Abstract: Maternal undernutrition during gestation can impair fetal muscle development, inducing lasting consequences for offspring growth and carcass quality. This study evaluated the effects of early- to mid-gestation nutrient restriction on postnatal skeletal muscle development in Boer goat offspring. Pregnant does (n = 12 per treatment) were assigned to either a control diet (CON; 100% of NRC recommendations) or a nutrient-restricted diet (NR; 60% of CON) from days 45 to 100 of gestation, then all does were realimented to the CON diet until parturition; male offspring (n = 6 per treatment) were maintained on a CON diet until 5 months of age. Longissimus dorsi muscle samples were collected for histological evaluation of fiber number, diameter, and collagen content, and for gene expression analysis of insulin receptor (IR), insulin receptor substrate-1 (IRS1), glucose transporter-4 (GLUT4), myogenic regulatory factors (MYF5, MYF6, MYOD, MYOG), and collagen genes (COL1A1 and COL3A1) using RT-PCR. Plasma glucose and cortisol were also measured. Muscle fiber number tended to be reduced (p = 0.06) in NR offspring, accompanied by decreased MYOG expression (p < 0.05) and trends for reduced MYF5 and MYF6 expression (p < 0.10), as well as reduced IR expression (p < 0.05). Collagen content did not differ, although COL3A1 expression was increased in NR offspring. Plasma glucose was lower (p < 0.05) at 3 months, and cortisol tended to be higher (p < 0.10) at 5 months. These results indicate that maternal undernutrition during early- to mid-gestation alters postnatal skeletal muscle development in Boer goats by reducing muscle fiber number and affecting myogenic and metabolic signaling pathways. Such changes may negatively affect the efficiency of muscle growth and meat quality.

Abstract: Caspases orchestrate metazoan apoptosis, regulating processes such as embryogenesis, the death of old and infected cells and immune tolerance. Structural orthologs of caspases have been identified in bacteria, plants, protists and fungi and regulated cell death has been demonstrated in these organisms. This led some researchers to conclude that fungal metacaspases might perform a similar function to caspases. This review discusses regulated cell death, beginning with an account of RCD and the central role of caspases in mammalian RCD. It goes on to give examples of RCD in fungi, compares the structure and activity of caspase orthologs and outlines examples of metacaspase-dependent and metacaspase-independent cell death in fungi, focusing on S. cerevisiae. Finally, it addresses the question “are metacaspases caspases?”, identifies alternative cell death proteases and recommends future research objectives.

Abstract: Background/Objectives: Operational Assistants (OAs), as non-teaching staff responsible for playground supervision and school hygiene, play a vital role in ensuring safe and well-organized school environments. Despite their importance, research on their working conditions, job satisfaction, and well-being remains limited. This study aims to examine the quality of work life (QoWL) and job satisfaction (JS) of OAs in public schools in the Municipality of Setúbal, Portugal, and to explore their relationship with absenteeism. Methods: A quantitative research design was adopted using a survey questionnaire administered to Operational Assistants (N = 119). Data were collected on QoWL, job satisfaction, and absenteeism indicators to assess patterns and relationships among these variables. Results: Findings indicate that OAs experience significant challenges related to working conditions and health risks, which are associated with higher levels of absenteeism. Nonetheless, many participants report valuing the stability associated with public-sector employment. Conclusions: The results highlight the need for targeted interventions, including improved working conditions, enhanced training, and mental health support, to reduce absenteeism and promote employee well-being. This study contributes to a better understanding of an often-overlooked workforce and offers practical implications for policymakers and school administrators aiming to foster more supportive and sustainable school environments.

Abstract: Anaplastic thyroid carcinoma (ATC) and poorly differentiated thyroid carcinoma (PDTC) represent the most aggressive thyroid malignancies, characterized by rapid progression, therapeutic resistance, and poor prognosis. Conventional treatments remain largely ineffective, highlighting the need for novel therapies. Metabolic reprogramming, particularly the Warburg effect (WE), has emerged as a potential therapeutic target. This review synthesizes current evidence on the role of WE in ATC and PDTC, integrating data from molecular profiling, preclinical studies, and emerging therapeutic strategies. Oncogenic alterations frequently observed in ATC and PDTC, including mutations in BRAF, RAS, TP53, and activation of PI3K/AKT/mTOR and HIF-1α signaling, converge to promote glycolytic reprogramming. This metabolic shift supports tumor proliferation, immune evasion, and metastasis through increased glucose uptake, lactate production, and microenvironmental remodeling. Key metabolic nodes, including glucose transporters, hexokinase, and monocarboxylate transporters, are regarded as promising targets. Preclinical studies demonstrate that pharmacological inhibition of these pathways reduces tumor growth, enhances radiosensitivity, and improves response to targeted therapies. Future efforts should focus on combination therapies, biomarker-driven patient stratification, and the development of targeted delivery systems to overcome toxicity and resistance. A deeper understanding of tumor metabolic heterogeneity will be essential for translating these approaches into clinical practice.

Abstract: Rice (Oryza sativa L.) is one of the world’s major staple foods. However, its production is severely constrained by rice blast disease, caused by Magnaporthe oryzae, which leads to substantial yield losses. Conventional management relies on fungicides and chemical treatments; however, these methods raise concerns regarding the development of pathogen resistance and potential environmental impacts. This study evaluated carrageenan from Hypnea musciformis, collected from the coast of Saint Martin (92°19′21.28″E and 20°37′38.12″N), located in the Bay of Bengal, Bangladesh, as a natural plant growth promoter as well asbiocontrol agent. Carrageenan was characterized by high sulfate (19–35%) and galactose (12–18%) contents, with FT-IR confirming characteristic κ-carrageenan functional groups. Application of 15% carrageenan significantly increased germination of seed (27%), seedling vigor (93%), shoot and root lengths (54% and 47%), and biomass compared with untreated controls. Carrageenan markedly suppressed M. oryzae, inhibiting mycelial growth (83%), reducing conidiogenesis and conidial germination, and decreasing lesion length in detached leaves and potted plants. Treated rice seedlings exhibited improved soluble sugars, photosynthetic pigments, proline, phenolic and flavonoid contents, and enhanced antioxidant enzyme activities such as CAT (catalase) and POD (peroxidase), while lowering oxidative stress markers such as H₂O₂ and MDA (malondialdehyde). These results demonstrate that carrageenan from H. musciformis enhances rice growth and elicits defense responses against rice blast, offering a sustainable and environment friendly alternative to chemical-based fungicides for integrated M. oryzae management.

Abstract: The formation of side products is often unavoidable in organic synthesis; however, analyzing these secondary species provides practical insights into reaction pathways, mechanisms, and competing processes. This understanding is essential for optimizing reaction conditions, increasing product yields, and improving overall safety and efficiency. Additionally, side products can sometimes reveal unexpected molecular structures with valuable properties. In this study, we present the characterization of a compound that formed as a side product in a modified Pictet-Spengler reaction. The molecular structure of 2,2′-(methylenebis(3,4-dimethoxy-6,1-phenylene))diacetic acid was elucidated using a combination of NMR, FTIR, UV–Vis, and HRMS spectroscopic techniques.

Abstract: We propose an information-geometric, entropic interpretation of wave function collapse based on the finite distinguishability between quantum states. By analyzing the second-order expansion of relative entropy, we show that collapse can be understood as a finite transition in state space governed by the Fisher information metric. This framework naturally assigns an energetic cost to collapse, identified with the canonical energy associated with the perturbation. The resulting picture provides a unified description linking quantum measurement, statistical distinguishability, and geometric structure.

Abstract: Sobolev spaces and their implications for nonlinear nonlocal Dirichlet problems governed by the fractional g−Laplacian. The analysis begins with a detailed investigation of the underlying structure of the functions N− and Orlicz functions, which constitute the functional setting for these spaces. Fundamental features such as completeness, separability, reflexivity, and their limiting behavior as s ↑ 1 are rigorously addressed. Within this setting, a fractional counterpart of the compact Rellich-Kondrachov embedding theorem is established. As a principal application, the existence and uniqueness of weak solutions to a non-linear Dirichlet problem are obtained through a variational approach, relying on monotonicity methods and the Minty–Browder framework. The results highlight the role of fractional Orlicz–Sobolev spaces in extending the functional analytic tools required for the treatment of non-local differential models.

Abstract: Digitalization of European enterprises is a prerequisite for their long-term success and competitiveness. Within the framework of the European Green Deal and the United Nations Sustainable Development Goals (SDGs), digitalization is increasingly linked to key objectives such as Decent Work and Economic Growth, Industry, Innovation and Infrastructure, Responsible Consumption and Production, and Climate Action. This study examines the level of digitalization in European small and medium-sized enterprises (SMEs), with a particular focus on Slovenia. It evaluates the position of Slovenian companies relative to the EU average and explores the relationship between digital maturity and the achievement of sustainability goals. The research combines qualitative and quantitative approaches. In-depth interviews with SME managers were used to identify key digitalization parameters, which guided as the basis for selection of relevant indicators from the Eurostat survey on ICT usage (2020–2024). In total, 558 indicators were analysed at the country level, with a subset of 60 sustainability-relevant indicators examined in detail. Additionally, the study analyses the relationship between the Digital Economy and Society Index (DESI) and the SDG Index across EU Member States. These findings contribute to a better understanding of the role of digitalization in the context of sustainable development.

Abstract: Aim: This study aimed to investigate the effect of pancreatic ductal adenocarcinoma (PDAC) on the alterations in red blood cell aggregation related to angiogenesis and hypoxia markers. Material and Methods: We studied 32 patients with confirmed PDAC. The aggregation of red blood cells (RBCs) were evaluated using a Laser-assisted Optical Rotational Cell Analyzer (LORCA). Serum vascular endothelial gfowth factor (VEGF) and hypoxia-inducible factor 1α (HIF-1α) levels were measured by an ELISA method. The following parameters specific to the aggregation process were estimated: the aggregation index (AI), the aggregation half-time (t1/2), and the threshold shear rate (γ_thr). Results: All measured RBC aggregation parameters among PDAC subjects differed from those in controls. The AI (P<0.05) and γ_thr (P<0.005) were significantly higher in the PDAC group, whereas t1/2 (P<0.01) and AMP (P<0.001) were significantly lower compared to the control group. The levels of VEGF (p<0.0001) and HIF-1α (p<0.0001) were significantly higher in the PDAC group than in the control group. There were significant correlations between RBC aggregation parameters and VEGF and HIF-1α. Conclusion: For the first time, we have demonstrated RBC aggregation disturbances in patients with pancreatic carcinoma. We also found that circulating levels of angiogenesis and hypoxia biomarkers are significantly elevated compared to the control group. Further studies are needed to explore the pathophysiological mechanisms linking PDAC to these aggregation indices.

Abstract: Zucchelli and Smith [1] described a biomathematical fatigue model for recreational passage-making — the Integrated Fatigue Model (IFM) — with output expressed as a blood-alcohol-concentration (BAC) equivalence anchored on Dawson and Reid [2]. The IFM reproduces the dose-response using simpler linear extrapolations for multi-day fatigue accumulation and a simpler pre-departure accounting; Zucchelli and Smith marked 48 hours as the primary validation horizon. This paper extends the scientific treatment to multi-day offshore passage-making: transits of 48 hours and longer, non-rested departures, and short-handed watch-rotation choices. The engine is rebuilt on the McCauley–Ramakrishnan unified model [3-5] — a two-state framework with fast homeostatic S and slow allostatic L — and recalibrated against the Dawson–Reid 17 h / 24 h anchors so the BAC output of Zucchelli and Smith [1] is preserved. Maritime tuning combines sea-state [6] and fragmentation [7] effects multiplicatively on the sleep time constant, yielding effective τ_s from 4.9 h (calm + deep at sea) to 25.5 h (storm + fragmented); the 4.2 h shore-laboratory baseline is not reachable at sea in the current calibration. A combinatorial consequence — the circadian-burden-distribution property of crew-rotation cycles whose length is coprime with the crew size — formalises the effectiveness of the Swedish watch system.

Abstract: Background/Objectives: Mental health and suicide prevention are increasingly recognized as critical components of sustainable development in the European Union (EU), especially following the mental health repercussions of the COVID-19 pandemic. This narrative review explores national suicide prevention strategies and mental health policy efforts across the EU through the lens of sustainable development. The focus is on alignment with the United Nations Sustainable Development Goals (SDGs), notably Goal 3: Good Health and Well-being. Methods: A narrative review design was applied. A purposive literature search focused on national strategies, epidemiological trends, policy papers, and peer-reviewed articles published from 2000 to 2025 was followed. Databases searched included PubMed, Scopus, PsychInfo, EmBase and Google Scholar, supplemented by grey literature from the World Health Organization (WHO), European Commission, and national health authorities. Selected evidence was critically appraised and synthesized thematically to extract commonalities and contrasts in policy implementation and effectiveness. Results: The review synthesized current practices, identified strengths and gaps, and provided a comparative evaluation of efforts in countries such as the United Kingdom, Sweden, Finland, Greece, and the Republic of Cyprus. Special attention was given to gender and age disparities, economic and social determinants, suicide method patterns, and the broader impact of COVID-19. The findings also underscored the need for robust, gender-sensitive, and data-driven national strategies that are contextually grounded and equitably resourced. Conclusions: This review concludes with recommendations for enhancing mental health sustainability across Europe, emphasizing cross-sectoral coordination, improved surveillance systems, and future research priorities.

Abstract: The purpose of this study was to determine the predictive role of adverse childhood experiences on attachment styles and generalized anxiety in 358 adults who had experienced violence in Trujillo, thereby directly contributing to SDG 3: Good Health and Well-being. The study was conducted using a quantitative, empirical approach with an explanatory focus, employing a non-experimental cross-sectional design. The findings confirmed that adverse childhood experiences significantly predict both the formation of emotional bonds and anxiety symptoms, with a stronger impact on attachment styles. It was determined that early adversity does not act in isolation but rather distorts the emotional bond; thus, attachment acts as a mediating bridge where the insecurity generated by adverse experiences is what ultimately triggers and sustains anxiety in adulthood. These results underscore that current psychological distress is the consequence of a chain of vulnerability initiated in childhood, where the distortion of the attachment system becomes the central mechanism that perpetuates anxiety symptoms in the face of an environment perceived as hostile.

Abstract: As the era of Industry 4.0 (i.e., the fourth-generation industrial revolution) develops, machine tools in particular are becoming interconnected, forming a collaborative community in smart factories. “Smart manufacturing” is becoming the norm, in a world where intelligent machines, systems, and networks are able to exchange information between them and respond independently, with autonomy to information, with the goal to manage industrial production processes. A key to building smart factories is the transformation of traditional machines into intelligent machines, machines with a high degree of self-determination. The concept of “smart machine tools” was first introduced about ten years ago. Such a state-of-the-art machine tool is capable of monitoring and controlling multiple process modules with its own decisions in order to self-correct processes and phenomena that could optimize the dynamic behavior of machining process. The basic components of smart machine tools are the main spindles, which will have to ensure, through the presence of the intelligent sensor-actuator module, the optimal dynamic behavior, depending on the dynamic cutting process. Spindle technologies have been widely developed to improve accuracy and capabilities, such as higher speeds, precision and reliability. The historical evolution, recent challenges and future trends of machine tool spindles were analyzed, noting that further development would be necessary to enable sensor/actuator integration to make the spindle unit an inherent quality assurance system. Our concluding are the smart spindle would be the future direction of machine tool construction in the 4.0 era.

Abstract: This study investigates the geometric parameters of commercially available or recently published models of catalyst substrates for passenger vehicles and provides a numerical evaluation of their influence on thermal behavior for use in urban areas. To ensure the plausibility of the results, a range of scenarios have been meticulously designed to replicate real operational conditions. To ensure a reliable basis for comparison, all geometries were standardized to the same gas-solid exchange surface by adapt the total monolith length. The simulation experiments conducted revealed that the primary role in question is played by the mass of the monolith and its internal surface area, while the heat transfer coefficient plays a secondary role. The necessity of adapting the geometry to the deployment scenario was demonstrated by comparing the performance of different scenarios. The employment of contemporary technologies, such as start-stop systems and automatic catalytic converter preconditioning, has also been factored. The study's findings suggest that geometries characterized by high cell density and low wall thickness, in conjunction with modern materials and intelligent engine control, are ready to become the norm for future ICE applications. This claim is particularly salient in the context of the requirements of mobile exhaust gas purification systems.

Abstract: Rootstock selection is a key component of sustainable vineyard planning, as it determines the vine’s capacity to adapt to soil properties and environmental constraints. This study presents Vitis, a modular, microservice‑based Decision Support System (DSS) designed to assist winegrowers and professionals in identifying the most suitable grapevine rootstocks for specific pedoclimatic conditions. The rootstock module (SR‑Vitis) integrates a process‑based model that evaluates 17 widely used European rootstocks by considering soil and site characteristics, including effective rooting depth, texture, coarse fragments, salinity, pH, active calcium carbonate, potential fertility, and risks of waterlogging and water deficit. Input data can be entered manually or retrieved automatically from a georeferenced database through geographic coordinates. The system outputs a list of rootstocks compatible with the given soil and environmental context and provides the user with an automatic report in PDF format. The model was applied to four vineyards located in two contrasting Italian winegrowing regions, Chianti Classico (Tuscany) and Alta Murgia (Apulia), revealing strong agreement between DSS recommendations and rootstocks currently adopted by growers, while also identifying cases where alternative choices could reduce agronomic risk. These results demonstrate the potential of SR‑Vitis as an objective and practical tool for enhancing vineyard design, improving decision-making, and supporting viticultural resilience under increasingly variable climatic conditions.

Abstract: Ergothioneine (ET) is a diet-derived compound that is avidly absorbed and retained by the human body using a specialised transporter, OCTN1. A substantial and growing body of evidence implicates ET in maintaining human health and protecting against age-related diseases, especially neurodegenerative diseases, and multiple studies indicate that low blood ET levels increase the risk of developing age-related diseases. Despite the growing interest in ET, much fundamental work remains to be done on its metabolism, intracellular and intercellular transport (especially in the brain), precise mechanisms of cytoprotection, interactions with the microbiome, mycobiome and human pathogens, and identifying the factors that control body ET levels. This narrative review explores these issues and suggests what research needs to be done to improve our understanding of ET biology.

Abstract: The COVID-19 pandemic highlighted the interplay between metabolic health and infectious disease outcomes, with conditions such as obesity, insulin resistance, micronutrient deficiencies, endothelial dysfunction, and sarcopenia associated with increased risk of severe illness and mortality. This study synthesizes evidence from immunology, metabolic medicine, clinical nutrition, microbiome research, and public health to examine the role of nutrition in supporting resilience in the post-COVID (“Meta-COVID”) context. Mechanistic pathways, including inflammatory signaling (e.g., NF-κB, NLRP3), immune regulation, mitochondrial function, and gut-lung axis interactions, are reviewed to illustrate how nutritional status may influence host defense. Key nutrients, including vitamin D, zinc, selenium, vitamin C, and omega-3 fatty acids, are discussed for their roles in modulating immune and inflammatory responses. Dietary patterns such as the Mediterranean diet are associated with improved metabolic health, reduced inflammation, and enhanced microbiome diversity. In addition, adequate protein intake and resistance exercise are considered important for mitigating sarcopenia and supporting immune function. The findings suggest that integrating nutritional strategies into clinical and public health frameworks may contribute to improved resilience against infectious and chronic diseases.

Abstract: Background/Objectives: Treatments of inflammatory ailments of the central nervous system including diseases of an aging brain and of mood disorders are not satisfactory or non-existent despite a great and increasing demand. We performed a preclinical study to measure and characterize anti-neuroinflammatory activity unexpectedly associated with rice protein. Methods: Rice protein was administered orally continuously at 12-48 mg, 3 times per day to rats with the spinal cord injury (SCI) and the anti-neuroinflammatory effect measured by the standardized macrophage count in the cavity of injury (COI) test to determine a dose-dependent effect at 7 days post-SCI. The dose of 24 mg was used to characterize the macrophage-lowering effect from 7-56 days postSCI.Results: Rice protein had macrophage-lowering effect which was enhanced with increasing dose. The macrophage-lowering effect of the highest dose tested, 48 mg, was 45.6% vs 100% in untreated rats at 7 days post-SCI. Continuous administration of a dose of 24 mg for 56 days resulted in consistent reduction of macrophage counts.Conclusions: Oral administration of rice protein potently reduced severe, destructive inflammation initiated by SCI. Rice protein, as a GRAS (generally accepted as safe) food supplement may contribute to effective dietary management of ailments of the brain with inflammatory pathogenesis.

Abstract: The present paper suggests a Federated Learning-based Distributed Solar Forecasting model based on GRU networks (FL-GRU) to smart buildings in Muscat, Oman. The growing adoption of rooftop photovoltaic (PV) systems in urban settings needs precise, privatizing, and scalable forecasting models able to manage geographically dispersed and statistically heterogeneous data. The suggested solution will include federated learning and gated recurrent unit (GRU) networks to train a global forecasting model across several smart buildings and avoid the exchange of raw energy data to overcome these challenges. The local GRU models are trained on local PV generation data and only parameters of the model are relayed to a central aggregation server. This provides privacy of data without compromising effectiveness of collaborative learning. The proposed framework is tested in a variety of realistic scenarios such as scalability analysis, non-identically distributed (non-IID) data, client dropout, communication constraints, seasonal variability, and privacy saving noise injection. Simulation outcomes show that the proposed FL-GRU model presents a final RMSE of 0.129, MAE of 0.100 and forecasting accuracy of 97%. When increasing the number of clients involved in the process, 2 to 10, RMSE decreases to 0.129, which supports the high scalability advantages. In non-IID scenarios, RMSE ranges between 0.129 and 0.167, and even with half of the clients dropping, the system is robust with RMSE of 0.172. The proposed FL-GRU is better than the benchmark models, Local GRU, centralized GRU, FL-LSTM, and FL-ANN with a maximum improvement of 22.29% in RMSE reduction. Also, the best predictive consistency is found with correlation analysis with R² = 0.957. On the whole, the suggested approach can offer an efficient, privacy-aware, and scalable solution to distributed solar energy prediction in smart cities.