Search Results (18,408)

Fibromyalgia, a syndrome characterized by hyperalgesia and ‘negative emotionality’, and major depressive disorder (MDD) demonstrate substantial overlaps in clinical, neurobiological, and therapeutic domains. Currently, treatment options for fibromyalgia remain limited; however, the epidemiology of this syndrome continues to grow worldwide. The use of animal models is indispensable for developing new treatment strategies for fibromyalgia. Meanwhile, the choice of animal paradigms is limited. Here, we used the ultrasound exposure of emotional stress on CBA, BALB/c, and C57BL/6 mouse strains to model this condition and to identify new molecular targets of fibromyalgia treatment. We exposed young male mice of three common strains to a three-week ultrasound stress (US) comprising emotionally negative and neutral frequencies of 20–25 kHz and 25–45 kHz, resulting in the development of altered pain sensitivity and signs of ‘negative emotionality’. Specifically, mice were studied for timid-like/aggressive behaviors and the tail flick response. Serum levels of corticosterone, cortisol, β-Endorphin, and brain-derived neurotrophic factor (BDNF), as well as brain gene expression of interleukin-33 (Il-33), Bdnf, and its receptor Trkb were investigated. Among the stressed mouse strains, C57BL/6 mice displayed augmented pain sensitivity, allodynia, and suppressed dominant behavior, whereas CBA and BALB/c mice demonstrated opposing changes. Glucocorticoid levels were increased in all stressed groups. Stressed C57BL/6 mice showed downregulated gene and protein expression of functionally inter-related BDNF and IL-33 molecules in the hippocampus, amygdala, and striatum, significantly correlating with behavioral outcomes, as well as lowered blood levels of β-Endorphin and elevated cortisol concentrations. Altogether, our study identified the BDNF/IL-33 regulatory pathway as a molecular correlate of fibromyalgia, and the use of US-exposed young C57BL/6 mice as a potential model that recapitulates this syndrome. Full article

Family and parental education are increasingly recognized as key levers for sustainable development and family well-being. This study examines whether an online parental intervention program focused on strengthening parental self-efficacy can improve parents’ relationship satisfaction and couple satisfaction. A sample of 50 Romanian parents with below-average levels of parental self-efficacy and relationship satisfaction was randomly assigned to an experimental group and a control group. Participants in the experimental group attended the “Confident Parents” program over three months, while the control group received no structured intervention. Pre- and post-test data were collected using standardized measures of parental self-efficacy, couple satisfaction, and relationship satisfaction. Data analysis combined non-parametric Wilcoxon signed-rank tests with linear regression and moderation analysis. The results showed significant pre–post improvements in parental self-efficacy, relationship satisfaction, and couple satisfaction in the experimental group, with no meaningful changes in the control group. Post-test, parental self-efficacy significantly predicted both relationship satisfaction and couple satisfaction, and moderation analyses indicated that this predictive relationship was stronger for parents in the intervention group. These findings suggest that parental education programs centered on self-efficacy can contribute to more satisfying couple and family relationships, supporting psychological well-being and the broader goals of sustainable family functioning. Full article

Percutaneous cable infection of left ventricular assist device (LVAD) patients is a significant source of morbidity, often caused by biofilm-producing or multidrug-resistant bacteria. We hypothesized that bacteriophage viruses can be identified from biological samples of patients with active driveline infection. Six patients with local percutaneous lead infections were enrolled. Microbiological samples were collected from the infected wound and other skin regions. The isolated viral strains and phages from wastewater samples were then tested against the pathogen bacterial cultures in vitro. Biofilm disruption assay and genetic analysis of the strains were also performed. Bacteriophages with lytic activity could be identified from samples of two patients. One patient contained four strains showing strong efficacy against his own Staphylococcus epidermidis. Furthermore, this bacterium was susceptible to phages identified from another patient and strains from wastewater samples. Genomic analysis suggested lysogenic lifestyle of the phages. However, none of them have shown any microbiological signs of lysogeny. In conclusion, we have been able to prove in vitro lytic activity of bacteriophages originating from the same LVAD patient. We also found effective phages in biological samples of other patients and wastewater samples, suggesting that patients implanted in the same center may share bacteriophage flora. Full article

Motor Imagery (MI)-based brain–computer interfaces (BCIs) offer promising solutions for enhancing communication and motor functions in individuals with neurological impairments. However, decoding EEG signals accurately is difficult because of their poor signal-to-noise ratio and variability across subjects and sessions. In addition, EEG signals are sensitive to noise. Moreover, the low spatial resolution of EEG signals makes model generalization unreliable due to differences between signals across subjects. While several deep learning models have been developed, a fair comparison remains difficult due to differences in pre-processing, training procedures, and evaluation protocols. This study provides a systematic, controlled comparison of five deep learning approaches for subject-specific classification—EEGNet, EEG-TCNet, ShallowConvNet, DeepConvNet, and CTNet—using the BCI Competition IV datasets 2a and 2b. To enable an unbiased comparison, all models are trained using the same pipeline, with uniform pre-processing and training. Apart from classical accuracy scores, the effect of a constant set of hyper-parameters on the training dynamics, generalization capacity, and the susceptibility to overfitting is evaluated. The performance of the above-stated models is evaluated based on training dynamics, computational efficiency, accuracy, and the quality of the features learned by the models. Using the five-dimensional analysis framework consisting of quantitative performance metrics, training curves, confusion matrix analysis, ROC analysis, and t-SNE visualization techniques, the performance of the brain–computer interfaces is comprehensively analyzed. The experimental analysis confirms that CTNet outperforms other models, with accuracy values of 82.56% and 86.42% on the BCI competition IV datasets 2a and 2b, respectively. The EEGNet model is recognized as having the most potential in the field of real-time applications, owing to its light structure; meanwhile, the DeepConvNet model shows signs of overfitting, despite showing good accuracy. These findings highlight that model training characteristics and sensitivity to the hyper-parameters are important factors in evaluating deep learning models for MI-EEG classification problems. Full article

This paper presents the Spike Processing Unit (SPU), a digital spiking neuron model based on a discrete-time second-order Infinite Impulse Response (IIR) filter. By constraining filter coefficients to powers of two, the SPU implements all internal operations via shift-and-add arithmetic on 6-bit signed integers, eliminating general-purpose multipliers. Unlike traditional models, computation in the SPU is fundamentally temporal; spike timing emerges from the interaction between input events and internal IIR dynamics rather than signal intensity accumulation. The model’s efficacy is evaluated through a temporal pattern discrimination task. Using Particle Swarm Optimization (PSO) within a hardware-constrained parameter space, a single SPU is optimized to emit pattern-specific spikes while remaining silent under stochastic noise. Results from cycle-accurate Python simulations and synthesizable VHDL implementations indicate that the learned temporal dynamics are preserved under hardware-constrained digital execution, supporting the feasibility of the proposed approach. This work demonstrates that discrete-time IIR-based neurons enable reliable temporal spike processing under strict quantization and arithmetic constraints. Full article

Terrestrial ecosystems play a critical role in regulating atmospheric CO₂ through land–atmosphere carbon exchange. While Net Ecosystem Exchange (NEE) serves as a key integrative metric for carbon dynamics, its robust global estimation remains challenging due to profound environmental heterogeneity and nonlinear ecosystem responses. In this study, we propose a dual-track experimental framework to invert annual global terrestrial NEE at a 0.1° spatial resolution for 2000–2024. Initially, a long-term historical baseline inversion (2000–2024) was developed using explicit multi-source environmental predictors. Subsequently, to overcome the representational limitations of conventional spectral indices over complex terrains, we integrated high-dimensional remote-sensing embeddings from the AlphaEarth framework for the 2017–2024 overlapping period. This approach was designed to explicitly quantify the added value of these advanced features. Our results demonstrate that embedding features substantially enhance inversion performance, reducing prediction errors and improving spatial coherence. Adopting the standard meteorological sign convention, global terrestrial NEE remained consistently negative. Based on the 2000–2024 baseline inversion, our predicted global NEE fluctuated between −3.50 and −4.38 Pg C yr⁻¹. To validate these long-term estimates, we systematically cross-validated our results against an independent, recently published multi-network fusion dataset, which reported a comparable range of −3.11 to −3.75 Pg C yr⁻¹. This comparison demonstrates consistent interannual dynamics and corroborates the magnitude of the global terrestrial carbon sink. Spatial patterns exhibit a stable latitudinal structure, with stronger net carbon uptake in low latitudes. Interannual variability is expressed mainly as magnitude fluctuations rather than systematic spatial reorganization. Overall, this study highlights that high-dimensional Earth observation embeddings provide significant, measurable information gains for global NEE inversion without introducing new process-based assumptions, thereby offering a robust and internally consistent basis for evaluating long-term carbon dynamics. Full article

The deployment of Motor Imagery Brain–Computer Interfaces (MI-BCI) is constrained by the inherent physiological variabilities of Electroencephalography (EEG) and parametric opacity. This paper presents a targeted technical audit of ten high-density MI-BCI computational pipelines, evaluating how existing literature addresses low Signal-to-Noise Ratio (SNR), intra-subject variability, and session-to-session instability. The investigation focuses on the contamination of data by ocular and muscular artifacts that overlap with the spectral components of Mu and Beta rhythms, often leading to algorithmic overfitting. Furthermore, the paper evaluates the impact of manifold drift where fluctuations in user state necessitate frequent recalibration as a primary hurdle for BCI portability. By applying a forensic evaluation framework to standardize the analysis across the ten selected studies, this paper identifies a high-performance landscape within standardized benchmarks, with classification accuracies reaching peak values of 95.42%. The audit specifically identifies a performance-reporting gap; while hybrid architectures demonstrate superior noise-rejection, they are frequently characterized by undocumented computational overhead. Additionally, while Neighborhood Component Analysis (NCA) emerges as a stable feature selection algorithm across the sampled literature, the systemic absence of reported execution times prevents a verified assessment of its low-latency viability. A critical technical finding is the widespread issue of Parametric Opacity, particularly regarding the omission of essential deterministic variables such as filter orders, windowing constants, and the final dimensionality of feature vectors. The audit reveals that the frequent failure to report the exact number of features utilized for classification masks potential overfitting and prevents an accurate assessment of the system’s generalization capabilities. Furthermore, only a specialized subset of the reviewed literature validates performance through formal statistical testing, such as Friedman ANOVA or Wilcoxon Signed-Rank tests, with most studies relying on peak accuracy metrics that may disguise filtered artifact residuals. This lack of granular documentation disguises the computational complexity of proposed methods and complicates their feasibility for hardware-in-the-loop validation. The findings establish that standardizing the reporting of preprocessing variables and feature-space dimensions is a prerequisite for overcoming current performance plateaus in universal BCI architectures. Full article

Background: Graft-versus-host disease (GVHD) is a common severe complication of allogeneic hematopoietic stem cell transplant. The current treatments are limited by steroid toxicity, broad immunosuppression, and the potential suppression of the graft-versus-tumor (GVT) effect. Developing less toxic therapies is an unmet need. We previously showed that systemically infused negatively charged immune-modifying microparticles (IMPs) composed of carboxylated poly-lactic-co-glycolic acid are taken up by inflammatory monocytes via the MARCO receptor, reducing symptoms and improving survival in inflammatory conditions. We hypothesized that IMPs could reduce acute GVHD manifestations. Methods: Acute GVHD was induced in an MHC-mismatched murine transplant model with radiation conditioning. IMPs were infused for five days; outcomes were compared to saline controls. We assessed organ histopathology, immune cell populations in the spleen and intestine, serum cytokine levels, and the GVT effect. Results: IMP-treated mice showed significant improvements in terms of clinical GVHD scores, histopathology, and survival. They had increased regulatory T-cells in the spleen and intestine and decreased colonic inflammatory monocytes and cytokines such as IL-6 and IFN-γ. IMPs were ineffective in MARCO knockout mice, confirming receptor dependence. Importantly, GVT activity was preserved, as evidenced by improved survival in mice with A20 lymphoma treated with IMPs. Conclusions: Systemic IMPs reduce clinical GVHD signs and improve survival, likely by decreasing inflammatory monocytes via MARCO and expanded regulatory T-cells numbers, while maintaining GVT activity. These findings support further investigation of IMPs as a targeted GVHD therapy. Full article

Background: Mega-events like the FIFA World Cup (FWC) present unique and substantial challenges for individuals in recovery from substance use disorders (SUDs), primarily by increasing the risk of relapse. We employed a qualitative design using reflexive thematic analysis to explore the behavior of patients with SUDs during the 2022 FWC and to evaluate institutional strategies for mitigating related risks. Methods: We purposively sampled 32 participants who were present at the Naufar Center during the 2022 FWC: (i) thirteen adult patients with SUDs who were receiving treatment, and (ii) nineteen healthcare practitioners. Semi-structured patient interviews were conducted, and focus group discussions were held with a multidisciplinary team, including psychologists, nurses, and physicians. Individuals’ experiences regarding patterns in substance use behavior, environmental triggers, and the effects of institutional interventions were examined. Thematic analysis was employed to identify patterns, risks, and effective strategies. Results: Most patients maintained abstinence and only had cravings for alcohol. Triggers included public celebrations, emotional excitement, and the increased availability of addictive substances. Psychologists and physicians reported signs of behavioral destabilization; nurses observed some behavioral changes and noted logistical challenges. The participants acknowledged the supportive measures provided by Naufar, including the accessibility of clinical services, individualized therapy, social and recreational programming, and protective fan zones, which enabled them to participate in various activities during the event. Conclusions: The 2022 FWC created considerable psychological and environmental triggers for high exposure to alcohol and other substances. The supportive structured activities and tailored interventions were helpful in mitigating the risk of relapse, maintaining treatment engagement and ensuring recovery. Further research is required to explore the implications for recovery-oriented practices during culturally and socially high-risk events. Full article

Odontogenic sinusitis (ODS) is a common cause of unilateral maxillary sinusitis arising from periapical lesions (PALs) or other dental sources. The infection is typically polymicrobial and dominated by anaerobic bacteria, which are often under detected by routine culture. Molecular approaches such as quantitative polymerase chain reaction (QPCR) and next-generation sequencing (NGS) may provide improved characterization of the microbial burden and community structure. This study aimed to compare culture-based methods, targeted quantitative PCR, and 16S rRNA sequencing in paired samples to characterize microbial composition of ODS and evaluate diagnostic performance. Paired sinus mucosal biopsy (SIN) and periapical lesion (PAL) samples were collected from 28 patients with clinically confirmed ODS. Bacterial detection was performed using conventional culture and targeted QPCR assays for ten clinically relevant taxa. In three randomly selected patients, paired samples were additionally analyzed by 16S rRNA gene amplicon sequencing. Microbial load, taxa richness, and similarity between the two anatomically connected sites were assessed using Wilcoxon signed-rank, McNemar, Jaccard distance, and Bray–Curtis dissimilarity analyses. Results: Culture showed low sensitivity, identifying a limited number of pathogens, primarily Staphylococcus aureus, Streptococcus anginosus, and Fusobacterium nucleatum, in a minority of samples. In contrast, QPCR demonstrated substantially higher detection rates, particularly in PAL samples. Porphyromonas gingivalis (96.8%), Fusobacterium spp. (100.0%), and the S. anginosus group (90.3%) were highly prevalent in PAL specimens, with overlapping but lower detection in SIN samples. PAL samples exhibited significantly higher bacterial loads and taxa richness than paired SIN samples (Wilcoxon p = 0.0004). 16S rRNA sequencing confirmed polymicrobial communities at both sites and identified additional taxa not included in the QPCR panel. Similarity analyses revealed pronounced interindividual variability, ranging from near-identical to highly divergent paired microbiota. Periapical lesions act as reservoirs of predominantly anaerobic bacteria that may seed the maxillary sinus in ODS. Although microbial overlap exists, sinus communities display lower burden and site-specific compositional shifts. Culture-based diagnostics underestimate ODS microbial complexity, whereas combined molecular approaches provide a more comprehensive and clinically informative assessment. Full article

Urban emptiness is a recurring spatial condition across contemporary cities, resulting from long-term planning decisions, functional transformations, and shifting socio-economic dynamics. Urban voids are often interpreted as signs of failure or neglect; however, they also represent flexible and open-ended spaces embedded within everyday urban environments. This study develops and tests the Adaptive Void Assessment Framework (AVAF), a five-dimensional typological instrument applied to n = 33 urban voids identified through a systematic grid-based field survey (100 × 100 m resolution) in the central urban zone of Nitra, Slovakia (March 2025–January 2026). The framework evaluates sites across nine indicators spanning openness, social appropriation, ecological succession, temporal persistence, and institutional flexibility, yielding composite Adaptivity Index scores and four dominant adaptive regimes. The findings demonstrate that 34% of identified voids function in a socially active regime while 14% exhibit ecological dominance, with a moderate positive correlation identified between temporal persistence and adaptive capacity (r = 0.46, p < 0.05). This challenges conventional deficit-based classifications and reframes urban voids as active components of the urban metabolism capable of enhancing ecological connectivity and spatial flexibility within post-industrial urban landscapes. This reframes urban voids from residual outcomes of urbanization to spaces with potential for green integration within sustainable contemporary cities. Full article

Synovitis is a key contributor to the development of OA, and early modulation of the synovial environment may help limit downstream cartilage damage. This study compared the clinical and biochemical effects of intra-articular triamcinolone acetonide (TA) and autologous conditioned serum (ACS) in an equine model of IL-1β–induced synovitis. Six healthy adult horses were used in a crossover design involving five groups: PBS (negative control), IL-1β (positive control), IL-1β + ACS, IL-1β + TA, and an exploratory ACS-alone group administered post hoc to isolate its effects without IL-1β interference. Both TA and ACS mitigated inflammation through distinct profiles. TA was superior in reducing joint heat, swelling, and effusion. Conversely, IL-1β + ACS provided greater lameness improvement at 24, 36, and 72 h compared to IL-1β. ACS demonstrated potential chondroprotective advantages, as it did not increase synovial glycosaminoglycan (GAG) concentrations, which were highest in the IL-1β + TA group. ACS treatment resulted in significantly higher synovial total nucleated cell counts and total protein, driven primarily by monocyte enrichment. This cellular profile suggests that ACS may support the restoration of joint homeostasis. While TA remains highly effective for visual inflammatory signs, ACS offers a promising biological alternative for modulating the synovial environment and protecting cartilage during acute synovitis. Full article

Introduction: Stroke remains a leading cause of mortality and long-term disability worldwide. Accurate prognosis prediction is essential for timely intervention and personalized treatment planning. However, previous studies have often overlooked the role of patients’ medical history, age-specific risk factors, and time-dependent mortality patterns. This study aimed to develop and evaluate machine learning models for predicting mortality in stroke patients by incorporating vital signs, blood test results, demographic characteristics, and medical history, while also exploring subgroup-specific factors. Methods: We retrospectively analyzed data from 1780 stroke patients admitted to Hallym University Sacred Heart Hospital between 2018 and 2023. Input features included both original and binarized forms of vital signs and blood test values, along with age and medical history. Random Forest models were developed to predict mortality at 1, 2, and 3 years post-admission, as well as overall mortality. Model performance was assessed using AUC and 95% confidence intervals, and variable importance was evaluated using Mean Decrease Gini and SHAP values. Results: The highest predictive performance was observed in a model for patients under 60 using binarized input features, achieving an AUC of 0.995 (CI: 0.98–1). Across all models, pulse rate consistently emerged as the most important predictor. Additional key features included platelet count and diastolic blood pressure. SHAP analysis revealed that pulse rate was associated with higher mortality risk. Subgroup analyses based on age and medical history improved interpretability and predictive power. Conclusions: This study demonstrates that integrating clinical indicators with demographic and medical history variables can significantly enhance the accuracy and interpretability of mortality prediction models in stroke patients. The results underscore the importance of stratified modeling and continuous monitoring of vital signs, particularly pulse rate, to support precision stroke care. Full article

The growing prevalence of chronic diseases, population aging, and the shift toward preventive and personalized care under Healthcare 5.0 have increased the need for continuous health monitoring beyond clinical settings. While wearable devices enable remote monitoring, their long-term use is often limited by user compliance, comfort issues, battery dependence, and disruption of daily routines. To address these limitations, unobtrusive home-based health monitoring systems have emerged, integrating sensing technologies into domestic environments and everyday objects. This review provides a system-level analysis of unobtrusive health monitoring technologies for smart homes. It examines seven major sensing approaches, including camera-, laser-, radar-, infrared-, mechanical-, bioelectrical-, and optical-based sensors, and their integration into four home environments: living areas, bathrooms, bedrooms, and home offices. For each sensing modality, the operating principles, monitored physiological parameters, representative applications, and key advantages and limitations are discussed. Overall, existing solutions reveal trade-offs among measurement accuracy, robustness in real home conditions, energy autonomy, privacy preservation, and user acceptance. Heart rate and respiratory rate are the most commonly monitored parameters, while multimodal and clinically validated systems remain limited. Although unobtrusive sensing technologies show strong potential for proactive and personalized healthcare, challenges related to accuracy, interoperability, privacy, and cost continue to hinder large-scale adoption. Full article

Alcohol-related liver disease (ALD) and ALD-related mortality are associated with hemolysis, increased erythrophagocytosis, and disturbed iron homeostasis. While macrophage-mediated erythrophagocytosis is well established, we investigated the contribution of liver sinusoidal endothelial cells (LSECs) to handling oxidatively damaged or ethanol-primed red blood cells (RBCs) in ALD. Live-cell imaging demonstrated that damaged RBCs were rapidly taken up by SK-HEP1 cells, an endothelial cell line with LSEC-like characteristics, and RBC uptake was associated with induction of heme oxygenase-1 (HO-1) and activation of its upstream regulator Nrf2. siRNA-mediated knockdown of the scavenger receptor Stabilin-1 attenuated RBC-induced HO-1 expression, supporting a role for Stabilin-1 in efferocytic signaling. Exposure of RBCs to ethanol concentrations as low as 25 mM induced phosphatidylserine externalization and rendered erythrocytes efferocytosis-competent. Lysed RBCs and free hemin elicited comparable oxidative stress responses. In murine models of hemolysis and chronic ethanol feeding, hemoglobin-derived signals were detected within sinusoidal structures showing a diffuse CD206-positive distribution pattern consistent with the sinusoidal scavenger compartment. Similar signals were observed in sinusoidal endothelial regions in human heavy drinkers with clinical signs of hemolysis. Together, these data suggest that LSECs may represent an additional component of RBC clearance in ALD, alongside macrophages and hepatocytes, with implications for hepatic iron handling. Full article