Search Results (24,318)

Bone integrity is maintained through continuous remodeling, orchestrated by the coordinated actions of osteocytes, osteoblasts, and osteoclasts. Once considered passive bystanders, osteocytes are now recognized as central regulators of this process, mediating biochemical signaling and mechanotransduction. Malfunctioning osteocytes contribute to serious skeletal disorders such as osteoporosis. Mesenchymal stromal cells (MSCs), multipotent stem cells capable of differentiating into osteoblasts, have emerged as promising agents for bone regeneration, primarily through the paracrine effects of their secreted exosomes. MSC-derived exosomes are nanoscale vesicles enriched with proteins, lipids, and nucleic acids that promote intercellular communication, osteoblast proliferation and differentiation, and angiogenesis. Notably, they deliver osteoinductive microRNAs (miRNAs) that influence osteogenic markers and support bone tissue repair. In vivo investigations validate their capacity to enhance bone regeneration, increase bone volume, and improve biomechanical strength. Additionally, MSC-derived exosomes regulate the immune response, creating pro-osteogenic and pro-angiogenic factors, boosting their therapeutic efficacy. Due to their cell-free characteristics, MSC-derived exosomes offer benefits such as diminished immunogenicity and minimal risk of off-target effects. These properties position them as promising and innovative approaches for bone regeneration, integrating immunomodulatory effects with tissue-specific regenerative capabilities. Full article

Disease resistance is one of the most important target traits for sugarcane genetic improvement. Sugarcane brown stripe (SBS) caused by Helminthosporium stenospilum is one of the most destructive foliar diseases, which not only reduces harvest cane yield but also sugar content. This study aimed to identify quantitative trait loci (QTL) and candidate genes associated with SBS resistance. Here, the phenotypic investigation in six field habitats showed a continuous normal distribution, revealing that the SBS resistance trait is a quantitative trait. Two high-density linkage maps based on the single-dose markers calling from the Axiom Sugarcane100K SNP chip were constructed for the dominant sugarcane cultivars YT93-159 (SBS-resistant) and ROC22 (SBS-susceptible) with a density of 2.53 cM and 2.54 cM per SNP marker, and mapped on 87 linkage groups (LGs) and 80 LGs covering 3069.45 cM and 1490.34 cM of genetic distance, respectively. A total of 32 QTL associated with SBS resistance were detected by QTL mapping, which explained 3.73–11.64% of the phenotypic variation, and the total phenotypic variance explained (PVE) in YT93-159 and ROC22 was 107.44% and 79.09%, respectively. Among these QTL, four repeatedly detected QTL (qSBS-Y38-1, qSBS-Y38-2, qSBS-R8, and qSBS-R46) were considered stable QTL. Meanwhile, two major QTL, qSBS-Y38 and qSBS-R46, could account for 11.47% and 11.64% of the PVE, respectively. Twenty-five disease resistance candidate genes were screened by searching these four stable QTL regions in their corresponding intervals, of which Soffic.01G0010840-3C (PR3) and Soffic.09G0017520-1P (DND2) were significantly up-regulated in YT93-159 by qRT-PCR, while Soffic.01G0040620-1P (EDR2) was significantly up-regulated in ROC22. These results will provide valuable insights for future studies on sugarcane breeding in combating this disease. Full article

Background: This study aimed to examine the effects of an 8-week plyometric training program on lower-limb explosive strength, jump performance, musculotendinous stiffness, reactive strength index (RSI), and multidirectional speed in elite Polish badminton players. Methods: Twenty-four athletes were randomly assigned to either an experimental group (n = 15), which supplemented their regular badminton training with plyometric exercises, or a control group (n = 15), which continued standard technical training. Performance assessments included squat jump (SJ), countermovement jump (CMJ), single-leg jumps, sprint tests (5 m, 10 m), lateral movements, musculotendinous stiffness, and RSI measurements. Results: The experimental group showed statistically significant improvements in jump height, power output, stiffness, and 10 m sprint and lateral slide-step performance (p < 0.05), with large effect sizes. No significant changes were observed in the control group. Single-leg jump improvements suggested potential benefits for addressing lower-limb asymmetries. Conclusions: An 8-week plyometric intervention significantly enhanced lower-limb explosive performance and multidirectional movement capabilities in young badminton players. These findings support the integration of targeted plyometric training into regular training programs to optimize physical performance, improve movement efficiency, and potentially reduce injury risk in high-intensity racket sports. Full article

Sleep disorders and stroke are intricately linked through a complex, bidirectional relationship. Sleep disturbances such as obstructive sleep apnea (OSA), insomnia, and restless legs syndrome (RLS) not only increase the risk of stroke but also frequently emerge as consequences of cerebrovascular events. OSA, in particular, is associated with a two- to three-fold increased risk of incident stroke, primarily through mechanisms involving intermittent hypoxia, systemic inflammation, endothelial dysfunction, and autonomic dysregulation. Conversely, stroke can disrupt sleep architecture and trigger or exacerbate sleep disorders, including insomnia, hypersomnia, circadian rhythm disturbances, and breathing-related sleep disorders. These post-stroke sleep disturbances are common and significantly impair rehabilitation, cognitive recovery, and quality of life, yet they remain underdiagnosed and undertreated. Early identification and management of sleep disorders in stroke patients are essential to optimize recovery and reduce the risk of recurrence. Therapeutic strategies include lifestyle modifications, pharmacological treatments, medical devices such as continuous positive airway pressure (CPAP), and emerging alternatives for CPAP-intolerant individuals. Despite growing awareness, significant knowledge gaps persist, particularly regarding non-OSA sleep disorders and their impact on stroke outcomes. Improved diagnostic tools, broader screening protocols, and greater integration of sleep assessments into stroke care are urgently needed. This narrative review synthesizes current evidence on the interplay between sleep and stroke, emphasizing the importance of personalized, multidisciplinary approaches to diagnosis and treatment. Advancing research in this field holds promise for reducing the global burden of stroke and improving long-term outcomes through targeted sleep interventions. Full article

This paper presents a distributed measurement system intended to effectively monitor the health status of switchgears under varying temperature conditions. In particular, thermocouples are deployed as temperature sensors for the continuous monitoring of a medium-voltage (MV) switchgear. Then, by integrating a low-cost microcontroller unit, the proposed system can implement previously trained unsupervised learning techniques for health status evaluation. This approach enables the early detection of potential faults by identifying anomalous temperature patterns, thus supporting predictive maintenance and extending the lifespan of switchgears. The results show strong clustering performance with low execution times, highlighting the suitability of the method for resource-constrained hardware. Furthermore, onboard temperature processing eliminates the need for data transmission to remote servers, reducing latency and communication overhead while improving system responsiveness. The paper includes a numerical analysis on synthetic data as well as a validation on real measurements. Overall, the presented distributed measurement system offers a scalable and cost-effective solution to enhance the reliability and safety of MV switchgears. Full article

This paper presents a novel EEG-based learning system designed to enhance the efficiency and effectiveness of studying by dynamically adjusting the difficulty level of learning materials based on real-time attention levels. In the training phase, EEG signals corresponding to high and low concentration levels are recorded while participants engage in quizzes to learn and memorize Chinese characters. The attention levels are determined based on performance metrics derived from the quiz results. Following extensive preprocessing, the EEG data undergoes severmal feature extraction steps: removal of artifacts due to eye blinks and facial movements, segregation of waves based on their frequencies, similarity indexing with respect to delay, binary thresholding, and (PCA). These extracted features are then fed into a k-NN classifier, which accurately distinguishes between high and low attention brain wave patterns, with the labels derived from the quiz performance indicating high or low attention. During the implementation phase, the system continuously monitors the user’s EEG signals while studying. When low attention levels are detected, the system increases the repetition frequency and reduces the difficulty of the flashcards to refocus the user’s attention. Conversely, when high concentration levels are identified, the system escalates the difficulty level of the flashcards to maximize the learning challenge. This adaptive approach ensures a more effective learning experience by maintaining optimal cognitive engagement, resulting in improved learning rates, reduced stress, and increased overall learning efficiency. This adaptive approach ensures a more effective learning experience by maintaining optimal cognitive engagement, resulting in improved learning rates, reduced stress, and increased overall learning efficiency. Our results indicate that this EEG-based adaptive learning system holds significant potential for personalized education, fostering better retention and understanding of Chinese characters. Full article

As geographic information visualization continues to gain prominence, dynamic symbols are increasingly employed in map-based applications. However, the optimal visual coding for dynamic point symbols—particularly concerning encoding type, animation rate, and modulation area—remains underexplored. This study examines how these factors influence user performance in visual search tasks through two eye-tracking experiments. Experiment 1 investigated the effects of two visual coding factors: encoding types (flashing, pulsation, and lightness modulation) and animation rates (low, medium, and high). Experiment 2 focused on the interaction between encoding types and modulation areas (fill, contour, and entire symbol) under a fixed animation rate condition. The results revealed that search performance deteriorates as the animation rate of the fastest target symbol exceeds 10 fps. Flashing and lightness modulation outperformed pulsation, and modulation areas significantly impacted efficiency and accuracy, with notable interaction effects. Based on the experimental results, three visual coding strategies are recommended for optimal performance in map-based interfaces: contour pulsation, contour flashing, and entire symbol lightness modulation. These findings provide valuable insights for optimizing the design of dynamic point symbols, contributing to improved user engagement and task performance in cartographic and geovisual applications. Full article

Background: Transfection is vital for gene therapy, mRNA treatments, CAR-T cell therapy, and regenerative medicine. While viral vectors are effective, non-viral systems like lipid nanoparticles (LNPs) offer safer, more flexible alternatives. This work explores emerging non-viral transfection technologies to improve delivery efficiency and therapeutic outcomes. Methods: This review synthesizes the current literature and recent advancements in non-viral transfection technologies. It focuses on the mechanisms, advantages, and limitations of various delivery systems, including lipid nanoparticles, biodegradable polymers, electroporation, peptide-based carriers, and microfluidic platforms. Comparative analysis was conducted to evaluate their performance in terms of transfection efficiency, cellular uptake, biocompatibility, and potential for clinical translation. Several academic search engines and online resources were utilized for data collection, including Science Direct, PubMed, Google Scholar Scopus, the National Cancer Institute’s online portal, and other reputable online databases. Results: Non-viral systems demonstrated superior performance in delivering mRNA, siRNA, and antisense oligonucleotides, particularly in clinical applications. Biodegradable polymers and peptide-based systems showed promise in enhancing biocompatibility and targeted delivery. Electroporation and microfluidic systems offered precise control over transfection parameters, improving reproducibility and scalability. Collectively, these innovations address key challenges in gene delivery, such as stability, immune response, and cell-type specificity. Conclusions: The continuous evolution of transfection technologies is pivotal for advancing gene and cell-based therapies. Non-viral delivery systems, particularly LNPs and emerging platforms like microfluidics and biodegradable polymers, offer safer and more adaptable alternatives to viral vectors. These innovations are critical for optimizing therapeutic efficacy and enabling personalized medicine, immunotherapy, and regenerative treatments. Future research should focus on integrating these technologies to develop next-generation transfection platforms with enhanced precision and clinical applicability. Full article

Machine learning and artificial intelligence have transformed fault detection and maintenance strategies for industrial machinery. This study applies well-established data-driven techniques to a rarely explored industrial application—the condition monitoring of high-precision paper cutting machines—enhancing condition-based maintenance to improve operational efficiency, safety, and cost-effectiveness. A key element of the proposed approach is the integration of an infrared pyrometer into vibration monitoring, utilizing accelerometer data to evaluate the state of health of machinery. Unlike traditional fault detection studies that focus on extreme degradation states, this work successfully identifies subtle deviations from optimal, which even expert technicians struggle to detect. Building on a feasibility study conducted with Tecnau SRL, a comprehensive diagnostic system suitable for industrial deployment is developed. Endurance tests pave the way for continuous monitoring under various operating conditions, enabling real-time industrial diagnostic applications. Multi-scale signal analysis highlights the significance of transient and steady-state phase detection, improving the effectiveness of real-time monitoring strategies. Despite the physical similarity of the classified states, simple time-series statistics combined with machine learning algorithms demonstrate high sensitivity to early-stage deviations, confirming the reliability of the approach. Additionally, a systematic analysis to downgrade acquisition system specifications identifies cost-effective sensor configurations, ensuring the feasibility of industrial implementation. Full article

As the penetration rate of new energy continues to increase, the active voltage support capability of the power system is decreasing. The grid-forming static var generator (GFM-SVG) features the advantages of fast dynamic response, strong reactive power support, and high overload capacity, which play an important role in maintaining voltage stability. However, the parameters of the GFM-SVG are often unknown due to trade secret reasons. Meanwhile, the parameters may be changed during the long-term operation of the system, which brings challenges to the system stability analysis and control. Aiming at this problem, a parameter identification method based on trajectory sensitivity analysis and the proximal policy optimization (PPO) algorithm is proposed in this paper. Firstly, through trajectory sensitivity analysis, the key influential parameters on the output characteristics of the GFM-SVG can be selected, which can reduce the dimensionality of the identification parameters and improve the identification efficiency. Then, a parameter identification framework based on the PPO algorithm is constructed for GFM-SVGs, which utilizes its adaptive learning capability to achieve accurate identification of the key parameters of the system. Finally, the effectiveness of the proposed parameter identification method is verified through simulation examples. The simulation results show that the identification error of the parameters in the GFM-SVG is small. The proposed method can characterize the output response of the GFM-SVG under different operating conditions. Full article

Effective management of fecal pollutants in rural sanitation is crucial for environmental health and public safety, especially in developing regions. In this study, temporal and regional variations in nutrient elements, heavy metals, pathogenic microorganisms (PMs), and antibiotic resistance genes (ARGs) of fecal samples from rural toilets in China were investigated. The moisture contents of the fecal samples average 92.7%, decreasing seasonally from 97.4% in summer to 90.6% in winter. The samples’ pH values range from 6.5 to 7.5, with a slight decrease in winter (6.8), while their electrical conductivity varies from 128.1 to 2150 μs/cm, influenced by regional diets. Chromium (9.0–49.7 mg/kg) and copper (31.9–784.4 mg/kg) levels vary regionally, with higher concentrations in Anhui and Guangxi Provinces due to dietary and industrial factors. Zinc contents range from 108.5 to 1648.9 mg/kg, with higher levels in autumn and winter, resulting from agricultural practices and Zn-containing fungicides, posing potential health and phytotoxicity risks. Seasonal and regional variations in PMs and ARGs were observed. Guangxi Province shows the high PM diversity in summer samples, while Jiangsu Province exhibits the high ARGs types in autumn samples. These findings highlight the need for improved waste management and sanitation solutions in rural areas to mitigate environmental risks and protect public health. Continued research in these regions is essential to inform effective sanitation strategies. Full article

To study and quantify the impact of water storage on lake slope stability after the closure of an open-pit mine, we targeted slope control measures by large-scale parallel computing methods and strength reduction theory. This was based on a three-dimensional refined numerical model to simulate the evolution of slope stability under different water storage levels and backfilling management conditions, and to quantitatively assess the risk of slope instability through the spatial distribution of stability coefficients. This study shows that during the impoundment process, the slope stability has a nonlinear decreasing trend due to the decrease in effective stress caused by the increase in pore water pressure. When the water storage was at 0 m, the instability range is the largest, and the surface range is nearly 200 m from the edge of the pit; when the water level continued to rise to 50 m, the hydrostatic pressure of the pit lake water on the slope support effect began to appear, and the stability was improved, but there is still a wide range of unstable areas at the bottom. In view of the unstable area of the steep slope with soft rock in the north slope during the process of water storage, the management scheme of backfilling the whole bottom to −150 m was proposed, and the slope protection and pressure footing were formed by discharging the soil to −40 m in steps to improve the anti-slip ability of the slope. Full article

Early-career teachers (ECTs) entered the profession during the COVID-19 pandemic, a period that introduced unique stressors to an already-demanding career phase. This systematic review examines empirical studies published between 2020 and February 2025 to explore how the pandemic influenced ECT wellbeing, with particular attention to stressors and protective factors impacting long-term retention and professional sustainability. Guided by PRISMA protocols, databases including Web of Science, ERIC, Google Scholar, and Scopus were searched, screening 470 records and identifying 30 studies that met inclusion criteria: peer-reviewed, empirical, focused on early-career teachers (within the first five years), and situated in or explicitly addressing the pandemic and its ongoing impacts. The results of Braun and Clarke’s thematic analysis (2006) revealed that pandemic-related challenges such as increased workload, professional isolation, disrupted induction processes, and emotional strain have persisted into the post-pandemic era, contributing to sustained risks of burnout and attrition. Regardless, protective factors identified during the pandemic—including high-quality mentoring, structured induction programmes, collegial support, professional autonomy, and effective individual coping strategies—continue to offer essential support, enhancing resilience and professional wellbeing. These findings underscore the necessity of institutionalising targeted supports to address the enduring effects of pandemic-related stressors on ECT wellbeing. By prioritising sustained mental health initiatives and structural supports, education systems can effectively mitigate long-term impacts and improve retention outcomes for early-career teachers in a post-pandemic educational landscape. Full article

Chemotherapy remains a cornerstone in the treatment of esophageal cancer (EC), yet chemoresistance remains a critical challenge, leading to poor outcomes and limited therapeutic success. Mitochondrial DNA (mtDNA) has emerged as a pivotal player in mediating these responses, influencing cellular metabolism, oxidative stress regulation, and apoptotic pathways. This review provides a comprehensive overview of the mechanisms by which mtDNA alterations, including mutations and copy number variations, drive chemoresistance in EC. Specific focus is given to the role of mtDNA in metabolic reprogramming, including its contribution to the Warburg effect and lipid metabolism, as well as its impact on epithelial–mesenchymal transition (EMT) and mitochondrial bioenergetics. Recent advances in targeting mitochondrial pathways through novel therapeutic agents, such as metformin and mitoquinone, and innovative approaches like CRISPR/Cas9 gene editing, are also discussed. These interventions highlight the potential for overcoming chemoresistance and improving patient outcomes. By integrating mitochondrial diagnostics with personalized treatment strategies, we propose a roadmap for future research that bridges basic mitochondrial biology with translational applications in oncology. The insights offered in this review emphasize the critical need for continued exploration of mtDNA-targeted therapies to address the unmet needs in EC management and other diseases associated with mitochondria. Full article

In clinical decision support systems (CDSSs), where accurate classification of drug–drug interactions (DDIs) can directly affect treatment safety and outcomes, identifying drug interactions is a major challenge, introducing a scalable approach for classifying DDIs utilizing a finely-tuned biomedical language model. The method shown here uses BiomedBERT, a domain-specific version of bidirectional encoder representations from transformers (BERT) that was pre-trained on biomedical literature, to reduce the number of resources needed during fine-tuning. Low-rank adaptation (LoRA) was used to fine-tune the model on the DrugBank dataset. The objective was to classify DDIs into two clinically distinct categories, that is, synergistic and antagonistic interactions. A pseudo-labeling strategy was created to deal with the problem of not having enough labeled data. A curated ground-truth dataset was constructed using polarity-labeled interaction entries from DrugComb and verified DrugBank antagonism pairs. The fine-tuned model is used to figure out what kinds of interactions there are in the rest of the unlabeled data. A checkpointing system saves predictions and confidence scores in small pieces, which means that the process can be continued and is not affected by system crashes. The framework is designed to log every prediction it makes, allowing results to be refined later, either manually or through automated updates, without discarding low-confidence cases, as traditional threshold-based methods often do. The method keeps a record of every output it generates, making it easier to revisit earlier predictions, either by experts or with improved tools, without depending on preset confidence cutoffs. It was built with efficiency in mind, so it can handle large amounts of biomedical text without heavy computational demands. Rather than focusing on model novelty, this research demonstrates how existing biomedical transformers can be adapted to polarity-aware DDI classification with minimal computational overhead, emphasizing deployment feasibility and clinical relevance. Full article