Sleep Cycle Research Articles

Temporal dynamics of negative emotional memory reprocessing during sleep

Memory reprocessing during sleep is a well-established phenomenon in numerous studies. However, it is unclear whether the intensity of memory reprocessing is consistently maintained throughout the night or exhibits dynamic changes. This study investigates the temporal dynamics of negative emotional memory reprocessing during sleep, with a specific focus on slow oscillation (SO)-spindle coupling and its role in memory reprocessing. In the first experiment (N = 40, mean age = 22.5 years), we detected the negative emotional memory reprocessing strength in each sleep cycle, we found that the 2nd sleep cycle after negative emotional memory learning constitute the most sensitive window for memory reprocessing, furthermore, SO-spindle coupling signals in this window plays a role in stabilizing negative emotional memory. To verify the role of SO-spindle coupling in negative emotional memory reprocessing, we utilized transcranial alternating current stimulation (tACS) to disrupt SO-spindle coupling during the 2nd sleep cycle (N = 21, mean age = 19.3 years). Notably, the outcomes of the tACS intervention demonstrated a significant reduction in the recognition of negative emotional memories. These findings offer new insights into the mechanisms that regulate emotional memory consolidation during sleep and may have implications for addressing psychiatric disorders associated with pathological emotional memory.

Caffeine Intake Alters Recovery Sleep after Sleep Deprivation.

Caffeine is a well-known psychostimulant reputed to alleviate the deleterious effects of sleep deprivation. Nevertheless, caffeine can alter sleep duration and quality, particularly during recovery sleep. We evaluated the effects of acute caffeine intake on the duration and quality of recovery sleep following total sleep deprivation (TSD), taking into account daily caffeine consumption. Forty-one participants performed a double-blind, crossover TSD protocol (38 h of continuous wakefulness) with acute caffeine or placebo. Caffeine (2.5 mg/kg) or placebo was administered twice during continuous wakefulness (last treatment 6.5 h before bedtime for the recovery night). Polysomnographic measurements were recorded using a connected headband. TSD was associated with a rebound in total sleep time (TST) on the recovery night (+110.2 ± 23.2 min, p < 0.001). Caffeine intake decreased this recovery TST (-30.2 ± 8.2 min p = 0.02) and the N3 sleep stage duration (-35.6 ± 23.2 min, p < 0.01). Caffeine intake altered recovery sleep continuity (increased number of long awakenings), stability (higher stage transition frequency), and organization (less time spent in complete sleep cycle) and decreased the delta power spectral density during NREM sleep. On the recovery night, habitual daily caffeine consumption was negatively correlated with TST in caffeine and placebo conditions and positively correlated with wake after sleep onset (WASO) duration and with the frequency of long (>2 min) awakenings in the caffeine condition only. Acute caffeine intake during TSD affects nighttime recovery sleep, with an interaction with daily consumption. These results may influence advice on caffeine intake for night-shift workers. (NCT03859882).

Relationship Between High Frequency Component of Heart Rate Variability and Delta EEG Power During Sleep in Women With Irritable Bowel Syndrome Compared to Healthy Women.

To explore the relationship between the high frequency (HF) heart rate variability (HRV) and electroencephalogram (EEG) delta band power in women with irritable bowel syndrome (IBS) versus healthy control women. Twenty women with IBS and twenty healthy controls were studied over three consecutive nights using polysomnography in a sleep laboratory. To avoid the first night effect, only second-night data were analyzed. Power spectral analysis was applied to HRV and EEG recordings. The linear system coherence/phase analysis assessed the relationship between normalized HF power of HRV and normalized delta band power of EEG during the first four NREM-REM sleep cycles. Women with IBS exhibited a significantly higher percentage of NREM sleep, higher normalized HF, lower normalized low frequency (LF) and decreased LF/HF ratio of HRV in the first four NREM-REM sleep cycles compared to controls. Additionally, their normalized delta band power was significantly lower in these sleep cycles and over the whole night. The phase shift between HF and delta band power was significantly longer in the IBS group. While the coherence between normalized HF and normalized delta band power was lower in the IBS group, the difference was not statistically significant. The coherence/phase analysis showed a dysregulated interaction between autonomic and central nervous systems in women with IBS, manifested by increased lag time between cardiac and EEG delta band power compared to healthy controls. Whether this dysregulation contributes to the pathophysiology of IBS remains to be determined.

Using cross-recurrence quantification analysis to compute similarity measures for time series of unequal length with applications to sleep stage analysis

Comparing time series of unequal length requires data processing procedures that may introduce biases. This article describes, validates, and applies Cross-Recurrence Quantification Analysis (CRQA) to detect and quantify correlation and coupling among time series of unequal length without prior data processing. We illustrate and validate this application using continuous and discrete data from a model system (study 1). Then we use the method to re-analyze the Sleep Heart Health Study (SHHS), a rare large dataset comprising detailed physiological sleep measurements acquired by in-home polysomnography. We investigate whether recurrence patterns of ultradian NREM/REM sleep cycles (USC) predict mortality (study 2). CRQA exhibits better performance compared with traditional approaches that require trimming, stretching or compression to bring two time series to the same length. Application to the SHHS indicates that recurrence patterns linked to stability of USCs are associated with all-cause mortality even after controlling for other sleep parameters, health, and sociodemographics. We suggest that CRQA is a useful tool for analyzing categorical time series, where the underlying structure of the data is unlikely to result in matching data points—such as ultradian sleep cycles.

Impact of school closure during COVID-19 pandemic on school-going children in a rural population of Haryana: A qualitative study

ABSTRACT Background: The Government of India undertook several measures for COVID-19 pandemic, of which school closure was one. This led to sudden shift of teaching methods from classroom-based to online mode. This study aimed to understand the effects of school closure on children during lockdown period. Methods: This was a community-based qualitative study conducted in Faridabad District, Haryana, India, from January 2022 to February 2022. Two focus group discussions (FGDs) with school students of 8th standard and above and two FGDs with parents of school students below 8th standard were conducted. In-depth interviews were conducted with two school headmasters, two teachers, a Senior Medical Officer (SMO), District Education Officer (DEO), and District Elementary Education Officer (DEEO). Responses were recorded, transcribed, and reviewed to identify themes, and thematic analysis was done. Four major themes were framed—academic, physical, behavioral, and psychosocial. Results: Decreased academic performance, teacher-student interaction, and distraction from the study were found in academic domain. There was reluctance to go back to school reopening. Low physical exercise, irregular sleep cycles meal skipping, and increased screen time were noticed. Limited mid-day meal nutrition was available. Increased fear, anxiety, and loneliness were faced along with an increase in household quarrels and violence. An increase in the number of child marriages was also reported. Few students had positive impacts like improvement in hygiene practices, learning new hobbies, and skills. Conclusion: School closure had impact on the physical, mental, socio-behavioral, and academic aspects of school-going children. In case of similar events in the future, these impacts must be kept in mind by policymakers to achieve a balance between effective pandemic control and fruitful childhoods.

Melatonin ameliorates chronic sleep deprivation against memory encoding vulnerability: Involvement of synapse regulation via the mitochondrial-dependent redox homeostasis-induced autophagy inhibition

Voluntary sleep curtailment is increasingly more rampant in modern society and compromises healthy cognition, including memory, to varying degrees. However, whether memory encoding is impaired after chronic sleep deprivation (CSD) and the underlying molecular mechanisms involved remain unclear. Here, using the mice, we tested the impact of CSD on the encoding abilities of social recognition-dependent memory and object recognition-dependent memory. We found that memory encoding was indeed vulnerable to CSD, while memory retrieval remained unaffected. The hippocampal neurons of mice with memory encoding deficits exhibited significant synapse damage and hyperactive autophagy, which dissipates during regular sleep cycles. This excessive autophagy appeared to be triggered by damage to mitochondrial DNA (mtDNA), resulting from oxidative stress within the mitochondria. The relief at the behavioral and molecular biological levels can be achieved with intraperitoneal injections of the antioxidant compound melatonin. Moreover, our in vitro experiments using HT-22 cells demonstrated that oxidative stress induced by hydrogen peroxide led to oxidative damage, including mtDNA damage, and activation of autophagy. Melatonin treatment effectively countered these effects, restoring redox homeostasis and reducing excessive autophagic activity. Notably, this protective effect was not observed when melatonin was administered as a pre-treatment. Together, our findings reveal the vulnerability of memory encoding during chronic sleep curtailment, which is caused by oxidative stress and consequent enhancement of autophagy, suggest a potential therapeutic strategy for addressing these effects following prolonged wakefulness through melatonin intervention, and reiterate the significance of adequate sleep for memory formation and retention.

Little grinder, big concern - a questionnaire survey

Background: Bruxism in paediatric dentistry refers to the habitual grinding or clenching of teeth in children. This condition can manifest during the day or night and may have various underlying causes. Bruxism has multifactorial etiology including disturbed sleep cycle and excessive gadget use in children. Present study aimed to evaluate the knowledge and awareness of parents about bruxism in children who visited the department. Bruxism is a common concern in paediatric dentistry, presenting challenges for dental professionals and parents alike in managing and addressing its effects on a child's oral health. Methods: The study was conducted among the parents visiting the Department with their children for the treatment. A self-administered questionnaire about knowledge and awareness regarding bruxism habit, sleep cycle, and gadget use in children was made. It was shared with parents using E-methods. Data was collected and statistical analysis was done. Results: In this study, parents stated that 74.1% of children grind their teeth, and 63% of children did so while using the device. Children most frequently utilized mobile devices (51.9%), then TV (13%), electronic toys and video games (11.10%), laptops/PCs and tablets (6.50%). Before falling asleep, 81.50% of the kids used their gadgets, and after using them, 60.20% of the kids reported having trouble sleeping. Conclusions: According to this study, children who clench their teeth have disrupted sleep, and clenching of teeth is more common in children who experience stress as a result of prolonged use of gadgets. Thus, a significant relationship was seen between children's use of gadgets, sleep disruption, and bruxism.

Predictive Modeling of Sleep Slow Oscillation Emergence on the electrode manifold: Toward Personalized Closed-Loop Brain Stimulation.

Sleep slow oscillations (SOs), characteristic of NREM sleep, are causally tied to cognitive outcomes and the health-promoting homeostatic functions of sleep. Due to these known benefits, brain stimulation techniques aiming to enhance SOs are being developed, with great potential to contribute to clinical interventions, as they hold promise for improving sleep functions in populations with identified SO deficits (e.g., mild cognitive impairment). SO-targeting closed-loop stimulation protocols currently strive to identify SO occurrences in real time, a computationally intensive step that can lead to reduced precision (compared to post-hoc detection). These approaches are also often limited to focusing on only one electrode location, thus inherently precluding targeting of SOs that is informed by the overall organization of SOs in space-time. Prediction of SO emergence across the electrode manifold would establish an alternative to online detection, thus greatly advancing the development of personalized and flexible brain stimulation paradigms. This study presents a computational model that predicts SO occurrences at multiple locations across a night of sleep. In combination with our previous study on optimizing brain stimulation protocols using the spatiotemporal properties of SOs, this model contributes to increasing the accuracy of SO targeting in brain stimulation applications. SOs were detected in a dataset of nighttime sleep of 22 subjects (9 females), acquired with polysomnography including 64 EEG channels. Modeling of SO occurrence was achieved for SOs in stage N3, or in a combination of stages N2 and N3 (N2&N3). We study SO emergence at progressively more refined time scales. First, the cumulative SO occurrences in successive sleep cycles were successfully fit with exponentials. Secondly, the SO timing in each individual was modeled with a renewal point process. Using an inverse Gaussian model, we estimated the probability density function of SO timing and its parameters μ (mean) and λ (shape, representing skewness) in successive cycles. We observed a declining trend in the SO count across sleep cycles, which we modeled using a power law relationship. The decay rate per cycle was 1.473 for N3 and 1.139 for N2&N3, with variances of the decay rates across participants being 1 and 0.53, respectively. This pattern mirrors the declining trend of slow wave activity (SWA) across sleep cycles, likely due to the inherent relationship between SWA and SO. Additionally, the SO timing model for N3 showed an increasing trend in the model parameters (μ, λ) across cycles. The increase rate per cycle followed a power law relationship with a rate of 0.83 and an exponential relationship with a rate of 4.59, respectively. The variances of the increase rates were 0.02 for μ and 0.44 for λ across participants. This study establishes a predictive model for SO occurrence during NREM sleep, providing insights into its organization in successive cycles and at different EEG channels, which is relevant to development of personalized stimulation paradigms. These findings imply that personalized model parameters can be estimated by incorporating SO information in the first sleep cycle, and hence SO timing can be predicted before its occurrence with a probability distribution, enabling more precise targeting of SOs.

Оценка эффективности нового лекарственного препарата Курс Успокоин таблетки для коррекции возрастных когнитивных нарушений у кошек

The treatment by Kurs Uspokoin tablets in a minimum initial daily dose of 7 mg/kg for the first three days, with a further increase to 14 mg/kg, showed a positive effect in correcting the behavior of older cats with progressive simptoms of cognitive disfunction. During the treatment were registered a statistically and clinically significant decrease in idiopathic vocalization, restoration of sleep cycles and wakefulness, elimination of house-soiling behavior, disorientation, increased activity, and improvement of social behavior. During the treatment by Kurs Uspokoin tablets and after its discontinuation no adverse reactions were registered.

Exploration of sleep quality and rest-activity rhythms characteristics in Bilateral Vestibulopathy patients

Sleep and circadian timing systems are constantly regulated by both photic and non-photic signals. Connections between the vestibular nuclei and the biological clock raise the question of the effect of peripheral vestibular loss on daily rhythms, such as the sleep-wake cycle and circadian rhythm. To answer this question, we compared the sleep and rest-activity rhythm parameters of 15 patients with bilateral vestibulopathy (BVP) to those of 15 healthy controls. Sleep and rest-activity cycle were recorded by a device coupling actimetry with the heart rate and actigraphy at home over 7 days. Subjective sleep quality was assessed by the Pittsburgh Sleep Quality Index (PSQI). Sleep efficiency and subjective sleep quality were significantly reduced, and sleep fragmentation was increased in BVP patients compared to controls. BVP patients displayed a damped amplitude of the rest-activity rhythm and higher sleep fragmentation, reflected by a higher nocturnal activity compared to controls. These results suggest that both rest-activity and sleep cycles are impaired in BVP patients compared to healthy controls. BVP patients seem to have greater difficulty maintaining good sleep at night compared to controls. BVP pathology appears to affect the sleep-wake cycle and disturb the circadian rhythm synchronization. Nevertheless, these results need further investigation to be confirmed, particularly with larger sample sizes.

Associations between Aircraft Noise, Sleep, and Sleep-Wake Cycle: Actimetric Data from the UK Biobank Cohort near Four Major Airports.

Nighttime aircraft noise may affect people's sleep, yet large-scale evidence using objective and subjective measures remains limited. Our aim was to investigate associations between nighttime aircraft noise exposure and objectively measured sleep disturbance using a large UK cohort. We used data from 105,770 UK Biobank cohort participants exposed and unexposed to aircraft noise who lived in 44 local authority districts near 4 international airports in England. We used a generalized linear regression model to examine cross-sectional associations between aircraft noise (23:00 hours-07:00 hours) and 7-d actimetric measures collected 2013-2015 (). We also used Logit and generalized estimating equations models to examine associations between and self-reported sleep measures at enrollment (2006-2010) and follow-up (2012-2013). This approach allowed us to compare and contrast the results and support potential future meta-analyses on noise-related sleep disturbance. Cross-sectional analyses of actimetric data suggested sleep disturbance associated with , showing higher level of movements during the least active continuous 8-h time period [: 0.12 milligravitational units; 95% confidence interval (CI): 0.013, 0.23]. We also saw disrupted sleep-wake cycles as indicated by index scores of lower relative amplitude (: ; 95% CI: , ), poorer interdaily stability (: ; 95% CI: , ), and greater intradaily variability (: 0.021; 95% CI: 0.019, 0.023), comparing dB with dB. Repeated cross-sectional analyses found a 52% higher odds of more frequent daytime dozing [odds ratio (OR) ; 95% CI: 1.32, 1.75] for dB in comparison with dB, whereas the likelihood for more frequent sleeplessness was more uncertain (; 95% CI: 0.92, 1.39). Higher effect sizes were seen in preidentified vulnerable groups, including individuals of age and those with diabetes or dementia. Individuals exposed to higher levels of aircraft noise experienced objectively higher levels of sleep disturbance and changes in sleep-wake cycle. https://doi.org/10.1289/EHP14156.

Sleep Cycles and Health: Role of Sleep Stages, Circadian Rhythms, and Lifestyle Factors on Optimizing Physical Performance and Mental Well-Being – a literature review

Sleep Cycles and Health: Role of Sleep Stages, Circadian Rhythms, and Lifestyle Factors on Optimizing Physical Performance and Mental Well-Being – a literature review

Amplitude-integrated electroencephalography monitoring results of hospitalized neonates in plateau areas

To investigate the amplitude-integrated electroencephalography (aEEG) monitoring results of hospitalized neonates in plateau areas. A retrospective analysis was conducted on 5 945 neonates who were admitted to the Department of Neonatology, Kunming Children's Hospital, and received aEEG monitoring from January 2020 to December 2022. According to the aEEG monitoring results, they were divided into a normal aEEG group and an abnormal aEEG group. The incidence rate of aEEG abnormalities was analyzed in neonates with various systemic diseases, as well as the manifestations of aEEG abnormalities and the consistency between aEEG abnormalities and clinical abnormalities. Among the 5 945 neonates, the aEEG abnormality rate was 19.28% (1 146/5 945), with an abnormality rate of 29.58% (906/3 063) in critically ill neonates and 8.33% (240/2 882) in non-critically ill neonates (P<0.05). The children with inherited metabolic diseases showed the highest aEEG abnormality rate of 60.77% (79/130), followed by those with central nervous system disorders [42.22% (76/180)] and preterm infants [35.53% (108/304)]. Compared with the normal aEEG group, the abnormal aEEG group had significantly lower age and gestational age, as well as a significantly lower birth weight of preterm infants (P<0.05). Among the 1 146 neonates with aEEG abnormalities, the main types of aEEG abnormalities were sleep cycle disorders in 597 neonates (52.09%), background activity abnormalities in 294 neonates (25.65%), and epileptiform activity in 255 neonates (22.25%), and there were 902 neonates (78.71%) with abnormal clinical manifestations. The sensitivity and specificity of aEEG monitoring for brain function abnormalities were 33.51% and 92.50%, respectively. In plateau areas, there is a relatively high rate of aEEG abnormalities among hospitalized neonates, particularly in critically ill neonates and those with smaller gestational ages and younger ages, suggesting a high risk of brain injury. Therefore, routine aEEG monitoring for the hospitalized neonates can help with the early detection of brain function abnormalities, the decision-making in treatment, and the formulation of brain protection strategies.

A Case of Anti-IgLON5 Disease with Aplastic Anemia and review of the literature

Anti-IgLON5 is a neurological condition with neurodegenerative and autoimmune etiology. A 64-year-old man with a 2-year history of aplastic anemia presented with symmetrical parkinsonism, fluctuating consciousness, supranuclear gaze palsy, mild fasciculation, and muscular atrophy. He had disturbances in his sleep cycle and brain MRI. CSF analysis was positive for the IgLON5 antibody. We initiated immunotherapy with high-dose methylprednisolone, intravenous immunoglobulin, and rituximab. The patient showed a mild to moderate response to treatment. We reviewed 29 published case reports regarding anti-Iglon-5 and examined the clinical manifestation. None of the cases showed aplastic anemia, which was the main presentation in our case. All of the patients experienced sleep disturbances, while other symptoms were heterogeneous. Anti-Iglon-5 is usually diagnosed late, leading to a weak prognosis. This study helped us establish a better understanding of the correlation between antiIglon5 disease and other autoimmune disorders like anemia.

The pulse of sleep: novel interventions in understanding the sleep-cardiovascular connection: a literature review.

Sleep disorders represent common complaints in different medical illnesses. They encompass a risk for diverse inflammatory, metabolic, and cardiovascular diseases. Sleep disorders include disorders of hypersomnolence, insomnia, parasomnias, sleep-related movement disorders, circadian rhythm sleep-wake-disorders, and sleep-related breathing disorders, each one of which was associated with increased cardiovascular disease risk in a different mechanism. In this review, the authors address the most recent research on the correlation between sleep and CVD. The literature on sleep disorders and their potential links to various cardiovascular diseases was reviewed in narrative form. For the published papers up to June 2023, the authors searched the databases of PubMed and Google Scholar. Literature demonstrating the relationship between these illnesses, pathophysiological mechanisms, diagnosis, and various therapeutic approaches was included. Sleep disorders were significantly linked to heart rate variability, hypertension, and obesity, which can eventually result in cardiovascular consequences and affect mortality and morbidity. The disruption in sleep cycles, which can be noticed in different sleep disorders, can obviously result in blood pressure, heart rate, and other cardiac functions. The clinical assessment acts as the cornerstone in the diagnosis of different spectrums of sleep disorders. The management of sleep disorders ranges from cognitive-behavioral therapy to continuous positive airway pressure (CPAP). Additional research on the topic is needed to pinpoint any potential links and pathological processes. To improve clinical treatment and preventive measures, further observational studies should emphasize the reliability of early diagnostic signs.

Multifractal analysis of heart rate variability in pregnancy during sleep.

Understanding the complex dynamics of heart rate variability (HRV) during pregnancy is crucial for monitoring both maternal well-being and fetal health. In this study, we use the Multifractal Detrended Fluctuations Analysis approach to investigate HRV patterns in pregnant individuals during sleep based on RR interval maxima (MM fluctuations). In addition, we study the type of multifractality within MM fluctuations, that is, if it arises from a broad probability density function or from varying long-range correlations. Furthermore, to provide a comprehensive view of HRV changes during sleep in pregnancy, classical temporal and spectral HRV indices were calculated at quarterly intervals during sleep. Our study population consists of 21 recordings from nonpregnant women, 18 from the first trimester (early-pregnancy) and 18 from the second trimester (middle-pregnancy) of pregnancy. Results. There are statistically significant differences ( -value < 0.05) in mean heart rate, rms heart rate, mean MM fluctuations, and standard deviation of MM fluctuations, particularly in the third and fourth quarter of sleep between pregnant and non-pregnant states. In addition, the early-pregnancy group shows significant differences ( -value < 0.05) in spectral indices during the first and fourth quarter of sleep compared to the non-pregnancy group. Furthermore, the results of our research show striking similarities in the average multifractal structure of MM fluctuations between pregnant and non-pregnant states during normal sleep. These results highlight the influence of different long-range correlations within the MM fluctuations, which could be primarily associated with the emergence of sleep cycles on multifractality during sleep. Finally, we performed a separability analysis between groups using temporal and spectral HRV indices as features per sleep quarter. Employing only three features after Principal Component Analysis (PCA) to the original feature set, achieving complete separability among all groups appears feasible. Using multifractal analysis, our study provides a comprehensive understanding of the complex HRV patterns during pregnancy, which holds promise for maternal and fetal health monitoring. The separability analysis also provides valuable insights into the potential for group differentiation using simple measures such as mean heart rate, rms heart rate, and mean MM fluctuations or in the transformed feature space based on PCA.

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.

Assessment of Sleep Stages in Unconscious Patients with Acute Severe Traumatic Brain Injury

Abstract Background The existence of a sleep cycle in deeply unconscious patients with severe traumatic brain injury (TBI) remains unknown. Evaluating the sleep patterns of these patients may provide insights into their consciousness and help predict functional recovery. Aims and Objectives This prospective observational study aimed to assess the usefulness of monitoring the sleep cycle in the prognostication of unconscious TBI patients. Materials and Methods A purposive sampling technique was employed to include 39 patients with severe TBI (Glasgow Coma Scale [GCS] ≤ 8). The average GCS score at admission was (5.76 ± 1.65). Sleep was monitored using an actigraphy smartwatch for 4 consecutive days, (3.15 ± 2.49) days postbrain injury. Sleep cycle monitoring tool and unconscious patient outcome monitoring tool were employed. An outcome assessment was done based on the GCS score. Results Severe TBI patients exhibit intact sleep cycle (i.e., deep sleep, light sleep, and rapid eye movement sleep) despite being deeply unconscious, even in the acute stage. Total sleep duration was found to be significantly higher at a mean of 19.97 hours as compared to the mean of 8 hours as per the smartwatch reference value for normal individuals. Patients with improved sleep cycles had a higher likelihood of GCS improvement (p &lt; 0.05). Conclusion This study is the first of its kind to demonstrate that the sleep cycle is a reliable prognostic factor for the recovery of consciousness in the acute phase of severe TBI among unconscious patients, with improvement in the sleep cycle mirroring neurological improvement.

Сontribution of circadian rhythms to the development of neurological disorders after prolonged anesthesia

Despite the improvement of pre-, intra- and postoperative care methods, the problem of the development of postoperative neurological complications following prolonged anesthesia remains. A mechanism of occurrence of these complications may be circadian rhythm disorders. This article presents a review of the literature data on the prevalence, pathophysiological mechanisms, and risk factors of postoperative autonomic and circadian disorders. Notably, circadian rhythm disorders in surgical patients are observed at several levels: disruption of the sleep and wakefulness cycle, decreased melatonin secretion, instability of cortisol levels and body temperature, and changes in the vegetative balance towards sympathetic regulation. These disorders contribute to the development of postoperative pain and cognitive and anxiety-depressive disorders, hinder effective rehabilitation, and may be associated with postoperative mortality.

Recent advances in sleep cycle regulation and hormonal imbalance: a comprehensive review

A vital physiological function, sleep is necessary for general health and wellbeing. It is essential for several body processes, including as metabolism, immunological response, emotional control, and cognitive function. A complicated mechanism involving intricate connections between brain circuits, neurotransmitters, and hormone cues governs the sleep-wake cycle. Scientific research has recently made significant progress in understanding the mechanisms behind the control of the sleep cycle and how it relates to hormone imbalance. The goal of this thorough review is to examine the most recent research in this area, with a particular emphasis on the interaction between hormone variations and sleep regulation.