Search Results (186)

Background: Chronic low back pain (cLBP) with kinesiophobia is difficult to treat, and traditional graded activity approaches often show limited adherence and short-term effects. Virtual reality (VR) may enhance treatment engagement by providing immersive game-based environments that encourage therapeutic movement. This randomized controlled trial aimed to examine the effects of VR interventions designed to promote lumbar spine flexion in individuals with cLBP and elevated movement-related fear. Methods: Participants were randomized to one of two nine-week VR game conditions that differed only in the amount of lumbar flexion required. Primary outcomes were changes in pain intensity and disability from baseline to one-week post-treatment. Secondary analyses examined lumbar flexion and expectations of pain/harm as potential mediators. Follow-up assessments were conducted at multiple time points through 48 weeks to assess maintenance of treatment gains. Results: Both VR groups showed significant and clinically meaningful reductions in pain and disability at post-treatment. Improvements were maintained throughout the 48-week follow-up period. Depression symptoms continued to improve during follow-up. Expectations of pain and harm decreased significantly during treatment and remained reduced, whereas objective lumbar flexion did not change appreciably over time. Mediator analyses indicated that improved expectations of pain/harm, rather than increased lumbar flexion, were more closely associated with treatment response. Conclusions: Immersive VR gaming produced sustained reductions in pain, disability, and movement-related fear in individuals with cLBP and kinesiophobia. Findings suggest that VR may enhance rehabilitation outcomes by modifying maladaptive expectations rather than altering lumbar motion. VR-based interventions represent a promising and engaging approach for long-term cLBP management. Full article

Microgravity during space travel induces significant regulatory changes in the body, posing health risks for astronauts, including alterations in cell morphology and cytoskeletal integrity. The Small Ubiquitin-like Modifier (SUMO) is crucial for cellular adaptation, regulating DNA repair, cytoskeletal dynamics, cell division, and protein turnover—all processes affected by microgravity. To determine the extent to which SUMO mediates the cellular response to microgravity stress, Saccharomyces cerevisiae cells were cultured under normal gravity and simulated microgravity (SMG) in rotating wall vessels. After 12 h of culture, we investigated changes in SUMO modified proteins and protein expression. We identified 347 SUMOylated proteins, 18 of which demonstrated a 50% change in abundance under SMG. Of 3773 proteins identified, protein expression for 34 proteins decreased and 8 increased by over 50% in SMG (p < 0.05). Differentially expressed proteins represented changes in cellular processes for DNA repair, cell division, histone modification, and cytoskeleton regulation. These findings underscore the pivotal role of SUMOylation in orchestrating cellular adaptation to the unique stress of microgravity, revealing potential targets for mitigating spaceflight-induced health risks. Full article

Medication-related osteonecrosis of the jaw (MRONJ) is a serious adverse effect of antiresorptive and antiangiogenic therapies, yet its molecular mechanisms remain poorly defined. The present study employed an analysis of microarray data (GSE7116) from peripheral blood mononuclear cells of patients with multiple myeloma, myeloma patients with MRONJ, and healthy controls. Differentially expressed genes were identified using the limma package, followed by functional enrichment analysis, weighted gene co-expression network analysis, and LASSO regression and CytoHubba network ranking. The predictive performance was validated by means of nested cross-validation, Firth logistic regression, and safe stratified 0.632+ bootstrap ridge regression. The profiling revealed distinct gene expression patterns between the groups: the upregulation of ribosomal and translational pathways, as well as the suppression of neutrophil degranulation and antimicrobial defense mechanisms, and identified key candidate genes, including PDE4B, JAK1, ETS1, EIF4A2, FCMR, IGKV4-1, and XPO7. These genes demonstrated substantial discriminatory capability, with an area under the curve ranging from 0.95 to 0.99, and were found to be functionally linked to immune system dysfunction, cytokine signaling, NF-κB activation, and a maladaptive stress response. These findings link MRONJ to systemic immune-inflammatory imbalance and translational stress disruption, offering novel insights and potential biomarkers for diagnosis and risk evaluation. Full article

Background/Objectives: Ground-glass opacity (GGO) refers to areas of increased lung opacity on computed tomography (CT) scans. Distinguishing malignant from benign lesions using CT scans remains significantly challenging. This study aims to compare the performances of a linear multivariate statistical regression and an AI deep learning method in their abilities to predict GGO malignancy, given a set of pixel features extracted from CT scans. Methods: This retrospective study investigated patients from the Carmel Medical Center with findings of GGO nodules in their lung CT scans. Forty-seven consecutive patients were found to have either pure or part-solid GGO lesions, as defined by two independent radiologists. After manually segmenting the GGOs in the CT scans, pixel features were extracted using the MaZda software package, which analyzes six different image texture features. These textural variables were then compiled as input for the multivariate statistical regression. Additionally, an AI deep learning method, developed by our group and hosted on the cloud, was applied to the CT images containing the GGOs. Results: Among the 47 patients, 32 were diagnosed by pathology with malignant lesions and 15 with benign findings. Using the multivariate statistical regression, we identified 19 variables with statistically significant or near-significant differences through univariate analysis. In subsequent multivariate analyses, two independent variables that could distinguish between benign and malignant GGO lesions were identified: S(4,4)AngScMom (p = 0.012) and WavEnLH_s-2 (p = 0.008). The regression formula based on these two variables yielded a sensitivity of 91% and a specificity of 67% AUC: 0.8 (95% CI: [0.65, 0.94]). The AI deep learning model demonstrated a sensitivity of 100% and a specificity of 80% AUC: 0.96 (95% CI: [0.86, 1.00]). Conclusions: This proof-of-concept study demonstrates the superior performance of the AI deep learning model compared to the multivariate statistical regression, particularly in terms of sensitivity and specificity. However, given the small sample size, these results could potentially change with larger patient cohorts. Full article

Cowpea production in Nigeria, the world’s largest producer, is insufficient to meet domestic demand due to significant yield gaps caused by various production constraints. Several high-yielding improved cowpea varieties have been developed and disseminated among smallholder farmers to improve productivity, but their adoption is low because breeding efforts have not adequately incorporated farmers’ and consumers’ preferred traits. To address this, a study was conducted to evaluate the performance of newly developed cowpea lines and identify those with traits preferred by farmers and consumers. Twenty-four cowpea lines were evaluated in multiple environments under sole and intercropped systems in Nigeria. The study revealed significant (p < 0.001) genotypic and genotype-by-environment interaction effects for grain yield, fodder yield, and other key agronomic traits. Three genotypes consistently outperformed the standard check, with UAM15-2157-4 exhibiting a 57.6% higher grain yield and superior seed quality. UAM15-2157-4 produced the highest grain yield (1289 kg ha⁻¹) under the intercropping system. GGE biplot analysis identified UAM15-2157-4 as the most stable genotype across all tested environments. This genotype, along with other promising lines, possesses desirable traits such as Striga resistance, large seed size, and preferred seed coat color, making them suitable for release and adoption to improve cowpea productivity in the region. Full article

Urban water supply networks are prone to complex noise interference, which significantly degrades the performance of data-driven forecasting models. Conventional denoising techniques, such as standard Variational Mode Decomposition (VMD), often rely on empirical parameter selection or optimize only a subset of parameters, lacking a robust mechanism for identifying noise-dominant components post-decomposition. To address these issues, this paper proposed a novel denoising framework termed Dynamic Hyperbolic Tangent PSO-optimized VMD (DHTPSO-VMD). The DHTPSO algorithm adaptively adjusts inertia weights and cognitive/social learning factors during iteration, mitigating the local optima convergence typical of traditional PSO and enabling automated VMD parameter selection. Furthermore, a dual-criteria screening strategy based on Variance Contribution Rate (VCR) and Correlation Coefficient Metric (CCM) is employed to accurately identify and eliminate noise-related Intrinsic Mode Functions (IMFs). Validation using pressure data from District A in Zhejiang Province, China, demonstrated that the proposed DHTPSO-VMD method significantly outperforms benchmark approaches (PSO-VMD, EMD, SABO-VMD, GWO-VMD) in terms of Signal-to-Noise Ratio (SNR), Mean Absolute Error (MAE), and Mean Square Error (MSE). Subsequent forecasting experiments using an Informer model showed that signals preprocessed with DHTPSO-VMD achieved superior prediction accuracy (R² = 0.948924), underscoring its practical utility for smart water supply management. Full article

Typical fertilizer applicators are often restricted in performance due to non-uniformity in distribution, required labor and time intensiveness, high discharge rate, chemical input wastage, and fostering weed proliferation. To address this gap in production agriculture, an automated variable-rate fertilizer applicator was developed for the cotton crop that is based on deep learning-initiated electronic control unit (ECU). The applicator comprises (a) plant recognition unit (PRU) to capture and predict presence (or absence) of cotton plants using the YOLOv7 recognition model deployed on-board Raspberry Pi microprocessor (Wale, UK), and relay decision to a microcontroller; (b) an ECU to control stepper motor of fertilizer metering unit as per received cotton-detection signal from the PRU; and (c) fertilizer metering unit that delivers precisely metered granular fertilizer to the targeted cotton plant when corresponding stepper motor is triggered by the microcontroller. The trials were conducted in the laboratory on a custom testbed using artificial cotton plants, with the camera positioned 0.21 m ahead of the discharge tube and 16 cm above the plants. The system was evaluated at forward speeds ranging from 0.2 to 1.0 km/h under lighting levels of 3000, 5000, and 7000 lux to simulate varying illumination conditions in the field. Precision, recall, F1-score, and mAP of the plant recognition model were determined as 1.00 at 0.669 confidence, 0.97 at 0.000 confidence, 0.87 at 0.151 confidence, and 0.906 at 0.5 confidence, respectively. The mean absolute percent error (MAPE) of 6.15% and 9.1%, and mean absolute deviation (MAD) of 0.81 g/plant and 1.20 g/plant, on application of urea and Diammonium Phosphate (DAP), were observed, respectively. The statistical analysis showed no significant effect of the forward speed of the conveying system on fertilizer application rate (p > 0.05), thereby offering a uniform application throughout, independent of the forward speed. The developed fertilizer applicator enhances precision in site-specific applications, minimizes fertilizer wastage, and reduces labor requirements. Eventually, this fertilizer applicator placed the fertilizer near targeted plants as per the recommended dosage. Full article

Background and Objectives: Discrimination between various causes of exudative pleural effusion (PE) remains a major clinical challenge, and to date, definitive biochemical markers for this discrimination remain lacking. An increasing number of studies have reported that serum C-reactive protein (CRPs), pleural fluid CRP (CRPpf), and CRPpf/CRPs ratio (CRPr) are useful for the differential diagnosis of exudative PE; however, their efficacy rate is not similar in these studies. The majority of these studies were conducted on small groups of subjects, and the efficacy of the gradient between CRPs and CRPpf (CRPg—calculated as CRPs—CRPpf) in this differentiation has not been previously investigated. This study aims to evaluate the efficacy rate of CRPs, CRPpf, CRPg, and CRPr in the differential diagnoses of various causes of exudative PE in a relatively large cohort of patients. Materials and Methods: The research group included 282 subjects with exudative PE—146 had parapneumonic effusion (PPE), 126 had malignant pleural effusion (MPE), and 10 had tuberculous pleural effusion (TPE). The values are presented as mean ± SD. Results: The mean CRPs level was significantly higher in the PPE group compared to the MPE group (p < 0.0001) and the TPE group (p < 0.001), and also significantly higher in the TPE group than in the MPE group (p = 0.0009). Similarly, the mean CRPpf level was significantly higher in the PPE group than in the MPE group (p < 0.0001) and the TPE group (p = 0.04), and also significantly higher in the TPE group than in the MPE group (p < 0.0001). The mean CRPg level was significantly higher in the PPE group than in both the MPE group (p < 0.0001) and the TPE group (p < 0.002). The mean CRPr level did not differ significantly among these groups of exudate. Conclusions: CRPs, CRPpf, and CRPg are effective in the differential diagnosis of exudative PE, while CRPr was not effective in this regard. The main limitation of this study is that the sample size of the TPE group is very small. Full article

The oxygen evolution reaction (OER) plays a central role as an anode in electrocatalytic processes such as energy conversion and storage and the generation of molecular oxygen from the electrolysis of water. Currently, precious metal oxides such as IrO₂ and RuO₂ are recognized as reference OER electrocatalysts with reasonably high activity; however, their widespread use in practical devices has been severely hindered by their high cost and scarcity. It is essential to design alternative OER electrocatalysts made of low-cost and abundant earth elements with significant activity and robustness. We report four new nanocellulose-derived Fe–zeolite nanocomposites, namely Fe/Zeolite@CCNC (1), Fe/Zeolite@CCNF (2), Fe/Zeolite/CNT@CCNC (3), and Fe/Zeolite/CNT@CCNF (4). Two different types of nanocellulose were investigated: nanocellulose nanofibrils and nanocellulose nanocrystals. Characterization with TEM, SEM-EDS, PXRD, and XPS is reported. The nanocomposites exhibited electrocatalytic activity for OER that varies based on the origin of biocarbon and the composition content. The effect of adding carbon nanotubes to the nanocomposites was studied, and an improvement in OER catalysis was observed. The electrochemical double-layer capacitance and electrochemical impedance spectroscopy of the nanocomposites are reported. The nanocomposite 3 exhibited the highest performance, with an onset potential value of 1.654 V and an overpotential of 551 mV, which exceeds the activity of RuO₂ for OER catalysis at 10 mA/cm² in the glassy carbon electrode. A 24 h chronoamperometry study revealed that the catalyst is active for ~2 h under continuous operating conditions. BET surface analysis showed that the crystalline nanocellulose-derived composite exhibited 301.47 m²/g, and the fibril nanocellulose-derived composite exhibited 120.39 m²/g, indicating that the increased nanoporosity of the former contributes to the increase in OER catalysis. Full article

Children may suffer knee injuries due to motor vehicle crashes, sports, and falls. Additionally, children can suffer from rheumatic, neurological, musculoskeletal, and neuromuscular disorders which restrict joint movement. These types of injuries and disorders often result in knee joint impairment, thereby affecting joint mobility. Understanding the range of motion (ROM) of the pediatric knee is vital in diagnosing, examining, and treating these injuries and disorders. This study was undertaken to establish normative values for passive (PROM) and active (AROM) range of motion of the pediatric knee and to examine the effects of anthropometric and demographic factors on knee joint ROM. Normative reference values for both passive and active knee ROM were established for 295 children in the United Arab Emirates (Arab and South Asian ethnicity). The subjects’ PROM averaged 131.2° (117.2°, 140.2°) for boys and 132.8° (120.9°, 140.3°) for girls. Similarly, the observed PROM for children was 132.2° (118.6°, 141.2°), versus 130.8° (119.9°, 139.3°) for adolescents. Conversely, the subjects’ AROM averaged 129.3° (118.8°, 137.9°) for boys and 130.5° (120.9°, 137.4°) for girls. The observed AROM averaged 130.2° (119.5°, 137.8°) for children and 128.6° (121.5°, 137.4°) for adolescents. Significant differences in knee ROM based on ethnicity were identified. Additionally, significant correlations were observed between anthropometric factors and knee joint ROM. The gender and age-based normative values established in this study can be used in medical and vehicle safety analyses of knee injuries sustained by children as well as in the evaluation of knee joint impairments due to rheumatic, neurological, musculoskeletal, and neuromuscular disorders, thereby improving the outcomes of knee injuries and the treatment of joint impairments in children. Full article

Accurate mechanical fault diagnosis of On-Load Tap Changers (OLTCs) remains crucial for power system reliability yet faces challenges from vibration signals’ non-stationary characteristics and limitations of conventional methods. This paper develops a hybrid framework combining metaheuristic-optimized decomposition with hierarchical temporal learning. The methodology employs a Subtraction-Average-Based Optimizer (SABO) to adaptively configure Time-Varying Filtered Empirical Mode Decomposition (TVFEMD), effectively resolving mode mixing through optimized parameter selection. The decomposed components undergo dual-stage temporal processing: A Temporal Convolutional Network (TCN) extracts multi-scale dependencies via dilated convolution architecture, followed by Gated Recurrent Unit (GRU) layers capturing dynamic temporal patterns. An experimental platform was established using a KM-type OLTC to acquire vibration signals under typical mechanical faults, subsequently constructing the dataset. Experimental validation demonstrates superior classification accuracy compared to conventional decomposition–classification approaches in distinguishing complex mechanical anomalies, achieving a classification accuracy of 96.38%. The framework achieves significant accuracy improvement over baseline methods while maintaining computational efficiency, validated through comprehensive mechanical fault simulations. This parameter-adaptive methodology demonstrates enhanced stability in signal decomposition and improved temporal feature discernment, proving particularly effective in handling non-stationary vibration signals under real operational conditions. The results establish practical viability for industrial condition monitoring applications through robust feature extraction and reliable fault pattern recognition. Full article

The objective of this manuscript is to investigate the (2+1)-dimensional Chiral nonlinear Schrödinger equation (CNLSE). We employ the traveling wave transformation to convert the nonlinear partial differential equation (NLPDE) into the nonlinear ordinary differential equation (NLODE). Utilizing the two new vital techniques to derive the solitary wave solutions, the generalized Arnous method and the Riccati equation method, we obtained various types of waves like bright solitons, dark solitons, and periodic wave solutions. Sensitivity analysis is also discussed using different initial conditions. Sensitivity analysis refers to the study of how the solutions of the equations respond to changes in the parameters or initial conditions. It involves assessing the impact of variations in these factors on the behavior and properties of the solutions. To better comprehend the physical consequences of these solutions, we showcase them through different visual depictions like 3D, 2D, and contour plots. The findings of this study are original and hold significant value for the future exploration of the equation, offering valuable directions for researchers to deepen knowledge on the subject. Full article

Accurate structural health monitoring (SHM) is crucial for ensuring safety and preventing catastrophic failures. However, conventional parameter identification methods often assume a fixed-base foundation, neglecting the significant influence of soil–structure interaction (SSI) on the dynamic response, leading to inaccurate damage assessments, especially under seismic loading. Therefore, we introduce a novel approach that explicitly incorporates SSI effects into parameter identification for frame structures, utilizing an optimized variational mode decomposition (VMD) technique. The core innovation is the application of the Subtraction Average-Based Optimizer (SABO) algorithm, coupled with permutation entropy as the fitness function, to optimize the critical VMD parameters. This SABO-VMD method was rigorously validated through a shaking table test on a 12-story frame structure on soft soil. Comparative analysis with EMD and conventional VMD demonstrated that SABO-VMD provides a superior time–frequency representation of the structural response, capturing non-stationary characteristics more effectively. A novel energy entropy index, derived from the SABO-VMD output with SSI, was developed for quantitative damage assessment. It revealed 8.1% lower degree of structural damage compared to the fixed-base assumption. The proposed SABO-VMD-based approach, by explicitly accounting for SSI, offers a substantial advancement in SHM of frame structures, leading to more reliable safety evaluations and improved seismic resilience. Full article

Skin cancer prevalence has increased during the last decades, with the last years serving as a pivotal moment for comprehending its epidemiological patterns and its impact on public health. Melanoma is one of the most frequently occurring malignancies, arising from a complex interplay of genetic factors, environmental factors, lifestyle and socio-economic conditions. Epigenetic changes play a critical role in tumor development, influencing progression and aggressiveness. Epigenetic therapies could represent novel therapeutic options, while drug repositioning may serve as a viable strategy for cancer treatment. Demethylating agents, commonly used in hematological malignancies, show promising results on solid tumors, including melanoma. Methylation patterns are responsible for tumor development by modulating gene expression, while histone acetylation influences DNA processes such as transcription, replication, repair, and recombination. This review aims to identify existing potential therapeutical approaches using therapeutic agents that can modulate DNA methylation and histone modification, which can lead to tumor inhibition, cell death initiation and reactivation of tumor suppressor genes. Full article

In this paper, pathohistological images of extramedullary hematopoiesis (EMH) in stomach mucosa in a 68-year-old female patient with treated osteomyelofibrosis are presented. The digestive system is a potential but uncommon site of EMH, with the gastric mucosa being particularly rare. According to the available literature, only 12 cases have been described. Full article