Search Results (34,868)

Atopic dermatitis (AD) is a chronic inflammatory skin disease with a complex pathogenesis involving epidermal barrier dysfunction, microbiome dysbiosis, and immune dysregulation. Despite significant advances in therapy, including biologics and targeted treatments, their use may be limited by adverse effects, highlighting the need for safe adjunctive strategies. Polyphenols are naturally occurring bioactive compounds that are abundant in plant-based foods and are known for their anti-inflammatory, antioxidant, and immunomodulatory properties, making them promising candidates for supportive AD management. This review integrates current evidence on the effects of polyphenols on epidermal barrier lipids, microbiome interactions, and key inflammatory pathways, including NF-κB and JAK/STAT signaling. Additionally, the role of polyphenols in modulating dendritic cell and neutrophil activity, and reducing reactive oxygen species (ROS) production and neutrophil extracellular trap (NET) formation, as well as their potential involvement in mitophagy regulation, is discussed. Polyphenols support epidermal barrier integrity by modulating the expression of key structural proteins, including filaggrin, involucrin, and loricrin, leading to a reduction in transepidermal water loss (TEWL). Furthermore, they interact bidirectionally with the gut microbiome, acting as metabolic substrates for beneficial bacteria and promoting the growth of short-chain fatty acid (SCFA)-producing species such as Lactobacillus, Bifidobacterium, and Akkermansia, while simultaneously inhibiting pathogenic strains. These findings highlight the role of polyphenols in maintaining microbiome homeostasis and supporting epidermal barrier integrity. The review encompasses findings from clinical studies, animal models, and mechanistic investigations, while also addressing limitations related to polyphenol bioavailability. Overall, polyphenols may represent a valuable adjunctive approach in AD management; however, further well-designed clinical and mechanistic studies are required to confirm their therapeutic potential. Full article

Subsoiling can break the plough pan and improve the root growth environment. The effect of the traditional subsoiler is poor, as it relies only on the chisel tine, but pneumatic subsoiling can improve the soil structure more efficiently through the negative pressure generated by the jet hole. This research used computational fluid dynamics and the discrete element method to optimize the biomimetic structure of the jet hole, model the pneumatic subsoiling process at a depth of 330 mm, and observe the movement of soil particles as airflow passes through. The effect of the jet hole at different positions and sizes on the plough pan soil was analyzed, and fluid domains and measurement areas were set up to observe the upward movement, diffusion, stabilization, and settling of soil particles under the action of airflow. The results of the soil bin experiment validated the accuracy of the simulation model through draft force and vertical force, and the average error between the simulation and experimental data was 2.8%. The study revealed that the increase in the rate of soil porosity reached a maximum of 3.65% when the jet hole was positioned above the chisel tine with a radius of 4 mm. The biomimetic jet hole pneumatic subsoiler designed in this study, along with the established CFD-DEM coupled simulation model capable of predicting pneumatic subsoiling performance, can provide references for the design and application of a pneumatic subsoiler. Furthermore, it also provides a theoretical basis for understanding the mechanism of airflow on soil during pneumatic subsoiling operations. Full article

Exchange rate volatility has intensified in recent decades, yet its systematic implications for banking-sector stability remain contested. This study investigates whether exchange rate volatility constitutes a meaningful source of financial fragility using a global panel of 103 countries over the period 2000–2021. Financial stability is proxied by the banking-sector Z-score, while exchange rate volatility is estimated using a EGARCH-based framework to capture time-varying uncertainty. To address cross-sectional dependence, heterogeneity, and endogeneity, the analysis employs Driscoll–Kraay fixed effects, two-step system GMM, and quantile regressions. The results reveal that exchange rate volatility exerts a statistically and economically significant negative effect on banking stability, reducing Z-scores across countries and income groups. The findings remain robust across alternative specifications and estimators. Bank-level fundamentals—capitalisation, liquidity, and credit—enhance stability, whereas higher non-performing loans and risk exposure amplify fragility. Macroeconomic conditions also matter, with stronger growth, institutional quality and external balances supporting resilience, while inflation, economic policy uncertainty and expansionary government spending weaken stability. By integrating time-varying volatility modelling with dynamic panel techniques in a large cross-country setting, this study provides new global evidence that exchange rate volatility is not merely a macroeconomic fluctuation but a structural source of banking-sector risk. The findings carry important implications for macroprudential policy, foreign-exchange management, and coordinated monetary–fiscal responses aimed at safeguarding financial stability in open economies. Full article

Background/Objectives: Clear cell renal cell carcinoma (ccRCC) involves complex interactions between immune evasion and metabolic reprogramming. This study aimed to characterize ccRCC through integrated immunometabolic profiling, develop a prognostic signature, and investigate the functional role of the key driver gene UCN using in vitro and in vivo approaches. Methods: Integrated immunometabolic profiling was performed to identify molecular subtypes and establish a prognostic gene signature. Two distinct molecular subtypes were identified, and a 9-gene Immune Metabolic Index (IMI) was constructed. The functional role of the key driver gene UCN was investigated through in vitro functional assays and in vivo xenograft models in BALB/c mice, including combination with PD-1 blockade. Results: Two molecular subtypes with significant survival differences (p < 0.001) were identified. The established IMI demonstrated high prognostic accuracy, with Area Under the Curve (AUC) values of 0.813, 0.751, and 0.779 at 1-, 3-, and 5-year intervals, respectively. UCN was identified as the highest-risk gene in the signature. Functional assays showed that UCN silencing significantly inhibited cell proliferation and migration (p < 0.05). In BALB/c mouse xenograft models, UCN silencing remodeled the tumor microenvironment by increasing CD8+ T cell infiltration and reducing regulatory T cells (p < 0.01). Furthermore, UCN knockdown significantly suppressed tumor growth and synergized with PD-1 blockade to enhance antitumor efficacy (p < 0.001). Conclusions: The IMI is a robust tool for risk stratification in ccRCC. Targeting the UCN-driven immunometabolic axis represents a promising therapeutic strategy to overcome immune resistance in ccRCC. Full article

In this study, we aimed to examine the multiplicative interaction model as a tool to assess the impact of plant extracts and fermentation products on the growth of mycelium of selected fungi. The materials used in the study included a total of 16 products. Plant extracts were obtained by the processes of ultrasound-assisted extraction (UAE) or supercritical CO₂ extraction, and the fermentation broths were produced by Enterobacter and Paenibacillus bacteria in a bioreactor. All these products were examined in vitro using 12 cultures of frequently occuring pathogenic fungi collected from cereals and oilseed rape cultivation. For mycelium diameter in all three examined concentrations, the Additive Main impacts and Multiplicative Interaction (AMMI) analyses showed substantial impacts of both the product and the pathogen as well as the product-by-pathogen interaction. It is advised that future plant protection techniques incorporate product E8, a plant extract (the CO₂ extract of a ginger plant belonging to the Zingiberaceae family), since it demonstrated excellent stability and good average mycelium diameter values across all concentrations examined. As far as the authors are aware, this is the first time the AMMI model has been used to evaluate the impact of product–pathogen interactions on mycelium diameter. Full article

Cardiac fibrosis is a central determinant of heart failure progression and arises from pathological remodeling characterized by fibroblast activation, myofibroblast differentiation, and excessive extracellular matrix deposition. In contrast, physiological remodeling permits adaptive cardiac growth without net fibrosis. Pregnancy represents an underexplored physiological model of reversible cardiac remodeling. In response to hemodynamic load, the maternal heart undergoes hypertrophic growth that resolves postpartum, constituting a natural paradigm of fibrosis-resistant cardiac adaptation. Pregnancy and lactation are accompanied by profound endocrine and immune reprogramming of maternal tissues. We propose that this hormonal milieu orchestrates coordinated crosstalk among endothelial cells, fibroblasts, and immune cell populations to suppress profibrotic pathways and preserve extracellular matrix homeostasis. Candidate regulators include estrogen, progesterone, prolactin family peptides, relaxin, oxytocin, and components of the renin–angiotensin–aldosterone system. During the postpartum and lactational period, prolactin and oxytocin may further promote reverse remodeling. These hormones likely act by modulating local cytokine and growth factor networks that otherwise drive fibroblast activation. By focusing on non-myocyte cardiac cells and extracellular matrix dynamics, this review positions pregnancy as a translational model to uncover endogenous anti-fibrotic mechanisms and identify novel therapeutic strategies for cardiac fibrosis. Full article

Brain tumors (BTs) arise from the abnormal growth of cells within brain tissue and may spread rapidly, making them a major cause of mortality worldwide. Early detection of BTs remains highly challenging due to the brain’s complex structure and the heterogeneous nature of tumors. Magnetic Resonance Imaging (MRI) provides detailed information about tumor size, location, and shape, thereby supporting clinical decision-making for treatments such as chemotherapy, radiation therapy, and surgery. Traditional machine learning (ML) approaches mainly rely on manual feature extraction, whereas recent advances in Computer-Aided Diagnosis (CAD) and deep learning (DL) have enabled more accurate detection of small and complex tumor regions. To improve automated tumor detection, we propose a hybrid Swin–YOLO framework that combines the Swin Transformer (ST) with the latest CNN-based YOLOv12 model. In this framework, the Swin Transformer serves as the main backbone for feature extraction, while the Feature Pyramid Network (FPN) and Path Aggregation Network (PANet) are employed in the neck to better capture multi-scale features. For training, we used the publicly available Br35H dataset and applied data augmentation to enhance the model’s robustness and generalization capability. The experimental results show that the proposed framework achieved 99.7% accuracy, 99.4% mAP@50, and 87.2% mAP@50:95. Furthermore, we incorporated Explainable Artificial Intelligence (XAI) techniques, including Grad-CAM and SHAP, to improve the interpretability of the model by visually highlighting the tumor regions that contributed most to the prediction. In addition, we developed NeuroVision AI, a web-based application designed to support faster and more accurate clinical decision-making. Although the proposed model demonstrated strong performance on the dataset, these results should be interpreted within the context of the current experimental setting. Full article

To support low-cost, non-destructive crop growth monitoring, this study systematically compared different vegetation indices, voxel sizes, and camera angles using a point cloud voxelization approach combined with a vegetation index weighted canopy volume index (CVM_VI) to assess aboveground biomass (AGB) in winter oilseed rape (Brassica napus L.). Field experiments were conducted from 2021 to 2024 at the Yangma Experimental Base of the Chengdu Academy of Agricultural and Forestry Sciences. Red, green, blue (RGB) imagery of oilseed rape was acquired using an unmanned aerial vehicle (UAV) during the following five key growth stages: seedling, bolting, flowering, podding, and maturity. Collected images were processed to generate point clouds, which were subsequently voxelized at four resolutions (0.03, 0.05, 0.07, and 0.1 m). CVM_VI was constructed by integrating vegetation indices (VIs) derived from the RGB data and the voxelized canopy structural information. Regression models were established between the CVM_VI values and field-measured AGB to estimate biomass. Model performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), and relative error (RE). There were strong correlations (r > 0.80) between the estimated and measured AGB across all voxelization treatments throughout the growth period. Among the 20 VIs tested, regression methods based on the blue green ratio index (BGI), color intensity index, blue red ratio index, vegetative index, and green red ratio index consistently showed superior estimation performance across three consecutive years, demonstrating their good applicability for estimating AGB in oilseed rape under varying agronomic conditions (different varieties, densities, and sowing dates). The cubic regression model CVM_BGI performed best under a 45° UAV camera angle, with the highest R² and lowest RMSE and RE (2021–2022: R² = 0.864, RMSE = 2414.18 kg/ha, RE = 14.8%; 2022–2023: R² = 0.754, RMSE = 2550.53 kg/ha, RE = 14.9%; 2023–2024: R² = 0.863, RMSE = 1953.61 kg/ha, RE = 22.9%). Since the estimation performance showed negligible differences among voxel sizes, and the 0.1–m voxel offered the smallest data volume and shortest analysis time, the CVM_BGI model with a 0.1–m voxel was selected as the preferred approach, providing a practical balance between estimation performance and processing demand. These findings highlight the application potential of point cloud voxelization technology for crop biomass estimation. This study proposes a novel, non-destructive, and efficient framework for estimating field crop AGB using low-cost UAV RGB imagery, facilitating the wider adoption of UAV technology in practical agricultural production. Full article

Peripheral nerve injuries involving critical-sized gaps remain a major clinical challenge. Although autologous nerve grafting is considered the gold standard for peripheral nerve repair, its clinical application is limited by the availability of donor nerve tissue and the risk of donor-site morbidity, including sensory deficits and functional impairment. Therefore, nerve guidance conduits (NGCs) have emerged as a promising alternative when combined with bioactive modulation strategies. In this study, we evaluated bisvinyl sulfonemethyl (BVSM)-crosslinked gelatin conduits integrated with electrical stimulation (ES) at different frequencies (0, 2, 20, and 200 Hz) in a rat sciatic nerve defect model over a 4-week recovery period (n = 10 per group). Structural regeneration was assessed by morphometric analysis, electrophysiology, macrophage infiltration, CGRP immunoreactivity, retrograde Fluorogold tracing, quantitative PCR of growth factors and inflammatory cytokines, and behavioral testing. Among all stimulation paradigms, low-frequency ES at 2 Hz produced the most pronounced regenerative effects. The 2 Hz group demonstrated significantly greater axon number, axonal density, and regenerated nerve area compared with control and high-frequency groups (p < 0.05). Electrophysiological assessments revealed improved nerve conduction velocity, higher MAP amplitudes, and shorter latencies. Enhanced macrophage recruitment and elevated CGRP expression were observed, suggesting coordinated neuroimmune and neurochemical activation. Gene expression analysis indicated upregulation of neurotrophic factors and balanced inflammatory cytokine responses under low-frequency stimulation. In contrast, high-frequency stimulation (200 Hz) failed to enhance overall regeneration and showed reduced axonal metrics, suggesting possible overstimulation-associated suppression. Collectively, these findings demonstrate that BVSM-crosslinked conduits provide a stable and biocompatible regenerative scaffold, and that appropriately tuned low-frequency electrical stimulation (2 Hz) optimally enhances structural, molecular, and functional recovery. The integration of material engineering with bioelectrical modulation represents a promising strategy for next-generation bioelectronic interfaces in peripheral nerve repair. Full article

Industrial chains in agriculture are currently fragmented and do not support developing resource-based competitive advantages. This is true under the Big Food Framework’s strategic orientation. This research seeks to develop a new analytical framework for evaluating pathways to the integration of agricultural industrial chains and their impact on the performance of companies engaged in food processing in Xinjiang. A mixed-method approach, employing both an exploratory and sequential design, will be used to do this. The primary method of data collection for this study is the case study method, along with the questionnaire method involving 145 agricultural enterprises. From these data, structural equation modeling (SEM) will be used to test the paths of causation among cognitive managers of firms who have implemented the BFF. Evidence will be presented to demonstrate the relationship among three types of integration (vertical, horizontal, and lateral) in the agricultural industrial chain, dynamic capabilities, and company performance. Additionally, network topology and optimization simulations will be conducted to determine how effectively structures are organized in training the respective companies. Important findings revealed in this research include the following: The managerial cognition constructs offered by BFFs play a key role in enhancing the depth and structural balance of industry chain integration. There were complementary performance effects found, and they are related to vertical integration achieving operational efficiency and financial efficiency; horizontal integration improving market competitiveness and brand competitiveness; and lateral integration facilitating innovative growth. Dynamic capabilities are a significant mediating mechanism linking institutional support and digital capability with the depth of integration across different modes of integration. The findings from network optimization suggest that there is a positive effect of balanced connectivity across the different dimensions of integration on overall system efficiency and reduced structural inefficiencies. Based on these findings, the authors recommend that organizations establish governance mechanisms that facilitate coordinated connectivity; strengthen adaptive capabilities within the firm; and promote balanced integration across industrial networks. Future researchers should consider applying these findings to conducting longitudinal studies on network evolution; integrating sustainability measures as part of their analysis; and conducting comparative validation studies across regions or industry systems. Full article

We introduce a minimal relational network model in which superconducting-like behavior emerges as a collective phase of constrained connectivity and phase coherence, without assuming microscopic electrons, phonons, or material-specific interactions. The model is formulated as a concrete instantiation of a previously introduced axiomatic relational–informational framework for emergent geometry and effective spacetime, in which geometry and effective forces arise from constrained information flow rather than from a background manifold. Mathematically, this construction is realized on a finite weighted graph with binary edge-activation variables and compact vertex phase variables, sampled through a Gibbs ensemble generated by an additive informational action. The system is represented as a finite weighted graph with weighted edges encoding transport or informational costs, augmented by dynamically activated low-cost channels and compact phase degrees of freedom defined at vertices. The effective edge costs induce a weighted shortest-path metric, providing an operational notion of emergent relational geometry. Using Monte Carlo simulations on two-dimensional periodic lattices, we show that the same informational action supports three distinct emergent regimes: a normal resistive phase, a fragile low-temperature–like superconducting phase characterized by noise-sensitive coherence, and a noise-robust high-temperature–like superconducting phase in which global phase coherence persists under substantial fluctuations. These regimes are identified using purely relational observables with direct graph-theoretic and statistical-mechanical interpretation, including percolation of low-cost channels, phase correlation functions, an operational phase stiffness (helicity modulus), and a geometric diagnostic based on relational ball growth. In particular, we extract an effective geometric dimension from the scaling of low-cost accessibility balls, using a ball-growth relation of the form $⟨B\left(r\right)⟩~r^{d_{eff}}$, revealing a clear monotonic hierarchy between normal, fragile superconducting, and noise-robust superconducting—like regimes. This demonstrates that superconducting-like behaviour in the present framework corresponds not only to percolation and phase alignment, but also to a qualitative reorganization of relational geometry. Robustness is tested via finite-size comparison between 8 × 8, 12 × 12 and 16 × 16 lattice realizations. Within this framework, normal and superconducting-like behavior arise from the same underlying relational mechanism and differ only in the structural stability of connectivity, coherence, and geometric accessibility under fluctuations. The aim of this work is structural rather than material-specific: we do not reproduce detailed experimental phase diagrams or microscopic pairing mechanisms, but identify minimal relational conditions under which low-dissipation, phase-coherent transport can emerge as a generic organizational regime of constrained relational systems. Full article

Blood glucose level (BGL) prediction, by providing early warnings regarding unsatisfactory glycaemic control and maximising the amount of time BGL remains in the target range, can contribute to minimising both acute and chronic complications related to diabetes. This paper aims to provide an overview of data-driven approaches for BGL prediction in type 1 diabetes mellitus (T1DM). This review summarises different aspects of developing and evaluating data-driven prediction models, including model strategy, model input, prediction horizon, and prediction performance. It also examines applications of recent artificial intelligence (AI) techniques, including deep learning, transfer learning, ensemble learning, and causal analysis in the management of T1DM. Recent studies indicate that machine learning approaches often outperform classical time-series forecasting models in BGL prediction, particularly when using multivariate inputs. These findings also highlight the potential of advanced AI methods to improve prediction accuracy. Moreover, applying appropriate statistical analyses is essential to enable valid comparisons between different BGL prediction models, especially given the considerable inter-individual variability among people with T1DM. The development of efficient methods for integrating affecting variables into BGL prediction requires further research. Given the promising performance of advanced AI techniques and the rapid growth of AI innovation, continued exploration of cutting-edge AI strategies will be crucial for further improving BGL prediction models. Full article

Biomethane is emerging as a key renewable gas in both mature and developing energy systems worldwide. Driven by climate-neutrality objectives, energy-security concerns, and rising waste-to-energy ambitions, global biomethane production is expected to expand rapidly in the coming decade. In Europe, this growth is accelerated by the REPowerEU target of 35 billion m³ by 2030. However, as biomethane production increases and natural gas demand declines over time, distribution networks face growing operational challenges, including pressure build-up and biomethane curtailment caused by supply and demand mismatches. This study evaluates whether surplus biomethane can be converted into electricity as a multi-commodity strategy to alleviate these constraints. Using hourly operational data from two Dutch Distribution System Operators (DSOs), a simulation model was developed to assess the impact of generator-based biomethane-to-power conversion on both gas and electricity distribution networks. The results show that, for RENDO, the approach increases effective biomethane injection by 49.0%, reduces natural gas deliveries from the transmission system by 20.0%, and lowers electricity imports by 9.2%. For Coteq, the corresponding impacts are 106.8%, 30.6%, and 16.2%, respectively. These findings indicate that multi-commodity coupling through biomethane-to-power conversion provides a promising strategy for increasing biomethane injection and renewable electricity generation. Full article

Background/Objectives: Oral cancer remains a major global health challenge, with persistent limitations in treatment efficacy and significant therapy-related morbidity. Probiotics, owing to their immunomodulatory, anti-inflammatory, and microbiota-regulating properties, have emerged as potential therapeutic and adjuvant agents. This scoping review aimed to systematically map and critically appraise preclinical and clinical evidence regarding the therapeutic and supportive effects of probiotics in oral cancer. Methods: A comprehensive literature search was conducted across PubMed, Scopus, Web of Science, and Google Scholar without temporal restrictions, including studies published up to February 2026. Eligible studies comprised in vitro, in vivo, and clinical investigations evaluating the effects of live or non-viable probiotic interventions on oral cancer biology and related clinical outcomes. Results: Twenty-one studies were included: 13 in vitro, 3 in vivo, and 6 clinical studies. Preclinical evidence indicates that strains such as Lactiplantibacillus plantarum, Lactobacillus acidophilus, and Lacticaseibacillus paracasei exert selective antiproliferative effects (up to 85% inhibition) via apoptosis induction, modulation of PTEN/MAPK and NF-κB signaling, and reduction in pro-inflammatory mediators. In vivo models demonstrated tumor growth suppression and improved survival without significant toxicity. Clinically, probiotics reduced treatment-induced oral mucositis, improved salivary function, and enhanced microbiota stability and patient-reported outcomes. However, evidence on direct oncological endpoints remains limited. Conclusions: Probiotics demonstrate biologically plausible, strain-specific antitumor and supportive effects, with the strongest evidence supporting their role as adjunctive agents, particularly in managing treatment-related complications. Further well-designed in vivo and clinical studies are required to define optimal strains, dosing strategies, and integration with standard oncologic treatments. Full article

The islet of Vaixell, off the west coast of Ibiza (Balearic Islands, Spain), is home to a native population of the Pityusic wall lizard, Podarcis pityusensis, with the largest body size recorded for the species. These lizards live in extreme environmental conditions on an islet with a small surface area covered by very sparse vegetation. The sex ratio is balanced, and a very high incidence of missing toes and autotomized tails is observed, indicating strong intraspecific competition involving both males and females. The body growth rate, adjusted using the Gompertz model, is intense and, apparently, juvenile lizards quickly reach relatively large body sizes. This fast body growth is probably a strategy against predation pressure from conspecifics. In P. pityusensis from Vaixell, the peak growth acceleration is prenatal and practically coincides with the moment of hatching. The diet consists mainly of aggregated prey, such as ants, with the inclusion of marine subsidies, such as halophyllous and littoral isopods, and a lower consumption of plant matter compared to other insular populations of lizards from the Balearic Islands. The lizards of Vaixell are an excellent example of the adaptive response of a lacertid lizard to the extreme conditions on the small coastal islets of the Mediterranean, with very small available areas, high population density, but a small population size, of about 50 to 100 lizards, which also reach a remarkable longevity. Full article