Search Results (70,788)

The intestinal mucus layer is a dynamic protective barrier that maintains gut homeostasis, supports immune defense, and regulates host–microbiota interactions. Rodent models have yielded valuable insights, but their intestinal structure and physiology differ from those of humans and pigs. By contrast, the omnivorous pig shares closer anatomical and immunological features with humans, making it a relevant large-animal model in translational studies. In this study, we established a histological workflow for porcine intestine by combining Carnoy’s fixation with Alcian Blue–Periodic Acid–Schiff and Mucicarmine staining. This enabled accurate visualization and quantification of goblet-cell density and mucus thickness across intestinal segments, with a particular focus on Peyer’s patches—key sites of immune surveillance. Both stains produced consistent results. We observed a clear proximal-to-distal gradient, from jejunum to colon, in mucus thickness: the colon displayed the thickest layer (~100 μm), whereas the follicle-associated epithelium over Peyer’s patches in the jejunum and ileum showed a markedly thinner layer (<12 μm) and fewer goblet cells. Immunofluorescence further revealed strong cytokeratin-18 expression in goblet cells, delineating their morphology and polarity. These findings demonstrate region-specific differences in mucus architecture and goblet-cell distribution that likely reflect specialized immune functions, advancing our understanding of the intestinal barrier and informing future strategies to support gut health and immunity. Full article

We characterize all additive biderivations on the incidence algebra $I(P,\mathcal{R})$ of a locally finite poset P over a commutative ring with unity $\mathcal{R}$. By decomposing P into its connected chains, we prove that any additive biderivation splits uniquely into a sum of inner biderivations and extremal ones determined by chain components. In particular, when every maximal chain of P is infinite, all additive biderivations are inner. Full article

The applicability of biochar as a coarse aggregate substitute in concrete to increase sustainability and multifunctionality was investigated. Biochar, a porous carbon-rich byproduct from biomass pyrolysis, was incorporated at various replacement ratios (5–20%) under four water-to-binder (w/b) conditions (0.25–0.40). The key physical, mechanical, thermal, and microstructural properties, including the unit weight, porosity, compressive strength, flexural strength, and thermal conductivity, were evaluated via SEM and EDS analyses. The results revealed that although increasing the biochar content reduced the mechanical strength, it significantly improved the thermal insulation performance because of the porous structure of the biochar. At low w/b ratios and 5–10% biochar content, sufficient mechanical properties were retained, indicating a viable design range. Higher replacement ratios (>15%) led to excessive porosity, reduced hydration, and impaired durability. This study quantitatively analyzed the interproperty correlations, confirming that the strength and thermal performance are closely linked to the internal matrix density and porosity. These findings suggest that biochar-based concrete has potential for use in thermal energy storage systems, high-temperature insulation, and low-carbon construction. The low-carbon effect is achieved both by sequestering stable carbon within the concrete matrix and by partially replacing cement, thereby reducing CO₂ emissions from cement production. Moreover, the results highlight a strong correlation between increased porosity, enhanced thermal insulation, and reduced strength, thereby offering a solid foundation for sustainable material design. In particular, the term ‘high temperature’ in this context refers to exposure conditions above approximately 200~400 °C, as reported in previous studies. However, this should be considered as a potential application to be validated in future experiments rather than a confirmed outcome of this study. Full article

Background/Objectives: The COVID-19 (Coronavirus Disease, 2019) pandemic affected all countries in a variety of ways, and forced policymakers to adapt national health infrastructure. In this context, the strategic adaptation and policy evolution of small island states are understudied. Therefore, the objective of this study was to quantitatively analyse the relationship between confirmed COVID-19 cases and health policy decisions in Northern Cyprus. We also examined the shifting management strategies employed during the pandemic using a replicable statistical analysis framework. Methods: In this mixed-methods study, we used systematic thematic analysis to categorise official policy decisions from March 2020 to December 2022. Yearly linear regression models using SPSS and Python correlated the monthly number of decisions with the number of confirmed COVID-19 cases. The analyses included R² values, p-values, and visualisations with 95% confidence intervals. Results: The findings of this study highlight a three-phase strategic period. In 2020, the results (R² = 0.03, p = 0.63) showed no significant relationship, indicating initial uncertainty. The results (R² = 0.60, p = 0.003) indicate a strong negative correlation in 2021, which reflects the consistency of the proactive suppression strategies adopted. Conversely, for 2022, the results (R² = 0.79, p < 0.001) show a strong positive correlation representing the shift to a reactive mitigation strategy, in which the government responded based on case peaks. Conclusions: This study’s primary finding is that strategic agility was key to managing the pandemic. For small island states in particular, the effectiveness of geographic advantages like border control depends on a coherent strategy that transcends initial uncertainty. Our data-driven framework provides a tool for analysing this strategic evolution and guiding responses to future pandemics. Full article

Background/Objectives: Gestational diabetes mellitus (GDM) alters maternal metabolism during pregnancy and may impact the biochemical composition of breast milk. Given the critical role of human milk in early-life metabolic programming, identifying metabolic alterations in GDM milk and understanding the effects of insulin therapy has important implications for neonatal health. This study aims to investigate the metabolomic profile of transitional breast milk in mothers with gestational diabetes mellitus compared with healthy controls and to evaluate the impact of insulin therapy on milk metabolite composition. Methods: Breast milk samples were collected between postpartum days 10 and 15 from 80 mothers with GDM and 80 matched controls. Metabolomic profiling was performed using gas chromatography–mass spectrometry (GC–MS), and data were analyzed using multivariate and univariate statistical techniques including PCA, PLS–DA, logistic regression, and ROC analysis. Conclusions: A total of 133 metabolites were identified, and GDM mothers exhibited a distinct metabolomic signature characterized by significant alterations in carbohydrate, amino acid, and microbial-derived metabolites. In particular, galactinol, arabitol, and pyrogallol were significantly decreased, while α-ketoglutaric acid and citric acid were elevated in the GDM group. Insulin-treated mothers showed unique metabolic changes involving glycolytic intermediates (glycerone phosphoric acid), purine metabolism (xanthine), and oxidative pathways (isocitric acid, gluconic acid lactone). Multivariate models based on the top metabolites achieved moderate discriminatory performance (AUC = 0.68). GDM is associated with substantial metabolic changes in transitional breast milk, and insulin therapy appears to modulate these alterations further. These findings suggest that maternal metabolic status and its treatment can shape the neonatal nutritional environment, potentially influencing early metabolic programming. Full article

Personalized, point-of-care testing of human tears is essential for ocular disease diagnosis, yet it is hampered by picomolar biomarker levels and microliter sample volumes. In this work, we developed an integrated, portable electrochemiluminescence (ECL)-based device for rapid and quantitative detection of tumor necrosis factor alpha (TNF-α), a pivotal inflammatory marker in ocular surface disease, with particular relevance to dry eye syndrome (DES). The device integrates a miniaturized electrochemical cell for ECL reactions and a compact silica photomultiplier for signal measurement. A vertical silica mesochannel (VSM)-coated ITO electrode is also integrated and further functionalized with TNF-α-specific aptamers. The VSM enables the enrichment of ECL luminophores, thus enabling further amplification of ECL signals and enhancing sensitivity. A wide linear range from 0.1 to 200 pg/mL was achieved using 10-fold dilution of 3 μL tear samples. Overall, this study provides a portable, highly sensitive platform for personalized analysis of TNF-α in tear fluid, enabling rapid point-of-care assessment of DES. Full article

Background/Objectives: This study analyzes the epidemiology of invasive pneumococcal disease (IPD) and the dynamics of Streptococcus pneumoniae (SP) serotypes in the Comunidad Valenciana (CV) region, Spain, over a 10-year period (2014–2024), with particular focus on vaccine coverage of PCV13 compared to the newer PCV20 and PCV21 formulations. Methods: A total of 2.014 isolates of SP obtained from sterile fluids were included, with available serotype, demographic data, and vaccination status, which were collected from the Epidemiological Surveillance System (AVE) and the Microbiological Surveillance Network of the CV region (RedMIVA). Results: Overall vaccination coverage was low (22.4%), with the highest rates observed in children under 10 years (78%) compared to only 16% in those aged 10–64 years and 22% in those over 64. Serotype distribution revealed 120 distinct serotypes, with serotype 8 (17.6%) and serotype 3 (14.7%) being the most frequent. Serotype 8 predominated among unvaccinated individuals, while serotype 3 remained highly prevalent despite inclusion in PCV13. Other relevant serotypes included 22F, 9N, 19A, 6C, and 23A. Temporal analysis showed that serotype 3 has continued to increase in recent years, whereas serotype 8 rose during the pandemic period but has remained stable in the most recent interval, while 19A, 15A, and 11A significantly declined. Among serotypes with <2% incidence, some, such as 4, 24F, and 38, showed upward trends. Conclusions: The findings suggest that PCV20 currently provides broad coverage of dominant serotypes, but PCV21 may offer advantages should serotypes like 23A, 9N, or 15A increase further due to serotype replacement. Continuous epidemiological surveillance is essential to guide evidence-based vaccine policy and anticipate future vaccine reformulations. Full article

Wind power is integral to the transformation of energy systems towards sustainability. However, the increasing number of wind turbines approaching the end of their service life presents significant challenges in terms of waste management and environmental sustainability. Rotor blades, typically composed of thermoset polymer composites reinforced with glass or carbon fibres, are particularly problematic due to their low recyclability and complex material structure. The aim of this article is to provide a system-level review of current end-of-life strategies for wind turbine components, with particular emphasis on blade recycling and decision-oriented comparison, and its integration into circular economy frameworks. The paper explores three main pathways: operational life extension through predictive maintenance and design optimisation; upcycling and second-life applications; and advanced recycling techniques, including mechanical, thermal, and chemical methods, and reports qualitative/quantitative indicators together with an indicative Technology Readiness Level (TRL). Recent innovations, such as solvolysis, microwave-assisted pyrolysis, and supercritical fluid treatment, offer promising recovery rates but face technological and economic as well as environmental compliance limitations. In parallel, the review considers deployment maturity and economics, including an indicative mapping of cost and deployment status to support decision-making. Simultaneously, reuse applications in the construction and infrastructure sectors—such as concrete additives or repurposed structural elements—demonstrate viable low-energy alternatives to full material recovery, although regulatory barriers remain. The study also highlights the importance of systemic approaches, including Extended Producer Responsibility (EPR), Digital Product Passports and EU-aligned policy/finance instruments, and cross-sectoral collaboration. These instruments are essential for enhancing material traceability and fostering industrial symbiosis. In conclusion, there is no universal solution for wind turbine blade recycling. Effective integration of circular principles will require tailored strategies, interdisciplinary research, and bankable policy support. Addressing these challenges is crucial for minimising the environmental footprint of the wind energy sector. Full article

Background: Treatment of inflammatory bowel diseases with biological therapies has significantly increased, with ever increasing numbers of patients receiving such treatment at the time of surgery. This study evaluates the perioperative safety of three commonly used biologics—Infliximab, Vedolizumab, or Ustekinumab—in patients undergoing intestinal surgery for IBDs. Materials and Methods: In this systematic review a comprehensive search was conducted in Scopus, Medline and PubMed up to January 2025 by two independent reviewers, and a total of 34 articles (retrospective studies in the majority of them) reporting total surgical complications of patients treated with these three agents, in comparison to a control group, were included. Relative risks were aggregated using the Mantel-Haenszel method, and the I² statistic was used to assess between-study heterogeneity. Subgroup analyses were conducted for particular complications, and direct comparisons among the biological agents were made. Results: In the primary analysis, INFL was not linked to a statistically significant rise in overall postoperative complications when compared to controls (RR = 1.13, 95% CI: 0.90–1.42, p = 0.31). VDLZ exhibited a non-significant inclination towards increased complications (RR = 1.26, 95% CI: 0.94–1.67, p = 0.12), although it was linked to a notably higher risk of postoperative ileus compared to INFL (RR = 2.29, 95% CI: 1.59–3.29, p < 0.00001). USTK also did not show significant differences from controls overall (RR = 0.55, 95% CI: 0.20–1.57, p = 0.26), though it was associated with a considerably lower risk of SSIs (RR = 0.35, 95% CI: 0.17–0.73, p = 0.005). There were no significant distinctions between the biological agents regarding SSIs or anastomotic leakage, although many comparisons faced challenges due to high heterogeneity and low event rates. Conclusions: USTK demonstrated the most favorable safety profile, while VDLZ was associated with higher rates of ileus and inflammatory complications. However, prospective studies are warranted. Full article

This study investigates the interpretation of hand gestures in nonverbal communication, with particular attention paid to cases where gesture form does not reliably convey the intended meaning. Hand gestures are a key medium for expressing impressions, complementing or substituting verbal communication. For example, the “Thumbs Up” gesture is generally associated with approval, yet its interpretation can vary across contexts and individuals. Using participant-generated descriptive words, sentiment analysis with the VADER method, and fuzzy membership modeling, this research examines the variability and ambiguity in gesture–intention mappings. Our results show that Negative gestures, such as “Thumbs Down,” consistently align with Negative sentiment, while Positive and Neutral gestures, including “Thumbs Sideways” and “So-so,” exhibit greater interpretive flexibility, often spanning adjacent sentiment categories. These findings demonstrate that rigid, category-based classification systems risk oversimplifying nonverbal communication, particularly for gestures with higher interpretive uncertainty. The proposed fuzzy logic-based framework offers a more context-sensitive and human-aligned approach to modeling gesture intention, with implications for affective computing, behavioral analysis, and human–computer interaction. Full article

Rhizobia have been used for decades as biopreparations, successfully replacing synthetic nitrogen fertilizers in legume cultivation. They have a beneficial effect on the growth and yield of these plants when cultivated in soils that are deficient in both nitrogen and indigenous rhizobia. However, such preparations, containing strains that are characterized by high effectiveness in reducing atmospheric dinitrogen, are not universal. Their use is ineffective when plants are grown in soils that are already rich in strains with low effectiveness, because such inoculant strains are unable to effectively compete with native soil populations. This review discusses issues related to the rhizobia–legume symbiosis, with particular emphasis on inter-strain competition occurring in the soil and in the colonized plant tissues. The importance of Nod factors (NFs) in symbiosis and their broad impact on plant physiological and developmental processes are also discussed. Research results on the effects of NF-containing biopreparations on legume growth and yield are summarized. Moreover, this review explains how such preparations can support the growth and yield of legumes growing in soils containing numerous populations of low-effectiveness rhizobia. Finally, the potential for the application of this technology to non-legume plants is presented. Full article

Fault detection is critical to the resilience and operational integrity of electrical power grids, particularly smart grids. In addition to requiring a lot of labeled data, traditional fault detection approaches have limited flexibility in handling unknown fault scenarios. In addition, since traditional machine learning models rely on historical data, they struggle to adapt to new fault patterns in dynamic grid environments. Due to these limitations, fault detection systems have limited resilience and scalability, necessitating more advanced approaches. This paper presents a hybrid technique that integrates supervised and unsupervised machine learning with Generative AI to generate artificial data to aid in fault identification. A number of machine learning algorithms were compared with regard to how they detect symmetrical and asymmetrical faults in varying conditions, with a particular focus on fault conditions that have not happened before. A key feature of this study is the application of the autoencoder, a new machine learning model, to compare different ML models. The autoencoder, an unsupervised model, performed better than other models in the detection of faults outside the learning dataset, pointing to its potential to enhance smart grid resilience and stability. Also, the study compared a generative AI-generated dataset (D2) with a conventionally prepared dataset (D1). When the two datasets were utilized to train various machine learning models, the synthetic dataset (D2) outperformed D1 in accuracy and scalability for fault detection applications. The strength of generative AI in improving the quality of data for machine learning is thus indicated by this discovery.By emphasizing the necessity of using advanced machine learning techniques and high-quality synthetic datasets, this research aims to increase the resilience of smart grid networks through improved fault detection and identification. Full article

Background/Objectives: Video laryngoscopy is one of the primary methods used by otolaryngologists for detecting and classifying laryngeal lesions. However, the diagnostic process of these images largely relies on clinicians’ visual inspection, which can lead to overlooked small structural changes, delayed diagnosis, and interpretation errors. Methods: AI-based approaches are becoming increasingly critical for accelerating early-stage diagnosis and improving reliability. This study proposes a hybrid Convolutional Neural Network (CNN) architecture that eliminates repetitive and clinically insignificant frames from videos, utilizing only meaningful key frames. Video data from healthy individuals, patients with vocal fold nodules, and those with vocal fold polyps were summarized using three different threshold values with the Structural Similarity Index Measure (SSIM). Results: The resulting key frames were then classified using a hybrid CNN. Experimental findings demonstrate that selecting an appropriate threshold can significantly reduce the model’s memory usage and processing load while maintaining accuracy. In particular, a threshold value of 0.90 provided richer information content thanks to the selection of a wider variety of frames, resulting in the highest success rate. Fine-tuning the last 20 layers of the MobileNetV2 and Xception backbones, combined with the fusion of extracted features, yielded an overall classification accuracy of 98%. Conclusions: The proposed approach provides a mechanism that eliminates unnecessary data and prioritizes only critical information in video-based diagnostic processes, thus helping physicians accelerate diagnostic decisions and reduce memory requirements. Full article

In the past few decades, a growing body of literature focused on the ‘return of religion’ has added important nuance to the discussion of pluralism, religion, and violence. This paper explores these postsecular critiques through the ancient parable of the Blind People and the Elephant. It argues that secularism maintains an ontology that assumes violence which forecloses the possibility of pluralism. Recent reappraisals of mysticism are at pains to highlight its ethical and political implications. This paper puts these bodies of literature in conversation to offer a mystical basis for pluralist ethics. To this end, a particular western Christian mystic, Nicholas of Cusa, in his work The Vision of God (1453) is shown to provide a theoretical and ethical basis for pluralism. The decision to focus on his mystical work The Vision of God is because the metatheoretical question of pluralism is addressed here in how unity with the divine means unity between the members of a community, which is worked out in an ethical practice of dialogue. By engaging Cusa’s mysticism in the context of postsecular critical theory, an alternate basis for pluralism is offered that sharply contrasts with that offered by secularism. Full article

The rapid development of deep learning (DL) has demonstrated its modular contributions to artificial intelligence (AI) techniques, such as large language models (LLMs). DL variants have proliferated across domains such as feature extraction, normalization, lightweight architecture design, and module integration, yielding substantial advancements in these subfields. However, the absence of a unified review framework to contextualize DL’s modular evolutions within AI development complicates efforts to pinpoint future research directions. Existing review papers often focus on narrow technical aspects or lack systemic analysis of modular relationships, leaving gaps in our understanding how these innovations collectively drive AI progress. This work bridges this gap by providing a roadmap for researchers to navigate DL’s modular innovations, with a focus on balancing scalability and sustainability amid evolving AI paradigms. To address this, we systematically analyze extensive literature from databases including Web of Science, Scopus, arXiv, ACM Digital Library, IEEE Xplore, SpringerLink, Elsevier, etc., with the aim of (1) summarizing and updating recent developments in DL algorithms, with performance benchmarks on standard dataset; (2) identifying innovation trends in DL from a modular viewpoint; and (3) evaluating how these modular innovations contribute to broader advances in artificial intelligence, with particular attention to scalability and sustainability amid shifting AI paradigms. Full article