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Exercise Addiction in Older Adults: Health Preservation or Fear of Death
No full textObjectives The aim of the study is to explore exercise addiction in older adults and the psychological, social and biological factors underlying the exercise addiction. Subjects and Methods This study employed a cross-sectional design. 254 participants aged 65 and above were divided into exercise addicted and non-exercise addicted groups in the study. The exercise addiction of participants was assessed using the Exercise Addiction Inventory (EAI). Health-preserving behaviours were evaluated with the Health Protection Behaviour Scale (HPBS). Anxiety related to health was measured using the Health Anxiety Inventory (HAI), while death anxiety was assessed using the Death Anxiety Scale (DAS). Results In intergroup analyses, the EAI, HPBS and TDAS scores were higher in the Exercise Addicted Group compared to the Non-Exercise Addicted Group (p < 0.001, p < 0.001, p = 0.009, respectively). However, the HAI score was lower in the Exercise Addicted Group (p = 0.021). In addition, a positive correlation was observed between HPBS and EAI scores (r = 0.454, p < 0.001). No correlation was found between EAI scores and age (r = 0.028, p = 0.654) or HAI (r = -0.088, p = 0.162). Conclusions This study found that individuals with exercise addiction had lower BMI, a lower proportion of women and higher education levels. Additionally, while positive relationships were observed between exercise addiction and both health-protective behaviours and death anxiety, a negative relationship was found with health anxiety. Correlation and regression analyses indicated that BMI and higher education level serve as protective factors against exercise addiction, whereas health-protective behaviours and death anxiety function as risk factors
Fixed-Time Synchronization of Fractional-Order Hopfield Neural Networks with Proportional Delays
No full textEl Abed, Assali/0000-0002-0949-0654This article explores the fixed-time synchronization of fractional-order Hopfield neural networks incorporating proportional delays. Unlike finite-time synchronization, where the convergence time varies based on the initial synchronization errors, fixed-time synchronization allows for a predetermined settling time that remains independent of initial conditions. To achieve fixed-time synchronization, two types of feedback control strategies incorporating fractional integrals are employed: one based on state feedback and another utilizing a controller designed with a Lyapunov function and an exponential function. By designing appropriate Lyapunov functions and employing inequality techniques, multiple sufficient conditions were established to guarantee the fixed-time synchronization of the considered systems under these control strategies. Finally, two numerical examples are presented to demonstrate the validity and practical relevance of the theoretical findings.Science Citation Index Expande
Decoding Preterm Birth: Non-Invasive Biomarkers and Personalized Multi-Omics Strategies
No full textA birth that occurs prior to 37 weeks of gestation is referred to as preterm birth (PTB). PTB is a health concern globally with significant outcomes including neonatal morbidity and mortality. Advancements in multi-omics have revolutionized the understanding of PTB pathogenesis, offering new opportunities for early prediction and risk categorization. This review highlights emerging liquid biomarkers derived from proteomic, metabolomic, genomic, transcriptomic, and epigenomic studies, emphasizing the integrative power of multi-omics approaches. Proteomic analyses have revealed key proteins in maternal and fetal compartments associated with inflammatory and extracellular matrix pathways, while metabolomics have identified lipid and metabolite profiles linked to energy metabolism and fetal development. Genomic and epigenomic studies have uncovered genetic variations and microRNAs involved in uterine contractility and immune modulation, providing novel insights into PTB's molecular underpinnings. Transcriptomic research further underscores the act of long non-coding RNAs (ncRNAs) in regulating gene expression and inflammatory responses. Multi-omics integration, coupled with machine learning models, has demonstrated superior predictive accuracy by synthesizing data across these domains, revealing intricate molecular interactions underlying PTB. Future research should prioritize longitudinal multi-omics studies to capture dynamic biological changes during pregnancy, expanding diverse population cohorts to enhance generalizability. Translating multi-omics insights into clinical practice necessitates collaborative efforts to develop cost-effective, accessible biomarker panels and establish standardized guidelines for implementation. These advancements hold the potential to transform prenatal care through personalized risk assessment and targeted preventive strategies, reducing the global burden of PTB. © © 2025 Elsevier Inc. All rights reserved.Science Citation Index Expande
A High-Efficiency Fourth-Order Iterative Method for Nonlinear Equations: Convergence and Computational Gains
No full textThis study introduces an optimal fourth-order iterative method derived by combining two established methods, resulting in enhanced convergence when solving nonlinear equations. Through rigorous convergence analysis using both Taylor expansion and the Banach space framework, the fourth-order optimality condition is verified. We demonstrate the superior efficiency and stability of this new method compared to traditional alternatives. Numerical experiments confirm its effectiveness, showing a reduction in the average number of iterations and computational time. Visual analysis with polynomiographs confirms the method's robustness, focusing on convergence area index, iteration count, computational time, fractal dimension, and Wada measure of basins. These findings underscore the potential of this optimal method for tackling complex nonlinear problems in various scientific and engineering fields. (c) 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC license (http:// creativecommons.org/licenses/by-nc/4.0/).Science Citation Index Expande
Muscle Tone and Stiffness Comparison in Ambulatory Children With Unilateral Spastic Cerebral Palsy: Implications for Postural Balance and Functional Mobility
No full textOBJECTIVES: To compare muscle tone and stiffness in ambulatory children with unilateral spastic cerebral palsy (UCP) with typically developing peers and explore their relationship with postural balance and functional mobility. METHODS: Forty ambulatory children with UCP and age-matched typically developing peers were assessed for tone and stiffness of lumbar spinal extensors, gastrocnemius, and hamstring muscles using a myotonometer. Functional mobility was evaluated with the 2-Minute Walk Test, and the Timed Up and Go Test, while postural balance was evaluated using the Pediatric Balance Scale and the Trunk Control Measurement Scale (TCMS). RESULTS: The gastrocnemius muscle tone and stiffness were higher on the affected side in UCP compared with the less affected side and typically developing peers (P .05). CONCLUSIONS: Our study highlights the importance of achieving muscle symmetry, particularly in the plantar flexors, for functional mobility in UCP children. While differences in ankle and knee muscle biomechanics were observed, they didn't significantly impact functional mobility or postural balance. Symmetry in lumbar spinal extensor biomechanics correlated with better outcomes, emphasizing the crucial role of trunk control in rehabilitation strategies for ambulatory children with UCP
Soliton Structures, Modulational Instability, and Chaotic Dynamics of the Coupled Schrödinger-Boussinesq Equation
No full textWe investigate the coupled Schr & ouml;dinger-Boussinesq (SB) system, a nonlinear model describing resonant interactions between short-and long-wave components in optics, plasma physics, and fluid mechanics. Using a traveling-wave reduction, we transform the governing PDEs into a canonical nonlinear ODE and derive a broad family of exact solutions, including solitary and singular solitons, finite-background localized states, and Jacobi elliptic periodic waves. We analyze the modulational instability of continuous-wave states, identifying parameter regimes where uniform wave trains destabilize into localized excitations and elucidating the interplay between dispersion, coupling, and nonlinearity. Recasting the reduced dynamics in phase space, we classify equilibria, phase portraits, and connecting orbits, thereby characterizing the conditions for solitary and periodic patterns. With weak external periodic forcing, we apply the Melnikov method to derive explicit thresholds for homoclinic orbit splitting and rigorously predict the onset of chaos. Together, these results establish a unified analytical framework connecting soliton formation, modulational instability, and chaotic dynamics in the SB system, thereby advancing the broader understanding of nonlinear wave phenomena in multiscale physical media
Peas, Natural Resources for a Sustainable Future: A Multifaceted Review of Nutritional, Health, Environmental, and Market Perspectives
No full textMutavski, Zorana/0000-0002-6998-0292The pea (Pisum sativum L.) is an emerging pillar in plant-based nutrition and sustainable food systems due to its high-quality proteins, diverse bioactive compounds, and agroecological benefits. This review provides an updated synthesis of the nutritional composition, health-promoting properties, and environmental relevance of peas, emphasizing recent scientific findings. Pea seeds typically contain 20%-40% protein, 45%-55% starch, and 10%-15% dietary fiber, alongside essential micronutrients such as vitamin C (40-60 mg/100 g), folate (60-70 mu g/100 g), vitamin K (30-45 mu g/100 g), iron (1.5-2.0 mg/100 g), and manganese (0.4-0.6 mg/100 g). Their storage proteins, primarily legumin and vicilin, offer high digestibility and amino acid profiles compatible with human requirements, supporting their rapidly growing use in protein isolates and meat- and dairy-alternative products. Peas represent a valuable source of phenolic acids, flavonoids, and saponins, which contribute to notable antioxidant (50-120 mu mol Trolox/g) and anti-inflammatory activities demonstrated in preclinical studies. Compared with other legumes, peas exhibit a lower glycemic index (35-45), making them suitable for metabolic health applications. Agronomically, pea cultivation enhances soil fertility through biological nitrogen fixation (up to 150 kg N/ha), supporting reduced fertilizer inputs and improved crop rotation performance, aligning with circular economy and climate-resilience strategies. Despite these advantages, global consumption and breeding innovation remain insufficient to meet the rising demand for alternative proteins. Future opportunities include improving protein extraction technologies, valorizing processing side-streams, and exploring underutilized phytochemicals to strengthen the nutritional and sustainability profile of pea-based food systems.COST Action DIVERSICROP [CA22146]; COST (European Cooperation in Science and Technology); COST Action [CA22146-Harnessing]; Ministry of Science, Technological Development, and Innovation, Republic of Serbia [451-03-136/2025-03/200015, 451-03-136/2025-03/200003]The author(s) declare that financial support was received for the research and/or publication of this article. This research was funded by COST Action DIVERSICROP (CA22146), supported by COST (European Cooperation in Science and Technology). COST Action CA22146-Harnessing the potential of underutilized crops to promote sustainable food production. This research was supported by the Ministry of Science, Technological Development, and Innovation, Republic of Serbia (Grants No. 451-03-136/2025-03/200015 and 451-03-136/2025-03/200003)
Cumulative Summation Test for Learning Curve in Vaginal Tightening
No full textYüksel Aybek, Özlem/0000-0003-0332-496X; Caliskan, Eray/0000-0002-6799-5909;Introduction and HypothesisThe objective was to evaluate the learning curve of vaginoplasty procedures performed by two trainees with different levels of experience using the Learning Curve Cumulative Summation (LC-CUSUM) method.MethodsThis prospective study included 80 consecutive vaginoplasty cases performed by two trainees after structured theoretical and hands-on training. Trainee 1 was a senior resident in the third year of the Obstetrics and Gynecology specialty training program, with no prior experience in urogynecological surgery, whereas Trainee 2 was a certified specialist with a relevant surgical background. Both trainees were evaluated using LC-CUSUM parameters based on predefined acceptable (10%) and unacceptable (17.5%) failure rates. The number of procedures required to reach competency was recorded.ResultsTrainee 1 reached the predefined performance level after 27 procedures, whereas Trainee 2 achieved it after 14. No major complications occurred. The LC-CUSUM curves confirmed gradual and safe skill acquisition in both cases. Patient satisfaction scores were high in both groups, with no statistically significant differences in complication rates.ConclusionThe LC-CUSUM method provides an objective and individualized tool to assess surgical competence in vaginoplasty. It enables dynamic monitoring of learning curves and may contribute to safer surgical training, especially in procedures where experience heavily influences outcomes
Fractional-Order Modeling of Infectious Diseases: A Stochastic Neural Network Procedure to Deal With Vaccination and Awareness Strategies
No full textThe aim of the work is to solve the fractional order infectious disease model with the awareness and vaccination effects by executing reliable neural network strategies. Fractional kinds of derivatives perform higher efficiency and accuracy in comparison with the derivatives of integer kinds. The fractional order infectious disease model with the awareness and vaccination effects is separated into susceptible class, vaccinated class, infected class, quarantined class, and removed class. A construction of the proposed neural network is accomplished by a single layer construction with log-sigmoid transfer function together with 24 neurons. The model is trained using the Adam optimizer along with the Bayesian regularization, a reliable solver to perform the results of nonlinear systems. The dataset obtained between 0 and 1 with the step size of 0.01, which is divided into three states: validation 10%, training 76%, and testing 14%. The numerical solutions of the infectious disease model with the awareness and vaccination effects are performed by taking three fractional order cases between 0 and 1. The solver's exactness is perceived by the identical solutions, optimal training and absolute error. The reliability of the proposed algorithm is achieved based on regression coefficients, state transitions, and best fitness
Electrochemical and Optical Biosensors for Periodontitis Detection
No full textPeriodontitis is a common chronic inflammatory disease that frequently results in tooth loss and systemic complications. Traditional diagnostic methods have limited sensitivity and often fail to detect early disease activity. Biosensors have emerged as promising tools for the early and accurate detection of specific biomarkers in saliva and gingival crevicular fluid. This review highlights recent advancements in electrochemical, optical, lab-on-a-chip technologies, and nanosensors for periodontal diagnosis. These innovations provide rapid, noninvasive, point-of-care capabilities, enabling improved monitoring and personalized treatment. While challenges remain in clinical translation, biosensors have significant potential to transform periodontal diagnostics and enhance patient outcomes. Clinical trial number: not applicable.Emerging Sources Citation Inde