Search Results (237)

Objective: To test whether inpatient electrical muscle stimulation (EMS) using Russian current (5 kHz carrier, 50 Hz modulation; 4 s ON/6 s OFF) improves mobility and balance in elderly people with chronic cerebral ischaemia. Design: Prospective single-centre controlled observational pilot, embedded in routine inpatient rehabilitation; no concealed randomisation (EMS + standard care; sham EMS + standard care; standard care only (control)). Methods: A single-centre controlled observational study with three groups was conducted (EMS n = 27, control n = 10, sham n = 7) with 3–9 sessions over 2 weeks (20 min; quadriceps and calves). Pre/Post Outcomes: Tinetti (balance/gait), Rivermead Mobility Index, Timed Up and Go (TUG), ankle extensor maximal voluntary force (MVF), stabilography (statokinesiogram path length (L), mean velocity of COP (V), sway area (S), and myotonometry; ANOVA, α = 0.05). Ethics approval and informed consent were obtained. Between-group differences in change scores were evaluated descriptively, and no formal hypothesis-testing was planned. Results: EMS showed significant gains versus control/sham—higher Tinetti total and Rivermead scores, faster TUG, higher MVF, and improved stabilography in the eyes-closed condition (reduced L, V, and S), with good tolerability and no serious adverse events (SAEs). Conclusions: Short-course Russian-current EMS is feasible and associated with clinically meaningful improvements in balance, gait, and strength in elderly patients with chronic cerebral ischaemia; however, larger randomised trials are warranted. Full article

Inflammatory bowel disease (IBD) arises from chronic dysregulation at the epithelial–stromal interface, creating a need for in vitro systems that better capture these interactions. In this study, we developed a 3D co-culture platform in which HT-29 intestinal epithelial cells and IMR-90 fibroblasts are embedded within an alginate–gelatin hydrogel, alongside a complementary interface model using a plasma-treated electrospun mesh to spatially compartmentalize stromal and epithelial layers. We first assessed metabolic activity, viability, and proliferation across several epithelial-to-fibroblast ratios and identified 1:0.5 as the most supportive of epithelial expansion. The A1G7 hydrogel maintained high viability (>92%) and sustained growth in all mono- and co-cultures. To evaluate inflammatory competence, models were stimulated with lipopolysaccharide (LPS), administered either within the hydrogel or through the culture medium. LPS exposure increased TNF-α and IL-1β secretion in both configurations, with the magnitude of the response depending on the delivery route. Treatment with dexamethasone consistently reduced cytokine levels, confirming the model’s suitability for pharmacological testing. Together, these results demonstrate that the alginate–gelatin system provides a reproducible epithelial–stromal platform with quantifiable inflammatory readouts, offering a practical foundation for mechanistic studies and early-stage screening of anti-inflammatory therapeutics in IBD. Full article

Industry 5.0 challenges higher education to adopt human-centred and sustainable uses of artificial intelligence, yet many current deployments still treat generative AI as a stand-alone tool, neurophysiological sensing as largely laboratory-bound, and governance as an external add-on rather than a design constraint. This article introduces Nested Learning as a neuro-adaptive ecosystem design in which generative-AI agents, IoT infrastructures and multimodal deep learning orchestrate instructional support while preserving student agency and a “pedagogy of hope”. We report an exploratory two-phase mixed-methods study as an initial empirical illustration. First, a neuro-experimental calibration with 18 undergraduate students used mobile EEG while they interacted with ChatGPT in problem-solving tasks structured as challenge–support–reflection micro-cycles. Second, a field implementation at a university in Madrid involved 380 participants (300 students and 80 lecturers), embedding the Nested Learning ecosystem into regular courses. Data sources included EEG (P300) signals, interaction logs, self-report measures of engagement, self-regulated learning and cognitive safety (with strong internal consistency; $\alpha /\omega \ge 0.82$), and open-ended responses capturing emotional experience and ethical concerns. In Phase 1, P300 dynamics aligned with key instructional micro-events, providing feasibility evidence that low-cost neuro-adaptive pipelines can be sensitive to pedagogical flow in ecologically relevant tasks. In Phase 2, participants reported high levels of perceived nested support and cognitive safety, and observed associations between perceived Nested Learning, perceived neuro-adaptive adjustments, engagement and self-regulation were moderate to strong ($r=0.41$–$0.63$, $p<0.001$). Qualitative data converged on themes of clarity, adaptive support and non-punitive error culture, alongside recurring concerns about privacy and cognitive sovereignty. We argue that, under robust ethical, data-protection and sustainability-by-design constraints, Nested Learning can strengthen academic resilience, learner autonomy and human-centred uses of AI in higher education. Full article

Global climate change demands modeling approaches that are both computationally efficient and physically faithful to the system’s long-term dynamics. Classical Energy Balance Models (EBMs), while valuable, are fundamentally limited by their memoryless exponential response, which fails to represent the prolonged thermal inertia of the climate system—particularly that associated with deep-ocean heat uptake. In this study, we introduce a fractional Energy Balance Model (fEBM) by replacing the classical integer-order time derivative with a Caputo fractional derivative of order $\alpha (0<\alpha \le 1)$, thereby embedding long-range memory directly into the model structure. We establish a rigorous mathematical foundation for the fEBM, including proofs of existence, uniqueness, and asymptotic stability, ensuring theoretical well-posedness and numerical reliability. The model is calibrated and validated against historical global mean surface temperature data from NASA GISTEMP and radiative forcing estimates from IPCC AR6. Relative to the classical EBM, the fEBM achieves a substantially improved representation of observed temperatures, reducing the root mean square error by approximately 29% during calibration (1880–2010) and by 47% in out-of-sample forecasting (2011–2023). The optimized fractional order $\alpha =0.75\pm 0.03$ emerges as a physically interpretable measure of aggregate climate memory, consistent with multi-decadal ocean heat uptake and observed persistence in temperature anomalies. Residual diagnostics and robustness analyses further demonstrate that the fractional formulation captures dominant temporal dependencies without overfitting. By integrating mathematical rigor, uncertainty quantification, and physical interpretability, this work positions fractional calculus as a powerful and responsible framework for reduced-order climate modeling and long-term projection analysis. Full article

Objectives: This study evaluated the effect of three root canal filling material (RCFM) removal techniques—mechanical, thermo-mechanical, and chemico-mechanical—on the micro push-out bond strength of fiber posts to root dentin in endodontically treated teeth. Materials and Methods: Forty-five single-rooted human premolars were endodontically treated and randomly allocated into three groups (n = 15) according to the RCFM removal technique used during post-space preparation: mechanical, thermo-mechanical, or chemico-mechanical. Fiber posts were luted using a dual-cure resin cement. Roots were embedded in resin and sectioned into coronal, middle, and apical thirds. Micro push-out bond strength was measured using a universal testing machine. Failure modes were examined under a stereomicroscope and validated using scanning electron microscopy. Statistical analysis used two-way ANOVA and Chi-square tests (α = 0.05). Results: Both the thermo-mechanical and mechanical groups showed significantly higher bond strength values than the chemico-mechanical group (p < 0.001). Across all groups, the coronal third recorded the highest bond strength, while the apical third presented the lowest values (p < 0.001). Adhesive failure at the dentin–cement interface was the most frequent failure mode. Conclusions: The gutta-percha removal technique and the root canal region significantly influence fiber-post bond strength. Solvent-based chemico-mechanical methods may adversely affect adhesion quality. Clinical Relevance: Thermo-mechanical and mechanical removal techniques may provide more reliable post retention during retreatment procedures, improving adhesion and reducing the risk of post debonding in daily practice. Full article

Poly(vinylidene fluoride) (PVDF) nanofibers have emerged as promising materials for flexible piezoelectric sensors, yet their performance is fundamentally constrained by the limited formation and alignment of the electroactive β-phase. In this study, we report a phase-engineering strategy that integrates ionic functionalization, inorganic nanofiller incorporation, and post-fabrication corona poling to achieve enhanced crystalline ordering and electromechanical coupling in electrospun PVDF nanofibers. Tetrabutylammonium perchlorate increases solution conductivity, enabling uniform, bead-free fiber formation, while barium titanate nanoparticles act as nucleation centers that promote β-phase crystallization at the expense of the non-polar α-phase. Subsequent corona poling further aligns molecular dipoles and strengthens remnant polarization within both the PVDF matrix and embedded nanoparticles. Structural analyses confirm the synergistic evolution of crystalline phases, and piezoelectric measurements demonstrate a substantial increase in peak-to-peak output voltage under dynamic loading conditions. This combined phase-engineering approach provides a simple and scalable route to high-performance PVDF-based piezoelectric sensors and highlights the importance of coupling crystallization control with dipole alignment in designing next-generation wearable electromechanical materials. Full article

Background: Frostbite injury creates an ischemic, hypoxic, and acidic microenvironment that often triggers severe oxidative stress and inflammation. Current therapeutic approaches are limited by low drug delivery efficiency and an inability to adequately regulate multiple pathological pathways. Although oxyresveratrol (OR) exhibits excellent antioxidant and anti-inflammatory activities, its application is hampered by poor aqueous solubility and low stability. Methods: We constructed Oxyresveratrol@Zeolitic Imidazolate Framework-8 nanoparticles (OR@ZIF-8) and further embedded them in a sodium hyaluronate (HA) matrix to form an OR@ZIF-8@HA composite hydrogel. The physicochemical properties and pH-responsive drug release behavior of the system were characterized. Its antioxidant activity, ability to promote cell migration, and capacity to modulate macrophage polarization were evaluated in cellular assays. The therapeutic efficacy was further investigated using a mouse frostbite model, with wound repair analyzed via histological staining. Results: The OR@ZIF-8 nanoparticles achieved a cumulative release rate of 75.46 ± 3.68% under acidic conditions within 36 h. In vitro experiments demonstrated that the formulation significantly scavenged TNF-α and IL-6, by 161.85 ± 19.43% and 125.37 ± 12.65%, respectively, and increased the level of IL-10 by 44.97 ± 4.57%. In a scratch assay, it promoted wound healing, achieving a closure rate of 97.55 ± 2.77% after 36 h. In vivo studies revealed that the OR@ZIF-8@HA treatment group achieved a wound healing rate of 96.14 ± 4.12% on day 14. Conclusions: The OR@ZIF-8@HA composite hydrogel effectively overcomes the limitations of OR application via intelligent pH-responsive delivery. Through synergistic multi-mechanistic actions, it significantly accelerates frostbite wound healing, offering a novel and efficient therapeutic strategy for frostbite management. Full article

This study proposes 4D-MRSimNet, a framework that employs attention mechanisms to focus on distinct dimensions. The approach applies enhancements to key responses in the spatial and spectral domains and provides a characterization of dynamic evolution in temporal domain, which extracts and integrates complementary emotional features to facilitate final classification. At the feature level, differential entropy (DE) and power spectral density (PSD) are combined within four core frequency bands ($\theta$, $\alpha$, $\beta$, and $\gamma$). These bands are recognized as closely related to emotional processing. This integration constructs a complementary feature representation that preserves both energy distribution and entropy variability. These features are organized into a 4D representation that integrates electrode topology, frequency characteristics, and temporal dependencies inherent in EEG signals. At the network level, a multi-resolution convolutional module embedded with SimAM attention extracts spatial and spectral features at different scales and adaptively emphasizes key information. A bidirectional GRU (BiGRU) integrated with temporal attention further emphasizes critical time segments and strengthens the modeling of temporal dependencies. Experiments show that our method achieves an accuracy of 97.68% for valence and 97.61% for arousal on the DEAP dataset and 99.60% for valence and 99.46% for arousal on the DREAMER dataset. The results demonstrate the effectiveness of complementary feature fusion, multidimensional feature representation, and the complementary dual attention enhancement strategy for EEG emotion recognition. Full article

Background: Although there is a considerable body of research evidence on active school travel (AST), the number of children walking to school has decreased over the years in Australia, as well as around the world. Different factors influence AST; the most cited in Melbourne is traffic safety perception. Traffic safety perception is influenced by built environment elements, and improving the built environment can enhance parental perception of traffic safety. Studies have shown that lateral separation from traffic and the provision of a buffer improve the perception of traffic safety, and this has to be explored for children walking to school based on the existing ground situation on a typical street near a school in Melbourne. Based on this background, a pilot study was carried out before the main study to test the reliability and validity of the survey questionnaire. Methods: The survey instrument was developed based on perceptions and/or AST studies, and included safety and probability aspects of the perception construct to elicit responses on perception. The perception of traffic safety was to be rated based on the streetscape videos embedded in the survey. The reliability was tested using Cronbach’s alpha and validity was explored through exploratory factor analysis. The study also checked the feasibility of the recruitment method and whether there would be an observable outcome from the study. The participants were recruited online through community Facebook groups. Results: The survey instrument had excellent reliability (α = 0.945) and was valid. The recruitment method through a Facebook community group was apt for recruiting participants. The preliminary analysis of the pilot data revealed a difference in perception ratings based on the streetscape element intervention. Conclusions: The survey instrument can be used for similar AST research, as it proved to be reliable and valid. Full article

We introduce a BiHom-type skew-symmetric bracket on general linear Lie algebra $GL\left(V\right)$ built from two commuting inner automorphisms $\alpha =A{d}_{\psi }$ and $\beta =A{d}_{\varphi }$, with $\psi ,\varphi \in GL\left(V\right)$ and integers $i,j$. We prove that $(GL\left(V\right),{[·,·]}_{(\psi ,\varphi )}^{(i,j)},\alpha ,\beta )$ is a BiHom–Lie algebra, and we study the Lax equation obtained by replacing the commutator in the finite nonperiodic Toda lattice by this bracket. For the symmetric choice $\varphi =\psi$ with $(i,j)=(0,0)$, the deformed flow is equivariant under conjugation and becomes gauge-equivalent, via $\tilde{L}={\psi }^{-1}L\psi$, to a Toda-type Lax equation with a conjugated triangular projection. In particular, scalar deformations amount to a constant rescaling of time. On embedded $2\times 2$ blocks, we derive explicit trigonometric and hyperbolic formulae that make symmetry constraints (e.g., tracelessness) transparent. In the asymmetric hyperbolic case, we exhibit a trace obstruction showing that the right-hand side is generically not a commutator, which amounts to symmetry breaking of the isospectral property. We further extend the construction to the weakly coupled Toda lattice with an indefinite metric and provide explicit $2\times 2$ solutions via an inverse-scattering calculation, clarifying and correcting certain formulas in the literature. The classical Toda dynamics are recovered at special parameter values. Full article

Transcription factors control the development of the endocrine pancreas in various mammals. In humans, paired box-4 (PAX4) and aristaless-related homeobox (ARX) allocate endocrine progenitor cells toward β-cell and α-cell specification, respectively. In adulthood, PAX4 contributes to reprogramming α-cells into β-cells and exocrine into endocrine cells; induction of ARX in β-cells drives them to reprogram into α-cells. Feline diabetes mellitus has a similar pathophysiology to human type 2 diabetes, but information about the role of these transcription factors is unavailable in diabetic cats. The study aim was to test whether diabetic cats have an increased number of pancreatic cells expressing developmental markers of β- and α-cells, respectively, suggesting reprogramming. In 9 diabetic and 9 well-matched control cats, pancreas was collected, formalin-fixed and paraffin-embedded. Tissue slides were labelled for insulin, glucagon, PAX4, and ARX. Positive cells for each marker and double-positive cells for their combinations were counted in the pancreas and compared between groups. Against controls, diabetic cats had fewer insulin-positive cells in the islets (p = 0.001) and exocrine pancreas (p = 0.038); glucagon-positive cells were similar. In the islets, diabetic cats had higher counts of insulin/glucagon-positive cells (p = 0.024), PAX4-positive cells (p = 0.038), as well as PAX4/insulin-positive cells (p = 0.027). In conclusion, in diabetic cats, the increased number of islet cells expressing PAX4 leads to the hypothesis that β-cells change to an earlier stage of differentiation or that novel β-cells are formed. Furthermore, the higher count of islet insulin/glucagon-positive cells might indicate that α-cells transform into β-cells or vice versa. Hence, reprogramming seems possible in diabetic cats, specifically in the islets. Full article

Background/Objectives: In recent years, people have been facing a lot of difficulties in handling stress, emotions, social, and behavioral issues, which have led to severe mental disorders. Schizophrenia is one disorder that requires more attention. This disorder is characterized by positive or psychotic symptoms, negative symptoms, and cognitive symptoms, which makes diagnosis and treatment complicated. The main objective is to identify the degree of severity of symptoms through multimodal data and classify them using the EEGNet framework. Methods: Multimodal data are collected. To identify the severity of symptoms of schizophrenia, initial screening is performed through assessment tools such as the Positive and Negative Symptoms Scale (PANSS), Brief Negative Symptom Scale (BNSS), Scale for Assessment of Negative Symptoms (NSA-16), and Scale for Assessment of Positive Symptoms (SAPS). Designed photo elicitation and VR box video stimuli are used for data collection. The patients are asked to express their thoughts upon viewing photos shown through a photo elicitation task. The patients are given Virtual Reality (VR) stimuli where videos will be played in a VR box and patients are asked to express their thoughts. Patients’ facial expressions and speech signals are captured through webcam while performing these tasks. Finally, the electrical activities of the patients are assessed through a 14-channel EEG headset. A novel method of fusing and embedding of normalized multimodality features into the EEGNet architecture is carried out that enables combined utilization of electrophysiological information from EEG and complementary behavioral–affective cues from other modalities, thereby enhancing classification performance while retaining the architectural efficiency of EEGNet. Results: The reliability and validity of the questionnaire are statistically analyzed and found to be α = 0.761. The sum of variance of PANSS is about 27.08, SAPS is about 28.61, and BNSS is about 29.92 with p < 0.05. This EEGNet model displays an accuracy of 0.99, precision of 0.99, recall of 0.98, and F1-score of 0.99 for healthy and a precision of 0.98, recall of 0.99, and F1-score of 0.99 for schizophrenia-affected patients and ROC AUC of about 0.9989. Conclusions: This system proves to be a promising method for the diagnosis of schizophrenia and thereby enhances the performance of the system. Full article

Composite materials based on low-density polyethylene (LDPE) embedded with iron-manganese nanoparticles with compositions Fe_0.9Mn_0.1 and Fe_0.8Mn_0.2 were prepared and investigated. The newly created composites were characterized using transmission electron microscopy (TEM), X-ray diffraction (XRD), X-ray absorption near-edge structure (XANES), extended X-ray absorption fine structure (EXAFS), and Mössbauer spectroscopy. The composition, electronic, and atomic structure of the nanoparticles were established. The study confirms that the nanoparticles possess a ‘core-shell’ structure, the nature of which depends on the manganese content. The nanoparticles of Fe_0.8Mn_0.2 in LDPE exhibit a three-layered structure: a metallic α-Fe core is coated with an intermediate oxidized layer structurally close to Fe₂O₃, while the outermost shell consists of manganese oxide (Mn₂O₃). In contrast, nanoparticles with lower Mn content Fe_0.9Mn_0.1 show a predominantly fully oxidized structure. This structural evolution is consistent with thermodynamic principles, where manganese, having a higher oxide formation enthalpy, migrates to the surface. The core–shell architecture is promising for applications requiring stable magnetic components or tailored catalytic interfaces within a polymer matrix. Full article

Central Core Disease (CCD) is a congenital myopathy predominantly caused by mutations in the gene encoding ryanodine receptor type-1 (RYR1), the intracellular Ca²⁺ release channel embedded in the skeletal muscle sarcoplasmic reticulum membrane. The I4898T mutation represents one of the most common RYR1 mutations associated with CCD. Unfortunately, there are no approved therapies for CCD or for other myopathies caused by mutations in this gene. This study aims to perform a top-down differential proteomic analysis on soleus muscle samples from wild-type mice (WT) and heterozygous knock-in mice carrying the I4895T (IT) mutation in RyR1, to investigate the pathogenic mechanisms and molecular pathways involved in this myopathy and to shed light on new potential biomarkers useful for future therapies. Proteomic analysis revealed 50 dysregulated protein species, and multivariate analysis showed that IT mice exhibit a distinct proteomic signature compared to WT mice, characterized by alterations in proteins associated with contractile and structural dysfunction, metabolism, and stress response. In particular, a significant increase in myosin fragments was observed in IT mice, likely due to muscle breakdown. In contrast, myotilin was downregulated, suggesting a weakening of the muscle cytoskeletal structure. There was a notable downregulation of proteins involved in glycolysis and the TCA cycle; conversely, there was an increase in proteins related to anaerobic glycolysis, suggesting a shift from aerobic to anaerobic glycolysis. Furthermore, proteins involved in fatty acid beta-oxidation and oxidative phosphorylation were also found to be upregulated in IT mice, indicating an attempt by the muscle to maximize energy production. Finally, we found a significant decrease in PGC1α, which could serve as potential therapy target and biomarker in CCD. Full article

Artificial intelligence is reshaping healthcare project management in Saudi Arabia, yet most deployments lack culturally grounded ethics. This paper synthesises global AI-ethics guidance and Islamic bioethics, then proposes a maqāṣid-al-sharīʿah-aligned conceptual framework for ANN-based decision support. Ethical signals derived from the preservation of life, dignity, justice, faith, and intellect are embedded as logic-gate filters on ANN outputs. The framework specifies a dual-metric evaluation that reports predictive performance (e.g., accuracy, MAE, AUC) alongside ethical compliance, with auditable thresholds for fairness (δ = 0.1) and confidence (α = 0.8) calibrated through stakeholder workshops. It incorporates a co-design protocol with clinicians, patients, Islamic scholars, and policymakers to ensure cultural and clinical legitimacy. Unlike UNESCO and EU frameworks, which remain principle-oriented, this study introduces a measurable dual-layer assessment that combines technical accuracy with ethical compliance, supported by audit artefacts such as model cards, traceability logs, and human override records. The framework yields technically efficient and Shariah-compliant recommendations and sets a roadmap for empirical pilots under Vision 2030. The paper moves beyond a general review by formalising an Islamic-values-driven conceptual framework that operationalises ethical constraints inside ANN–DSS pipelines and defines auditable compliance metrics. This paper combines a critical review of AI in healthcare project management with the development of a maqāṣid-aligned conceptual framework, thereby bridging systematic synthesis with an implementable proposal for ethical AI. Full article