Search Results (771)

Background/Objectives: Obstructive sleep apnea (OSA) is a highly prevalent sleep-related breathing disorder caused by recurrent upper-airway collapse during sleep, leading to intermittent hypoxia, sleep fragmentation, and excessive daytime sleepiness. Although continuous positive airway pressure (CPAP) remains the first-line therapy, long-term adherence is often suboptimal, underscoring the need for more tolerable and flexible treatment options. This review aims to summarize the pathophysiological rationale for pharmacotherapy in OSA and to discuss recent developments in drug-based interventions. Methods: We conducted a narrative review of the literature on pharmacological interventions for OSA, with a systematic search of PubMed, Embase, Cochrane Library, and ClinicalTrials.gov up to 4 August 2026. We included randomised controlled trials, observational studies, meta-analyses, and mechanistic human or animal studies that reported relevant sleep and respiratory outcomes, and graded evidence according to the principles of GRADE framework. Results: Several drug classes have shown promise: agents that increase upper-airway dilator muscle activity, respiratory stabilizers that reduce loop gain, medications that raise the arousal threshold, topical anti-inflammatory drugs for mucosal edema, and systemic metabolic modulators such as glucagon-like peptide-1 receptor agonists and dual incretin receptor agonists. Emerging strategies, including gene therapy directed at the hypoglossal motor system, are also under investigation at the preclinical stage. Moreover, combining pharmacotherapy with CPAP or other devices can produce synergistic benefits, enabling lower device pressures and enhanced patient comfort. Conclusions: Pharmacotherapy for OSA is progressively moving from exploratory research towards targeted, phenotype-driven personalized treatment. Future studies should focus on robust patient phenotyping, multi-mechanistic combination regimens, and long-term clinical outcome evaluations to facilitate the integration of drug-based therapies into routine OSA management, particularly in specific subgroups such as those with COMISA. Full article

The aim of the present study was to develop an instrument that enables evaluation of AI-based mental health apps, which are promising digital interventions for promoting psychological well-being. The instrument was used to conduct an initial evaluation of an early pilot stage of the well-being app MALU. As part of a non-representative hypothesis-testing longitudinal study, N = 11 participants aged 18 to 34 used the app over a period of two weeks. The participants were surveyed at three points regarding perceived stress (Perceived Stress Scale), sleep problems (short version of the Insomnia Severity Index), and chatbot usability (Chatbot Usability Scale). The results showed a significant decrease in perceived stress between the first and third measurement points (Z = −2.31, p = 0.01), as well as for perceived sleep problems between the second and third measurement points (Z = −1.86, p = 0.03). Perceived chatbot usability increased significantly over the course of the study (Z = 2.37, p = 0.01). The results suggest potential effectiveness of the app in reducing stress and sleep problems as well as an improvement in the user experience regarding the chatbot interaction over time. The evaluation instrument proved suitable for use in early development phases. Full article

Objectives: Sleep staging from polysomnographic (PSG) recordings is clinically critical for diagnosing sleep-related disorders, yet manual scoring by certified technologists remains time-consuming, costly, and subject to inter-rater variability. Methods: This study presents an automated, explainable, and multimodal framework for five-class sleep stage classification using simultaneously acquired electroencephalography (EEG), electrooculography (EOG), and electromyography (EMG) signals. A total of 1946 annotated 30 s epochs from 30 healthy adult recording sessions (Sleep-EDF Expanded and Sleep Cassette subset) were processed through a 37-dimensional multimodal feature extraction pipeline encompassing temporal amplitude statistics, frequency-domain spectral band powers, nonlinear entropy and complexity measures, and Daubechies-4 discrete wavelet transform (DWT) energy coefficients. Four classical machine learning classifiers -Random Forest (RF), Support Vector Machine with radial basis function kernel (SVM-RBF), Gradient Boosting (GB), and K-Nearest Neighbours (KNN, k = 7) were benchmarked under stratified five-fold cross-validation. Results: SVM-RBF achieved the highest macro-averaged F1-score of 0.7322 (Cohen’s kappa 0.6784, overall accuracy 75.18%). N3 deep slow-wave sleep achieved the highest per-class F1 of 0.879, while N1 light sleep was the most challenging (F1 = 0.668). SHapley Additive exPlanations (SHAP) and RF mean decrease in Gini impurity (MDGI) analysis jointly identified EMG root mean square amplitude (MDGI = 0.0805), gamma band power (0.0784), and permutation entropy (0.0434) as the three most discriminative features. As a novel methodological contribution, sixteen categories of signal sculpting visualisations were developed, translating abstract multivariate features into clinically interpretable graphical representations. Conclusions: The proposed framework achieves substantial kappa agreement approaching the lower bound of expert inter-rater reliability (0.76–0.82) while providing full model transparency, with direct implications for wearable sleep monitoring device design. Full article

Automatic sleep staging is fundamental for diagnosing sleep disorders and enabling long-term sleep monitoring with wearable devices. Although Deep Learning has significantly improved classification performance, balancing accuracy with computational efficiency remains challenging, particularly for resource-constrained systems. This paper proposes a lightweight two-stage Deep Learning framework for five-class sleep staging based on optimized multimodal physiological features extracted from electroencephalogram (EEG), electrooculogram (EOG), and photoplethysmography (PPG) signals. The framework is trained and tested using the Bitbrain Open Access Sleep (BOAS) database, considering a 31-subject dataset partitioned into training (24 subjects) and independent test (7 subjects) sets. Feature selection is performed using the minimum Redundancy Maximum Relevance (mRMR) algorithm, followed by Principal Component Analysis (PCA) for EEG and EOG features, while respiratory and cardiac features derived from PPG are directly incorporated into the multimodal representation. A hierarchical Long Short-Term Memory (LSTM) architecture first classifies sleep into Wake, REM, and NREM, then further distinguishes the N1, N2, and N3 stages. On an independent test set, the classifier achieves 88.2% five-class accuracy on the multimodal feature set (EEG + EOG + PPG) with a model size of 3.14 MB, and 87.1% accuracy on the EEG-only feature set using only 2.95 MB of memory. Leave-One-Subject-Out (LOSO) cross-validation yields 86.9% accuracy, supporting subject-independent generalization. Inference latency ranged from 2.55 ms (EEG-only) to 4.02 ms (multimodal), with measured energy per inference of 2.88–10.9 mJ across feature sets. Additional validation on the RichSleep and ISRUC datasets demonstrates robustness across different recording conditions, achieving mean accuracies of 78.2% and 77.3%, respectively. The proposed framework provides a favorable trade-off among classification performance, complexity, and memory footprint, suggesting its potential suitability for wearable and edge-based sleep-monitoring systems. Full article

Objective: To explore the association of glucocorticoid (GC) exposure with objectively assessed sleep architecture in patients with rheumatoid arthritis (RA). Methods: A single-center exploratory pilot study involving 20 consecutive patients with RA (9 GC users and 11 non-users; mean age 64.9 years) was conducted. Using single-channel electroencephalography (EEG) and accelerometry, objective sleep architecture and autonomic balance were evaluated. Sleep parameters included wake after sleep onset (WASO), sleep stages, and delta EEG power during the first sleep cycle, while heart rate variability, expressed as low frequency/high frequency (LF/HF) ratio, was used as an index of autonomic balance. RA disease activity was evaluated using the Disease Activity Score in 28 joints based on C-reactive protein (DAS28-CRP). Associations between clinical variables and objective sleep parameters were evaluated using Spearman rank correlation analysis. Results: Disease activity, assessed by DAS28-CRP, did not differ significantly between GC users and non-users. Compared with non-users, GC users had longer WASO (p = 0.011), shorter non-rapid eye movement (NREM) stage N3 duration (p = 0.010), and lower delta power (p = 0.014) than non-users. A higher nighttime-to-daytime LF/HF ratio was also observed in GC users, although this difference did not reach statistical significance. Total sleep time, sleep latency, and sleep efficiency were comparable between the groups. WASO was positively correlated with age, GC use, and GC dose, whereas NREM stage N3 duration and delta power were negatively correlated with GC exposure. No significant associations were observed between age or sex and objective measures of deep sleep. Conclusions: Among patients with RA, GC exposure was associated with poorer sleep continuity and reduced deep sleep despite comparable disease activity. Given the small sample size of this exploratory single-center study, these findings should be interpreted cautiously. Larger longitudinal studies using objective sleep measures are warranted to confirm these associations. Full article

Background/Objectives: Automatic sleep staging from polysomnography (PSG) is particularly challenging in clinical cohorts with neurological disorders. Methods: This study presents SleepStageNet, a compact model (1.075M parameters) that integrates a dual-branch convolutional epoch encoder, feature gating, a bidirectional gated recurrent unit, and multi-head self-attention for five-class staging from five PSG channels (C3, C4, EOG1, EOG2, and chin EMG). The individual operations are adapted from established architectures; the study contribution is their compact integration and controlled evaluation in an Indian acute stroke cohort. Results: Of the 100 recordings in the Indian Sleep Polysomnography (iSLEEPS) resource, 95 satisfied the five-channel extraction criteria, yielding 78,323 annotated epochs. Subject-independent 10-fold stratified group cross-validation produced an accuracy of 73.91 ± 2.24%, a macro F1-score of 67.29 ± 1.96%, and a Cohen’s $\kappa$ of $0.634\pm 0.029$ (sample standard deviations). A matched single-branch encoder obtained $\kappa =0.635$ (full minus single branch: $\Delta \kappa =-0.001$, Holm-adjusted $p=0.846$), while matched C4-only input obtained $\kappa =0.600$ (full minus C4-only: $\Delta \kappa =0.033$, Holm-adjusted $p=0.008$). Grad-CAM and temporal attention visualizations provided qualitative evidence of physiologically plausible focus, while channel occlusion quantified the contribution of each signal. Without fine-tuning, a 10-model ensemble obtained $\kappa =0.614$ on ISRUC-SLEEP Subgroup III (10 healthy subjects; 8889 epochs). Conclusions: These results establish a reproducible reference for this clinical cohort while identifying the need for broader external and prospective validation. Full article

Wearable sleep monitoring systems provide a scalable and low-burden alternative to laboratory-based polysomnography, but overnight physiological recordings collected from wearable sensors are frequently corrupted by poor skin contact, sensor displacement, flatline behavior, saturation, abrupt autoscaling, outliers, and non-physiological noise. These low-quality segments can prevent reliable extraction of clinically relevant biomarkers, including heart rate, heart rate variability, pulse rate, oxygen saturation, and electrodermal activity, thereby limiting the robustness of downstream sleep stage classification and sleep apnea prediction. In this work, we present a multistage hybrid anomaly detection framework for signal quality assessment in a custom-designed multimodal wearable sleep monitoring platform. The proposed framework is shared across modalities: each sensor stream is segmented into windows, screened using signal processing algorithms for obvious sensor failures, and then analyzed using an autoencoder trained on normal windows to detect subtler morphology-level deviations from normal physiological patterns. On chest ECG recordings collected over 10 overnight sessions, the hybrid fusion detector achieves an accuracy of 0.9067, AUROC of 0.9068, F1 score of 0.8739, precision of 0.9470, and recall of 0.8202, outperforming rule-based detection and autoencoder-based detection alone. Additional experiments on PPG, EDA and ExG recordings show consistently high F1 scores of 0.9431, 0.9567 and 0.8728, respectively. These results demonstrate that hybrid anomaly detection can serve as an effective quality-control layer for multimodal wearable sleep monitoring and provide a foundation for future work on robust sleep stage and apnea prediction under real-world sensing conditions. Full article

Automatic sleep stage classification has been extensively studied in adults but remains less explored in children because clinical pediatric polysomnography (PSG) datasets are limited and sleep physiology changes throughout development. In this work, we present a clinical pediatric PSG dataset collected from the archives of Victor Gomoiu Children’s Hospital (VGCH), consisting of 20 overnight recordings from children aged 9–17 years. Using AttnSleep, we assess EEG channel relevance, the contribution of EOG and EMG signals, and adult-to-pediatric transfer learning from SHHS and Sleep-EDFx datasets. Among the individual EEG derivations, P4 achieves the highest cross-validation (CV) means, with 79.5% accuracy and 70.8% balanced accuracy, while several temporal and posterior derivations perform similarly. Adding both EOG channels increases mean accuracy to 82.9%, macro-$F1$ to 74.9%, and balanced accuracy to 75.0%, whereas EMG produces smaller changes. The highest performance is obtained after Sleep-EDFx EEG+EOG pretraining and full fine-tuning on VGCH, reaching 84.0% accuracy, 76.4% macro-$F1$, and 76.4% balanced accuracy. Within this cohort of 20 recordings, the observed patterns highlight the potential value of combining EEG and EOG and support further evaluation of adult-to-pediatric transfer learning for pediatric sleep-stage classification. Full article

Background/Objectives: This study aimed to comprehensively evaluate the interaction between periodontal disease, sleep quality, and quality of life and to investigate the effects of the presence and severity of periodontal disease on individuals’ sleep patterns and oral health-related quality of life. Materials and Methods: A total of 195 individuals were included in the study. Participants’ sociodemographic data and dental habits were recorded. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), while the impact of oral health on quality of life was evaluated using the Oral Health Impact Profile-14 (OHIP-14). Results: A significant difference was observed in OHIP-14 scores among the groups (p < 0.001), with median values of 6, 13, and 22 in the healthy, gingivitis, and periodontitis groups, respectively. OHIP-14 scores also increased significantly with the stage and grade of periodontitis (p < 0.001). PSQI scores differed significantly among the groups (p < 0.001), with median values of 4, 8, and 10 in the healthy, gingivitis, and periodontitis groups, respectively. However, no significant differences were observed in total PSQI scores across the stages or grades of periodontitis. In addition, a strong positive correlation was found between total PSQI and OHIP-14 scores (r = 0.63, p < 0.001). Multiple linear regression analysis confirmed that periodontal status remained independently associated with both PSQI and OHIP-14 scores after adjusting for age, sex, education level, and smoking status, with periodontitis showing the strongest association in both models (PSQI: B = 5.03; OHIP-14: B = 15.80, both p < 0.001). Conclusions: A significant relationship was identified between periodontal disease, sleep quality, and oral health-related quality of life. Furthermore, the strong positive correlation between sleep quality and oral health-related quality of life suggests that deterioration in sleep quality may be associated with poorer oral health-related quality of life. These findings indicate that periodontal health is not limited to oral tissues but is also closely associated with an individual’s overall well-being. Full article

Background/Objectives: This study aimed to compare disease severity and cognitive performance between Parkinson’s disease (PD) patients with good and poor sleep quality. Methods: This cross-sectional observational study included 69 patients with PD. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Participants were classified as having good sleep quality (PSQI ≤ 5; n = 32) or poor sleep quality (PSQI > 5; n = 37). Disease severity was evaluated using the Unified Parkinson’s Disease Rating Scale (UPDRS) and Modified Hoehn and Yahr Scale (mH&Y). Global cognition, executive functions, and visuospatial performance were assessed using the Montreal Cognitive Assessment (MoCA), Stroop Test TBAG Form, and Clock Drawing Test, respectively. Results: Patients with poor sleep quality had significantly higher UPDRS scores (p = 0.004), longer disease duration (p = 0.043), and more advanced mH&Y (p < 0.001). They also demonstrated lower MoCA scores (p = 0.039) and lower Clock Drawing Test scores (p = 0.019). In addition, completion times on all Stroop test cards were significantly longer in the poor sleep quality group (p = 0.011–0.042), indicating poorer attention–executive performance. Moderate effect sizes were observed for disease severity and cognitive outcomes. Conclusions: Poor sleep quality was associated with greater disease severity and lower cognitive performance in early-to-mid-stage PD patients who were largely cognitively preserved. Routine assessment of subjective sleep quality may help identify patients with a higher clinical and cognitive burden and support more individualized, person-centered healthcare strategies in PD. Full article

Wearable technologies are reshaping women’s health by extending observation beyond episodic clinical encounters into daily life. Across the reproductive lifespan, they can capture physiological, behavioral, symptom, and functional trajectories that are often missed in routine care. Yet more data do not automatically translate into better care. Clinical value depends on whether multimodal signals can be modeled and interpreted in relation to reproductive biology, temporal change, and meaningful clinical or functional endpoints. In this perspective, we examine how women’s health wearables can move beyond consumer tracking toward validated digital phenotyping across menstruation, fertility, pregnancy, postpartum recovery, and menopause. We propose a four-layer framework spanning data capture, physiological domain mapping, computational phenotyping, and actionable translation. We then apply this framework across key reproductive life stages. Menstrual health and fertility applications illustrate the shift from calendar-based prediction toward physiological, metabolic, and hormone-aware monitoring. Pregnancy and postpartum applications highlight the need for safety-focused validation, maternal–infant risk awareness, and clinician-governed escalation pathways. Menopause and midlife health represent underdeveloped areas where longitudinal digital phenotyping may better capture vasomotor, sleep, mood, fatigue, and functional symptoms. Across these domains, we identify key barriers to translation, including limited hormone-linked validation, inconsistent evidence standards, underrepresentation of diverse populations, privacy risks, algorithmic bias, and weak workflow integration. By organizing wearable-derived signals across reproductive life stages and identifying major translational barriers, this perspective provides a roadmap toward biologically grounded, equitable, and clinically actionable digital phenotyping for women’s health. Full article

Background: Cardiometabolic risk is shaped not only by the amount of food intake and physical activity but also by their timing and regularity across the 24 h cycle. We propose rhythm coherence as a hypothesis-generating framework describing the temporal stability and alignment of eating, physical activity, and sleep. Its vascular and glycemic relevance has not yet been prospectively validated. Methods: This semi-structured narrative review was supported by a transparent, staged evidence-identification process using PubMed, Scopus, and Web of Science Core Collection. Main searches used a 2020–2026 window, with a broader 2018–2026 window for targeted athlete-focused searches. After cross-database deduplication, 1081 unique records entered broad screening; 694 underwent strict screening, yielding 257 core candidate records. A targeted athlete-focused search contributed 46 additional records, producing a 303-record candidate evidence pool for topic mapping. From this pool, 131 high-priority records underwent full-text narrative synthesis, and 92 sources were ultimately cited directly in the manuscript. These counts represent successive review stages rather than a PRISMA-defined systematic-review inclusion set. Results: Earlier or more regular eating patterns, postprandial physical activity, and stable sleep–wake schedules were associated with more favorable cardiometabolic profiles in selected studies. However, the evidence was heterogeneous and included mechanistic studies, observational analyses, and intervention trials. These findings support further investigation but do not establish an effect of integrated eating–activity–sleep coherence. Numerical effects reported for individual interventions should not be attributed to the proposed integrated framework or to the Rhythm Coherence Index (RCI). Conclusions: Coordinated assessment of eating, activity, and sleep timing may offer a useful research direction. The RCI is an author-proposed candidate composite, not an existing validated instrument or clinical decision rule. Its formula, component weights, missing-data procedures, thresholds, reliability, responsiveness, predictive value, and external validity require prospective evaluation before clinical application can be considered. Full article

Background/Objectives: Systemic mastocytosis (SM) is a rare clonal mast cell disorder associated with mediator-related symptoms and a broad range of neurocognitive and sleep-related complaints. Fatigue, insomnia, and brain fog are frequently reported in clinical practice, but objective polysomnographic data in this patient population remain limited. As overweight and obesity are established contributors to sleep-disordered breathing, polysomnography (PSG) findings in patients with SM should be interpreted with caution when a control group is not available. This study aimed to characterize sleep architecture, sleep continuity, and sleep-disordered breathing in adult patients with SM who underwent PSG. Methods: This single-center observational study included nine adult patients diagnosed with SM from the Ege University Mastocytosis Database between January 2023 and January 2024. No control group was included. All patients underwent overnight laboratory PSG after discontinuation of antihistamine therapy for at least seven days. Demographic characteristics, SM subtype, baseline serum tryptase levels, Epworth Sleepiness Scale (ESS) scores, and brain fog-like cognitive complaints were recorded. PSG parameters included total sleep time, sleep efficiency, sleep onset latency, wake after sleep onset, rapid eye movement (REM) latency, sleep stage distribution, arousal burden, apnea–hypopnea index (AHI), oxygenation parameters, and periodic limb movements. Results: The study included four women and five men, with a median age of 50 years. Eight patients had a body mass index (BMI) ≥ 25 kg/m², indicating that the cohort was predominantly overweight or obese. The most common SM subtype was indolent SM. The median baseline serum tryptase level was 55.9 ng/mL, and the median ESS score was 9. Only two patients had excessive daytime sleepiness according to ESS > 10, whereas six patients reported brain fog-like complaints. Median sleep efficiency was 91.4%, total sleep time was 344 min, sleep onset latency was 9 min, and wake after sleep onset was 11 min. Median REM latency was 128.5 min. Sleep stage distribution showed median N1, N2, N3, and REM sleep proportions of 3%, 54%, 26%, and 16.7%, respectively. One patient had no observed REM sleep, and two patients had no measurable N3 sleep. The median number of arousals was 55. The median AHI was 8.2 events/hour. Although eight of nine patients met PSG criteria for obstructive sleep apnea (OSA), including five with mild and three with severe disease, this finding should be interpreted in the context of the high prevalence of overweight/obesity in the cohort. All patients with BMI ≥ 25 kg/m² had OSA, whereas the only normal-weight patient did not. Conclusions: In this single- center observational study, PSG demonstrated frequent sleep-disordered breathing, REM-related changes, and variable arousal burden in patients with SM; however, these findings cannot be separated from the predominance of overweight/obesity and the absence of a control group. Brain fog-like complaints were common, although excessive daytime sleepiness was present in only a minority of patients. Because most patients were overweight or obese and no control group was included, these findings cannot be attributed directly to SM. However, the results suggest that objective sleep assessment may be useful in selected patients with SM, particularly when fatigue, non-restorative sleep, or cognitive complaints are present. Larger controlled studies with BMI-matched groups are needed to clarify the relationship between SM, mast cell mediator activity, and sleep abnormalities. Full article

Background: This study evaluated the use of circulation time (Tcirc) calculated from polysomnogram (PSG) with pulse oximetry to identify poor cardiac function with low left ventricular ejection fraction (EF). Methods: Subjects over 18 years of age with sleep apnea (apnea-hypopnea index (AHI) > 5/h diagnosed by PSG who had transthoracic echocardiography (TTE) within 1 year of PSG were included in this retrospective study. Tcirc of each sleep stage (N2, N3, and REM) were measured and averaged and EF was recorded. Statistical analysis was carried out using the Wilcoxon rank sum test, logistic regression and Youden index. Results: There were 89 subjects who met the inclusion criteria, 14 with EF ≤ 45% (Group A) and 75 with EF ≥ 50% (Group B). The normal Tcirc is <20 s, but Group A subjects had a prolonged overall Tcirc with a median time of 27.8 s (range 14.1–39.6 s), compared to Group B subjects with a median Tcirc of 23.5 s (range 14.3–37.6 s), p = 0.311. The optimal cut-point for overall sleep Tcirc with moderate discrimination (AUC = 0.6) was 28.6 s. Those with total sleep Tcirc ≥ 28.6 s were 2.5× more likely to have low EF with OR = 2.56 (95% CI, 0.55–11.16). Conclusions: In sleep apnea patients, total sleep Tcirc > 28.6 s is associated with low ejection fraction with specificity = 0.78. Full article

Melatonin has chronobiotic, antioxidant, anti-inflammatory, and neuromodulatory properties, and its oral effects may vary according to formulation. This study compared conventional melatonin with a lipid-multiparticulate (LMP) formulation in two acute pharmacological mouse paradigms. Eighty-four male BALB/c mice were assigned to caffeine-evoked cortical activity or pentobarbital-induced sedative-hypnotic experiments. Mice received caffeine (7.5 mg/kg, i.p.) alone or with standard or LMP-melatonin (10 or 20 mg/kg, oral). ECoG recorded cortical electrical activity under urethane anaesthesia. In a separate cohort, latency to and duration of pentobarbital-induced loss of the righting reflex were measured. Circulating neurochemical and redox markers, as well as neurotransmitter- and inflammation-related gene and protein expression, were evaluated. Caffeine increased spike frequency and altered ECoG amplitude, together with unfavorable changes in circulating neurochemical and redox markers and in neurotransmitter receptor- and inflammation-related outcomes. Both melatonin formulations attenuated several of these changes in a dose-dependent manner, with the strongest effects generally observed in the high-dose LMP-melatonin group. Compared with conventional melatonin, LMP-melatonin produced greater modulation of ECoG activity, circulating biochemical markers, neurotransmitter-receptor expression, and IL-6 and TNF-α-related outcomes. LMP-melatonin also produced a greater reduction in the latency to and a greater prolongation of the duration of pentobarbital-induced loss of the righting reflex. LMP-melatonin produced greater formulation-dependent effects than conventional melatonin on caffeine-evoked cortical electrical activity, pentobarbital-induced sedative-hypnotic responses, and selected biochemical and molecular outcomes. However, pharmacokinetic exposure, physiological sleep stages, sleep architecture, sleep quality, and direct measures of neuronal injury were not assessed. Therefore, the findings do not directly establish enhanced bioavailability, physiological sleep promotion, or neuroprotection. Full article