Search Results (5,913)

Ischemic stroke triggers a dynamic immune response that influences both acute damage and long-term recovery. This review synthesizes a decade of evidence on immunological and inflammatory biomarkers in ischemic stroke, emphasizing their prognostic and therapeutic significance. Following ischemic insult, levels of pro-inflammatory cytokines, such as interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α), and chemokines like interleukin-8 (IL-8) rapidly rise, promoting blood–brain barrier disruption, leukocyte infiltration, and neuronal death. Conversely, anti-inflammatory mediators such as interleukin-10 (IL-10) and transforming growth factor-β (TGF-β) facilitate repair, neurogenesis, and immune regulation in later phases. The balance between these pathways determines outcomes and is reflected in circulating biomarkers. Composite hematological indices including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) offer accessible and cost-effective prognostic tools. Several biomarkers correlate with infarct size, neurological deterioration, and mortality, and may predict complications like hemorrhagic transformation or infection. Therapeutic strategies targeting cytokines, especially IL-1 and IL-6, have shown promise in modulating inflammation and improving outcomes. Future directions include personalized immune profiling, real-time cytokine monitoring, and combining immunotherapy with neurorestorative approaches. By integrating immune biomarkers into stroke care, clinicians may enhance risk stratification, optimize treatment timing, and identify candidates for novel interventions. This review underscores inflammation’s dual role and evolving therapeutic and prognostic relevance in ischemic stroke. Full article

Exercise-induced muscle damage (EIMD) is the most common health risk in physical exercise. However, instant and non-invasive methods for EIMD prediction have not been reported. Urine is a promising tool for EIMD prediction. However, urinary metabolite variations after EIMD occurrence have not been revealed, and potential biomarkers have not been identified. In this study, eighteen young students without regular exercise habits were recruited to perform high-intensity rowing exercise. EIMD occurrence was determined using blood biochemical analyses and pain assessment. The changes in urinary metabolites were revealed by quasi-targeted metabolomics. Results demonstrated that high-intensity rowing exercise induced EIMD and obviously changed urinary metabolites, including 23 upregulated metabolites and 26 downregulated metabolites. These differential metabolites were related to energy metabolism, exercise performance, and antioxidant metabolism. Among these metabolites, potential urinary biomarkers were identified with high sensitivity and specificity. Full article

Traditional nutritional diagnosis suffers from inefficiency, high cost, and damage when predicting the nitrogen, phosphorus, and potassium content of sugarcane leaves. Non-destructive nutritional diagnosis of sugarcane leaves based on traditional machine learning and deep learning suffers from poor generalization and lower accuracy. To address these issues, this study proposes a novel convolutional neural network called WT-ResNet. This model incorporates wavelet transform into the residual network structure, enabling effective feature extraction from sugarcane leaf images and facilitating the regression prediction of nitrogen, phosphorus, and potassium content in the leaves. By employing a cascade of decomposition and reconstruction, the wavelet transform extracts multi-scale features, which allows for the capture of different frequency components in images. Through the use of shortcut connections, residual structures facilitate the learning of identity mappings within the model. The results show that by analyzing sugarcane leaf images, our model achieves R² values of 0.9420 for nitrogen content prediction, 0.9084 for phosphorus content prediction, and 0.8235 for potassium content prediction. The accuracy rate for nitrogen prediction reaches 88.24% within a 0.5 tolerance, 58.82% for phosphorus prediction within a 0.1 tolerance, and 70.59% for potassium prediction within a 0.5 tolerance. Compared to other algorithms, WT-ResNet demonstrates higher accuracy. This study aims to provide algorithms for non-destructive sugarcane nutritional diagnosis and technical support for precise sugarcane fertilization. Full article

Damaged material invariably exhibits a lower resonance frequency than undamaged material due to its reduced stiffness. Under fatigue loading, damage accumulates until failure, so changes in resonance frequency can be utilised as a variable to predict fatigue life. Conventional fatigue life prediction methods have a low success rate, prompting the exploration of alternative approaches. We have presented a novel method for predicting the fatigue life of spruce wood based on changes in resonance frequency during fatigue, using a representative specimen (i.e., one out of five specimens tested, with four used for static strength reference). We conducted a low-cycle fatigue test and monitored the resonance frequency alongside the dynamic and static modulus of elasticity. All three types of data were employed to predict fatigue life using between 40% and 100% of the measurement data. Of the two fatigue life prediction methods investigated, the Weibull cycle density distribution using resonance frequency measurements proved most appropriate. The error decreases monotonically with the amount of resonance frequency measurement data used for fatigue life prediction, reaching its lowest value of 1% when the full resonance frequency dataset is used. The proposed fatigue life prediction method should be further validated with a larger sample size, as fatigue is inherently a statistical phenomenon. Full article

Plants must contend with oxidative stress, a paradoxical phenomenon in which reactive oxygen species (ROS) can cause cellular damage while also serving as key signaling molecules. Environmental stressors, such as drought, salinity, and temperature extremes, promote ROS accumulation, affecting plant growth and productivity. To maintain redox homeostasis, plants rely on antioxidant systems comprising enzymatic defenses, such as superoxide dismutase, catalase, and ascorbate peroxidase, and non-enzymatic molecules, including ascorbate, glutathione, flavonoids, and emerging compounds such as proline and nano-silicon. This review provides an integrated overview of antioxidant responses and their modulation through recent biotechnological advances, emphasizing the role of emerging technologies in advancing our understanding of redox regulation and translating molecular insights into stress-resilient phenotypes. Omics approaches have enabled the identification of redox-related genes, while genome editing tools, particularly those based on clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) proteins, offer opportunities for precise functional manipulation. Artificial intelligence and systems biology are accelerating the discovery of regulatory modules and enabling predictive modeling of antioxidant networks. We also highlight the contribution of synthetic biology to the development of stress-responsive gene circuits and address current regulatory and ethical considerations. Overall, this review aims to provide a comprehensive perspective on molecular, biochemical, and technological strategies to enhance oxidative stress tolerance in plants, thereby contributing to sustainable agriculture and food security in a changing climate. Full article

Understanding three-dimensional (3D) auxetic structural responses to impact loading remains challenging due to large deformations involving failure evolution and the interaction between geometric and material instabilities. In this study, the Generalized Interpolation Material Point Method (GIMP) is used to investigate representative auxetic structures, with the focus on the negative Poisson’s ratio effect on the responses to impact loading. Using a cubic lattice model for 3D re-entrant structures, simulations with different impact speeds are performed to evaluate corresponding energy absorption characteristics and deformation behaviors. Three constitutive models for lattice materials (linear elasticity, elastoplasticity, and damage) are employed to analyze the corresponding variations in auxetic structural performance. The computational results indicate that distinct deformation mechanisms are mainly associated with microstructural geometry, while the constitutive modeling effect is not significant. The findings demonstrate the importance of the process–structure–property relationship in the impact performance of protective structures. Verification against theoretical predictions of the Poisson’s ratio–strain relationship confirms the potential of GIMP in effectively engineering auxetic structures for general applications. Full article

Background/Objectives: Antibody–drug conjugates are a rapidly evolving class of cancer therapeutics that combine the specificity of monoclonal antibodies with the potency of cytotoxic drugs. This review explores experimental and computational advances in ADC design, focusing on structural elements and optimization strategies. Methods: We examined recent developments in the mechanisms of action, antibody engineering, linker chemistries, and payload selection. Emphasis was placed on experimental strategies and computational tools, including molecular modeling and AI-driven structure prediction. Results: ADCs function through both internalization-dependent and -independent mechanisms, enabling targeted drug delivery and bystander effects. The therapeutic efficacy of ADCs depends on key factors: antigen specificity, linker stability, and payload potency. Linkers are categorized as cleavable or non-cleavable, each with distinct advantages. Payloads—mainly tubulin inhibitors and DNA-damaging agents—require extreme potency to be effective. Computational methods have become essential for antibody modeling, developability assessment, and in silico optimization of ADC components, accelerating candidate selection and reducing experimental labor. Conclusions: The integration of experimental and in silico approaches enhances ADC design by improving selectivity, stability, and efficacy. These strategies are critical for advancing next-generation ADCs with broader applicability and improved therapeutic indices. Full article

This study reviews existing research on the effects of the interaction between in-plane (IP) and out-of-plane (OOP) behaviors on the seismic response of non-framed unreinforced masonry (URM) structures. During earthquakes, masonry buildings exhibit complex behaviors. First, walls may experience simultaneous IP and OOP actions, or pre-existing IP and OOP damage, deformation, or loads that can alter their unidirectional IP or OOP seismic response. Second, the IP and OOP action of one wall can affect the behavior of its intersecting walls. However, the effects of these behaviors, referred to as “direct IP-OOP interactions” and “Flange effects”, respectively, are often disregarded in design and assessment provisions. To address this gap, this study explores findings from experimental and numerical research conducted at the wall level currently available in the literature, identifying the nature of these interaction effects and the key parameters that affect their extent. The available body of work includes only a few experimental studies on interaction effects, whereas numerical investigations are more extensive. However, most numerical studies focus on how OOP pre-damage/deformation influences the IP behaviors (OOP/IP interactions) and the role of flanges in IP response (F/IP interactions), leaving significant gaps in understanding the effects of IP pre-damage/deformation on the OOP response (IP/OOP interactions) and the OOP response in the presence of flanges (F/OOP interactions). Among the parameters studied, boundary conditions, wall height-to-length aspect ratio, and vertical overburden are found to have the most significant influence on interaction effects because of their relevance for the IP and OOP failure mechanisms. Other parameters, such as the restriction of top uplift, the presence of openings, or changes in slenderness ratio, are not comprehensively studied, and the available data are insufficient for definitive conclusions. Methodologies available in the literature for extrapolating the findings observed at the wall level to building-level analyses are reviewed. The current predictive equations primarily address the effects of OOP pre-load and Flange effects on IP response. Furthermore, only a few macro-element models are proposed for cost-effective, large-scale building simulations. To bridge these gaps, future research must expand experimental investigations, develop more comprehensive design and assessment equations, and refine numerical modeling techniques for building-level applications. Full article

Coal mining disturbance induces progressive damage and fracturing in overlying rock (OLR), forming a complex fracture network. This process triggers groundwater depletion, ecological degradation, and severely compromises mine safety. Based on field drilling sampling and mechanical experiments, this paper reveals the occurrence properties and characteristics of weakly cemented overlying rock (WCOLR). At the same time, similar simulation experiments, DIC speckle analysis system, and fractal theory are used to explain the development and evolution mechanism of mining-induced fractures under this special geological condition. The OLR fracture is determined based on the grid fractal dimension (D) distribution. A stress arch-bed separation (BS) co-evolution model is established based on dynamic cyclic BS development and stress arch characteristics, enabling identification of BS horizons. The results show that the overlying weak and extremely weak rock accounts for more than 90%. During the process of longwall face (LF) advancing, the D undergoes oscillatory evolution through five distinct stages: rapid initial growth, constrained slow growth under thick, soft strata (TSS), dimension reduction induced by fracturing and compaction of TSS, secondary growth from newly generated fractures, and stabilization upon reaching full extraction. Grid-based D analysis further categorizes fracture zones, indicating a water conducting fracture zone (WCFZ) height of 160~180 m. Mining-induced fractures predominantly concentrate at dip angles of 0–10°, 40–50°, and 170–180°. Horizontally BS fractures account for 70.2% of the total fracture population, vertically penetrating fractures constitute 13.1% and transitional fractures make up the remaining 16.7%. The stress arch height is 314.4 m, and the stable BS horizon is 260 m away from the coal seam. Finally, an elastic foundation theory-based model was used to predict BS development under top-coal caving operations. This research provides scientific foundations for damage-reduced mining in ecologically vulnerable Western China coalfields. Full article

Wood–plastic composites (WPCs) offer a sustainable alternative to solid wood, yet their heterogeneous structure presents challenges in laser machining due to thermal sensitivity and inconsistent material behaviour. This study investigates the optimization of CO₂ laser-cutting parameters for WPCs, focusing on feed rate and assist-gas pressure. Using a 1500 W CO₂ laser, a full factorial experimental design was employed to cut 18 mm thick WPC panels at varying feed rates (1000–3000 mm/min) and gas pressures (1–3 bar). Statistical analyses including MANOVA and linear regression were conducted to evaluate their effects on key machining responses: cutting depth, heat-affected zone (HAZ) width, cut-edge quality, and surface finish. Results indicated that feed rate significantly influences both cutting depth and thermal damage, while gas pressure plays a major role in improving surface quality and reducing HAZ. Optimal combinations were identified for various performance goals, and validation trials at the selected parameters confirmed alignment with predicted outcomes. The optimized settings yielded high-quality cuts with reduced HAZ and enhanced surface characteristics. This study demonstrates the effectiveness of a statistical optimization approach in refining CO₂ laser-cutting conditions for WPCs, offering insights for improved process control and sustainable manufacturing applications. This study also introduces a multi-objective optimization approach that verifies the interaction effects of feed rate and assist-gas pressure, enabling precise and efficient CO₂ laser cutting of 18 mm thick WPCs. Full article

Silicon carbide (SiC), a wide-bandgap semiconductor, is renowned for its exceptional performance in power electronics and extreme-temperature environments. However, precision low-loss laser slicing of SiC is impeded by energy divergence and crack delamination induced by refractive-index-mismatch interfacial aberrations. This study presents an integrated laser slicing system based on a liquid crystal on silicon spatial light modulator (LCOS-SLM) to address aberration-induced focal elongation and energy inhomogeneity. Through dynamic modulation of the laser wavefront via an inverse ray-tracing algorithm, the system corrects spherical aberrations from refractive index mismatch, thus achieving precise energy concentration at wanted depths. A laser power attenuation model based on interface reflection and the Lambert–Beer law is established to calculate the required laser power at varying processing depths. Experimental results demonstrate that aberration correction reduces focal depth to approximately one-third (from 45 μm to 15 μm) and enhances energy concentration, eliminating multi-layer damage and increasing crack propagation length. Post-correction critical power measurements across depths are consistent with model predictions, with maximum error decreasing from >50% to 8.4%. Verification on a 6-inch N-type SiC ingot shows 90 μm damage thickness, confirming system feasibility for SiC laser slicing. The integrated aberration-correction approach provides a novel solution for high-precision SiC substrate processing. Full article

Diffuse axonal injury (DAI) is one of the most severe consequences of traumatic brain injury (TBI), characterized by widespread axonal damage in the cerebral white matter. DAI plays a crucial role in determining clinical outcomes, significantly contributing to long-term disability and mortality in severe cases. Despite advancements in neuroscience and clinical management, the diagnosis and prognosis of DAI remain challenging due to its complex pathophysiology and the difficulty of detecting axonal damage in its early stages. This study critically analyzes the clinical and post-mortem methodologies used to assess DAI, highlighting their strengths and limitations. Traditional histopathological grading systems provide valuable insights into disease progression, yet their correlation with long-term functional outcomes remains controversial. Advanced neuroimaging techniques, such as diffusion-weighted MRI, have improved lesion detection, although their routine clinical application is still limited. Additionally, emerging approaches involving biomarkers and artificial intelligence-based models hold promise for enhancing diagnostic accuracy and prognostic predictions. By synthesizing current knowledge on DAI, this work aims to outline a comprehensive framework for improving diagnosis and outcome assessment. Furthermore, it seeks to foster collaboration among clinicians and researchers, ultimately advancing the understanding of DAI and refining strategies to improve patient care. Full article

Icing represents a significant hazard to the flight safety of unmanned aerial vehicles (UAVs), particularly affecting critical aerodynamic surfaces such as air intakes, wings, and empennages. While conventional adhesive electrothermal de-icing systems are straightforward to operate, they present safety concerns, including a 15–25% increase in system weight, elevated anti-/de-icing power consumption, and the risk of interlayer interface delamination. To address the objectives of reducing weight and power consumption, this study introduces an innovative electrothermal–structural–durability co-design strategy. This approach successfully led to the development of a glass fiber-reinforced polymer (GFRP) component that integrates anti-icing functionality with structural load-bearing capacity, achieved through an embedded hot-pressing process. A stress-damage cohesive zone model was utilized to accurately quantify the threshold of mechanical performance degradation under electrothermal cycling conditions, elucidating the evolution of interfacial stress and the mechanism underlying interlayer failure. Experimental data indicate that this novel component significantly enhances heating performance compared to traditional designs. Specifically, the heating rate increased by approximately 202%, electrothermal efficiency improved by about 13.8% at −30 °C, and interlayer shear strength was enhanced by approximately 30.5%. This research offers essential technical support for the structural optimization, strength assessment, and service life prediction of UAV anti-icing and de-icing systems in the aerospace field. Full article

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by widespread immune dysregulation and the production of autoantibodies targeting nuclear, cytoplasmic, and cell surface antigens. These autoantibodies are central to disease pathogenesis, contribute to immune complex formation and organ damage, and serve as essential diagnostic and prognostic markers. Their detection supports disease classification, guides clinical decision-making, and offers insight into disease activity and therapeutic response. Traditional markers such as anti-nuclear antibodies (ANA), anti-dsDNA, and anti-Sm antibodies remain diagnostic cornerstones, but growing attention is given to anti-C1q, anti-nucleosome antibodies (ANuA), anti-ribosomal P, antiphospholipid, and anti-cytokine antibodies due to their associations with specific disease phenotypes and activity. These markers may reflect disease activity, specific organ involvement, or predict flares. The mechanisms underlying their persistence include B cell tolerance failure and long-lived plasma cell activity. The aim of this review is to summarize current knowledge on the major autoantibodies in SLE, appraise available detection methods, highlight their clinical utility and limitations and present evidence on the association between antibodies and disease phenotypes. Full article

Cellular senescence, a state of permanent cell cycle arrest, represents a double-edged sword in biology—providing tumor-suppressive functions while contributing to tissue degeneration, chronic inflammation, and age-related diseases when senescent cells persist. A key driver of senescence is oxidative stress, primarily mediated by excessive reactive oxygen species that damage mitochondrial DNA, modulate redox-sensitive signaling pathways, and trigger the senescence-associated secretory phenotype. Emerging evidence highlights the pathogenic role of SASP in promoting local inflammation, immune evasion, and senescence propagation. This review explores the intricate interplay between redox imbalance and cellular senescence, emphasizing mitochondrial dysfunction, SASP dynamics, and their implications in aging and cancer. We discuss current senotherapeutic strategies—including senolytics, senomorphics, antioxidants, gene therapy, and immunotherapy—that aim to eliminate or modulate senescent cells to restore tissue homeostasis. Understanding the heterogeneity and context-specific behavior of senescent cells remains crucial for optimizing these therapies. Future research should focus on addressing key knowledge gaps, including the standardization of senescence biomarkers such as circulating miRNAs, refinement of predictive preclinical models, and development of composite clinical endpoints. These efforts are essential to translate mechanistic insights into effective senotherapeutic interventions and enable the safe integration of senescence-targeting strategies into routine clinical practice. Full article