Search Results (222)

Background/Objectives: Alterations in liver vascularization play a remarkable role in liver disease development, including hepatocellular carcinoma (HCC), but remain understudied. This study evaluated the hepatic microvascular imaging method and provided high-resolution quantitative anatomical data on the characteristics and architecture of liver vasculature in wild-type (WT) mice and HCC mouse models. Methods: C57BL/6 mice were injected with Akt/Ras or Sleeping Beauty transposon to induce HCC. Liver tissues from normal and Akt/Ras mice underwent hematoxylin and eosin, Masson’s trichrome, Ki67, and lymphatic endothelial receptor-1 staining. Using cutting-edge high-definition fluorescence micro-optical sectioning tomography, high-precision microvascular visualization of the liver was performed in WT and Akt/Ras HCC mice. Results: The sectioned volumes of normal and HCC liver tissues were 204.8 mm³ and 212.8 mm³, respectively. The microvascular systems associated with the tissues of the Akt/Ras HCC mouse were twisted, disordered, and compressed by tumor nodules. In the four tumor nodules, the path of the hepatic artery was more around the tumor edge, whereas the portal vein occupied the central position and constituted the main blood vessel entering the tumors. The porosity of HCC and paracancerous cirrhotic tissues was significantly less than that of normal tissues. The radii of the central vessels in the hepatic sinusoid of paratumoral cirrhotic tissues were significantly higher than those of normal tissues; however, the hepatic sinusoid density of paratumoral cirrhotic tissues was lower. Conclusions: This research provides a deeper understanding of the normal liver microvasculature and alterations in cases of cirrhosis and HCC, which complements scientific insights into liver morphology and physiology. This straightforward research approach involving the novel 3D liver microvasculature can be used in multiscale physiological and pathophysiological studies regarding liver diseases. Full article

The automatic classification of sleep stages and Cyclic Alternating Pattern (CAP) subtypes from electroencephalogram (EEG) recordings remains a significant challenge in computational sleep research because of the short duration of CAP events and the inherent class imbalance in clinical datasets. Background/Objectives: The research introduces a domain-specific deep learning system that employs an LSTM network optimized through a PSO-Hyperband hybrid hyperparameter tuning method. Methods: The research enhances EEG-based sleep analysis through the implementation of hybrid optimization methods within an LSTM architecture that addresses CAP sequence classification requirements without requiring architectural changes. Results: The developed model demonstrates strong performance on the CAP Sleep Database by achieving 97% accuracy for REM and 96% accuracy for stage S0 and ROC AUC scores exceeding 0.92 across challenging CAP subtypes (A1–A3). The model transparency is improved through the application of SHAP-based interpretability techniques, which highlight the role of spectral and morphological EEG features in classification outcomes. Conclusions: The proposed framework demonstrates resistance to class imbalance and better discrimination between visually similar CAP subtypes. The results demonstrate how hybrid optimization methods improve the performance, generalizability, and interpretability of deep learning models for EEG-based sleep microstructure analysis. Full article

Sleep disorders and stroke are intricately linked through a complex, bidirectional relationship. Sleep disturbances such as obstructive sleep apnea (OSA), insomnia, and restless legs syndrome (RLS) not only increase the risk of stroke but also frequently emerge as consequences of cerebrovascular events. OSA, in particular, is associated with a two- to three-fold increased risk of incident stroke, primarily through mechanisms involving intermittent hypoxia, systemic inflammation, endothelial dysfunction, and autonomic dysregulation. Conversely, stroke can disrupt sleep architecture and trigger or exacerbate sleep disorders, including insomnia, hypersomnia, circadian rhythm disturbances, and breathing-related sleep disorders. These post-stroke sleep disturbances are common and significantly impair rehabilitation, cognitive recovery, and quality of life, yet they remain underdiagnosed and undertreated. Early identification and management of sleep disorders in stroke patients are essential to optimize recovery and reduce the risk of recurrence. Therapeutic strategies include lifestyle modifications, pharmacological treatments, medical devices such as continuous positive airway pressure (CPAP), and emerging alternatives for CPAP-intolerant individuals. Despite growing awareness, significant knowledge gaps persist, particularly regarding non-OSA sleep disorders and their impact on stroke outcomes. Improved diagnostic tools, broader screening protocols, and greater integration of sleep assessments into stroke care are urgently needed. This narrative review synthesizes current evidence on the interplay between sleep and stroke, emphasizing the importance of personalized, multidisciplinary approaches to diagnosis and treatment. Advancing research in this field holds promise for reducing the global burden of stroke and improving long-term outcomes through targeted sleep interventions. Full article

Obstructive sleep apnea (OSA) is a prevalent disorder linked to metabolic complications such as diabetes and cardiovascular disease. By fragmenting normal sleep architecture, OSA perturbs the growth hormone/insulin-like growth factor (GH/IGF) axis and alters circulating levels of IGF-binding proteins (IGFBPs). A prior clinical observation of elevated IGFBP4 in OSA patients motivated the present investigation in a controlled animal model. Building on the previously reported protocol, OSA was induced in male C57BL/6 mice (9–12 weeks old) through intralingual injection of polytetrafluoroethylene (PTFE), producing tongue hypertrophy, intermittent airway obstruction, and hypoxemia. After 8–10 weeks, the study assessed (1) hypoxia biomarkers—including HIF-1α and VEGF expression—and (2) neurobehavioral outcomes in anxiety and cognition using the open-field and novel object recognition tests. PTFE-treated mice exhibited a significant increase in circulating IGFBP4 versus both baseline and control groups. Hepatic Igfbp4 mRNA was also upregulated. Behaviorally, PTFE mice displayed heightened anxiety-like behavior and impaired novel object recognition, paralleling cognitive deficits reported in human OSA. These findings validate the PTFE-induced model as a tool for studying OSA-related hypoxia and neurocognitive dysfunction, and they underscore IGFBP4 as a promising biomarker and potential mediator of OSA’s systemic effects. Full article

Continuous blood pressure (BP) monitoring provides valuable insight into the body’s dynamic cardiovascular regulation across various physiological states such as physical activity, emotional stress, postural changes, and sleep. Continuous BP monitoring captures different variations in systolic and diastolic pressures, reflecting autonomic nervous system activity, vascular compliance, and circadian rhythms. This enables early identification of abnormal BP trends and allows for timely diagnosis and interventions to reduce the risk of cardiovascular diseases (CVDs) such as hypertension, stroke, heart failure, and chronic kidney disease as well as chronic stress or anxiety disorders. To facilitate continuous BP monitoring, we propose an AI-powered estimation framework. The proposed framework first uses an expert-driven feature engineering approach that systematically extracts physiological features from photoplethysmogram (PPG)-based arterial pulse waveforms (APWs). Extracted features include pulse rate, ascending/descending times, pulse width, slopes, intensity variations, and waveform areas. These features are fused with demographic data (age, gender, height, weight, BMI) to enhance model robustness and accuracy across diverse populations. The framework utilizes a Tab-Transformer to learn rich feature embeddings, which are then processed through an ensemble machine learning framework consisting of CatBoost, XGBoost, and LightGBM. Evaluated on a dataset of 1000 subjects, the model achieves Mean Absolute Errors (MAE) of 3.87 mmHg (SBP) and 2.50 mmHg (DBP), meeting British Hypertension Society (BHS) Grade A and Association for the Advancement of Medical Instrumentation (AAMI) standards. The proposed architecture advances non-invasive, AI-driven solutions for dynamic cardiovascular health monitoring. Full article

Accurate sleep staging and sleep-disordered breathing (SDB) severity prediction are critical for the early diagnosis and management of sleep disorders. However, real-world polysomnography (PSG) data often suffer from modality heterogeneity, label scarcity, and non-independent and identically distributed (non-IID) characteristics across institutions, posing significant challenges for model generalization and clinical deployment. To address these issues, we propose a federated multi-task learning (FMTL) framework that simultaneously performs sleep staging and SDB severity classification from seven multimodal physiological signals, including EEG, ECG, respiration, etc. The proposed framework is built upon a hybrid deep neural architecture that integrates convolutional layers (CNN) for spatial representation, bidirectional GRUs for temporal modeling, and multi-head self-attention for long-range dependency learning. A shared feature extractor is combined with task-specific heads to enable joint diagnosis, while the FedAvg algorithm is employed to facilitate decentralized training across multiple institutions without sharing raw data, thereby preserving privacy and addressing non-IID challenges. We evaluate the proposed method across three public datasets (APPLES, SHHS, and HMC) treated as independent clients. For sleep staging, the model achieves accuracies of 85.3% (APPLES), 87.1% (SHHS_rest), and 79.3% (HMC), with Cohen’s Kappa scores exceeding 0.71. For SDB severity classification, it obtains macro-F1 scores of 77.6%, 76.4%, and 79.1% on APPLES, SHHS_rest, and HMC, respectively. These results demonstrate that our unified FMTL framework effectively leverages multimodal PSG signals and federated training to deliver accurate and scalable sleep disorder assessment, paving the way for the development of a privacy-preserving, generalizable, and clinically applicable digital sleep monitoring system. Full article

Background: The brain is never truly at rest. Even in the absence of external tasks, it remains active, continuously organizing itself into large-scale resting-state networks involved in shaping our internal thoughts and experiences. Understanding the networks’ structure and dynamics is key to uncovering how the brain functions as a whole. While previous studies have mapped resting-state networks and explored the roles of individual brain regions (network vertices), the relevance of the time-dependent functional interactions (network edges) between them remains largely unexplored. Methods: Here, we assess this relevance by elucidating the time-evolving importance of both brain regions and their interactions, associated with the networks’ constituents, using the fundamental concept of centrality. We investigate long-term electrophysiological recordings of brain dynamics from more than 100 participants and reveal new insights into how resting-state networks are organized over longer times. Results: Our findings reveal that the functional architecture of brain networks in a resting state is critically shaped by the dynamic interplay between brain regions. We identified functional importance backbones–core sets of dynamically central vertices and edges–whose configuration varies significantly between subgroups and further varies with different brain states, including wakefulness and sleep. Notably, regions associated with the default mode network exhibited adaptable patterns of centrality, challenging the notion of static network cores. Conclusions: By considering the temporal evolution of both vertices and edges, we provide a more comprehensive understanding of intrinsic brain activity and its functional relevance. This dynamic perspective reveals how the brain’s intrinsic activity is coordinated across space and time, highlighting the existence of functional importance backbones that adapt to different brain states. Full article

Background/Objectives: Imposed sleep restriction leads to increased feelings of appetite and hedonic eating behaviors (or food rewards). No study to date has assessed home-based measures of sleep with appetite and food rewards exclusively in Black emerging adults (ages 18–28 years), despite higher risks of short sleep and obesity in this population. We examined associations between 6-month changes in sleep with changes in appetite and food reward in Black emerging adults. Methods: Fifteen Black emerging adults (12 females; age, 21 ± 2.5 years; body mass index, 25.7 ± 4.5 kg/m²; body fat, 25.8 ± 11.9%) completed two identical 7-day measurement bursts at baseline and 6 months. Sleep (duration, efficiency, and architecture) was captured via 7 days of actigraphy and 2 nights of in-home polysomnography. During a laboratory visit, participants completed appetite measures (desire to eat, hunger, fullness, and prospective food consumption) via visual analog scales before and for 3 h following standard breakfast intake. The food reward for the fat and sweet categories of food was measured before lunch with the Leeds Food Preference Questionnaire. Results: Fasting fullness scores decreased from baseline to 6 months (−8.9 mm, p < 0.01) despite increases in body weight (2.6 kg, p < 0.01) and waist circumference (2.4 cm, p = 0.03). Increases in actigraph-measured sleep duration were associated with decreases in fasting desire to eat (r = −0.58, p = 0.04). Increases in actigraph-measured sleep efficiency were also associated with decreases in explicit liking for sweet foods (r = −0.60, p = 0.03). Conclusions: Our findings suggest that improvements in sleep duration and sleep efficiency may lead to decreased feelings of appetite and food reward in Black emerging adults. Full article

Automated sleep stage classification is essential for objective sleep evaluation and clinical diagnosis. While numerous algorithms have been developed, the predominant existing methods utilize single-channel electroencephalogram (EEG) signals, neglecting the complementary physiological information available from other channels. Standard polysomnography (PSG) recordings capture multiple concurrent biosignals, where sophisticated integration of these multi-channel data represents a critical factor for enhanced classification accuracy. Conventional multi-channel fusion techniques typically employ elementary concatenation approaches that insufficiently model the intricate cross-channel correlations, consequently limiting classification performance. To overcome these shortcomings, we present MCAF-Net, a novel network architecture that employs temporal convolution modules to extract channel-specific features from each input signal and introduces a dynamic gated multi-head cross-channel attention mechanism (MCAF) to effectively model the interdependencies between different physiological channels. Experimental results show that our proposed method successfully integrates information from multiple channels, achieving significant improvements in sleep stage classification compared to the vast majority of existing methods. Full article

Obstructive sleep apnea (OSA) is the most prevalent sleep disorder caused by breathing difficulties, characterized by repeated episodes of airway blockage while sleeping. Various interventions have been used to improve the symptoms and overall health of individuals with OSA. However, few studies have focused on the impact of exercise on OSA. Objectives: The objective of this review was to evaluate the impact of exercise on individuals with OSA, providing an update on the exercise management of OSA. Methods: This review examined the current literature, including experimental studies and systematic reviews with meta-analysis, that investigated the impact of exercise (oropharyngeal exercises, respiratory muscle training, and therapeutic exercise training) in OSA patients. Studies were identified by searching databases (PubMed, CHINAL, EBSCO) using the following keywords: obstructive sleep apnea, OSA, exercise, oropharyngeal exercises, respiratory muscle training. Inclusion criteria were based on the PICO framework. Results: Forty-three studies were included in this review following the original search, all of which investigated the effects of exercise interventions in OSA. Most of the studies observed various significant health-related improvements following exercise interventions; however, none of them combined or compared all these exercise regimes together. In addition, there is limited information regarding the impact of exercise on sleep architecture. Conclusions: Overall, the findings suggest that exercise, regardless of its regime, benefits individuals with OSA. Full article

This study investigates the impact of heatwaves on the thermal comfort and well-being of elderly individuals living alone during heatwaves, focusing on two contrasting residential typologies in the Atlantic climate of Spain: a dense urban area and low-density peri-urban setting. A mixed-methods approach was used, combining in situ environmental monitoring, adaptive comfort modelling, and user-centred data from surveys and interviews based on the De Jong-Gierveld Loneliness Scale. The results show that both dwellings exceeded recommended indoor temperature thresholds during heatwaves, especially at night, contributing to sleep disturbance, cardiovascular stress, and emotional discomfort. Despite 85% of participants indicating that outdoor activities help them to mitigate not-wanted loneliness, architectural barriers often hinder such engagement. Over half reported having no balcony or terrace, which may have further intensified social isolation. Field data collected during 2022 summer heatwaves recorded maximum daytime temperatures of 30 °C and night-time peaks of 28.7 °C, exceeding the 25 °C threshold. The adaptive comfort evaluation classified both cases as Class 4 (severe discomfort). The urban dwelling showed consistent moderate discomfort (Category 3), likely due to poor ventilation and urban heat island effects. The peri-urban case, despite lacking the heat island influence, showed worse thermal conditions, especially during the day. Architectural barriers, poor thermal performance, and the lack of semi-outdoor spaces may exacerbate isolation among elderly people during extreme heat events. Full article

Health and well-being, both mental and physical, depend largely on adequate sleep. Many conditions arise from a disrupted sleep cycle, significantly deteriorating the quality of life of those affected. The analysis of the sleep cycle provide valuable information about sleep stages, which are employed in sleep medicine for the diagnosis of numerous diseases. The clinical standard for sleep data recording is polysomnography (PSG), which records electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and other signals during sleep activity. Recently, machine learning approaches have exhibited high accuracy in applications such as the classification and prediction of biomedical signals. This study presents a hybrid neural network architecture composed of convolutional neural network (CNN) layers, bidirectional long short-term memory (BiLSTM) layers, and attention mechanism layers in order to process large volumes of EEG data in PSG files. The objective is to design a framework for automated feature extraction. To address class imbalance, an epoch-level random undersampling (E-LRUS) method is proposed, discarding full epochs from majority classes while preserving the temporal structure, unlike traditional methods that remove individual samples. This method has been tested on EEG recordings acquired from the public Sleep EDF Expanded database, achieving an overall accuracy rate of 78.67% along with an F1-score of 72.10%. The findings show that this method proves to be effective for sleep stage classification in patients. Full article

This paper describes the modelling of Key Health Indicators (KHI) of frail individuals through non-invasive sensors located in their environment and wearable devices. Primary care professionals defined four indicators for daily health monitoring: sleep patterns, excretion control, physical mobility, and caregiver social interaction. A minimally invasive and low-cost sensing architecture was implemented, combining indoor localisation and physical activity tracking through environmental sensors and wrist-worn wearables. The health outcomes are modelled using a knowledge-based framework that integrates knowledge graphs to represent control variables and their relationships with data streams, and fuzzy logic to linguistically define temporal patterns based on expert criteria. The proposed approach was validated in a real-world case study with an older adult living independently in Granada, Spain. Over several days of deployment, the system successfully generated interpretable daily summaries reflecting relevant behavioural patterns, including rest periods, bathroom usage, activity levels, and caregiver proximity. In addition, supervised machine learning models were trained on the indicators derived from the fuzzy logic system, achieving average accuracy and F1 scores of 93% and 92%, respectively. These results confirm the potential of combining expert-informed semantics with data-driven inference to support continuous, explainable health monitoring in ambient assisted living environments. Full article

Sleep is essential for physical and mental health, playing a critical role in memory consolidation, behavioral stability, and the regulation of immune and metabolic functions. The incidence of sleep disorders, particularly sleep deprivation (SD), increases with age and is prevalent in neurodegenerative and psychiatric disorders such as Alzheimer’s disease (AD). Nearly 40% of AD patients experience significant chronic sleep impairments. The clinical distinction between late-life sleep disorders and AD is often challenging due to overlapping symptoms, including cognitive decline and behavioral impairments. Although the exact causal relationship between SD and AD remains complex and multifaceted, strong evidence suggests a bidirectional link, with AD patients frequently exhibiting disrupted sleep architecture, reduced slow-wave activity, and shorter total sleep duration. On a pathophysiological level, SD contributes to neuroinflammation, amyloid-β plaque deposition, and tau tangles, which are key features of AD. Current treatments, such as sedatives and antidepressants, often have limitations, including inconsistent efficacy, dependency risks, and poor long-term outcomes/recurrence, highlighting the need for safer and more effective alternatives. This review examines the interplay between SD and AD and proposes omega (n)-3 fatty acids (FAs) as a potential therapeutic intervention. Preclinical and clinical studies suggest that n-3 supplementation may improve sleep onset/quality, reduce neuroinflammation, support synaptic function, and decrease amyloid-β aggregation, thereby alleviating early AD-related neurological changes. Given their safety profile and neuroprotective effects, n-3 FAs represent a promising strategy for managing the comorbidity of sleep disorders in AD. Full article

Currently, approximately 40% of patients with dementia develop some form of sleep disorder. Benzodiazepines are widely prescribed but pose the risk of tolerance and cognitive decline; however, Z-drugs may offer safer alternatives. Therefore, this systematic review aimed to analyze the effect of benzodiazepines and Z-drugs on sleep disorders in patients with dementia. Two authors conducted a systematic search in PubMed, Scopus, Web of Science, Espistemonikos, and ACCESSSS for studies published between 2019 and 2024 using the MeSH terms “dementia”, “sleep disorders”, and “pharmacotherapy”. Randomized clinical trials comparing benzodiazepines, Z-drugs, or innovative medications with placebo or other drugs were included. Sleep and cognitive outcomes were assessed using validated instruments; the ROB-2 tool evaluated the risk of bias. The protocol was registered in “PROSPERO”. Three randomized clinical trials involving a total of 192 patients were included in the review. Zopiclone increased the main duration of nighttime sleep by 81 min, Zolpidem reduced nighttime awakenings by 21 min, and Eszopiclone improved sleep quality, benefited the progression of sleep architecture, and reduced mental symptoms such as fear and anxiety. Z-drugs show superior efficacy and safety over benzodiazepines, improving sleep and cognitive symptoms in dementia. Personalized treatment and further research across dementia subtypes are needed to optimize long-term outcomes. Full article