Search Results (3,554)

Brain and muscle Arnt-like protein 1 (BMAL1) is a transcription factor that forms heterodimers with circadian locomotor output cycles kaput (CLOCK) and drives transcription from E-box elements, thereby regulating the circadian rhythms of gene expression. The kidney expresses numerous rhythmic genes and exhibits circadian physiological function regulation. Circadian rhythm abnormalities, such as sleep disorders and excessive daytime sleepiness, are particularly frequent in patients with chronic kidney disease (CKD). Furthermore, reduced amplitude and phase disruption in clock gene expression rhythms have been reported in mouse CKD models. These results suggest that circadian disruption is associated with renal pathophysiology. However, the role of BMAL1 in the repair process following acute kidney injury (AKI) remains unclear; therefore, this study aimed to elucidate its role in kidney repair following ischemia–reperfusion injury (IRI). We found that the tamoxifen (TAM)-inducible global Bmal1 knockout (BKO) mouse kidneys exhibited increased lipid accumulation, enhanced fibrosis, and delayed kidney repair post-IRI, and that these abnormalities were associated with reduced Peroxisome proliferator-activated receptor alpha (Pparα) expression. Furthermore, treatment with a PPARα agonist reduced these abnormalities in BKO mice. Collectively, our findings demonstrate that the BMAL1–PPARα axis promotes post-AKI kidney repair. Full article

Previous studies indicate that the main predictors of stress-related disorders in firefighters are pre- and post-trauma factors, rather than intensity or type of traumatic event. This study aimed to identify risk and protective factors contributing to the development of Post-Traumatic Stress Disorder (PTSD) and other stress-related conditions in Portuguese firefighters who battled the 2017 forest fires. To assess the prevalence of PTSD and related conditions, a set of self-report measures—including PHQ-15 (somatic symptoms), PCL-5 (PTSD), PSQI (sleep quality), and DASS-21 (depression, anxiety, stress)—was completed by 96 firefighters and 96 individuals from the general population, who served as a comparison group. In addition, semi-structured interviews were conducted with 79 firefighters, focusing on their perceptions of PTSD, exposure to duty-related traumatic events, and coping strategies employed to manage stress. Findings indicated that firefighters reported higher levels of somatic symptoms, sleep disturbance, and PTSD than the general population. Organizational support, working conditions, professional experience and training were identified as protective factors, while a sense of belongingness and peer relationship were considered resources for managing stress reactions. Firefighters also associated social and media pressures with the development or exacerbation of stress-related symptoms. Collectively, these results highlight the relevance of both subjective and contextual factors and may inform prevention, intervention, and treatment strategies for stress-related psychopathologies. Full article

Background/Objectives: Freezing of gait (FOG) and sleep disturbances are common in people with Parkinson’s disease (PwPD). A bidirectional association between them has been suggested, but quantitative evaluations are scarce. This study aimed to compare sleep disturbances in mild-to-moderate PwPD with (PD+FOG) and without FOG (PD−FOG), and to assess the association between FOG severity and sleep parameters. Methods: Data from 54 PwPD with disease stage <4 and no severe cognitive decline were included (27 PD+FOG and 27 propensity score-matched for age, sex, and disease duration PD−FOG). Demographics and clinical variables were collected. Clinical assessment included the new freezing of gait questionnaire (NFOG-Q), Parkinson’s Disease Sleep Scale (PDSS-2), Epworth Sleepiness Scale (ESS) and Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Mann–Whitney U, Fisher’s exact and Spearman’s tests were used for group comparisons and correlations, respectively. Results: Significant differences were observed between PD+FOG and PD−FOG groups in MDS-UPDRS part II (p = 0.011) and part IV (p = 0.011), with higher scores in PD+FOG participants. No significant differences were found in PDSS-2 or ESS between the two groups. A significant moderate positive correlation was found between NFOG-Q score and PDSS-2 (ρ = 0.416; p = 0.044) in PD+FOG participants. Conclusions: FOG severity was positively associated with sleep disturbances within the PD+FOG group. However, no significant difference in sleep quality or excessive daytime sleepiness was found between PD+FOG and PD−FOG after propensity score matching. PD+FOG participants experienced more severe motor complications and greater impairment in daily activities compared to PD−FOG. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder primarily caused by the degeneration of dopamine-producing neurons in the substantia nigra, leading to characteristic motor symptoms such as tremors, rigidity, and bradykinesia, as well as non-motor manifestations including depression, sleep disturbances, and speech impairments. Among these symptoms, speech abnormalities affect approximately 90% of individuals with PD, making acoustic analysis a promising non-invasive cue for early detection. However, subtle speech variations are often imperceptible to the human ear, and speech-only analysis may overlook complementary visual manifestations, such as hypomimia—reduced facial expressivity commonly observed in PD patients. To address these limitations, we propose Parkinson’s Detection via Attentional Fusion Network (PDAF-Net), a novel multimodal deep learning framework for early PD detection that jointly models acoustic and facial dynamic features in a binary classification setting. The proposed architecture consists of a Dual-Stream Feature Encoder (DSFE), with an audio branch based on a one-dimensional convolutional neural network (1D-CNN) and bidirectional long short-term memory (BiLSTM), and a visual branch built upon a two-dimensional convolutional neural network (2D-CNN) and a Transformer encoder. Multimodal integration is achieved through a Cross-Attention-guided Attentional Feature Fusion (CA-AFF) module, which explicitly models bidirectional cross-modal interactions and performs adaptive feature recalibration via an iterative attentional fusion mechanism. We conducted experiments on a self-collected Chinese multimodal dataset comprising 100 PD patients and 100 healthy controls. Although the data are balanced at the subject level, sliding-window segmentation introduces sample-level imbalance; to address this issue, a class-balanced focal loss is employed. Model performance was evaluated using subject-wise five-fold cross-validation. The results demonstrate that PDAF-Net consistently outperforms unimodal baselines across multiple evaluation metrics, achieving an accuracy of 89.3%, an F1-score of 0.884, and an AUC of 0.916. These findings highlight the effectiveness of explicit cross-modal interaction modeling and adaptive feature fusion for improving automated early PD screening in real-world clinical settings. Full article

Sleep disturbances are common in multiple sclerosis (MS) and contribute to increased symptom burden. Behavioral sleep hygiene practices are potentially modifiable factors influencing sleep and related symptoms, yet their role in MS remains insufficiently explored. This cross-sectional study comprised 175 MS patients. Sleep hygiene was assessed using a behavioral checklist covering a regular sleep schedule, avoidance of daytime naps, limitation of evening caffeine intake, reduced evening screen exposure, and optimization of the sleep environment. The instruments included the Fatigue Severity Scale (FSS), the Modified Fatigue Impact Scale (MFIS), the Athens Insomnia Scale (AIS), the Epworth Sleepiness Scale (ESS), and the Hospital Anxiety and Depression Scale (HADS). Higher sleep hygiene adherence was associated with lower daytime sleepiness (ESS: r = −0.18, p = 0.020), anxiety (HADS-A: r = −0.16, p = 0.034), and depression (HADS-D: r = −0.15, p = 0.047). Patients with higher adherence (score ≥ 3) demonstrated significantly lower MFIS, AIS, ESS, and HADS-D scores compared with those with lower adherence (all p < 0.05). Multivariate regression showed that sleep hygiene adherence was independently associated with lower MFIS (β = −3.24, 95% CI: −6.06 to −0.41, p = 0.025), ESS (β = −0.85, 95% CI: −6.06 to −0.41, p = 0.016), HADS-A (β = −0.67, 95% CI: −1.23 to −0.11, p = 0.019), and HADS-D scores (β = −0.62, 95% CI: −1.17; −0.08, p = 0.026). Better adherence to sleep hygiene practices may be associated with a lower symptom burden in MS. Full article

Undergraduate students often engage in nighttime activities and electronic device usage that may adversely affect sleep quality and academic performance; therefore, factors associated with sleep quality are important. The objective of this study was to investigate the prevalence and associated factors of poor sleep quality in undergraduate students. Four hundred and five undergraduate students participated in a cross-sectional study and had no history of psychological disorders or use of medications affecting sleep. Data was collected using the Pittsburgh Sleep Quality Index (PSQI), musculoskeletal discomfort questionnaire, electronic device usage questionnaire and Depression Anxiety Stress Scales (DASS-21). Multivariate logistic regression was employed to analyze the factors associated with poor sleep quality. Among the undergraduate students in this study, 65.93% reported having poor sleep quality. The factors associated with poor sleep quality were stress (OR, 1.03; 95% CI, 1.01–1.06) and musculoskeletal discomfort (OR, 1.92; 95% CI, 1.23–2.99) after controlling for other variables. Undergraduate students frequently experience poor sleep quality, with stress and musculoskeletal discomfort being major contributors. These findings highlight the importance of mental health support and stress management programs in improving sleep quality and overall well-being, as well as in preventing long-term detrimental consequences for undergraduate students’ mental health, physical health and academic performance. Full article

Background/Objectives: Sleep-disordered breathing (SDB), including obstructive sleep apnea, is common in children and is associated with mouth breathing, snoring, and neurobehavioral disturbances. In pediatric orthodontic patients, oral habits and craniofacial imbalances may contribute to airway dysfunction, making orthodontic evaluation a potential setting for early identification of SDB. This study aimed to estimate the prevalence of SDB and to evaluate its associations with parent-reported behavioral symptom profiles in a cohort of pediatric orthodontic patients. Methods: A multicenter cross-sectional study was conducted in 186 children aged 7–13 years attending orthodontic clinics in Oradea and Târgu Mureș, Romania. Parents completed a structured questionnaire on oral habits, the 22-item Pediatric Sleep Questionnaire (PSQ), with SDB defined as 8 or more positive responses, and a parent-reported behavioral screening form assessing ADHD symptom subtypes, oppositional-defiant disorder (ODD), conduct disorder, and anxiety/depression. These behavioral outcomes were based on screening measures and were not intended as clinical psychiatric diagnoses. Associations were analyzed using chi-square or Fisher’s exact tests, and multivariable logistic regression analyses were performed adjusting for age, sex, and weight status. Results: Mouth breathing was reported in 61.8% of participants, snoring in 26.9%, and SDB in 13.4%. Positive screens for ADHD-inattentive (p < 0.001), ADHD-hyperactive/impulsive (p < 0.001), ADHD-combined (p < 0.001), ODD (p < 0.001), and anxiety/depression (p < 0.001) were significantly more frequent among children with SDB. In multivariable analysis, SDB remained independently associated with ADHD-combined subtype (OR = 6.22), ADHD-hyperactive/impulsive symptoms (OR = 5.84), oppositional-defiant disorder (OR = 4.91), and anxiety/depression (OR = 4.38). Conclusions: SDB was identified in a meaningful proportion of pediatric orthodontic patients and was significantly associated with multiple screening-defined behavioral symptom domains. These findings support consideration of brief airway- and sleep-oriented screening during orthodontic assessment, particularly in school-aged children presenting with mouth breathing, snoring, or behavioral concerns. Given the cross-sectional and questionnaire-based design, the findings should be interpreted as associative and warrant confirmation in prospective studies using objective sleep measures. Full article

Sleep disorders, such as insomnia, obstructive sleep apnea (OSA), narcolepsy, REM sleep behavior disorder, and circadian rhythm disturbances, represent a rapidly expanding global health burden that is strongly associated with cardiovascular, metabolic, neurological, and psychiatric diseases. Advancements in wearable sensing technologies and Internet of Medical Things (IoMT) infrastructures have expanded the possibilities for continuous, home-based sleep assessment beyond conventional polysomnography laboratories. These Sleep Health Internet of Things (S-HIoT) systems combine multimodal physiological sensing (EEG, ECG, SpO₂, respiratory effort and actigraphy) with wireless communication and cloud-based analytics for automated sleep-stage classification and disorder detection. Nonetheless, the digitization of sleep medicine brings about significant cybersecurity concerns. The constant transmission of sensitive biomedical information makes S-HIoT networks open to anomalous traffic flows, signal manipulation, replay attacks, spoofing, and data integrity violation. Existing studies mostly focus on analyzing physiological signals and network intrusion detection independently, resulting in a systemic vulnerability of cyber–physical sleep monitoring ecosystems. With the aim of addressing this empirical deficiency, this review integrates emerging advances (2022–2026) in the AI-assisted categorization of sleep phases and IoMT anomaly detector designs on the finer analysis of CNN, LSTM/BiLSTM, Transformer-based systems, and a component part of federated schemes and the lightweight, edge-deployable intruder assessor models available. The aim of this study is to uncover a gap in the literature: integrated architectures to trade off audiences of faithfulness of physiological modeling with communication-layer security. To counter it, we present a single framework to include CNN-based spatial feature extraction, Bidirectional Long Short-Term Memory (BiLSTM)-based temporal models and Random Forest-based ensemble classification using a dual task-learning approach. We propose a multi-objective optimization framework to jointly optimize the performance of sleep-stage prediction and that of network anomaly detection. Performance on publicly available datasets (Sleep-EDF and CICIoMT2024) confirms that hybrid integration can be tailored to achieve high accuracy [99.8% sleep staging; 98.6% anomaly detection] whilst being characterized by low inference latency (<45 ms), which is promising for feasibility in real-time deployment in view of targeting edge devices. This work presents a comprehensive framework for developing secure, intelligent, and clinically robust digital sleep health ecosystems by bridging chronobiological signal modeling with cybersecurity mechanisms. Furthermore, it highlights future research directions, including explainable AI, federated secure learning, adversarial robustness, and energy-aware edge optimization. Full article

It is noteworthy that disturbances in circadian rhythms and irregular sleep patterns can exert influence over the onset of Type 2 Diabetes (T2DM). Similarly, they can impact the development of Type 1 Diabetes (T1DM). In recent decades, there has been a notable trend towards both reduced and extended sleep durations, with a concurrent rise in occurrences of compromised sleep quality attributable to sleep fragmentation. These sleep disturbances, along with clinically recognized sleep disorders such as sleep apnea and insomnia, have been increasingly associated with a range of detrimental health outcomes. Of particular concern is the growing evidence linking sleep dysregulation to an augmented risk of metabolic diseases, including diabetes. In addition to sleep duration and quality, emerging research suggests that an individual’s chronotype, reflecting their preferred time for going to sleep, may also exert an influence on disease development, particularly T2DM. The habit of going to bed late when compared to the tendency of going to bed early tends to cause significant disruptions to daily social engagements. Eventually, this misalignment may lead to discrepancies in sleep schedules between weekdays and weekends, commonly referred to as social jetlag. The current review aims to discuss the complex relationship between circadian rhythm misalignment, triggered by improper sleep habits such as short or long sleep duration, disrupted chronotype, social jetlag, and sleep disorders, on the subsequent impact on the development of diabetes. Overall, current evidence suggests that circadian rhythm disruption and sleep disorders contribute significantly to metabolic dysregulation and diabetes risk, highlighting the importance of sleep health in prevention and management of diabetes. Full article

Background and Objectives: Migraine is a common neurological condition that can affect daily life and well-being. Lifestyle factors such as physical activity, sleep, diet, and stress are often discussed in relation to migraine, but their role is not always consistent. This study aimed to examine how selected lifestyle factors are related to migraine frequency and intensity in adults, with a focus on physical activity and dietary habits. Materials and Methods: A cross-sectional study was conducted among 300 adults recruited through an online migraine-focused community from 1 January to 28 February 2026. Participants completed a questionnaire addressing migraine history, frequency and duration of attacks, pain intensity, physical activity, sleep duration, perceived stress, and dietary habits. Associations were assessed using Spearman correlation and multiple linear regression analyses. Results: Most participants were female (88%), and 48% reported physician-diagnosed migraine. Mean migraine intensity was 7.44 ± 1.72. Migraine intensity was positively correlated with migraine frequency (rs = 0.31, p < 0.001), episode duration (rs = 0.48, p < 0.001), perceived stress (rs = 0.17, p < 0.05), and sleep duration (rs = 0.16, p < 0.05). Migraine frequency was correlated with fast food consumption (rs = 0.23, p < 0.01) and BMI (rs = 0.25, p < 0.01). In regression analysis, migraine frequency (β = 0.17, p = 0.005), perceived stress (β = 0.23, p = 0.006), and aura (β = −0.19, p = 0.033) were significant predictors of migraine intensity. Physical activity was not significantly associated with migraine intensity or frequency. Conclusions: Migraine intensity was most consistently related to perceived stress and migraine frequency, whereas migraine frequency was associated with dietary factors and BMI. Physical activity was not associated with migraine outcomes. These findings suggest that lifestyle factors are related to migraine characteristics, although the cross-sectional design does not allow conclusions about causality. Full article

Background and Objectives: Pregnancy causes various physiological and hormonal changes that disrupt sleep architecture and modify respiratory patterns, increasing the risk of sleep-related breathing disorders (SBDs) such as obstructive sleep apnea (OSA) and potentially exacerbating pre-existing conditions. These disorders have been linked to adverse maternal and fetal outcomes. However, current screening tools remain inadequate, and data, including from Portugal, remain limited. This study aimed to assess the prevalence of SBD symptoms suggestive of sleep-disordered breathing during pregnancy, characterize the population, and explore associations with demographic and anthropometric parameters. Materials and Methods: A prospective observational study was conducted from July to December 2024 at Hospital da Luz Lisboa, involving pregnant women ≥ 18 years attending routine consultations. Participants completed a structured questionnaire that assessed demographic and anthropometric data, comorbidities, ten SBD symptoms, and the Epworth Sleepiness Scale (ESS). Results: The cohort included 289 participants, with a mean age of 34.4 years and pre-pregnancy body mass index (BMI) of 23.6 kg/m². On average, women reported 3.1 SBD symptoms, with fatigue (65.4%), memory/concentration impairment (52.2%), and non-restorative sleep (50.5%) being the most common. Excessive daytime sleepiness (ESS >10) was present in 22.8% of the population. Snoring was significantly associated with older age and higher BMI (p = 0.0009 and p < 0.0001, respectively). Both the number of symptoms and ESS scores tended to increase with gestational age, particularly in the third trimester. Women with diabetes had higher odds of reporting snoring, nocturnal dyspnea, and witnessed apneas, with odds ratios of 4.65, 8.77, and 11.38, respectively. Conclusions: SBD symptoms and daytime sleepiness are highly prevalent in pregnancy and typically increase with advancing gestation. These findings emphasize the need for improved clinical strategies to enable early identification and management of SBD in pregnant women, thereby reducing maternal-fetal complications. Full article

Background/Objectives: Pediatric obstructive sleep apnea (OSA) is a complex disorder with a variable presentation and often challenging diagnostic testing. The history and physical examination in pediatric OSA frequently differ from those in adults. The treatment options are multifaceted and must be tailored to the individual patient. Artificial intelligence (AI) modalities currently employed in pediatric sleep medicine face several important limitations: modality fragmentation, lack of explainability, and limited semantic integration. Method: Our team proposes a new vision for AI and pediatric sleep medicine. This platform is based on a knowledge graph (KG) framework integrating structured and unstructured data to enable reasoning, personalization, and clinical decision support. Results: This framework represents a conceptual architecture; it has not yet been empirically implemented, and the use cases described herein are illustrative of its intended capabilities. Components of the infrastructure developed for similar applications have been successfully implemented. The quantitative feasibility pilot KG represented 100% multimodal data with >90% semantic completeness. Conclusions: Fully realized and deployed into the clinical space, this pediatric OSA KG system will enhance tertiary care programs and help project tertiary-level pediatric care into underserved regions. Full article

Internet Gaming Disorder (IGD) has emerged as an increasingly prevalent behavioral health concern among late adolescent university students, a vulnerable population with emotional distress due to the developmental changes and academic pressures. This research explored the direct correlations between IGD and Mental Health Disorder (MHD), such as depression, anxiety, and stress in Saudi Arabia (SA) with study discipline as a moderator. A total of 480 students participated in the developed self-structured questionnaire, and Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to analyze the obtained data. The results showed that IGD can exert a positive and significant association with all three aspects of MHD. Moreover, the PLS-SEM slope analysis indicated that study discipline can significantly moderate the link from IGD to both anxiety and depression, with university students in health, science, and engineering fields displaying higher symptoms of depression and anxiety as compared to their peers in humanities and social sciences. However, study discipline failed to moderate the link from IGD to stress. These findings can be interpreted through maladaptive coping mechanisms and behavioral addiction, whereby extreme IG can contribute to social withdrawal, reduce sleep quality, and worsen stress regulation, specifically during the late adolescence period. The results extend current research on IGD by emphasizing the disciplinary differences in mental health vulnerability and offering more empirical evidence from a Middle Eastern context. The study highlighted the urgent need for discipline-oriented mental health screening and targeted proactive interventions to deal with unsettled IG attitude within a higher education context. Full article

Obstructive sleep apnea (OSA) is a widely known sleep disorder that leads to serious health problems and complications. The standard diagnosis method of OSA is polysomnography. However, the process is time-intensive, expensive, and not readily accessible. Machine learning (ML) has been increasingly applied in various medical imaging modalities; however, there is still a lack of research on applying ML to ultrasound imaging for OSA classification. Previous studies on ML applications in medical imaging adopt X-rays, Computed Tomography, and Magnetic Resonance Imaging, leaving ultrasound as an underexplored area. Using the You-Only-Look-Once version 8 algorithm and static tongue ultrasound images, we classified OSA severity: normal, mild, moderate, and severe. A total of 280 ultrasound images were augmented to 838 images using brightness scaling, which enhanced the training process of the model. The system was tested on 60 images, achieving an overall classification accuracy of 85%. The results demonstrate the possibility and potential of using machine learning and ultrasound imaging for classifying the severity of OSA, suggesting potential assistance to clinicians in diagnosing and intervening in this condition. Full article