GluN2B increased (P<0.05) in the model group. In the EA group and the PRX group, the protein expression of PSD95, ARC, BDNF, GluN2A and GluA1 in the hippocampus was elevated (P<0.05), and that of GluN2B reduced (P<0.05) when compared with the model group. Electroacupuncture at "Baihui" (GV 29) and "Zusanli" (ST 36) can ameliorate anxiety-like behavior, fear extinction disorder and abnormal sleep phase in SPS mice, which may be related to the regulation of synaptic transmission and synaptic plasticity expression in the hippocampus.

Decreased Delta/Beta ratio index as the sleep state-independent electrophysiological signature of sleep state misperception in Insomnia disorder: A focus on the sleep onset and the whole night

PurposeSleep State Misperception (SSM) is described as the tendency of Insomnia Disorder (ID) patients to overestimate Sleep Latency (SL) and underestimate Total Sleep Time (TST). Literature exploring topographical components in ID with SSM is scarce and does not allow us to fully understand the potential mechanisms underlying this phenomenon. This study aims to evaluate the existence of sleep EEG topography alterations in ID patients associated with SSM compared to Healthy Controls (HC), focusing on two distinct periods: the Sleep Onset (SO) and the whole night. MethodsTwenty ID patients (mean age: 43.5 ± 12.7; 7 M/13F) and 18 HCs (mean age: 41.6 ± 11.9; 8 M/10F) underwent a night of Polysomnography (PSG) and completed sleep diaries the following morning upon awakening. Two SSM indices, referring to the misperception of SL (SLm) and TST (TSTm), were calculated by comparing objective and subjective sleep indices extracted by PSG and sleep diary. According to these indices, the entire sample was split into 4 sub-groups: ID +SLm vs HC –SLm; ID +TSTm vs HC –TSTm. ResultsConsidering the SO, the two-way mixed-design ANOVA showed a significant main effect of Groups pointing to a decreased delta/beta ratio in the whole scalp topography. Moreover, we found a significant interaction effect for the sigma and beta bands. Post Hoc tests showed higher sigma and beta power in anterior and temporo-parietal sites during the SO period in IDs +SLm compared to HC –SLm.Considering the whole night, the unpaired t-test revealed in IDs +TSTm significantly lower delta power during NREM, and lower delta/beta ratio index during NREM and REM sleep compared to HCs –TSTm.Finally, we found diffuse significant negative correlations between SSM indices and the delta/beta ratio during SO, NREM, and REM sleep. ConclusionThe main finding of the present study suggests that higher SL overestimation and TST underestimation are both phenomena related to diffuse cortical hyperarousal interpreted as a sleep state-independent electrophysiological correlate of the SSM, both during the SO and the whole night.

The first-night effect of sleep occurs over nonconsecutive nights in unfamiliar and familiar environments.

The first night in an unfamiliar environment is marked by reduced sleep quality and changes in sleep architecture. This so-called first-night effect (FNE) is well established for two consecutive nights and lays the foundation for including an adaptation night in sleep research to counteract FNEs. However, adaptation nights rarely happen immediately before experimental nights, which raises the question of how sleep adapts over nonconsecutive nights. Furthermore, it is yet unclear, how environmental familiarity and hemispheric asymmetry of slow-wave sleep (SWS) contribute to the explanation of FNEs. To address this gap, 45 healthy participants spent two weekly separated nights in the sleep laboratory. In a separate study, we investigated the influence of environmental familiarity on 30 participants who spent two nonconsecutive nights in the sleep laboratory and two nights at home. Sleep was recorded by polysomnography. Results of both studies show that FNEs also occur in nonconsecutive nights, particularly affecting wake after sleep onset, sleep onset latency, and total sleep time. Sleep disturbances in the first night happen in both familiar and unfamiliar environments. The degree of asymmetric SWS was not correlated with the FNE but rather tended to vary over the course of several nights. Our findings suggest that nonconsecutive adaptation nights are effective in controlling for FNEs, justifying the current practice in basic sleep research. Further research should focus on trait- and fluctuating state-like components explaining interhemispheric asymmetries.

Construction and validation of clinical prediction model of tongue base collapse under drug-induced sleep endoscopy in OSA patients

Objective: To analyze the correlation between drug-induced sleep endoscopy (DISE), results, polysomnography (PSG) indicators, and clinical parameters in patients with obstructive sleep apnea (OSA), and to establish and validate a predictive model for tongue base plane obstruction. Methods: This retrospective study analyzed 117 OSA patients diagnosed via PSG and treated at the Department of Otolaryngology-Head and Neck Surgery, Nanfang Hospital, Southern Medical University, between October 2014 and March 2022. The cohort comprised of 114 males and 3 females, with an age range of 20 to 54 years (mean age 38.1±8.4 years). Data on DISE results, PSG results, and clinical indicators were collected for all 117 patients. Logistic regression analysis was performed to identify relevant indicators, and a predictive model for tongue base plane obstruction was constructed and internally validated using the R programming language. Results: Univariate logistic regression analysis identified four independent risk factors for predicting tongue root plane obstruction: tonsil grading, N2, N3, and rapid eye movement sleep(REM) stage [OR:0.412(0.260~0.652),1.045(1.012~1.079),0.943(0.903~0.984),0.961(0.925~0.998),P <0.05]. Multivariate logistic regression analysis confirmed tonsil grading and N3 sleep stage (12.48±12.22%) as significant predictors. A nomogram model incorporating these factors demonstrated good predictive performance, with an area under curve(AUC) of 0.82 (95%CI: 0.548-1.000), an optimal cutoff of 0.519, a specificity of 80.0%, and a sensitivity of 86.7%. Internal validation of the model in the validation cohort yielded an AUC of 0.751 (95%CI: 0.625-0.876). Conclusions: Tongue base plane obstruction observed during DISE in OSA patients is associated with tonsil grading and N3 sleep stage duration. The predictive model developed for tongue base plane obstruction based on DISE demonstrates good efficacy, as evidenced by its internal validation.

Automated remote sleep monitoring needs uncertainty quantification.

Wearable electroencephalography devices emerge as a cost-effective and ergonomic alternative to gold-standard polysomnography, paving the way for better health monitoring and sleep disorder screening. Machine learning allows to automate sleep stage classification, but trust and reliability issues have hampered its adoption in clinical applications. Estimating uncertainty is a crucial factor in enhancing reliability by identifying regions of heightened and diminished confidence. In this study, we used an uncertainty-centred machine learning pipeline, U-PASS, to automate sleep staging in a challenging real-world dataset of single-channel electroencephalography and accelerometry collected with a wearable device from an elderly population. We were able to effectively limit the uncertainty of our machine learning model and to reliably inform clinical experts of which predictions were uncertain to improve the machine learning model's reliability. This increased the five-stage sleep-scoring accuracy of a state-of-the-art machine learning model from 63.9% to 71.2% on our dataset. Remarkably, the machine learning approach outperformed the human expert in interpreting these wearable data. Manual review by sleep specialists, without specific training for sleep staging on wearable electroencephalography, proved ineffective. The clinical utility of this automated remote monitoring system was also demonstrated, establishing a strong correlation between the predicted sleep parameters and the reference polysomnography parameters, and reproducing known correlations with the apnea-hypopnea index. In essence, this work presents a promising avenue to revolutionize remote patient care through the power of machine learning by the use of an automated data-processing pipeline enhanced with uncertainty estimation.

Effect of Physical Exercise on Sleep Quality in College Athletes in Malaysia

Purpose: The aim of the study was to analyze the effect of physical exercise on sleep quality in college athletes in Malaysia. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Research on college athletes in Malaysia reveals that regular physical exercise significantly improves sleep quality by increasing sleep duration and efficiency. Exercise reduces sleep latency, helping athletes fall asleep faster, and enhances deep sleep stages crucial for recovery. Additionally, physical activity aids in managing stress and anxiety, which are linked to poor sleep. However, the impact of exercise can vary depending on its intensity and timing. Unique Contribution to Theory, Practice and Policy: The theory of exercise and sleep interactions, the cognitive behavioral theory of insomnia (CBT-I) &amp; the circadian rhythm theory may be used to anchor future studies on the effect of physical exercise on sleep quality in college athletes in Malaysia. Athletic trainers and coaches should design personalized exercise regimens that include a mix of aerobic, resistance, and flexibility exercises tailored to individual athletes' needs. Sports organizations and educational institutions should establish policies that recommend optimal timing for workouts. Research indicates that morning sessions are less disruptive to sleep than evening sessions, and such guidelines can help in scheduling training sessions to promote better sleep quality.

Relationship between Sports Participation and Academic Discipline in Middle School Students in Egypt

Purpose: The aim of the study was to analyze the relationship between sports participation and academic discipline in middle school students in Egypt. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Research on college athletes in Malaysia reveals that regular physical exercise significantly improves sleep quality by increasing sleep duration and efficiency. Exercise reduces sleep latency, helping athletes fall asleep faster, and enhances deep sleep stages crucial for recovery. Additionally, physical activity aids in managing stress and anxiety, which are linked to poor sleep. However, the impact of exercise can vary depending on its intensity and timing. Unique Contribution to Theory, Practice and Policy: Self-determination theory (SDT), social cognitive theory (SCT) &amp; achievement goal theory (AGT) may be used to anchor future studies on the relationship between sports participation and academic discipline in middle school students in Egypt. Schools should implement structured sports programs that emphasize not only physical skills but also the development of discipline, teamwork, and time management. Schools should adopt policies that promote the integration of sports and academics, ensuring that sports participation is not viewed as a distraction but as a complementary activity that enhances academic discipline.

Scheduled feeding improves sleep in a mouse model of Huntington's disease.

Sleep disturbances are common features of neurodegenerative disorders including Huntington's disease (HD). Sleep and circadian disruptions are recapitulated in animal models, providing the opportunity to evaluate the effectiveness of circadian interventions as countermeasures for neurodegenerative disease. For instance, time restricted feeding (TRF) successfully improved activity rhythms, sleep behavior and motor performance in mouse models of HD. Seeking to determine if these benefits extend to physiological measures of sleep, electroencephalography (EEG) was used to measure sleep/wake states and polysomnographic patterns in male and female wild-type (WT) and bacterial artificial chromosome transgenic (BACHD) adult mice, under TRF and ad lib feeding (ALF). Our findings show that male, but not female, BACHD mice exhibited significant changes in the temporal patterning of wake and non-rapid eye movement (NREM) sleep. The TRF intervention reduced the inappropriate early morning activity by increasing NREM sleep in the male BACHD mice. In addition, the scheduled feeding reduced sleep fragmentation (# bouts) in the male BACHD mice. The phase of the rhythm in rapid-eye movement (REM) sleep was significantly altered by the scheduled feeding in a sex-dependent manner. The treatment did impact the power spectral curves during the day in male but not female mice regardless of the genotype. Sleep homeostasis, as measured by the response to six hours of gentle handling, was not altered by the diet. Thus, TRF improves the temporal patterning and fragmentation of NREM sleep without impacting sleep homeostasis. This work adds critical support to the view that sleep is a modifiable risk factor in neurodegenerative diseases.

Sleep stages detection based on analysis and optimisation of non-linear brain signal parameters

The analysis and detection of sleep stages continue to preoccupy researchers, particularly bioinformaticians and neurologists aiming to understand various aspects and functioning of the brain during sleep cycles. This understanding is crucial for developing systems capable of early diagnosis of sleep disorders and adapting these systems for use in operating rooms to monitor the brain activity of patients under sedation or anesthesia. In this study, we apply a set of methods to process and decompose the single-channel EEG neural signal into different brain waves such as beta, alpha, theta, and delta. We then focus on the analysis and optimization of the extracted non-linear features using Principal Component Analysis (PCA) to improve the performance and robustness of the classification of six sleep stages. Classifier validation is performed using the cross-validation method. The results show that when the dimensions of the combined non-linear features are reduced, the RF-PCA model achieves an accuracy of 95.2%, outperforming other models such as MLP, SVM, DT, and GB, as well as recent studies. These findings validate the effectiveness of the system, indicating its potential for embedded implementation in practical applications for sleep disorder diagnosis and monitoring.

A foundational transformer leveraging full night, multichannel sleep study data accurately classifies sleep stages.

To investigate whether a foundational transformer model using 8-hour, multichannel data from polysomnograms can outperform existing artificial intelligence (AI) methods for sleep stage classification. We utilized the Sleep Heart Health Study (SHHS) visits 1 and 2 for training and validation and the Multi-Ethnic Study of Atherosclerosis (MESA) for testing of our model. We trained a self-supervised foundational transformer (called PFTSleep) that encodes 8-hour long sleep studies at 125 Hz with 7 signals including brain, movement, cardiac, oxygen, and respiratory channels. These encodings are used as input for training of an additional model to classify sleep stages, without adjusting the weights of the foundational transformer. We compared our results to existing AI methods that did not utilize 8-hour data or the full set of signals but did report evaluation metrics for the SHHS dataset. We trained and validated a model with 8,444 sleep studies with 7 signals including brain, movement, cardiac, oxygen, and respiratory channels and tested on an additional 2,055 studies. In total, we trained and tested 587,944 hours of sleep study signal data. Area under the precision recall curve (AUPRC) scores were 0.82, 0.40, 0.53, 0.75, and 0.82 and area under the receiving operating characteristics curve (AUROC) scores were 0.99, 0.95, 0.96, 0.98, and 0.99 for wake, N1, N2, N3, and REM, respectively, on the SHHS validation set. For MESA, the AUPRC scores were 0.56, 0.16, 0.40, 0.45, and 0.65 and AUROC scores were 0.94, 0.77, 0.87, 0.91, and 0.96, respectively. Our model was compared to the longest context window state-of-the-art model and showed increases in macro evaluation scores, notably sensitivity (3.7% increase) and multi-class REM (3.39% increase) and wake (0.97% increase) F1 scores. Utilizing full night, multi-channel PSG data encodings derived from a foundational transformer improve sleep stage classification over existing methods.

The Impact of Lunch Timing on Nap Quality.

Previous research has established that food intake is a biological regulator of the human sleep-wake cycle. As such, the timing of eating relative to sleep may influence the quality of sleep, including daytime naps. Here, we examine whether the timing of lunch (1 h vs. 2 h interval between lunch and a napping opportunity) impacts the quality of an afternoon nap. Using a randomized within-subject design over two separate experimental sessions (7 days apart), participants (n = 40, mean age = 25.8 years) consumed lunch 1 h and 2 h prior to an afternoon nap opportunity. Polysomnography and subjective self-reports were used to assess sleep architecture, sleepiness levels, and nap quality. Results revealed no significant differences in subjective ratings of sleep quality and sleepiness, or in sleep architecture (total sleep time, sleep efficiency, sleep onset latency, sleep stages) between the 1 h and 2-h lunch conditions. All sleep measures were similar when napping followed eating by either 1 h or 2 h, suggesting that eating closer to nap onset may not negatively impact sleep architecture and quality. Future research should continue to identify conditions that improve nap quality, given the well-documented benefits of naps to reduce sleep pressure and improve human performance.

The Interactions between Smoking and Sleep.

Smoking a cigarette before bed or first thing in the morning is a common habit. In this review, the relationship between smoking and sleep is investigated based on the existing literature. Out of 6504 unique items that were identified via a PubMed search related to smoking and sleep, 151 were included in this review. Tobacco smoking disrupts sleep architecture by reducing slow wave and rapid eye movement (REM) sleep and undermining sleep quality. Furthermore, smoking affects sleep-related co-morbidities, such as obstructive sleep apnea-hypopnea syndrome (OSAHS), insomnia, parasomnias, arousals, bruxism, and restless legs, as well as non-sleep-related conditions such as cardiovascular, metabolic, respiratory, neurologic, psychiatric, inflammatory, gynecologic and pediatric issues, while poor sleep quality also seems to worsen the chances of successful smoking cessation. In conclusion, the existing literature suggests that there is a wicked relation between smoking and sleep.

GBA-AAV mitigates sleep disruptions and motor deficits in mice with REM sleep behavior disorder

Sleep disturbances, including rapid eye movement sleep behavior disorder (RBD), excessive daytime sleepiness, and insomnia, are common non-motor manifestations of Parkinson’s disease (PD). Little is known about the underlying mechanisms, partly due to the inability of current rodent models to adequately mimic the human PD sleep phenotype. Clinically, increasing studies have reported that variants of the glucocerebrosidase gene (GBA) increase the risk of PD. Here, we developed a mouse model characterized by sleep–wakefulness by injecting α-synuclein preformed fibronectin (PFF) into the sublaterodorsal tegmental nucleus (SLD) of GBA L444P mutant mice and investigated the role of the GBA L444P variant in the transition from rapid eye movement sleep behavior disorder to PD. Initially, we analyzed spectral correlates of REM and NREM sleep in GBA L444P mutant mice. Importantly, EEG power spectral analysis revealed that GBA L444P mutation mice exhibited reduced delta power during non-rapid eye movement (NREM) sleep and increased theta power (8.2–10 Hz) in active rapid eye movement (REM) sleep phases. Our study revealed that GBA L444P-mutant mice, after receiving PFF injections, exhibited increased sleep fragmentation, significant motor and cognitive dysfunctions, and loss of dopaminergic neurons in the substantia nigra. Furthermore, the over-expression of GBA-AAV partially improved these sleep disturbances and motor and cognitive impairments. In conclusion, we present the initial evidence that the GBA L444P mutant mouse serves as an essential tool in understanding the complex sleep disturbances associated with PD. This model further provides insights into potential therapeutic approaches, particularly concerning α-synuclein accumulation and its subsequent pathological consequences.

GRU-powered sleep stage classification with permutation-based EEG channel selection

We present a new approach to classifying the sleep stage that incorporates a computationally inexpensive method based on permutations for channel selection and takes advantage of deep learning power, specifically the gated recurrent unit (GRU) model, along with other deep learning methods. By systematically permuting the electroencephalographic (EEG) channels, different combinations of EEG channels are evaluated to identify the most informative subset for the classification of the 5-class sleep stage. For analysis, we used an EEG dataset that was collected at the International Institute for Integrative Sleep Medicine (WPI-IIIS) at the University of Tsukuba in Japan. The results of these explorations provide many new insights such as the (1) drastic decrease in performance when channels are fewer than 3, (2) 3-random channels selected by permutation provide the same or better prediction than the 3 channels recommended by the American Academy of Sleep Medicine (AASM), (3) N1 class suffers the most in prediction accuracy as the channels drop from 128 to 3 random or 3 AASM, and (4) no single channel provides acceptable levels of accuracy in the prediction of 5 classes. The results obtained show the GRU’s ability to retain essential temporal information from EEG data, which allows capturing the underlying patterns associated with each sleep stage effectively. Using permutation-based channel selection, we enhance or at least maintain as high model efficiency as when using high-density EEG, incorporating only the most informative EEG channels.

Association of Sleep Patterns and Respiratory Disturbance Index with Physiological Parameters in Pediatric Patients with Self-Perceived Short Stature.

To investigate the relationships of sleep patterns and respiratory disturbance index (RDI) with key physiological parameters (height, body mass index (BMI), bone age (BA), and IGF-1 levels) in children aged 6 to 16 years with self-perceived short stature. For this cross-sectional study, conducted from October 2019 to November 2021, 238 children aged 6 to 16 years with self-perceived short stature were enrolled. The primary outcomes of sleep patterns and the RDI were non-invasively collected at home using the LARGAN Health AI-Tech Sleep Apnea and Sleep Quality Examination System, which operates based on polygraphy. Additionally, various physiological parameters, including height, BMI, bone age, and IGF-1 levels, were measured to assess their associations with sleep patterns and RDI. Significant age-related reductions were observed in both the total and deep sleep durations. Children aged 6-9 years averaged 8.5 ± 1.0 h of total sleep, which decreased to 8.1 ± 1.1 h in ages 10-11 and further to 7.5 ± 0.9 h in ages 12-16 (p < 0.0001). Deep sleep followed a similar pattern, decreasing from 4.4 ± 1.1 h in the youngest group to 3.3 ± 1.0 h in the oldest (p < 0.0001). Notably, girls experienced significantly longer deep sleep than boys, averaging 4.0 ± 1.2 h compared to 3.6 ± 1.2 h (p = 0.0153). In a multivariable regression analysis, age (beta = 4.89, p < 0.0001) and RDI (beta = -0.54, p = 0.0022) were significantly associated with body height. Age and deep sleep duration (beta = -0.02, p = 0.0371) were significantly associated with BMI. The results demonstrate significant age-related decreases in the total and deep sleep duration among children with self-perceived short stature, along with a notable association between RDI and body height and an association between deep sleep duration and BMI. These findings suggest that sleep disturbances in pediatric endocrine patients are intricately linked with physiological growth parameters. The identified correlations underline the importance of monitoring sleep patterns in this demographic to better understand the impact of endocrine disorders on developmental health. Further research is needed to explore interventions that could alleviate these sleep disturbances, thereby potentially improving outcomes for the affected children.

Melatonin targets the paraventricular thalamus to promote non-rapid eye movement sleep in C3H/HeJ mice

Melatonin (MLT) is an important circadian signal for sleep regulation, but the neural circuitries underlying the sleep-promoting effects of MLT are poorly understood. The paraventricular thalamus (PVT) is a critical thalamic area for wakefulness control and expresses MLT receptors, raising a possibility that PVT neurons may mediate the sleep-promoting effects of MLT. Here, we found that MLT receptors were densely expressed on PVT neurons and exhibited circadian-dependent variations in C3H/HeJ mice. Application of exogenous MLT decreased the excitability of PVT neurons, resulting in hyperpolarization of membrane potential and reduction of action potential firing. MLT also inhibited the spontaneous activity of PVT neurons at both population and single-neuron levels in freely behaving mice. Furthermore, pharmacological manipulations revealed that local infusion of exogeneous MLT into the PVT promoted non-rapid eye movement (NREM) sleep and increased NREM sleep duration, whereas MLT receptor antagonists decreased NREM sleep. Moreover, we found that selectively knocking down endogenous MLT receptors in the PVT decreased NREM sleep and correspondingly increased wakefulness, with particular changes shortly after the onset of the dark or light phase. Taken together, these results demonstrate that PVT is an important target of MLT for promoting NREM sleep.

Cyclooxygenase-2 (COX-2)-dependent mechanisms mediate sleep responses to microbial and thermal stimuli

Prostaglandins (PGs) play a crucial role in sleep regulation, yet the broader physiological context that leads to the activation of the prostaglandin-mediated sleep-promoting system remains elusive. In this study, we explored sleep-inducing mechanisms potentially involving PGs, including microbial, immune and thermal stimuli as well as homeostatic sleep responses induced by short-term sleep deprivation using cyclooxygenase-2 knockout (COX-2 KO) mice and their wild-type littermates (WT). Systemic administration of 0.4 µg lipopolysaccharide (LPS) induced increased non-rapid-eye movement sleep (NREMS) and fever in WT animals, these effects were completely absent in COX-2 KO mice. This finding underscores the essential role of COX-2-dependent prostaglandins in mediating sleep and febrile responses to LPS. In contrast, the sleep and fever responses induced by tumor necrosis factor α, a proinflammatory cytokine which activates COX-2, were preserved in COX-2 KO animals, indicating that these effects are independent of COX-2-related signaling. Additionally, we examined the impact of ambient temperature on sleep. The sleep-promoting effects of moderate warm ambient temperature were suppressed in COX-2 KO animals, resulting in significantly reduced NREMS at ambient temperatures of 30 °C and 35 °C compared to WT mice. However, rapid-eye-movement sleep responses to moderately cold or warm temperatures did not differ between the two genotypes. Furthermore, 6 h of sleep deprivation induced rebound increases in sleep with no significant differences observed between COX-2 KO and WT mice. This suggests that while COX-2-derived prostaglandins are crucial for the somnogenic effects of increased ambient temperature, the homeostatic responses to sleep loss are COX-2-independent. Overall, the results highlight the critical role of COX-2-derived prostaglandins as mediators of the sleep-wake and thermoregulatory responses to various physiological challenges, including microbial, immune, and thermal stimuli. These findings emphasize the interconnected regulation of body temperature and sleep, with peripheral mechanisms emerging as key players in these integrative processes.

The effect of central sleep apnea on sleep architecture in children with obstructive sleep apnea

ObjectiveThis study aimed to investigate how central sleep apnea (CSA) impacts sleep patterns in children with obstructive sleep apnea (OSA). MethodsChildren undergoing polysomnography (PSG) were enrolled and sorted into two groups: those with OSA alone (Group A) and those with both OSA and CSA (CAI <1 nd: children with 10 % CSA or more and less than 50 %, Group B). Statistical analysis was conducted to compare sleep structure and clinical features between Group A and Group B. ResultsGroup B exhibited significantly higher respiratory events, apnea hypoventilation index, apnea index and oxygen desaturation index (ODI) compared to Group A (p < 0.05). Group B also showed higher total sleep time and arousal index than Group A (P < 0.05). The proportion of time spent in stage N3 was lower in Group B than in Group A (P < 0.05). Moreover, mean heart rate and minimum heart rate were higher in Group B compared to Group A (P < 0.05).Minimum oxygenation levels (including non-rapid eye movement (NREM) stages) were lowe in Group B than in Group A (P < 0.05). Additionally, the prevalence of positional obstructive sleep apnea (P-OSA) was greater in Group B than in Group A (P < 0.05). ConclusionIn comparison to those with OSA alone, children with OSA and concurrent CSA exhibited distinct sleep patterns, including reduced N3uration, higher arousal index, longer respiratory events, higher ODI, and lower oxygen saturation, higher heart rate.

Safinamide effect on sleep architecture of motor fluctuating Parkinson's disease patients: A polysomnographic rasagiline-controlled study

IntroductionSleep problems commonly occur in Parkinson's disease (PD) and significantly affect patients' quality of life. A possible effect on subjective sleep disturbances of monoamine oxidase-B inhibitors (MAOB-Is) has been described. MethodsThis prospective, observational, single-centre study involved 45 fluctuating PD patients complaining sleep problems as documented by the PD Sleep Scale -2nd version (PDSS-2 ≥18) starting rasagiline 1 mg/daily or safinamide 100 mg/daily, according to common clinical practice, and maintaining antiparkinsonian therapy unchanged. Polysomnography (PSG), sleep questionnaires (PDSS-2, Epworth Sleepiness Scale - ESS), and motor function were evaluated at baseline (T0) and after 4 months of treatment (T1). ResultsSafinamide was prescribed in thirty patients and rasagiline in fifteen patients. Both drugs induced a significant improvement in Movement Disorder Society Unified PD Rating Scale III scores. Patients treated with rasagiline showed a significant increase in stage 1 (N1) Non-REM sleep compared to T0, with no significant effects on sleep scales. Patients treated with safinamide showed a significant increase in stage 3 of Non-REM sleep and sleep efficiency and a reduction in the rate of periodic limb movements, matching a significant reduction in PDSS-2 and ESS scales compared to T0. ConclusionThis study showed that safinamide, in addition to having a significant effect on PD motor symptoms, like the other MAOB-Is, may exert a specific beneficial effect on subjective and objective sleep, probably driven by its dual mechanism of action, which involves both dopaminergic and glutamatergic neurotransmission.

A Deep Transfer Learning Approach for Sleep Stage Classification and Sleep Apnea Detection Using Wrist-Worn Consumer Sleep Technologies.

Obstructive sleep apnea (OSA) is a common, underdiagnosed sleep-related breathing disorder with serious health implications Objective - We propose a deep transfer learning approach for sleep stage classification and sleep apnea (SA) detection using wrist-worn consumer sleep technologies (CST). Methods - Our model is based on a deep convolutional neural network (DNN) utilizing accelerometers and photo-plethysmography signals from nocturnal recordings. The DNN was trained and tested on internal datasets that include raw data from clinical and wrist-worn devices; external validation was performed on a hold-out test dataset containing raw data from a wrist-worn CST. Results - Training on clinical data improves performance significantly, and feature enrichment through a sleep stage stream gives only minor improvements. Raw data input outperforms feature-based input in CST datasets. The system generalizes well but performs slightly worse on wearable device data compared to clinical data. However, it excels in detecting events during REM sleep and is associated with arousal and oxygen desaturation. We found; cases that were significantly underestimated were characterized by fewer of such event associations. Conclusion - This study showcases the potential of using CSTs as alternate screening solution for undiagnosed cases of OSA. Significance - This work is significant for its development of a deep transfer learning approach using wrist-worn consumer sleep technologies, offering comprehensive validation for data utilization, and learning techniques, ultimately improving sleep apnea detection across diverse devices.