Search Results (430)

Introduction: Multiple sclerosis (MS) is a chronic neurodegenerative disease that entails high costs, progressive disability, and reduced quality of life (QoL). Telerehabilitation (TR), supported by new technologies, is emerging as an alternative or complement to in-person rehabilitation, potentially lowering socioeconomic impact and improving QoL. Aim: The objective of this study was to evaluate the effect of TR on the QoL of people with MS compared with in-person rehabilitation or no intervention. Materials and methods: A systematic review of randomized clinical trials was conducted (March–May 2025) following PRISMA guidelines. Searches were run in the PubMed-Medline, EMBASE, PEDro, Web of Science, and Dialnet databases. Methodological quality was assessed with the CASP scale, risk of bias with the Risk of Bias 2 tool, and evidence level and grade of recommendation with the Oxford Classification. The protocol was registered in PROSPERO (CRD420251110353). Results: Of the 151 articles initially found, 12 RCTs (598 total patients) met the inclusion criteria. Interventions included (a) four studies employing video-controlled exercise (one involving Pilates to improve fitness, another involving exercise to improve fatigue and general health, and two using exercises focused on the pelvic floor muscles); (b) three studies using a monitoring app to improve manual dexterity, symptom control, and increased physical activity; (c) two studies implementing an augmented reality system to treat cognitive deficits and sexual disorders, respectively; (d) one platform with a virtual reality headset for motor and cognitive training; (e) one study focusing on video-controlled motor imagery, along with the use of a pain management app; (f) a final study addressing cognitive training and pain reduction. Studies used eight different scales to assess QoL, finding similar improvements between groups in eight of the trials and statistically significant improvements in favor of TR in four. The included trials were of good methodological quality, with a moderate-to-low risk of bias and good levels of evidence and grades of recommendation. Conclusions: TR was more effective in improving the QoL of people with MS than no intervention, was as effective as in-person treatment in patients with EDSS ≤ 6, and appeared to be more effective than in-person intervention in patients with EDSS between 5.5 and 7.5 in terms of QoL. It may also eliminate some common barriers to accessing such treatments. Full article

Background/Objectives: Adequate sleep has a fundamental role in human health, mainly in cognitive and physiological functions. However, the daily demands of modern society have led to a constant pursuit of better living conditions, requiring more active hours at the expense of sleeping hours. This sleep deprivation has been associated with human health deterioration, namely an increase in Diabetes Mellitus incidence. This metabolic disease is a chronic pathology that imposes a big burden on health systems and is associated with the rise in insulin resistance. In this sense, the aim of this review is to analyze the relation between sleep deprivation and insulin resistance, emphasizing the metabolic parameters and hormones that may be involved in the subjacent mechanism. Methods: A literature review of the last 10 years was performed with specific terms related to “sleep deprivation” and “insulin resistance”. Results: Overall, the studies analyzed showed a decrease in insulin sensitivity in cases of sleep deprivation, even with different study protocols. In addition, an association between sleep deprivation and increased non-esterified fatty acids was also noticeable; however, other parameters such as cortisol, metanephrines, and normetanephrines showed no consistent results among the studies. Conclusions: This review allowed us to confirm the relationship between sleep deprivation and insulin resistance; however, despite the difficulties to monitor sleep, more research is needed to understand the related mechanisms that have not yet been clarified. Full article

The parallel global increase in obesity and Alzheimer’s disease (AD) underscores an urgent public health challenge, with converging evidence indicating that metabolic dysfunction strongly contributes to neurodegeneration. Obesity is now recognized not only as a systemic metabolic condition but also as a modifiable risk factor for AD, acting through mechanisms such as chronic low-grade inflammation, insulin resistance, and adipose tissue dysfunction. Among the molecular mediators at this interface, adipokines have emerged as pivotal regulators linking metabolic imbalance to cognitive decline. Adipokines are hormone-like proteins secreted by adipose tissue, including adiponectin, leptin, and resistin, that regulate metabolism, inflammation and can influence brain function. Resistin, frequently elevated in obesity, promotes neuroinflammation, disrupts insulin signaling, and accelerates β-amyloid (Aβ) deposition and tau pathology. Conversely, adiponectin enhances insulin sensitivity, suppresses oxidative stress, and supports mitochondrial and endothelial function, thereby exerting neuroprotective actions. The imbalance between resistin and adiponectin may shift the central nervous system toward a pro-inflammatory and metabolically compromised state that predisposes to neurodegeneration. Beyond their mechanistic relevance, adipokines hold translational promise as biomarkers for early risk stratification and therapeutic monitoring. Importantly, natural compounds, including polyphenols, alkaloids, and terpenoids, have shown the capacity to modulate adipokine signaling, restore metabolic homeostasis, and attenuate AD-related pathology in preclinical models. This positions adipokines not only as pathogenic mediators but also as therapeutic targets at the intersection of diabetes, obesity, and dementia. By integrating mechanistic, clinical, and pharmacological evidence, this review emphasizes adipokine signaling as a novel axis for intervention and highlights natural compound-based strategies as emerging therapeutic approaches in obesity-associated AD. Beyond nutraceuticals, antidiabetic agents also modulate adipokines and AD-relevant pathways. GLP-1 receptor agonists, metformin, and thiazolidinediones tend to increase adiponectin and reduce inflammatory tone, while SGLT2 and DPP-4 inhibitors exert systemic anti-inflammatory and hemodynamic benefits with emerging but still limited cognitive evidence. Together, these drug classes offer mechanistically grounded strategies to target the adipokine–inflammation–metabolism axis in obesity-associated AD. Full article

Background: Healthcare 5.0 represents a shift toward intelligent, human-centric care systems. Intensive care units generate vast amounts of data that require real-time decisions, but current decision support systems lack comprehensive frameworks for safe integration of artificial intelligence. Objective: We developed and validated the Learn–Predict–Monitor–Detect–Correct (LPMDC) framework as a methodology for systematic artificial intelligence integration across the critical care workflow. The framework improves predictive analytics, continuous patient monitoring, intelligent alerting, and therapeutic decision support while maintaining essential human clinical oversight. Methods: Framework development employed systematic theoretical modeling integrating Healthcare 5.0 principles, comprehensive literature synthesis covering 2020–2024, clinical workflow analysis across 15 international ICU sites, technology assessment of mature and emerging AI applications, and multi-round expert validation by 24 intensive care physicians and medical informaticists. Each LPMDC phase was designed with specific integration requirements, performance metrics, and safety protocols. Results: LPMDC implementation and aggregated evidence from prior studies demonstrated significant clinical improvements: 30% mortality reduction, 18% ICU length-of-stay decrease (7.5 to 6.1 days), 45% clinician cognitive load reduction, and 85% sepsis bundle compliance improvement. Machine learning algorithms achieved an 80% sensitivity for sepsis prediction three hours before clinical onset, with false-positive rates below 15%. Additional applications demonstrated effectiveness in predicting respiratory failure, preventing cardiovascular crises, and automating ventilator management. Digital twins technology enabled personalized treatment simulations, while the integration of the Internet of Medical Things provided comprehensive patient and environmental surveillance. Implementation challenges were systematically addressed through phased deployment strategies, staff training programs, and regulatory compliance frameworks. Conclusions: The Healthcare 5.0-enabled LPMDC framework provides the first comprehensive theoretical foundation for systematic AI integration in critical care while preserving human oversight and clinical safety. The cyclical five-phase architecture enables processing beyond traditional cognitive limits through continuous feedback loops and system optimization. Clinical validation demonstrates measurable improvements in patient outcomes, operational efficiency, and clinician satisfaction. Future developments incorporating quantum computing, federated learning, and explainable AI technologies offer additional advancement opportunities for next-generation critical care systems. Full article

This paper explores the development and implementation of a brain–computer interface (BCI) system that utilizes electroencephalogram (EEG) signals for real-time monitoring of attention levels to control unmanned aerial vehicles (UAVs). We propose an innovative approach that combines spectral power analysis and machine learning classification techniques to translate cognitive states into precise UAV command signals. This method overcomes the limitations of traditional threshold-based approaches by adapting to individual differences and improving classification accuracy. Through comprehensive testing with 20 participants in both controlled laboratory environments and real-world scenarios, our system achieved an 85% accuracy rate in distinguishing between high and low attention states and successfully mapped these cognitive states to vertical UAV movements. Experimental results demonstrate that our machine learning-based classification method significantly enhances system robustness and adaptability in noisy environments. This research not only advances UAV operability through neural interfaces but also broadens the practical applications of BCI technology in aviation. Our findings contribute to the expanding field of neurotechnology and underscore the potential for neural signal processing and machine learning integration to revolutionize human–machine interaction in industries where dynamic relationships between cognitive states and automated systems are beneficial. Full article

Traumatic brain injury (TBI) remains a major global health problem, contributing significantly to morbidity and mortality worldwide. Despite advances in understanding its complex pathophysiology, current therapeutic strategies are insufficient in addressing the long-term cognitive, emotional, and neurological impairments. While the primary mechanical injury is immediate and unavoidable, the secondary phase involves a cascade of biological processes leading to neuroinflammation, blood–brain barrier (BBB) disruption, and systemic immune activation. The heterogeneity of patient responses underscores the urgent need for reliable biomarkers and targeted interventions. Emerging evidence highlights the gut–brain axis as a critical modulator of the secondary phase, with microbiota-derived extracellular vesicles (MEVs) representing a promising avenue for both diagnosis and therapy. MEVs can cross the intestinal barrier and BBB, carrying biomolecules that influence neuronal survival, synaptic plasticity, and inflammatory signaling. These properties make MEVs promising biomarkers for early detection, severity classification, and prognosis in TBI, while also offering therapeutic potential through modulation of neuroinflammation and promotion of neural repair. MEV-based strategies could enable tailored interventions based on the individual’s microbiome profile, immune status, and injury characteristics. The integration of multi-omics with artificial intelligence is expected to fully unlock the diagnostic and therapeutic potential of MEVs. These approaches can identify molecular subtypes, predict outcomes, and facilitate real-time clinical decision-making. By bridging microbiology, neuroscience, and precision medicine, MEVs hold transformative potential to advance TBI diagnosis, monitoring, and treatment. This review also identifies key research gaps and proposes future directions for MEVs in precision diagnostics and gut microbiota-based therapeutics in neurotrauma care. Full article

Integrating human–robot collaboration (HRC) into construction sites has significantly enhanced efficiency and quality. However, it also introduces new or intensifies existing risks as it brings in new entities, relationships, and construction activities. Safety remains the top priority and a persistent concern in HRC systems. However, the current literature on human–robot collaboration safety (HRCS) is vast yet fragmented, and a systematic exploration of its status and research trends in the construction context is still lacking. This paper explores advances in HRCS over the past two decades through a mixed quantitative and qualitative analysis method. Initially, 287 related articles were identified by keyword-searching in Scopus, followed by bibliometric analysis using CiteSpace to uncover the knowledge structure and track emerging research trends. Subsequently, a qualitative discussion highlights achievements in HRCS across five dimensions: (1) optimization of remote intelligent machinery; (2) hazard analysis and risk assessment in HRCS; (3) digital twin for safety monitoring; (4) cognitive and psychological impacts; (5) organizational management perspective. This study quantitatively maps the scientific landscape of HRCS at a macro level and qualitatively identifies key research areas. It provides a comprehensive foundation for understanding the evolution of HRCS and exploring future research directions and applications. Full article

Exercise-induced fatigue can degrade athletic performance and increase injury risk, yet traditional fatigue assessments often rely on subjective measures. This study proposes an objective fatigue recognition approach using high-fidelity motion capture data and deep learning. This study induced both cognitive and physical fatigue in 50 male participants through a dual task (mental challenge followed by intense exercise) and collected three-dimensional lower-limb joint kinematics and kinetics during vertical jumps. A bidirectional Gate Recurrent Unit (GRU) with an attention mechanism (BiGRU + Attention) was trained to classify pre- vs. post-fatigue states. Five-fold cross-validation was employed for within-sample evaluation, and attention weight analysis provided insight into key fatigue-related movement phases. The BiGRU + Attention model achieved superior performance with 92% classification accuracy and an Area Under Curve (AUC) of 96%, significantly outperforming the single-layer GRU baseline (85% accuracy, AUC 92%). It also exhibited higher recall and fewer missed detections of fatigue. The attention mechanism highlighted critical moments (end of countermovement and landing) associated with fatigue-induced biomechanical changes, enhancing model interpretability. This study collects spatial data and biomechanical data during movement, and uses a bidirectional Gate Recurrent Unit (GRU) model with an attention mechanism to distinguish between non-fatigue states and fatigue states involving both physical and psychological aspects, which holds certain pioneering significance in the field of fatigue state identification. This study lays the foundation for real-time fatigue monitoring systems in sports and rehabilitation, enabling timely interventions to prevent performance decline and injury. Full article

This review is devoted to a comprehensive analysis of modern forms of information warfare in the context of digitalization and global interconnectedness. The work considers fundamental theoretical foundations—cognitive distortions, mass communication models, network theories and concepts of cultural code. The key tools of information influence are described in detail, including disinformation, the use of botnets, deepfakes, memetic strategies and manipulations in the media space. Particular attention is paid to methods of identifying and neutralizing information threats using artificial intelligence and digital signal processing, including partial digital convolutions, Fourier–Galois transforms, residue number systems and calculations in finite algebraic structures. The ethical and legal aspects of countering information attacks are analyzed, and geopolitical examples are given, demonstrating the peculiarities of applying various strategies. The review is based on a systematic analysis of 592 publications selected from the international databases Scopus, Web of Science and Google Scholar, covering research from fundamental works to modern publications of recent years (2015–2025). It is also based on regulatory legal acts, which ensures a high degree of relevance and representativeness. The results of the review can be used in the development of technologies for monitoring, detecting and filtering information attacks, as well as in the formation of national cybersecurity strategies. Full article

This study introduces a machine learning–driven extended reality (XR) interaction framework that leverages electroencephalography (EEG) for decoding consumer intentions in immersive decision-making tasks, demonstrated through functional food purchasing within a simulated autonomous vehicle setting. Recognizing inherent limitations in traditional “Preference vs. Non-Preference” EEG paradigms for immersive product evaluation, we propose a novel and robust “Rest vs. Intention” classification approach that significantly enhances cognitive signal contrast and improves interpretability. Eight healthy adults participated in immersive XR product evaluations within a simulated autonomous driving environment using the Microsoft HoloLens 2 headset (Microsoft Corp., Redmond, WA, USA). Participants assessed 3D-rendered multivitamin supplements systematically varied in intrinsic (ingredient, origin) and extrinsic (color, formulation) attributes. Event-related potentials (ERPs) were extracted from 64-channel EEG recordings, specifically targeting five neurocognitive components: N1 (perceptual attention), P2 (stimulus salience), N2 (conflict monitoring), P3 (decision evaluation), and LPP (motivational relevance). Four ensemble classifiers (Extra Trees, LightGBM, Random Forest, XGBoost) were trained to discriminate cognitive states under both paradigms. The ‘Rest vs. Intention’ approach achieved high cross-validated classification accuracy (up to 97.3% in this sample), and area under the curve (AUC > 0.97) SHAP-based interpretability identified dominant contributions from the N1, P2, and N2 components, aligning with neurophysiological processes of attentional allocation and cognitive control. These findings provide preliminary evidence of the viability of ERP-based intention decoding within a simulated autonomous-vehicle setting. Our framework serves as an exploratory proof-of-concept foundation for future development of real-time, BCI-enabled in-transit commerce systems, while underscoring the need for larger-scale validation in authentic AV environments and raising important considerations for ethics and privacy in neuromarketing applications. Full article

(1) Background: The convergence of digital twin technology, artificial intelligence, and multimodal biomarkers heralds a transformative era in neuropsychological assessment and intervention. Digital twin cognition represents an emerging paradigm that creates dynamic, personalized virtual models of individual cognitive systems, enabling continuous monitoring, predictive modeling, and precision interventions. This systematic review comprehensively examines the integration of AI-driven biomarkers within biomimetic neuropsychological frameworks to advance personalized cognitive health. (2) Methods: Following PRISMA 2020 guidelines, we conducted a systematic search across six major databases spanning medical, neuroscience, and computer science disciplines for literature published between 2014 and 2024. The review synthesized evidence addressing five research questions examining framework integration, predictive accuracy, clinical translation, algorithm effectiveness, and neuropsychological validity. (3) Results: Analysis revealed that multimodal integration approaches combining neuroimaging, physiological, behavioral, and digital phenotyping data substantially outperformed single-modality assessments. Deep learning architectures demonstrated superior pattern recognition capabilities, while traditional machine learning maintained advantages in interpretability and clinical implementation. Successful frameworks, particularly for neurodegenerative diseases and multiple sclerosis, achieved earlier detection, improved treatment personalization, and enhanced patient outcomes. However, significant challenges persist in algorithm interpretability, population generalizability, and the integration of healthcare systems. Critical analysis reveals that high-accuracy claims (85–95%) predominantly derive from small, homogeneous cohorts with limited external validation. Real-world performance in diverse clinical settings likely ranges 10–15% lower, emphasizing the need for large-scale, multi-site validation studies before clinical deployment. (4) Conclusions: Digital twin cognition establishes a new frontier in personalized neuropsychology, offering unprecedented opportunities for early detection, continuous monitoring, and adaptive interventions while requiring continued advancement in standardization, validation, and ethical frameworks. Full article

This narrative review synthesizes our current understanding of sepsis and septic shock burden in intensive care units (ICUs) worldwide. Based on a comprehensive but non-systematic literature search from 2000 to 2025, this review synthesizes our current understanding across eight key domains: epidemiology, pathophysiology, diagnostics, management strategies, long-term outcomes, disparities, and future directions. The global burden of sepsis, especially in the developed and developing world, is great: over 48 million cases per year, with mortality rates at the ICU level in the range of 30 to 50%, depending on geography and resources. The pathophysiological progression from an initial hyper-inflammatory state to immune paralysis underlies organ failure and complicates therapeutic targeting. Diagnostic approaches, including clinical scoring systems, biomarkers (e.g., procalcitonin, MR-proADM), and emerging AI tools, offer improved early detection but face challenges in reliability and accessibility. Management in the ICU remains anchored in timely antimicrobial administration, hemodynamic stabilization with balanced fluids and vasopressors, source control, and organ support, including lung-protective ventilation and kidney replacement therapy. Novel adjuncts, such as immunomodulators and extracorporeal therapies, show promise but demand further validation. Importantly, survivors face significant long-term sequelae—post-intensive care syndrome (PICS)—encompassing physical, cognitive, and psychological impairments, which require structured rehabilitation and follow-up. The future of sepsis care lies in integrating precision medicine—through molecular diagnostics, individualized immunotherapy, and AI-supported monitoring—with scalable, equitable implementation strategies that bridge the gap between high- and low-income settings. Addressing disparities and expanding rehabilitation services are essential to improving survival and long-term quality of life in sepsis survivors. Full article

Emerging evidence highlights the role of chronic low-grade inflammation and dysregulated cytokines in both obesity and schizophrenia, suggesting overlapping immune system pathways that contribute to cognition and nervous system inflammation. Excess adipose tissue functions as an active endocrine organ, releasing pro-inflammatory mediators that may serve as potential biomarkers, while the use of antipsychotic agents in schizophrenia further modifies cytokine profiles and immune responses. A key knowledge gap lies in understanding how adipose-related inflammation modifies the severity of psychotic symptoms, cognitive deficits, and the efficacy of antipsychotic medications. This review aims to present excess adipose tissue as a potential contributor to the development of SCZ or a modifier of treatment efficacy, emphasizing the role of immune imbalance, inflammatory pathways, and metabolic dysfunction. By synthesizing current findings, we aim to present obesity not only as a frequent comorbidity in schizophrenia but also as a potential driver of neuroinflammation and disease progression. Here, we demonstrate that excess adiposity may perpetuate a vicious cycle linking metabolic dysfunction, immune activation, and psychiatric symptomatology. Situating these findings within a broader context, the review underscores the clinical need for inflammation-informed, individualized management strategies that integrate psychiatric care with metabolic monitoring. Ultimately, clarifying the shared inflammatory pathways of obesity and schizophrenia may open new avenues for biomarker development and targeted interventions. Full article

Monitoring dopamine (DA) has attracted increasing attention due to alterations in DA levels associated with brain disorders. In addition, the urinary DA concentration plays a significant role in the sympathoadrenal system. A decrease in DA can impair reward-seeking behavior and cognitive flexibility. Therefore, accurate and precise DA detection is necessary. In this study, a poly(3-aminobenzylamine)/functionalized multi-walled carbon nanotube (PABA/f-CNT) composite thin film was fabricated by electrochemical synthesis, or electropolymerization, of 3-aminobenzylamine (3-ABA) monomer and f-CNTs through cyclic voltammetry (CV) on a fluorine-doped tin oxide (FTO)-coated glass substrate, which also served as a working electrode for label-free DA detection in artificial urine. The formation of the film was confirmed by the obtained cyclic voltammogram, electrochemical impedance spectroscopy (EIS) plots, and scanning electron microscope (SEM) and transmission electron microscope (TEM) images. The chemical components of the films were analyzed using attenuated total reflection–Fourier transform infrared (ATR–FTIR) spectroscopy and X-ray photoelectron spectroscopy (XPS). For label-free DA detection, various concentrations (50–1000 nM) of DA were determined in buffer solution through differential pulse voltammetry (DPV). The fabricated PABA/f-CNT film presented two linear ranges of 50–400 nM (R² = 0.9915) and 500–1000 nM (R² = 0.9443), with sensitivities of 1.97 and 0.95 µA·cm⁻²·µM⁻¹, respectively. The limit of detection (LOD) and the limit of quantity (LOQ) were 119.54 nM and 398.48 nM, respectively. In addition, the PABA/f-CNT film provided excellent selectivity against common interferents (ascorbic acid, uric acid, and glucose) with high stability, reproducibility, and repeatability. For potential future medical applications, DA detection was further performed in artificial urine, yielding a high percentage of recovery. Full article

Grassland fire risk perception constitutes a fundamental element of fire risk assessment and underpins the evaluation of response capacities in grassland regions. This study examines Qinghai Province, the fourth-largest pastoral region in China, as a case study to develop an evaluation index system for assessing residents’ perceptions of grassland fire risk. Using micro-level survey data, the study quantifies these perceptions and applies a quantile regression model to investigate influencing factors. The results indicate that: (1) the average grassland fire risk perception index among residents in Qinghai Province’s grassland areas is 0.509, with response behaviors contributing the most and response attitudes contributing the least; (2) Residents in agricultural areas perceive higher risks than those in semi-agricultural/semi-pastoral or purely pastoral areas, and individuals in regions with moderate dependency ratios and moderate fire-susceptibility conditions demonstrate the highest performance, whereas those in pastoral and high-susceptibility zones exhibit signs of “risk desensitization”; (3) risk communication and information dissemination are the primary drivers of enhanced perception, followed by climate variables, whereas individual characteristics of residents attributes exert no significant effect. It is recommended to monitor the impacts of climate change on fire risk patterns, update risk information dynamically, address deficits in residents’ cognition and capabilities, strengthen behavioral guidance and capacity-building initiatives, and foster a transition from passive acceptance to active engagement, thereby enhancing both cognitive and behavioral responses to grassland fires. Full article