Search Results (2,186)

ER-low breast cancer (1–9% ER expression) represents a biologically and clinically distinct entity at the interface between ER-positive and ER-negative disease. Although traditionally managed as hormone receptor-positive, mounting evidence indicates that ER-low tumors share molecular signatures, aggressive behavior, and chemotherapeutic responsiveness with triple-negative breast cancer. Accurate ER assessment is hindered by methodological variability and interpretative challenges, leading to potential misclassification and suboptimal treatment choices. While the benefit of endocrine therapy remains uncertain, ER-low tumors consistently show sensitivity to chemotherapy and promising responses to neoadjuvant chemo-immunotherapy, paralleling outcomes observed in triple-negative breast cancer cohorts. Emerging artificial intelligence tools, including digital pathology and multimodal deep learning, may enhance ER quantification, reduce observer variability, and enable more precise patient stratification. This review synthesizes current pathological and clinical insights into ER-low breast cancer and highlights evolving therapeutic strategies, with a forward-looking perspective on AI-driven approaches to optimize personalized treatment for this challenging subtype. Full article

Hepatoblastoma is the predominant primary liver malignancy in children, and outcomes remain poor for patients with metastatic disease. Long non-coding RNAs (lncRNAs) regulate tumor behavior, but their role in metastatic hepatoblastoma is not well defined. This study investigates the expression and functional significance of the lncRNA, maternally expressed gene 3 (MEG3), in a metastatic hepatoblastoma model. RNA sequencing comparing the metastatic hepatoblastoma cell line, HLM_2, with its parental HuH6 cell line identified MEG3 as being significantly upregulated in metastatic cells. MEG3 expression was examined using hepatoblastoma patient datasets and validated using qPCR in cell lines, orthotopic tumors, and COA67 patient-derived xenografts. The effects of siRNA MEG3 knockdown in HLM_2 cells on clonogenicity, migration, and invasion were evaluated. The effects of MEG3 overexpression on migration and invasion were assessed in HuH6 cells. MEG3 was significantly upregulated in metastatic cells and orthotopic tumors compared with controls. MEG3 silencing reduced clonogenicity, tumorsphere formation, migration, and invasion. MEG3 overexpression increased migration and invasion. These findings indicate that MEG3 contributes to an aggressive tumor phenotype, highlighting the need for further examination into its mechanistic role in hepatoblastoma and its potential as a biomarker or therapeutic target. Full article

Machined surfaces contain rich information about machining conditions and system behavior and are typically assessed using off-line, small-area metrology. This study developed and validated an image-based methodology for process-oriented surface texture analysis of end-milled Spheroidal Graphite Iron (SGI), enabling scalable, non-contact monitoring suitable for in-line deployment. End milling trials were conducted under optimized and aggressive cutting conditions and in two orthogonal feed directions (X,Y). Surface topography from White Light Interferometry (WLI) was complemented by Charge-Coupled Device (CCD) microscope imaging. Image processing comprised automatic orientation correction, intensity profile extraction, and frequency-domain analysis via Fast Fourier Transform and power spectral density estimation. Texture metrics (RMS amplitude, skewness, kurtosis, dominant wavelength) were derived from intensity profiles, and two spectral indices were introduced: a Change Index (CI), capturing high-frequency content linked to process disturbances, and a Surface Anisotropy Metric (SAM), quantifying texture directionality. Aggressive cutting increased RMS by 28.5% and shifted skewness by 274% with strong statistical significance. Directional analysis showed 22% higher texture amplitude in Y than X, indicating axis-dependent machine behavior. CI correlated with the machining parameters and stability, while SAM reflected the machine and setup characteristics. Trends were consistent with WLI, supporting the method as a rapid, complementary tool for surface quality and machine condition monitoring. Full article

Ultra-dense networks (UDNs) enable next-generation wireless systems by providing high capacity through aggressive base-station densification. However, dense deployments increase interference and energy consumption, making Quality-of-Service (QoS) aware performance evaluation and optimization challenging. Stochastic geometry (SG) provides a tractable framework for modeling large-scale UDNs, but its use is often limited by simplifying assumptions and simulation requirements. In parallel, Deep Learning (DL) offers scalable tools for capturing complex network behavior from data. This paper proposes a scalable analytical and data-driven framework for performance evaluation and energy efficiency (EE) optimization in UDNs. SG-based analysis is used to derive expressions for key metrics, including coverage probability and EE, under practical QoS constraints such as base-station density, transmit power, activation probability, and SINR thresholds. These results are used to construct a supervised learning dataset, where network parameters and SG derived metrics serve as inputs, and simulation outcomes act as labels. A DL model is trained to capture the nonlinear mapping between network configurations and performance metrics. Results show that the proposed framework predicts coverage probability and EE accurately for unseen UDN scenarios while substantially reducing computational complexity compared to conventional SG-based methods, without violating QoS constraints. Full article

Background: Cutaneous melanoma is an aggressive malignancy driven by complex interactions between tumor cells and the host immune system. Tumor progression is shaped not only by intrinsic tumor characteristics but also by immune-mediated processes within the tumor microenvironment. Cytokines, particularly interleukins, are key regulators of inflammation, immune cell recruitment, and tumor behavior. Cytokine profiling provides an integrated assessment of soluble immune mediators from tumor and stromal cells, reflecting both local and systemic immune responses. Methods: This narrative review summarizes and synthesizes the current literature addressing the biological and clinical relevance of selected interleukins, including IL-6, IL-8, IL-10, IL-2, IL-17, and IL-18, in cutaneous melanoma. Published data were evaluated with a focus on their immunomodulatory functions and potential implications for prognostic assessment. Results: Interleukins demonstrated distinct and context-dependent prognostic and predictive relevance in cutaneous melanoma. Elevated IL-2 levels correlated with sentinel lymph node positivity, supporting its prognostic value in early disease. Increased circulating IL-6 and IL-8 were consistently associated with tumor burden, advanced disease, and reduced survival. IL-10 expression reflected tumor progression and immune modulation. IL-17 signatures predicted response to combined immune checkpoint inhibition, particularly in BRAFV600-mutant melanoma. IL-18 exhibited dual roles, associating with both immune activation and favorable outcomes depending on tumor context. Conclusions: Interleukin profiling offers a biologically relevant framework for understanding immune regulation in cutaneous melanoma. Integrating interleukin signatures into prognostic models may support more refined risk stratification and advance the implementation of personalized medicine approaches in melanoma management. Full article

Recently, due to the expansion of telecommunication and power networks, as well as other structures, the demand for designing efficient and durable tall supporting columns has increased. Efficient steel columns are well known, including prestressed stayed beam–column systems. However, because of their relatively high cost, designers often turn to reinforced concrete structures, which are not only relatively cheaper but also sufficiently strong and resistant to aggressive external influences. Nevertheless, the large self-weight of reinforced concrete structures and considerable material consumption encourage the search for new efficient solutions. One such solution is the use of spun reinforced concrete structures. Compared to conventional reinforced concrete structures, these solutions not only reduce material consumption but also increase durability. This study examines an innovative prestressed stayed beam–column structure consisting of a spun reinforced concrete core and supporting prestressed steel tension ties. The behavior of such a composite structure is analyzed, and calculations of internal forces and displacements are presented. The rational parameters of the composing elements of this new prestressed stayed beam–column structure are discussed, and their influence on the stress–strain state of the structure is evaluated. Expressions are provided for calculating the rational bending moments of the spun reinforced concrete core. The obtained solutions make it possible to select rational cross-sections of the core and ties, as well as the required prestressing of the tension ties, without iterative calculations. Full article

The increasing need for sustainable construction materials has prompted research into alternatives to Ordinary Portland Cement (OPC), a major contributor to global CO₂ emissions. Geopolymers, synthesized via alkali activation of aluminosilicate precursors such as metakaolin and fly ash, are a promising alternative, reducing up to 80% of carbon emissions. However, their long-term durability in aggressive chemical environments, particularly when up against organic acids, remains insufficient. While mineral and inorganic acid resistance have been studied, the impact of naturally occurring organic acids like oxalic acid (Ox)—commonly found in soils and organic-rich sediments—has received limited attention. Ox is known to chelate metal ions and alter mineral phases, potentially affecting the integrity of geopolymer matrices. This study investigates the degradation behavior of geopolymers under continuous exposure to Ox (0.2, 0.4, and 0.6 M) at 25 °C using a flow-through reactor. Mass loss over time was monitored to determine reaction kinetics, while SEM, FT-IR, XRD, and EDS analyses were conducted to evaluate microstructural and chemical changes. The results revealed significant alterations in the geopolymers’ structures due to Ox exposure, providing key insights into their vulnerability to organic acid attack. These findings indicate the importance of considering organic acid interactions in long-term performance assessments of geopolymers. Full article

Polymer modification is a well-established strategy for improving the performance and extending the service life of cementitious and other construction materials, with direct implications for environmental sustainability and infrastructure resilience. Among these polymers, polyvinylpyrrolidone (PVP), a non-ionic, water-soluble, and highly compatible polymer, has emerged as a uniquely versatile additive for mitigating degradation in aggressive environments. This review provides a critical and comprehensive synthesis of the state-of-the-art research on PVP’s roles in cement, mortar, concrete, and asphalt systems. The novelty of this work lies in its mechanistic integration and system-level interpretation, which consolidate fragmented knowledge across multiple domains—ranging from rheology and durability to nanotechnology and interfacial engineering—into a unified and coherent framework. Through cross-study comparison, this approach establishes a comprehensive understanding of PVP’s role in cementitious systems while outlining clear pathways for future research and practical implementation. This review provides the first integrated framework that connects PVP’s molecular structure, adsorption behavior, and ion-coordination mechanisms to its macroscopic influence on rheology, hydration, microstructure, and long-term durability. The review critically analyzes the underlying mechanisms, including physical pore-filling and crack-bridging, as well as chemical ion-coordination, which collectively govern PVP’s performance. Key quantitative findings are consolidated, showing that optimal PVP addition can reduce water absorption by over 35%, increase fracture toughness by ~47%, and, when used as an interfacial modifier, enhance the strain capacity of fiber-reinforced composites by over 100%. Reported benefits include improved workability, enhanced mechanical performance and toughness, superior durability under chemical and frost exposure, and the development of functional materials such as self-sensing concretes and photocatalytic coatings that support structural health monitoring and pollution mitigation. Overall, this review synthesizes current knowledge, consolidates experimental evidence in tabular form, and identifies future opportunities for leveraging PVP in the design of sustainable, low-impact, and environmentally resilient construction materials and infrastructures. Full article

This paper presents a modeling framework for large-capacity lithium-ion battery energy storage systems (BESSs), developed within the Modelica LIBSystems library and focused on system-level integration. The framework builds on a combined analysis of the electrical, thermal and degradation behavior at the cell level to model the BESS interconnection to the electrical grid. A semi-empirical aging model was incorporated following its validation at the cell level against capacity loss experimental measurements. Two case studies were conducted for a 10.5 MW/15 MWh BESS installed in the isolated power system of Terceira Island. The first analyzed the short-term response to a 5% load step decrease under 60% and 80% renewable penetration scenarios, yielding a frequency nadir improvement of 3 mHz and 21 mHz, respectively. The second projected long-term degradation under two dispatch strategies: one derived from historical time series, and another synthetically constructed to induce more frequent and deeper cycling. After 1000 days of operation, the state of health declined to 95.2% in the historical-based case and to 93.5% under the aggressive profile. The proposed framework establishes a unified, cross-domain modeling workbench for Li-ion BESS applications, enabling evaluation of the system design, control strategies, operation conditions, and system-level performance across both dynamic and long-term horizons. Full article

Head and neck squamous cell carcinoma (HNSCC) remains a major global health challenge due to its aggressive behavior, late-stage diagnosis, and high incidence of therapy resistance. At the cellular level, these clinical limitations are driven by profound alterations in oncogenic signaling, stress adaptation, DNA damage response pathways, and immune regulation within the tumor microenvironment. Advances in nanotechnology offer powerful opportunities to address these challenges by enabling targeted interference with cellular processes that govern tumor growth, survival, and therapy resistance. “Ancient” (i.e., established, long-studied) nanostructures, including mineral-based nanoparticles, natural biopolymers, and plant-derived nanovesicles, provide inherently biocompatible and bioactive platforms capable of modulating cellular signaling, redox balance, and immune responses. In parallel, emerging nanosystems—such as nanobodies, engineered exosomes, DNA origami, and stimuli-responsive smart nanoparticles—allow precise molecular targeting, controlled cargo release, and direct manipulation of intracellular pathways and intercellular communication. This manuscript synthesizes historical and contemporary developments in nanostructure design, highlighting how the integration of ancient materials with advanced nanotechnology can reshape therapeutic strategies for HNSCC. By targeting key cellular and microenvironmental processes, including DNA damage response signaling, redox homeostasis, immune regulation and stress-adaptive survival mechanisms, rather than drug delivery alone, these integrated nano-platforms offer promising avenues to overcome resistance mechanisms, reprogram the tumor microenvironment, and improve therapeutic precision and patient outcomes. Full article

Externalizing problems are influenced by family dynamics, yet the specific mechanisms linking parental control to distinct externalizing problem behaviors remain unclear. This study examined the effects of parental behavioral control on proactive aggression, reactive aggression, and delinquent behavior, focusing on the mediating role of child disclosure. Data were collected from 3818 adolescents (aged 10–18) and their parents in Hong Kong. Results revealed that child disclosure served as a robust mediator. For mothers, full mediation was observed across all three outcomes. For fathers, full mediation was found for both subtypes of aggression, whereas partial mediation was observed for delinquent behavior. The indirect pathways were invariant across gender, suggesting the mechanism is universal. Notably, the model significantly predicted reactive aggression through a full mediation model from both mother and father. The study highlights the unique dual-pathway role of fathers—combining structural deterrence for delinquent behavior with relational communication for aggression and supports the efficacy of trust-based interventions for diverse externalizing problems. These findings suggest that effective parenting operates primarily by fostering a disclosure-promoting context rather than mere surveillance. Full article

Reservoir computing (RC) has emerged as an energy-efficient paradigm for temporal information processing, offering reduced training complexity by fixing recurrent dynamics and training only a simple readout layer. Among RC models, Echo State Networks (ESNs) and Liquid State Machines (LSMs) represent two distinct approaches based on continuous-valued and spiking neural dynamics, respectively. In this work, we present a comparative evaluation of ESNs and LSMs on the Mackey–Glass chaotic time-series prediction task, with emphasis on scalability, overfitting behavior, and robustness to reduced numerical error precision. Experimental results show that ESNs achieve lower prediction error with relatively small reservoirs but exhibit early performance saturation and signs of overfitting as reservoir size increases. In contrast, LSMs demonstrate more consistent generalization with increasing reservoir size and maintain stable performance under aggressive reservoir quantization. These findings highlight fundamental trade-offs between accuracy and hardware efficiency, and suggest that spiking RC models are well suited for energy-constrained and neuromorphic computing applications. Full article

This paper presents a comprehensive framework for evaluating the robustness and adaptability of human–robot collaboration (HRC) controllers under a spectrum of dynamic and unpredictable human intentions. Building upon variable admittance controller (VAC) frameworks augmented with Radial Basis Function Neural Network (RBFNN) online adaptation, we introduce two key innovations: (1) an intent-aware human force generator capable of simulating aggressive, hesitant, oscillatory, conflicting, and nominal behaviors, through the modulation of force gains and the introduction of stochastic noise, and (2) the extension of VAC to incorporate variable stiffness as an adaptive control parameter alongside damping and inertia. The adaptive parameters are jointly tuned online using a self-supervised learning (SSL) mechanism driven by motion error metrics and interaction dynamics. The framework is simulated in a dual-arm collaborative manipulation scenario involving two 7-DoF Franka Emika Panda robots transporting a shared object in a high-fidelity simulation environment. Simulation results demonstrate the system’s capability to maintain stable behavior and minimize tracking error despite abrupt changes in human intent. This work provides a novel and systematic tool for stress-testing adaptive controllers in HRC, with implications for the design of resilient, safe, and reliable robotic systems in real-world collaborative environments. Full article

In intensive pig production systems, limited space and lack of enrichment materials (EMs) restrict natural behaviors, inducing chronic stress and impairing welfare and health. Conventional EMs such as straw and sawdust improve comfort but increase NH₃ and particulate emissions and hinder manure management on slatted floors. This study compared rice-straw silage (RS), sawdust (SD), and sling belt (SB) as EMs for growing-finishing pigs to evaluate their effects on growth performance, behavior, body lesions, cleanliness score of body, and pen air quality. A total of 344 crossbred pigs ([Landrace × Yorkshire] × Duroc, 30.5 ± 3.10 kg) were randomly allocated to four treatments: Control, 50% slatted and 50% solid flooring; RS, 100% solid flooring with a 7-cm layer of RS; SD, 100% solid flooring with a 7-cm layer of SD; SB, 50% slatted and 50% solid flooring with 10 SBs (1.5 m long and 75 mm wide). At week 10, the RS pigs had the lowest body weight. At week 0, the RS and SD pigs exhibited more positive behaviors, although the SD pigs also showed the highest number of injurious interactions at week 3. Between weeks 0 and 5, the SD pigs spent less time lateral lying and more time sternal lying, while during weeks 8–11, sitting was more prevalent. Both RS and SD groups exhibited lower cleanliness scores at week 6 and higher NH₃ and CO₂ levels at week 10. In conclusion, bedding materials such as RS and SD promoted positive behaviors during the early phase; however, prolonged use without adequate management impaired hygiene, air quality, resting behavior, and growth performance. These findings highlight the importance of the appropriate selection and management of EMs in intensive pig production systems. Full article

Triple-negative breast cancer (TNBC) represents significant therapeutic challenges due to its aggressive behavior, metabolic plasticity, and lack of targeted treatments, prompting investigation of biologically active triterpene glycosides from the starfish Solaster pacificus. This study evaluated the ability of pacificusoside D (SpD) to synergistically enhance the anticancer efficacy of the glycolytic inhibitor 2-deoxy-D-glucose (2-DG) in TNBC MDA-MB-231 cells by targeting mitochondrial oxidative phosphorylation (OXPHOS). Methods included metabolic profiling via glucose uptake, lactate, and glutamate Glo assays; IC₅₀ determination by MTS and trypan blue assays; colony formation evaluation using a soft agar assay; and molecular mechanism elucidation by Western blot, fluorescence microscopy and spectrometry, and flow cytometry analyses. Results demonstrated that MDA-MB-231 cells predominantly utilized glycolysis under basal conditions, shifting to OXPHOS with 2-DG (0.5 mM). IC₅₀ values were 8.0/8.4 mM for 2-DG and 0.3/0.25 μM for SpD after 24 h of cell treatment. SpD exhibited a significant decrease in the number of colonies in MDA-MB-231 cells and possessed synergistic anticancer effects with 2-DG. Mechanistically, SpD increased tumor suppressor VHL expression level, down-regulated expression level of electron transport chain enzymes, generated reactive oxygen species, induced mitochondrial dysfunction, and triggered Bax/Bak-mediated apoptosis. These findings highlighted the synergistic anticancer potential of SpD in combination with 2-DG in aggressive breast cancer, offering insights into improved clinical outcomes in the future. Full article