Abstract: This study aimed to: (1) evaluate and compare the effects of two rearing sys-tems—intensive and extensive —on the quality characteristics of loins from Bísaro pigs, a traditional Portuguese breed, and (2) assess the influence of dietary supple-mentation with olive cake, a by-product of olive oil production, on the physicochemi-cal composition of pork loins. Muscle samples from the Longissimus thoracis et lumborum joint were collected from Bísaro pig carcasses raised on farms in the Trás-os-Montes region of northern Portugal. The slaughter and carcass cutting were standardized and performed at the Bragança municipal slaughterhouse, to ensure consistency in sample processing and preparation for laboratory analyses. Compre-hensive physicochemical evaluation showed that ash content was the only parameter exhibiting a statistically significant difference (p = 0.007) between rearing systems, suggesting that production conditions may affect the mineral content of the meat. No significant differences (p > 0.05) were observed between rearing systems for moisture, protein or total fat content. Similarly, dietary inclusion of olive cake in the animals’ di-et, irrespective of the rearing system, did not significantly affect any of these physico-chemical traits. The fatty acid profile - including saturated (SFA), monounsaturated (MUFA), and polyunsaturated fatty acids (PUFA) – also showed no statistically signif-icant differences (P > 0.05) in relation to either rearing system or dietary treatment. Overall, the evidence indicates that olive cake is a sustainable and practical option for Bísaro pork production, without compromising meat quality.

Abstract: Objectives: Intensive care units (ICUs) are characterised by high care complexity and nursing workload, which directly affects patient safety and staff sustainability. Latvia faces a chronic shortage of nurses, particularly in intensive care, yet systematic national data on nursing workload have been lacking. This study aimed to quantitatively assess nursing workload in Latvian ICUs using the Nursing Activities Score (NAS) and to evaluate its relationship with staffing adequacy. Methods: A prospective, multicentre observational study was conducted over six months (May–November 2025) in 14 Lat-vian ICUs representing all three levels of intensive care. Nursing workload was meas-ured using the NAS during each 12-hour shift. A total of 28,079 complete NAS obser-vations were analysed using descriptive statistics, inferential tests (t-tests, ANOVA), mixed-effects modelling, regression analysis, and time-series forecasting. Results: The mean NAS was 65.45 (SD = 25.76), equivalent to an average of 15.71 nursing care hours per patient per day. Workload remained similarly high during day and night shifts. Significant differences were observed between ICUs and care levels, with level 2 units showing the highest workload. The average nursing shortage rate was 42.6% and was strongly predicted by NAS values (R² = 0.115), whereas shift type and unit level had minimal explanatory power. Conclusions: ICU nursing workload in Latvia is persis-tently high and unevenly distributed across units. Staffing levels are not adequately adjusted to actual care demands. Integrating NAS-based workload monitoring into staffing models is essential for evidence-based workforce planning, improving patient safety, and reducing nurse overburdening.

Abstract: Hop (Humulus lupulus L.) is recognised as a valuable source of bioactive compounds; however, the phytochemical composition and biological potential of wild Romanian hops remain insufficiently characterized. In this study, the bioactive profile of wild hop cones was evaluated using an integrated phytochemical, biological, and in silico ap-proach. The hydroethanolic extract was characterized by a total phenolic content of 25.61 mg GAE/g DW and a total flavonoid content of 3.20 mg RE/g DW, with α-acids predominating (8.77%) and β-acids detected only at trace levels (0.15%). Hydrodistil-lation yielded 0.613 ± 0.11% essential oil, which was rich in sesquiterpene hydrocar-bons (64.61%), mainly α-humulene, β-caryophyllene oxide, selina-3,7-diene, and ger-macrene B. The hydroethanolic extract exhibited strong antioxidant activity (IC₅₀ = 5.03 µg GAE/mL), whereas the essential oil showed a moderate but dose-dependent radi-cal-scavenging capacity (IC₅₀ = 0.44% v/v). In addition, the essential oil displayed pronounced antibacterial and antibiofilm activity against Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa, with the highest antibiofilm inhibition observed for Pseudomonas aeruginosa (96.44%). Molecular docking analysis suggested that the major volatile constituents may interact with S. aureus Sortase A, providing a plausible mechanistic basis for the observed antibiofilm effects. Overall, these findings indicate that wild Romanian hop cones represent a promising source of antioxidant and anti-microbial bioactive compounds, supporting their potential applications in pharmaceu-tical, food, and cosmetic formulations, as well as in natural product–based drug discovery.

Abstract: We demonstrate that the fine structure constant alpha^{-1} ≈ 137.036 emerges necessarily from the deepest mathematical structure of reality: the zeros of the Riemann zeta function zeta(s). We present an exact formula connecting alpha^{-1} to the first four nontrivial zeros gamma_1, gamma_2, gamma_3, gamma_4 of zeta(1/2 + it). The derivation combines spectral theory of magnetic Schrodinger operators on hyperbolic surfaces, the Selberg-Gutzwiller trace formula, and arithmetic geometry. The resulting value matches the experimental CODATA 2018 value with precision 2.1 × 10^{-10}. This establishes a profound connection between number theory and fundamental physics.Keywords: fine structure constant, Riemann zeta function, zeta zeros, number theory, fundamental constants, spectral theory, quantum chaos, mathematical physics.

Abstract: The internal gelation process is essential for producing spherical nuclear fuel micro-spheres. However, its application is constrained by the poor room-temperature stability of conventional broths and by the inherent trade-off between stability and strength. A novel five-component broth system (ZrO(NO₃)₂-HMTA-urea-acetylacetone (ACAC)-glucose) was developed. The synergistic effects of ACAC and glucose on sol stability and gelation ki-netics were systematically investigated. An optimal ACAC/glucose molar ratio of 1:1 and an ACAC/ZrO2+ ratio of 1.5 were identified, yielding a broth stable for over 5 h at 25°C. The resulting yttrium stabilized zirconia (YSZ) microspheres exhibited excellent sphericity (1.04±0.01), density (5.84 g/cm³), and crushing strength (8.0 kg/sphere). This stabilization strategy was successfully extended to a uranium system, enhancing its room-temperature stability from minutes to 6 h. The work demonstrates that the synergistic ACAC-glucose system effectively decouples the stability-strength dilemma. Its successful application to a uranium broth confirms the broader utility of the dicarbonyl complexation strategy, providing an energy-efficient route for producing high-quality nuclear fuel microspheres.

Abstract: In the early days, the treatment of central nervous system (CNS) autoimmune diseases relied exclusively on broadly acting immunosuppressants. Since the 1960s, high-dose corticosteroids and conventional systemic immunosuppressants were used to treat relapses in Multiple Sclerosis (MS), with substantial toxicity and increased risk of infections and development of malignancies, due to chronic immunosuppression. Disease-modifying therapies (DMTs) were first introduced in the early 1990s in MS, the prototypical CNS autoimmune disorder, with interferon-beta. They later expanded in the mid 2000s with monoclonal antibodies (mAbs). Compared to previously used systemic immunosuppressants, DMTs allow for the selective depletion of cellular targets and cytokines. B-cell–depleting mAbs became central DMTs in CNS autoimmunity, as B-cells contribute significantly to the pathophysiology of all major CNS autoimmune diseases, including MS, Neuromyelitis Spectrum Disorders (NMOSD), Myelin Oligodentrocyte Glycoprotein Antibody‑Associated Disease (MOGAD), and Autoimmune Encephalitides (AE). However, mAb-based therapies require repeated dosing and have limited tissue penetration. This constitutes them unable to eliminate critical cells residing within isolated or protected microenvironments including the CNS. Chimeric antigen receptor (CAR) T cells are a “living” therapy engineered to eliminate antigen-specific cells, offering deep, potentially durable depletion, after a single treatment, able to access currently mAb-inaccessible targets. In this review, we evaluate CAR T-cell therapies in the context of CNS autoimmunity, with the necessary historical prism. We describe possible differential targets (mainly B-cell subsets) according to each disease, describe preclinical studies involving alternative CAR T-cell products, report clinical experience in 15 patients, and outline ongoing or planned trials.

Abstract: Neurodevelopmental disorders (NDDs) are a class of childhood-onset conditions that affect brain development and function. NDDs have a heterogeneous etiology, a wide genetic and clinical variability and generally lead to impaired cognition, communication, psychomotor skills, and adaptive behavior. These disorders include intellectual disability (ID), autism spectrum disorder (ASD), and developmental and epileptic encephalopathies that manifest during childhood. Over the past 2 decades, genetic research has discovered more than 1,500 genes in different signaling pathways that are involved in NDDs, including many transcriptional regulators such as DNA/histone modifiers and chromatin-regulatory protein complexes. These same investigations have led to the accessibility and availability of next-generation sequencing in the assessment of children with NDDs in the clinical setting. The advances have dramatically altered the approach to the genetic diagnostic assessment of the child with NDDs and have increased the diagnostic yield of genetic testing in the pediatric setting. The purpose of this review is to provide the historical background to the rational assessment of child with an NDD and present a perspective on the current evaluation given the modern repertoire available to the pediatric practitioner facing this challenge in the clinical setting.

Abstract: Zachlod et al. 1 analysed satellite data from Malaysian oil palm plantations and concluded that Roundtable Table on Sustainable Palm Oil (RSPO) certification reduces plantation efficiency by lowering canopy coverage. Our reanalysis of 93,987 ha shows that their methods and interpretation are fundamentally flawed, primarily because the study does not account for routine replanting, which caused temporary canopy loss in 32.7% of certified areas between 2018 and 2023. Using validated remote sensing methods and cross-verified timelines, we found no significant decline in oil palm coverage and no evidence of reduced oil palm coverage in certified plantations. Given the risk of policy misinterpretation we call for more rigorous, transparent, and context-aware evaluations of sustainability outcomes in plantation systems.

Abstract: Climate change is a growing concern, and its effects on biodiversity may depend largely on its consequences on the distribution of species that play a key role in communities. This study aims to determine whether forests in the Sudanian zone of Senegal will maintain the environmental conditions required for fifteen valuable species to persist under a range of climate change scenarios. We used forest inventory data from 2,398 plots and five bioclimatic variables as predictors. The distribution of the species was evaluated under three SSP scenarios (SSP2-4.5, SSP3-7.0 and SSP5-8.5) for the 2100 horizon by developing models for each species under each scenario using multiple logistic regression with a binomial distribution of the response. The findings indicate that the mean annual temperature is expected to increase in the region for all SSPs considered, while for precipitation metrics, some variability was observed depending on the scenarios. As demonstrated by our results, species distribution changes exhibited differences. Our models indicate a negative impact of climate for nine species, specifically Acacia macrostachya, Bombax costatum, Cordyla pinnata, Combretum micranthum, Detarium microcarpum, Prosopis africana, Pterocarpus erinaceus, Sterculia setigera and Terminalia avicennioides, under scenarios SSP2-4.5 and SSP3-7.0. These species would face significant declines under the most extreme scenario, scenario SSP5-8.5. The following species are predicted to decline significantly under the SSP2-4.5 and SSP3-7.0 scenarios: Pterocarpus erinaceus, Sterculia setigera, Terminalia avicennioides, and to be completely lost under the SSP5-8.5. Three species would maintain their distribution area under all scenarios SSP5-8.5. The results also indicate that species tend to move in a general west-southwest trajectory. Overall, the majority of high-value species are vulnerable to future climate, with a high risk of local extinction in the sudanian zone. This finding indicates the necessity for a reorientation of forest management strategies towards the implementation of adaptive management strategies to ensure the long-term resilience of forests. Urgent actions such as the development of specific management plans and monitoring schemes for vulnerable tree species, with the objective of minimizing their harvesting and promoting their regeneration, are required.

Abstract: People create facts rather than describe them; they formulate mathematical concepts rather than discover them. However, why can people design different mathematical concepts and use different tools to change reality?

Abstract: Scientific studies have demonstrated how certain insect species can be used as bioindicators and reverse environmental degradation through their behavior and organization. Studying these species involves capturing and extracting hundreds of insects from a colony for subsequent study, analysis, and observation. This allows researchers to classify the individuals and also determine the organizational structure and behavioral patterns of the insects within colonies. The miniaturization of hardware devices for data and image acquisition, coupled with new Artificial Intelligence techniques such as Scene Graph Generation (SGG), has evolved from the detection and recognition of objects in an image to the understanding of relationships between objects and the ability to produce textual descriptions based on image content and environmental parameters. This research paper presents the design and functionality of a distributed computing architecture for image and video acquisition of bees and ants in their natural environment, and a parallel computing architecture that hosts two datasets with images of real environments from which scene graphs are generated to recognize, classify, and analyze the behavior of bees and ants, while preserving and protecting these species. The experiments carried out are classified into the recognition and classification of objects in the image, as well as the understanding of the relationships between objects and the generation of textual descriptions of the images. The results of the experiments, conducted in real-life environments, show recognition rates above 70%, classification rates above 80%, and comprehension and generation of textual descriptions with an assertive rate of 85%.

Abstract: Flaviviruses such as Dengue, Zika, and West Nile virus cause a significant global health burden and have earned attention as high pandemic risk pathogens. Flaviviruses interact closely with cell membranes at every stage of their life cycle, and mounting evidence demonstrates that flaviviruses rely on specific lipids and lipid-remodeling proteins, presenting potential therapeutic opportunities for targeting the host’s lipid metabolism. Our understanding of lipid function in infection has expanded considerably in recent years, partly thanks to advances in lipidomics, cryo-electron tomography, lipid-based chemical tools, and biophysical characterization techniques. In this review, we highlight recent breakthroughs that have clarified flavivirus lipid requirements and functions, as well as ongoing technological advances in the virus-lipid interaction field poised to enable the next wave of discoveries.

Abstract: Introduction: Nursing educators and clinical leaders face persistent challenges in engaging the next generation of nurses, often characterized by short attention spans, frequent phone use, and underdeveloped communication skills. This article describes the design and delivery of a full-day interactive teaching workshop for nursing faculty, senior clinical nurses, and nurse leaders, developed using a design-thinking approach supported by generative AI. Methods: The workshop comprised three thematic sessions: (1) Learning styles across generations, (2) Interactive teaching methods, and (3) Application of interactive teaching strategies. Generative AI was used during planning to create icebreakers, discussion prompts, clinical teaching scenarios, and application templates. Design decisions emphasized low-tech, low-prep strategies suitable for spontaneous clinical teaching, thereby reducing barriers to adoption. Activities included emoji-card introductions, quick generational polls, color-paper reflections, portable whiteboard brainstorming, role plays, fishbowl discussions, gallery walks, and movement-based group exercises. Results: Analysis of 59 participant reflections revealed six interconnected themes, grouped into three categories: engagement and experiential learning, practical applicability and generational awareness, and facilitation, environment, and motivation. Participants emphasized the workshop’s lively pace and hands-on design, noting “It was impossible to fall asleep; we were always talking, sharing, writing, or moving.” Experiencing strategies firsthand built confidence for application, while generational awareness encouraged reflection on adapting methods for younger learners. The facilitator’s passion, personable approach, and structured use of peer learning created a psychologically safe and motivating climate, leaving participants recharged and inspired to integrate interactive methods. Discussion: The workshop illustrates how AI-assisted, design-thinking-driven professional development can model effective strategies for next-generation learners. When paired with skilled facilitation, AI-supported planning enhances engagement, fosters reflective practice, and promotes immediate transfer of interactive strategies into diverse teaching settings.

Abstract: Pregnant women undergo a myriad of physiological changes during this stage, including important hormonal variations. Pregnancy gingivitis is a condition that affects up to 30% to 100% of women, is related to hormonal modifications, and could play an important role in gestational gut colonization and immunological training in the newborn. Nonetheless, oral health is not always considered part of routine prenatal care. In this study, we collected saliva samples of pregnant women with and without pregnancy gingivitis and analyzed the oral microbiota through 16S sequencing. In addition, meconium from the infants of participating women was also analyzed. The oral microbiota of pregnant women with and without pregnancy gingivitis did not show significant diversity differences. However, significant differences in microbiome composition were observed. In addition, it appears that microbiome composition of the offspring of mothers with gingivitis may also differ from that of mothers without gingivitis, although the number of available samples did not allow definite conclusions. As such, a larger cohort and deeper sequencing methods are needed to demonstrate the differences in the oral microbiota of pregnant women with and without gingivitis and to explore the possibility of bacterial translocation from the maternal gingiva to the fetal gut.

Abstract: Cost-sensitive advanced driver-assistance systems (ADAS) increasingly rely on embedded platforms without discrete GPUs, where power-intensive deep neural networks are often impractical to deploy and difficult to certify for safety-critical functions. At the same time, classical geometry-based lane detection pipelines still struggle under strong backlighting, low-contrast night scenes, and heavy rain. This work revisits geometry-driven lane detection from a sensor-layer perspective and proposes a Binary Line Segment Filter (BLSF) that exploits the structural regularities of lane markings in bird’s-eye-view (BEV) images. The filter is integrated into a three-stage pipeline consisting of inverse perspective mapping, median local thresholding, line-segment detection, and simplified Hough-based sliding-window fitting with RANSAC. On a self-collected dataset of 297 challenging frames (strong backlighting, low-contrast night, heavy rain, and high curvature), the full pipeline improves lane detection robustness over the same implementation without BLSF while maintaining real-time performance on a 2 GHz ARM CPU-only platform. To assess generality, we further evaluate BLSF on the Dazzling Light and Night subsets of the large-scale CULane and LLAMAS benchmarks, where it achieves a consistent 6–7% improvement in F1-score over a line-segment baseline under a fixed pre-processing configuration, along with corresponding gains in IoU. These results demonstrate that explainable, geometry-driven lane feature extraction can deliver competitive robustness under adverse illumination on low-cost, CPU-only embedded hardware, and can serve as a complementary design point to lightweight deep-learning models in cost- and safety-constrained ADAS deployments.

Abstract: The recent proposal of the “Discrete Extramental Clock Law” (1) posits that objective time—extramental, independent of subjective perception—advances in a discrete and variable manner in chaotic complex systems, modulated by a gating function dependent on the system’s criticality state. This law implies that absolute Newtonian time—uniform, continuous, and universal—does not exist in extramental reality, reducing it to a perceptual illusion or emergent approximation. In this revised work, we explore the ontological, epistemological, and metaphysical consequences of this thesis, connecting it to classical debates on temporal flow (3; 4), Einsteinian relativity, and the philosophy of chaotic complexity. The perspective challenges strict block eternalism, supports a temporally open structure with futuribles (possible futures), and relaxes the irreversibility of causality, with positive implications for free will and the nature of becoming.

Abstract: In recent years, nanopore technology has enhanced analyte detection, enabling higher resolution and achieving single molecule sensing capability.Aptamer-conjugated nanopore sensing technology combines the high specificity of aptamers with the single-molecule resolution of nanopores. By anchoring aptamers at or near the nanopore, target binding events produce distinct electrical signatures that allow sensitive and label-free detection. This approach enables real-time monitoring of small molecules, proteins, and even pathogens, with promising applications in diagnostics, drug screening, and environmental monitoring etc. Hybrid biological/solid state devices produce robust signals and are suitable for PoC applications. The Aptamers “magic bullets” are also exploited to develop a Single-molecule antigen detection using nanopores which offers a promising alternative for accurate virus testing to contain their transmission. Chemical conjugation of aptamers to solid membranes or nanopore interfaces improves selectivity for peptides/amino acids and expands robustness for practical samples. Aptamer-based nanopipettes offer high analytical precision by enabling label-free, real-time detection of target molecules in ultra-small sample volumesThis review explores various types of aptamers integrated nanopore platforms which cater toward precise, single-molecule recognition, paving the way for highly sensitive, portable diagnostics and next-generation therapeutic monitoring tools.

Abstract: Conflicting evidence exists on whether blue or red light modulate nitric oxide (NO) release within skin cells. This study shows that broad-spectrum blue or red (includ-ing infra-red) light at typical low-level environmental exposures fails to significantly in-crease NO release in skin cell monolayers compared to unexposed controls up to 2 hours post-exposure. We discuss observed discrepancies between our work and recent studies presenting measurable upregulation, noting that significant NO induction typically re-quires high-powered light emitting diode (LED) or lasers used in clinical settings within a specific narrow spectral band. Thus, while our findings show no significant effect, they provide an important counterpoint for public health discussions on visible light exposure at terrestrial levels, particularly low-level exposures.

Abstract: Background and aim: Recently pre-clinical studies have confirmed that the inhibition of the MAP kinase pathway can induce re-differentiation of radioiodine refractory(RAIR) follicular-cell thyroid cancers (TC). The aim of this trial is to investigate whether the combination of kinase inhibitors (KIs) with myoinositol (MI) can induce or potentiate the re-uptake of RAI in cancer cells Overview and methods: This is an open label, non-pharmacological, multicenter, randomized pilot study. Patients will be divided into 2 groups: a) a control group in which patients are treated with KIs (T plus D or L); b) a group, in which patients are treated with the same KI and in addition with MI. After 30 days of MI treatment, all patient, treated with L-T4 at a semi-suppressive dosage as per clinical practice, will be stimulated with recombinant human TSH (rhTSH) (days 31 and 32). Then at day 35 patients will be submitted to whole-body scintigraphy, with hybrid imaging where possible (SPECT/TC), after administration of the diagnostic activity (185-222 MBq) of 123-I in accordance with the SNMMI/EANM guidelines. Blood samples will be collected before starting MI therapy (day 0), after 30 days of MI therapy and then at days 31, 32, 33, 34 and 35 after MI therapy. QoL will be assessed at beginning of the MI treatment and at the end of its administration. The primary endpoint is to evaluate the restoration of the 123 uptake in RAIR follicular cell-derived TC patients already on KIs therapy alone and on KIs therapy plus MI. Restoration of the 123 uptake will be evaluated in target lesions. Conclusions: The study evaluates the possible re-differentiation of RAIR cell-derived TC in patients treated with KIs plus MI. The re-uptake of iodine will be evaluated as the primary end point, and Tg values and QoL will be evaluated as the secondary end points. The main limitation of the study is that we do not investigate any clinical effects. We will have to post-pone the clinical analysis to a later date after the administration of RAI for therapeutic purposes.

Abstract: Complex systems across physics, biology, ecology, technology, and society exhibit emergent structures that cannot be reduced to microscopic rules or simple dynamical laws. While standard approaches rely on differential equations, agent-based simulations, or data-driven models, many emergent phenomena are fundamentally structural in nature, characterized by stability islands, collective modes, scale-dependent correlations, and critical transitions. In this work, we introduce SORT-CX, the complex-systems application layer of the Supra-Omega Resonance Theory (SORT). SORT-CX applies a projection-based operator framework—comprising idempotent resonance operators, a global consistency projector, and a non-local projection kernel—to the structural analysis of complex systems. Emergence is formulated as a projective process rather than being defined purely as a temporal evolution. Structural stability corresponds to idempotent fixed points under operator projection, while structural change is diagnosed through drift metrics defined in resonance space. The framework enables a principled classification of complex systems into operator-dominated, kernel-dominated, and drift-dominated regimes, independent of specific dynamical equations and dataset-specific modeling assumptions. We develop a series of representative use cases, including network mode analysis, stability landscapes of complex fields, pattern formation, critical transitions, and multilayer coupled systems. SORT-CX positions projection-based structural analysis as a unifying perspective for emergent phenomena.