Search Results (12,027)

Breast cancer is the most common malignant tumor in women worldwide. Triple-negative cancers have the worst prognosis, due to the low effectiveness of current therapies. In recent years, research has been conducted on the relationship between inflammatory process and the development of malignant tumors, including breast cancer. One of the elements influencing the inflammatory process is interleukins. These are small protein molecules belonging to the cytokine family that participate in the function of the human immune and hematopoietic systems. Interleukins are still being studied, and this is an area with significant knowledge gaps. More than 60 cytokines have been designated as interleukins over time, but not all of these designations are consistently used or universally accepted. In the available literature, we have only found information on 41. This is the first review to detail all 41 interleukins and their effects on breast cancer development. The review shows that interleukins affect the development of both locally advanced breast cancer and the development of distant metastases, mainly to the bones. Clinical trials are also underway in these areas: some have failed, and others are still ongoing. Due to the lack of success in the use of interleukins in the treatment of breast cancer, the latest strategies are based on combining several elements of the inflammatory process pathway occurring in breast cancer. This can probably bring us closer to therapeutic success in this area. Full article

This article compares selected fire safety regulatory systems in Japan, China, the United States, and the EU/UK, interpreted through the lens of responsive architecture and the implementation of digital technologies—building information modelling (BIM), digital twins (DTs), artificial intelligence (AI), and the Internet of Things (IoT). The study adopts a qualitative approach based on a structured review of legal acts, technical standards, public-sector reports, and the scientific and professional literature, organised using a common analytical framework. First, the analysis identifies shared foundations across regimes: the primacy of life safety, mandatory detection and alarm functions, fire compartmentation, requirements for protected means of exit, and the increasing importance of documenting the operational status of protection measures. Then, it contrasts key differences, including the permissibility of performance-based design (PBD), the degree to which digital documentation is formally recognised, organisational enforcement models, and cybersecurity approaches for integrated fire alarm/voice alarm/building management/IoT ecosystems. Japan and selected Chinese cities combine stringent requirements with openness to dynamic solutions and urban-scale data platforms. The USA relies on a decentralised code-based ecosystem with a strong role for professional and industry bodies, while the EU/UK continues to strengthen harmonised standards and digital building registers, reinforced by lessons after the Grenfell Tower fire. Against this background, Poland is discussed as broadly aligned in goals and baseline technical requirements yet lagging behind in implementing PBD pathways, digital registers, formal BIM/DT integration, and minimum cybersecurity requirements. The proposed directions for change aim to create a more predictable regulatory and technical framework for the development of responsive architecture and dynamic fire safety systems in Poland. The study contributes to the sustainability literature by framing regulatory readiness for digital fire safety as a lifecycle resilience strategy, directly relevant to safe, resource-efficient, and inclusive built environments. Full article

The literature on the Commercial Determinants of Health has primarily focused on the political power and interventions of corporations versus countermeasures by civil society. However, there is a gap in the discourse concerning industry use of legal threats and actions to silence public health organizations and their public health communication campaigns (PHCCs). This legal review aims to understand legal challenges brought against PHCCs and provide best practices for effective campaigns while minimizing legal risk. We used jurisprudence in Mexico and the United States as proxies for civil and common law countries, supplemented by international law and principles to identify the best defenses and mitigation strategies against defamation and trademark infringement actions. Legal frameworks in these countries demonstrate the ability of aggressive evidence-based public health campaigns to withstand these legal challenges. Based on these legal protections, we recommend that advocates implement PHCCs that (1) contain facts that are true and substantiated with evidence, (2) frame messaging as opinion or satire, (3) highlight the non-commercial purpose of informing people about the impact of their consumption choices on health and well-being and (4) explain how the messaging advances the rights to health and access to information. A PHCC’s success in court can also set important precedents regarding the right of advocates to disseminate and the right of populations to receive important health messages. Following these recommendations and best practices, health advocates can minimize legal risk and continue to provide effective evidence-based PHCCs that promote and protect public health. Full article

Urinary stone disease is common, recurrent, and increasingly managed through imaging-driven pathways, yet standard-dose CT of the kidneys, ureters, and bladder (CT KUB) raises concerns about cumulative radiation exposure and the limited use of quantitative imaging information for risk stratification. This review synthesizes contemporary evidence on dose-optimized CT, advanced spectral technologies, and artificial intelligence (AI)-enabled analytics that are reshaping diagnosis, treatment selection, and triage. This review summarizes data supporting low-dose and ultra-low-dose CT protocols that preserve diagnostic accuracy while substantially reducing dose, and discusses how dual-energy CT, photon-counting CT, and radiomics facilitate noninvasive stone characterization and extraction of imaging biomarkers beyond size and location. It also reviews AI approaches for automated detection, segmentation, and volumetric quantification across CT, KUB, and ultrasounds, highlighting their potential to standardize stone-burden metrics. It further examines predictive models, including logistic regression, nomograms, and machine learning, for perioperative infectious complications, emergency department admission or intervention, procedure success, and long-term recurrence, and outlines reporting and validation frameworks and implementation considerations, including software as a medical device regulation and human oversight. In contrast to prior reviews that consider imaging and AI separately, this review integrates dose reduction, spectral characterization, and AI-driven analytics within real-world clinical pathways to distinguish established clinical applications from those that remain investigational. Integrating advanced CT and AI outputs into well-validated prediction models embedded in real-world workflows may enable safer imaging, more consistent triage, and more personalized follow-up for urinary stone disease. Full article

Large language models (LLMs) have been widely applied to knowledge graph question answering (KGQA) systems. Recent Text-to-SPARQL studies have demonstrated that generation performance can achieve an F1 score exceeding 90%. Further error analysis has categorized common errors into entity translation errors, entity position errors, and resource description framework (RDF) triple-count errors, with the latter accounting for 24% of all errors. Notably, nearly 90% of RDF triple-count errors occur when the triples involve class entities. Previous research has shown that incorporating prompts can effectively enhance model performance. Based on the results, we predicted whether a question contains a class entity and the number of RDF triples in the corresponding query to reduce RDF triple-count errors in large language models by providing precise task-related information through prompt design. Since both strategies are classification-oriented, two implementation paradigms were established: traditional classification architectures and generative modeling. They were compared in terms of performance. For classification-based architectures, we employed Bidirectional Encoder Representations from Transformers (BERT) and the Robustly Optimized BERT Approach (RoBERTa) to obtain question embeddings for classification. For the generative approach, we adopted the Instruction-Tuned Text-to-Text Transfer Transformer (Flan-T5). Experimental results show that the generative model slightly outperforms conventional classification architectures, indicating that generative approaches can achieve higher prediction accuracy and provide more reliable information without the need for additional complex encoder designs, thereby improving the overall quality of Text-to-SPARQL generation. Full article

Deep learning models have achieved high accuracy in forecasting problems, but at the cost of large computational energy demand. Brain-inspired spiking neural networks (SNNs) offer a promising, low-power alternative, yet their adoption for time-series forecasting has been limited by information loss from binary spikes and degraded performance in deeper networks. This paper proposes a fully spiking framework that bridges this gap by improving both the encoding and propagation of information in SNNs. The framework introduces a hybrid Delta-Rate encoding mechanism that captures both abrupt changes and gradual trends in time-series data, and a Mem-Spike mechanism that transmits analog membrane potential values to preserve fine-grained information between spiking layers. We further employ residual membrane connections to maintain signal flow in deep spiking networks. Using two public energy load datasets, our enhanced SNNs consistently outperform conventional spiking models, improving prediction accuracy by up to 61.6% and mitigating degradation in multi-layer networks. Notably, it narrows the gap to the selected deep learning baseline (LSTM), achieving comparable accuracy in some settings while requiring only about 10% of the estimated inference energy of that baseline under a common operation-level model. These results show that, within the empirical scope considered here, enhanced conventional SNNs can improve time-series forecasting accuracy while retaining favorable estimated efficiency. Full article

This study develops a new concept of computer-assisted exercises (CAX) on constructive simulation systems and how the proposed concept affects the strategy and teaching methods. The current state of affairs in the field of defense and security, both in Europe and in the world, requires the acquisition of competencies (European Qualifications Framework—EQF: knowledge, skills, independence, and responsibility), i.e., the education and training of a significantly larger number of personnel in the field of defense and security than has been the case in the last 70 years. In addition, an important specificity of today is that students need to acquire some competencies that were almost unknown until recently. Most of these competencies are the result of the rapid development of technology, which has significantly changed human life in all areas. In order to respond to the modern requirements of conducting operations, where the transfer of information both horizontally and vertically is exponentially accelerated, current concepts of preparation and implementation of education and training, of which exercises are often the most important part, need to be replaced with new concepts, and one such concept is developed in this paper. New information introduced is mostly related to the new weapons that are being introduced (unmanned systems, hypersonic missiles, weapons based on microwaves and lasers, etc.), which all result in necessary changes to the traditional approach to conducting war, i.e., tactics, techniques, and procedures (TTP). This novel exercise concept allows for the simultaneous implementation of training for up to three or four hierarchical levels (e.g., TF Div, brigade, battalion, and company) in one exercise, while in most countries, including the NATO alliance, it is still common for such exercises to be conducted according to a concept that is over 20 years old and, as a rule, is focused on the implementation of exercises for one or two hierarchical levels. This approach allows key personnel from the headquarters of units from four hierarchical levels to be simulated in real time, which is not provided by current concepts for preparing and conducting exercises. The new concept was applied as a multi-level, computer-assisted exercise (CAX) on constructive simulation systems. In addition, significant advantages of the new concept relate to the flexibility and adaptability of the proposed concept to be applied in addition to operational units and in training institutions such as academies and higher education institutions. In addition to the above, the new concept requires a shorter planning period as well as fewer total resources needed for the preparation and implementation of the exercise. The management, organizational, and technological components of the proposed exercise concept are implemented in the CAX model. The hypotheses in this paper will be tested in an applied study, which was evaluated through an external evaluation body. The implemented CAX model was tested in Croatia on the example of using exercises at the Croatian Defense Academy. Full article

Background: Endometrial cancer (EC) is the most common gynecologic malignancy in developed countries and one of the few solid tumors with a steadily rising incidence, paralleling global trends in obesity and insulin resistance. Its strong epidemiologic association with systemic metabolic dysfunction positions EC as a uniquely accessible model for metabolically informed chemoprevention. Methods: This narrative review was conducted through a systematic search of PubMed/MEDLINE and Embase using the following terms: “endometrial cancer” AND (“insulin resistance” OR “metabolic syndrome” OR “PI3K” OR “chemoprevention” OR “bariatric surgery” OR “metformin” OR “cellular senescence”). Searches were limited to English-language publications; no date restriction was applied for foundational molecular studies, while clinical and translational evidence was reviewed from 2000 to 2025. Additional references were identified through manual review of reference lists of included articles. Results: We examine metabolic amplification as a conceptual framework in which hyperinsulinemia, inflammatory reinforcement, and redox-epigenetic modulation intensify proliferative signaling in biologically susceptible endometrial tissue, particularly within molecular subtypes enriched for PI3K pathway activation such as tumors lacking a specific molecular profile (NSMP). Bariatric surgery offers the strongest human evidence supporting the principle that durable metabolic correction can substantially reduce EC incidence. In contrast, pharmacologic interventions including metformin, anti-inflammatory agents, and nutraceutical compounds demonstrate variable or limited preventive efficacy, and short-term biomarker modulation cannot substitute for validated reduction in cancer risk. The endometrial intraepithelial neoplasia (EIN) model provides a uniquely accessible platform for biomarker-guided intervention. Conclusions: Integration of genomic subtype classification with metabolic profiling may enable precision prevention strategies in clearly defined high-risk populations. Effective chemoprevention will require molecular enrichment, confirmation of tissue-level target engagement, and clinically meaningful endpoints, while acknowledging the translational limits of pathway-directed approaches. Full article

The current literature on electrical system maintenance highlights three technology domains—Building Information Modeling (BIM), Digital Twin (DT), and extended reality (XR)—that have independently demonstrated strong potential for improving lifecycle information management, predictive analytics, and operational support. However, their convergence remains largely underexplored, particularly in electrical system maintenance. This paper provides a structured review of BIM–DT–XR convergence in electrical system lifecycle management, examining their roles across lifecycle phases and their integration through literature synthesis and cross-domain implementation evidence. BIM is analyzed as a basis for modeling and integrating facility management with electrical asset lifecycles; DT as a framework for dynamic system representation and applications in electrical and power systems; and XR as a means of visualizing and interacting with BIM-DT environments. Cross-domain implementation evidence from an industrial electrical facility and a tertiary smart-building pilot shows that BIM–DT–XR integration is technically feasible at pilot scale. However, the analysis identifies five structural integration gaps: semantic misalignment between building-oriented IFC and grid-oriented CIM ontologies; fragmented standard adoption; inconsistent data governance and naming practices; validation approaches focused on syntactic rather than dynamic model fidelity; and the separation of XR visualization from predictive DT capabilities. The implementation evidence further indicates that real-world deployment remains constrained by data quality limitations, integration complexity, cost factors, and interoperability with legacy systems. The review concludes that, despite the maturity of individual technologies, their effective application depends on advances in semantic alignment, lifecycle data governance, validation of dynamic models, and scalable integration frameworks, enabling the transition toward integrated, interoperable, and lifecycle-aware infrastructures for electrical system maintenance. Full article

Introduction: Paget’s disease of bone (PDB), the second most common bone disease after osteoporosis, is still of unknown etiology and thus deserves attention. PDB and chronic kidney disease (CKD) are chronic diseases characterized by alterations in bone turnover, mineralization, volume, and strength. Both conditions carry an increased cardiovascular risk, as well as increased morbidity and mortality. Both are common in the Western world and primarily affect men over 50 years of age. Despite these similarities, little data exists on their coexistence. Purpose and Methodology: By evaluating the available literature, we found extensive documentation on individual diseases, which has led to consolidated guidelines. The coexistence of the two diseases has provided sporadic studies describing individual cases or small case series. More limited information is available on patients who have received or are eligible for kidney transplants and also have PDB. This narrative (non-systematic) review aims to examine the topic with a particular focus on the relationship between PDB and CKD, especially concerning issues around kidney transplantation. The overlapping factors of the two diseases, and their impact on PDB diagnosis and treatment are discussed. Additionally, examining CKD patients may offer valuable insights for the design of prospective longitudinal or cross-sectional studies aimed at expanding our understanding of PDB. Limitations: The different points of discussion that emerged from the examination of this topic may be useful in the management of PDB-CKD patients but, at the moment, there are not enough data available to draw definitive conclusions to support clinical practice. Conclusions and Future Directions: The coexistence of PDB and CKD is not a rare phenomenon; studying patients with both diseases could provide insights into new research avenues. Above all, and more immediately, attention to the coexistence of the two diseases could improve patient management with personalized choices based on their renal function. Full article

We developed two distinct automatic packet reporting system (APRS) payload designs to establish a reliable backup communication system for disaster risk reduction and management. The payloads are designed to perform a significant key operation, primarily APRS digital repeating (DP), enabling continuous communication access even in areas where conventional ground-based infrastructure is damaged by natural disasters through the relay of APRS packets to extend communication coverage. A detailed framework is designed using the standard amateur packet radio (AX.25 protocol). It specifies the structure of APRS data frames and packets, which are used to transmit alerts, emergency status updates, and text messages. This structure ensures that important information is transmitted reliably and effectively during an emergency. The designs for the APRS payloads share a common overall operating system architecture but differ in their very high frequency transceiver modules used for the amateur radio (Radiometrix BiM1H very high frequency (VHF) Narrowband Transceiver and Dorji DRA818V VHF Band Voice Transceiver Module). Full article

Wide-field infrared imaging systems are often constrained by detector size, cooling requirements, and payload limitations, leading to the need for multi-FOV detector sharing. However, conventional geometric multiplexing introduces severe spatial aliasing, which significantly degrades target localization performance. This paper proposes a polarization-encoded field-of-view multiplexing method for recovering spatial information from aliased detector measurements. The imaging plane is divided into multiple FOV regions, each assigned a distinct polarization state. After optical folding, the modulated sub-images are superimposed onto a common detector region. Six-channel polarization measurements are used to reconstruct pixel-wise Stokes vectors, and the spatial origin of each pixel is identified through polarization-domain similarity matching and target-level voting. MATLAB-based simulations were conducted using a nine-region multiplexing configuration. The proposed method achieves 97.3% pixel-level classification accuracy under ideal conditions and maintains over 95% accuracy at a noise level of σ = 0.02. The normalized Stokes reconstruction error is below 0.02, and stable performance is observed under polarization modulation deviations within ±10°. By introducing polarization as an additional encoding dimension, the proposed framework enables efficient separation of multiplexed spatial information without increasing detector resources, demonstrating its potential for compact wide-field infrared sensing applications. Full article

The European Union is increasingly confronted with a convergence of sustainability, democratic, and security-related challenges that affect the conditions for long-term transformation. While sustainable development and democratic resilience are often discussed separately, their interdependence has become more visible in the context of geopolitical instability, geoeconomic competition, hybrid threats, and growing societal polarization. This article examines the relationship between sustainable development and democratic resilience in the European Union and analyses how external pressures shape both agendas. The study employs a qualitative, concept-driven research design that combines the analysis of EU strategic and policy documents, a structured review of relevant scholarly literature, and triangulation with selected sustainability and governance indicators. The findings suggest that the implementation of sustainable development goals depends not only on regulatory and economic capacity, but also on social cohesion, public trust, and the resilience of democratic institutions, which together shape the legitimacy, continuity, and political feasibility of long-term transformative policies. At the same time, energy dependence, supply-chain vulnerabilities, technological dependencies, and information threats increasingly constrain the EU’s sustainability agenda. In response, the article proposes the concept of Sustainable Democratic Security as an analytical framework linking sustainability governance, democratic resilience, and strategic-security capacity. The article contributes to the literature by conceptualising these dimensions as mutually conditioning components of a common governance framework and by outlining their implications for integrated EU policymaking under conditions of geopolitical and geoeconomic pressure. Full article

Higher rates of food insecurity have been observed among Hispanic immigrants, yet these individuals have traditionally been excluded from food insecurity survey development. The most common Spanish translated food insecurity scale—the Spanish Translation of the U.S. Household Food Security Survey Module (S-FSSM)—may not be capturing how Spanish-speaking immigrant parents conceptualize food insecurity. The purpose of this study was to gain insight into how Spanish-speaking immigrant parents with low English literacy conceptualize household food insecurity within the 18-item S-FSSM and to use this information to revise the S-FSSM instrument. Researchers conducted two rounds of cognitive interviews with Spanish-speaking adults at a community center in Houston, TX, USA (N = 19; Round 1: n = 9, October 2023; Round 2: n = 10, July 2024). Researchers used participants’ feedback to refine the S-FSSM. All participants were female (Rounds 1 and 2 = 100%) and most born in Mexico (Round 1 = 66.7%; Round 2 = 50%). In Round 1, eight items were combined to enhance cultural relevance and to add definitions. Follow-up questions were added to improve clarity. Two items were revised for relevancy, two items had no change, six items were deleted. In Round 2, modifications to wording occurred and one item was added. The revised scale, named Food Access Measure for Immigrant Latinos In America (FAMILIA), resulted in 17 survey items. Study findings suggested that the S-FSSM needed refinement to enhance relevancy for Spanish-speaking immigrant parents with low English literacy. Full article

This systematic review and meta-analysis aims to determine the diagnostic yield of whole-exome and targeted-panel sequencing in children with hearing loss. We searched PubMed, Google Scholar, and the Cochrane Library to identify studies describing cohorts of >50 families undergoing whole exome or targeted panel sequencing. Studies were excluded if they pre-screened for common deafness genes without including the data in final analyses, focused on syndromic hearing loss, or lacked diagnostic yield information. Meta-analysis employed a random-effects model of single proportions to determine yield across included studies. The pooled diagnostic yield for bilateral hearing loss was ~47%, while unilateral cases demonstrated a yield of only ~5% across both testing methods. These findings demonstrate that the diagnostic yield for bilateral hearing loss exceeds that of other conditions frequently requiring clinical genetic testing, such as epilepsy and intellectual disability, though this advantage does not extend to unilateral hearing loss. These results have important implications for healthcare policy decisions regarding genetic testing guidelines and funding. Full article