Search Results (7,849)

The tumor microenvironment [TME] is a dynamic ecosystem where spatial organization and metabolic reprogramming play a crucial role in immune response, tumor progression, and therapeutic response. Recent breakthroughs in spatial transcriptomics, metabolomics, and multiplexed imaging studies have shown that complex immunometabolic niches are involved in therapeutic resistance, including conventional and immunotherapeutic approaches. Artificial intelligence [AI] technology has been recognized as a revolutionary concept that allows the integration of complex data, thereby facilitating the scalable extraction of spatial, molecular, and cellular features from routine histopathology and multi-omics platforms. This review of the current evidence on AI-based spatial immunometabolic studies of the tumor microenvironment aims to provide a comprehensive overview of the current evidence, including AI-based spatial immunometabolic studies of the tumor mi-croenvironment, with special reference to digital pathology, spatial transcriptomics, and multimodal data fusion. The current challenges, including data heterogeneity, model interpretability, generalizability, and biological validation, will be discussed. The emerging trends in AI-based spatial immunometabolism, including multimodal foundation models, federated learning, and spatially resolved target discovery, will be discussed. AI-based spatial immunometabolism will be a cornerstone in precision oncology, with the potential to improve patient stratification, therapeutic approaches, and clinical translation. Full article

The overexpression of cyclin-dependent kinase inhibitor p16 (INK4a) is widely recognized as a surrogate marker for high-risk human papillomavirus (HPV) in anogenital malignancies, but its significance in invasive breast carcinoma is complex and remains frequently debated. While historically investigated as a viral proxy, emerging evidence suggests that elevated p16 levels in breast tissue may instead reflect intrinsic cell-cycle dysregulation and retinoblastoma (Rb) pathway disruption, though direct molecular confirmation is lacking in this area of research. This study aims to evaluate the role of p16 as an indicator of tumor aggressiveness for high-risk phenotypes. We conducted a retrospective study of 100 female patients with invasive breast carcinoma. Employing a purposive sampling strategy rather than a consecutive series, we analyzed a targeted cohort consisting predominantly of triple-negative breast cancer (TNBC) and high-grade tumors to evaluate biomarker patterns specifically in advanced disease contexts. Immunohistochemical assessment was performed using a standardized cumulative nuclear and cytoplasmic scoring system, with expression thresholds defined by receiver operating characteristic (ROC) curve analysis optimized for histological grade. p16 overexpression was a predominant characteristic of these aggressive tumors and was identified in 64% of cases. Statistical evaluation revealed a robust and significant correlation between p16 overexpression and the triple-negative molecular subtype, as well as a marked inverse relationship with estrogen receptor (ER) status. Although p16 levels were frequently associated with specific aggressive phenotypes, no statistically significant difference in overall survival was observed between expression groups, a finding attributable to the uniformly high-risk nature of the selected cohort. This study suggests an association between p16 expression levels and aggressive tumor features, although the study design limits causal inferences. A non-significant trend towards p16 overexpression was observed in ductal carcinomas compared to lobular subtypes, while high p16 expression was noted exclusively in G3 tumors within this selected cohort, a finding influenced by the purposive sampling strategy and the ROC-based cutoff definition. Tumor necrosis was more prevalent in p16-overexpressing tumors. Furthermore, p16 levels showed a strong inverse relationship with estrogen receptor (ER) status, as they were significantly elevated in ER-negative and triple-negative tumors compared to luminal phenotypes. Full article

Background: The emergence of multidrug-resistant (MDR) Gram-negative pathogens, namely Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii and Helicobacter pylori, necessitates urgent therapeutic alternatives. This scoping review aimed to summarize the current evidence on the efficacy of lytic bacteriophages against these critical MDR pathogens, and to identify existing research gaps and implementation challenges. Methods: The literature search was conducted by searching PubMed, Web of Science, and Scopus AI for studies published from 2015 to 2025. The inclusion criteria focused on experimental and human studies evaluating phage therapy against MDR, extensively drug-resistant (XDR), or pan-drug-resistant (PDR) strains in the four target species. A total of 172 articles were included. Results: A number of studies showed an increasing trend (2015–2025), focusing mainly on K. pneumoniae (n = 65), P. aeruginosa (n = 55), and A. baumannii (n = 48). No eligible studies for MDR H. pylori were found. All 172 studies confirmed lytic activity, with phage cocktails showing superior antibacterial activity than single phages in four studies. Phages also demonstrated antibiofilm activity (n = 44). Most animal studies reported successful bacterial reduction in animals treated with phages, and 87.5% of 23 human case studies reported patient improvement or infection clearance. However, heterogeneity in the types of animal models used and in dosage and administration routes in human studies was notable. Conclusions: Lytic bacteriophages exhibit strong potential as a new therapeutic option. Key challenges include the lack of data for MDR H. pylori, heterogeneity in animal models, and a paucity of large-scale human clinical trials. Future research must prioritize standardization, mechanistic studies, and conducting robust human trials to enable clinical translation and regulatory acceptance. Full article

The accelerated processes of industrialization and urbanization have led to increasingly prominent environmental risks by emerging organic contaminants (EOCs) in wastewater. These contaminants are characterized by low concentrations, high toxicity, and complex composition, making their efficient removal crucial for safeguarding ecological security and human health. Advanced oxidation processes exhibit significant potential for the removal of EOCs due to their high degradation efficiency. However, current treatment paradigms remain constrained by several critical issues. Notably, the routine over-oxidation of low-toxicity small-molecule organics solely aims to satisfy chemical oxygen demand (COD) compliance standards. This unnecessary practice not only increases operational costs and carbon footprint but also leads to energy waste and reduced overall treatment efficiency. Based on the current technological landscape, this paper analyzes the core challenges in the removal of EOCs at present. In light of policy orientations and technological trends, it outlines future research directions and industrial development pathways, providing insights for achieving the synergistic goals of efficient removal of EOCs, low carbon emissions, and cost-effective operation. Full article

Jordan is witnessing a growing market trend of retrofitting existing buildings. The annual construction work on existing buildings in Amman, based on building consents, increased by approximately 46% between 2007 and 2017, while the annual newly built areas decreased by around 33%. This paper aims to establish a solid understanding of the current shift towards existing building adaptation in Jordan by exploring the drivers for this trend and the Government’s role in regulating and, possibly, encouraging it. Ten local experts with extensive experience in retrofitting projects in Jordan and around the region were interviewed. The qualitative and quantitative analysis of experts’ answers was performed using the software NVivo. Findings highlight nine main drivers for retrofitting existing buildings in Jordan, namely: (1) land value and location; (2) reducing capital costs compared to new builds; (3) architectural heritage conservation; (4) social and cultural considerations; (5) adapting to population increase; (6) reusing, adapting, and retrofitting to extend the life of buildings; (7) increasing tourism capacity; (8) improving building performance and resource efficiency; and (9) municipal incentives. Not all these drivers have the same value as they depend on the client and the project context. The experts’ ranking of drivers in terms of priority showed higher consideration for land value and location benefits, social–cultural aspects, and population increase, while municipal incentives emerged as low priority. Further research is needed to design context-specific effective retrofit policies, contributing to the literature in this emerging field in Jordan and beyond. Full article

Background: Post-infarction heart failure (HF) remains a major contributor to morbidity and mortality despite advances in reperfusion and pharmacological management. However, the combined influence of clinical background, myocardial injury, neuro-hormonal activation, and angiographic disease on HF severity is not fully defined. Methods: We retrospectively analyzed 181 patients with confirmed myocardial infarction treated in a tertiary cardiology center. Demographics, cardiovascular risk factors, prior chronic HF, inflammatory markers (CRP, fibrinogen, ESR, leukocyte indices), and high-sensitivity troponin (hs-Tn) were measured at admission (pre-intervention), immediately after percutaneous coronary intervention (PCI), and at 48 h, angiographic lesion distributions were collected. HF severity was graded on a five-level scale and further dichotomized as no/mild HF (grade 0–1) versus moderate–severe HF (grade ≥ 2). Group comparisons and multivariable logistic regression were used to identify independent determinants of severe HF. Results: Moderate–severe HF occurred in 42.5% of patients (77/181). Compared to HF 0–1, the HF ≥ 2 group was older (64.0 vs. 60.5 years, p = 0.042) and exhibited substantially higher systemic inflammation (CRP 41.5 vs. 9.75 mg/L, p < 0.001; fibrinogen 435 vs. 346 mg/dL, p = 0.0002; ESR 28 vs. 18 mm/h, p = 0.0004). hs-Tn levels and NT-proBNP were significantly elevated in HF ≥ 2 (NT-proBNP 3449 vs. 1243 pg/mL, p = 0.0003), while left ventricular ejection fraction was reduced. Prior HF increased the likelihood of HF ≥ 2 (54.5% vs. 33.7%, p = 0.0078), and conservative therapy was associated with adverse outcomes (87.5% vs. 40.5%, p = 0.0235). In multivariable analysis, NT-proBNP remained the only independent predictor of moderate–severe HF, while CRP showed a positive but non-significant trend after adjustment. Conclusions: Post-MI HF severity reflects the combined influence of myocardial injury, neurohormonal stress, and systemic inflammatory activation. However, in multivariable analysis, NT-proBNP emerged as the dominant independent predictor of moderate–severe HF, while CRP reflected an associated but non-independent inflammatory signal. Full article

Vertical-cavity surface-emitting lasers (VCSELs) have emerged as essential light sources for atomic-precision measurement, quantum-secure communication, high-speed optical transmission, and laser coherent scanning detection, owing to their low power consumption, high-quality beam characteristics, and ease of two-dimensional integration. However, the fundamental limitation on linewidth narrowing in VCSELs arises from their inherently short resonator, resulting in a natural linewidth on the order of 50–100 MHz. This limitation prevents conventional VCSELs from meeting the stringent requirements of advanced applications, making the ultra-narrow linewidth a key focus in optoelectronics research. This review analyzes representative achievements and application scenarios of narrow-linewidth VCSELs, evaluates the merits and limitations of industrial-grade devices, and envisions future directions in next-generation optoelectronic systems. Distinct from existing reviews, it integrates key single-mode fabrication techniques, quantitative linewidth requirements across applications, silicon photonic integration, and scalable manufacturing trends, establishing a complete mechanism–technology–application–industry analytical framework. Full article

Financial fraud has become a critical challenge for modern financial systems due to the rapid growth of digital transactions, online banking services, and electronic payment platforms. Traditional rule-based fraud detection systems are increasingly inadequate in addressing the evolving and adaptive strategies employed by fraudsters. Consequently, Machine Learning (ML) and Deep Learning (DL) techniques have emerged as powerful tools for detecting fraudulent activities in large-scale financial datasets. This paper presents a comprehensive survey of ML/DL approaches for financial fraud detection. The survey systematically reviews existing research across multiple methodological paradigms, including classical supervised learning, anomaly detection, graph-based methods, deep neural networks, multimodal architectures, and cost-sensitive learning frameworks. Particular emphasis is placed on emerging techniques such as graph neural networks, transformer-based architectures, and federated learning approaches designed to address privacy and scalability challenges. In addition to reviewing model architectures, this work analyzes key challenges inherent to fraud detection systems, including extreme class imbalance, concept drift, adversarial behavior, data privacy constraints, and real-time deployment requirements. Furthermore, the survey examines evaluation methodologies, highlighting the limitations of commonly used metrics and discussing more realistic evaluation strategies that incorporate operational costs and risk management considerations. This paper also provides a structured taxonomy of fraud detection methods, comparative analyses of commonly used datasets, and a synthesis of current research trends. Finally, open challenges and promising research directions are identified, including adaptive learning systems, interpretable Artificial Intelligence models, graph-based behavioral modeling, and privacy-preserving collaborative fraud detection frameworks. Full article

Indium oxide (In₂O₃) has recently emerged as a promising semiconductor for advanced electronics due to its high electron mobility and wide bandgap. In this article, the lateral scaling characteristics of top-gate In₂O₃ thin-film transistors (TFTs) featuring a 1.5 nm thick channel and a 7 nm thick HfO₂ gate dielectric are investigated by two-dimensional device simulation. The analysis covers short-channel effects, DC characteristics, transconductance behavior, and small-signal radio frequency (RF) metrics across a gate-length (L_G) range of 20 nm to 700 nm. Simulation results identify a critical gate length near 100 nm for the transition from long-channel to short-channel behavior. For L_G ≤ 100 nm, pronounced short-channel effects emerge, featuring a significant negative V_TH shift and a drain-induced barrier lowering (DIBL) coefficient up to ~130 mV/V. A non-classical gm scaling behavior is observed, where g_{m_max} initially increases with L_G, then remains within a narrow range and eventually evolves toward the conventional long-channel trend. Further analysis of the lateral electric field distribution, field-dependent mobility, and transconductance efficiency indicates that this behavior originates from a crossover between short-channel field-assisted transport and gate-controlled channel modulation. The devices show strong RF potential, with f_T and f_max reaching 124.32 GHz and 157.64 GHz, respectively, at L_G = 20 nm. The high-mobility In₂O₃ channel leads to a less distinct f_T scaling transition from the classical 1/L²_G dependence to the short-channel 1/L_G dependence, while f_max scaling evolves through different regimes governed by capacitance-related limitations, intrinsic transport enhancement, and short-channel non-idealities. This work provides physical insight into the lateral scaling behavior of ultrathin top-gate In₂O₃ TFTs and highlights their potential for high-frequency and power-dense applications. Full article

This research paper examines the spatial and time variation of demographic dependency in Europe in a 30-year horizon of the evolution of the demographic dividend regarding the economic dependency ratio (ADR1). We used the Curve Fit Forecast tool to estimate the trends of ADR1 in each of the EU Member States using data on Eurostat projections and a sophisticated geostatistical analysis tool developed in ArcGIS Pro 3.2.2. The findings indicate that the dependency in all countries has increased significantly in a statistically significant manner as the Gompertz function has appeared as the best curve in a third of the cases. It is an S-shaped asymptotic behaviour of this function that effectively describes the nonlinear patterns of acceleration and saturation of demographic ageing. As indicated in the analysis, the European regions are increasingly moving apart, with the southern and eastern nations such as Romania demonstrating the most alarming decline in ADR1. These trends highlight the need to reform labour market policies and social protection mechanisms to an ageing population. The paper combines the curve-fitting, descriptive statistics (median, skewness, interquartile range (IQR)) with time clustering (value, correlation, and Fourier) to provide an effective, replicable approach to early warning and policy prioritisation. Overall, the results highlight the importance of integrating predictive spatial modelling and demographic economics to support anticipatory and evidence-based policy decisions. The proposed approach proves to be a robust and transferable framework, applicable to a wide range of socio-economic phenomena characterised by inertia and structural change. Future research should extend the analysis to subnational levels, incorporate additional explanatory variables, and develop scenario-based simulations, including multivariate Gompertz-type models, to further enhance both predictive accuracy and policy relevance in the context of emerging structural labour scarcity. Full article

Background: Language impairment is a core feature of Major Neurocognitive Disorder (MND), yet the domain-specific relationship between language functioning and everyday functional status remains insufficiently characterized. Methods: We conducted a retrospective observational study in 125 older adults diagnosed with MND according to DSM-5 criteria with mild-to-moderate cognitive impairment measured with Mini-Mental State Examination (MMSE). Language performance was assessed using semantic, phonemic verbal fluency and confrontation naming. Functional status was evaluated using basic (BADL) and instrumental activities of daily living (IADL). Ordinal logistic regression models examined associations between language domains and functional outcomes, adjusting for global cognitive status (MMSE), demographic variables, multimorbidity, and depressive symptoms. Model fit was evaluated using the Akaike Information Criterion. Results: Semantic fluency emerged as the best-performing predictor of BADL across all hierarchical models, remaining statistically significant after full adjustment for MMSE and clinical covariates (β ≈ 0.60, p < 0.05). Phonemic fluency showed the most robust association with IADL, with a stable effect across models, reaching a trend toward statistical significance in the fully adjusted analyses (β ≈ 0.22–0.27, p = 0.069). Naming ability did not influence functional outcomes. All observed associations persisted after controlling for MMSE, demographic variables, multimorbidity, and depressive symptoms. Conclusions: Language abilities showed differential associations across language domains with functional status in this sample of patients with MND. Semantic fluency was associated with basic self-care, while phonemic fluency showed a trend toward association with instrumental daily activities. These relationships remained observable after adjustment for global cognitive impairment, suggesting verbal fluency as a potentially sensitive marker of functional vulnerability. Full article

In recent years, increasing consumer demand for healthier and more natural foods has driven the food industry to replace artificial additives. Among these, colorants play a crucial role, as they influence the sensory perception and acceptance of food products. However, the widespread use of synthetic colorants has raised growing concerns due to their potential association with adverse health effects. In addition, several regulatory agencies have restricted or banned the use of certain synthetic colorants, requiring their replacement with natural alternatives. In this context, anthocyanins have emerged as a promising substitute for artificial colorants, owing to their similar color properties. Despite their potential, their use as a food colorant still faces several challenges, particularly regarding stability, incorporation into food matrices, and regulatory constraints. Therefore, this review examines the challenges and current trends in natural colorants, highlighting the potential of anthocyanins as substitutes for synthetic red colorants in food products. Full article

Smart hydrogel systems with stimuli-responsive properties are increasingly being investigated in combination with advanced additive manufacturing techniques for targeted drug delivery and wound healing in regenerative medicine; however, their clinical translation remains limited by challenges related to material performance, design complexity, and manufacturing scalability. This review analyzes recent developments in smart hydrogel design and 4D-printed scaffolds, with emphasis on programmable and stimuli-responsive architectures. The literature is selectively evaluated based on relevance to (i) hydrogel structure–property relationships, (ii) 3D/4D printing strategies, and (iii) demonstrated performance in drug delivery and wound healing applications. The analysis highlights design approaches enabling spatiotemporal control of drug release and dynamic scaffold behavior, while also examining how fabrication methods influence functional outcomes. Major limitations are critically assessed, including issues of reproducibility, mechanical stability, long-term performance, and the gap between experimental studies and clinical application. Challenges in defining and implementing 4D printing in biomedical contexts are discussed as well. Overall, this review identifies current design trade-offs, outlines priorities for improving reliability and translational potential, and synthesizes emerging trends in 3D and 4D printed hydrogel scaffolds for precision drug delivery and regenerative wound therapy. Full article

Environmental pollution constitutes an increasingly complex global challenge, largely driven by industrial expansion and the consequent release of toxic species such as Cd²⁺, Pb²⁺, Cu²⁺, Hg²⁺, Fe³⁺, As³⁺, and Rh³⁺ into natural ecosystems. These contaminants pose significant risks to environmental integrity and public health, motivating the development of analytical technologies capable of sensitive, selective, and reliable detection. In this context, graphene-based electrochemical sensors have emerged as versatile platforms for monitoring a broad range of analytes, particularly in environmental applications involving heavy-metal detection. The intrinsic physicochemical properties of graphene derivatives have enabled low detection limits, rapid response times, and tunable selectivity. Despite analytical advances, critical challenges persist regarding operational stability in complex matrices, inter-batch reproducibility, and robustness to interfering species, which continue to hinder large-scale deployment and real-world applicability. However, challenges remain regarding stability and performance in complex arrays, reproducibility, and resistance to interference, necessitating innovative strategies for functionalization and molecular recognition. This review article establishes a comparative framework based on functionalization strategies (covalent, non-covalent, and hybrid), the chemical nature of graphene (GO, rGO, and doping), and various types of polymers (conductors and insulators), using statistical metrics such as the limit of detection (LOD), linear range, working potential, stability, and interferences, employing a bibliometric analysis using the PRISMA 2020 methodology. This comparative framework enables analysis and explanation of performance trends, and the generation of design and functionalization recommendations for versatile applications, including criteria for reproducibility and sustainability. Full article

Computational chemistry has played a central role in early-stage drug discovery by accelerating target selection, hit identification, and lead optimization. This review summarizes recent developments in molecular docking, pharmacophore modeling, molecular dynamics (MD), and virtual screening (VS), with a focus on their application in practical drug discovery workflows. Advances in docking protocols, including consensus scoring, physics-based rescoring, and ensemble approaches, addressed the challenges of receptor flexibility. Both ligand-based and structure-based pharmacophore models facilitated scaffold hopping and guided library prioritization. MD simulations were used to assess binding pose stability, identify cryptic binding pockets, and characterize solvent interactions. These simulations also supported free-energy calculations using endpoint and alchemical methods. Large-scale VS campaigns employed curated compound libraries, often composed of make-on-demand molecules, and relied on high-performance computing or cloud infrastructure to screen up to 10⁹ compounds. Hits were validated using orthogonal biophysical assays and filtered by absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions. Integrated pipelines combining pharmacophore modeling, docking, MD, and free-energy calculations improved enrichment rates and reduced the number of compounds requiring synthesis. Several case studies demonstrated the identification of nanomolar-affinity leads from ultra-large screening campaigns. The review also addressed ongoing challenges, such as inconsistent scoring of binding affinity, protonation, and tautomeric errors, dataset bias, and reproducibility issues. Strategies to mitigate these limitations included standardized library preparation, adherence to FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, and the use of prospective benchmarking protocols. The review discussed emerging trends, including the use of quantum chemistry for electronic structure refinement, ensemble docking guided by cryo-electron microscopy (cryo-EM) data, and the integration of computational tools with automated synthesis and high-throughput screening in closed-loop discovery systems. These approaches have the potential to accelerate the design–make–test cycle, increase hit novelty, and improve decision-making in early drug development programs. Full article