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Multiple myeloma (MM) is an incurable plasma cell malignancy characterized by high metabolic activity, chronic endoplasmic reticulum stress, and persistent redox imbalance. Excessive immunoglobulin synthesis and adaptation to the hypoxic bone marrow microenvironment lead to sustained production of reactive oxygen species (ROS). Their excessive accumulation promotes genomic instability, disease progression, osteolytic bone disease, and resistance to therapy. Paradoxically, MM cells adapt to oxidative stress by activating antioxidant and metabolic defense mechanisms, including Nuclear factor erythroid 2-related factor 2 (NRF2)- and Heme Oxygenase 1 (HMOX1)-dependent pathways, metabolic reprogramming, and overexpression of ROS-scavenging enzymes such as peroxiredoxin 6 (PRDX6), allowing survival at the threshold of oxidative toxicity. Evidence indicates that biomarkers of oxidative stress—such as lipid and protein oxidation products, antioxidant enzyme activity, and the Oxidative Stress Score—correlate with disease stage, prognosis, and treatment response. Redox-modulating therapeutic strategies, including pharmacological ROS induction, inhibition of antioxidant defenses, and the use of natural pro-oxidant compounds, are emerging as promising adjuncts to standard MM therapies. Recent studies also highlight the gut microbiota as an indirect regulator of oxidative balance, immune modulation, and metabolic homeostasis in MM. This review summarizes current knowledge on oxidative stress in multiple myeloma, emphasizing its role in pathogenesis, drug resistance, biomarker development, and emerging therapeutic and supportive strategies. Full article

The dynamic self-assembly and phase separation of donor–acceptor blends are processes that dictate the nanoscale morphology in organic solar cells. Here, we employ a fluidics-inspired framework, integrating dissipative particle dynamics simulations with percolation theory, to investigate the morphogenesis of two non-fullerene systems: P3HT-PPerAcr and P3HT-PFTBT. We analyze monomeric and homopolymer blends, and copolymer macrostructures, focusing on how key parameters such as temperature and polymer chain flexibility govern the dynamic evolution towards percolating networks. Our simulations captured the fundamental fluidic behavior and universal scaling near the critical percolation threshold (χ_c). The critical exponent β revealed distinct universality classes dictated by system compatibility and flexibility: monomeric and flexible homopolymer blends below the critical temperature (T_c) exhibit mean field behavior (β ≈ 1). In contrast, monomeric systems above χ_c and flexible copolymers below χ_c display 3D percolation behavior (β ≈ 0.45). In the case of flexible copolymeric macromolecules, above percolation threshold a quasi-bidimensional behavior emerge with (β ≈ 0.1). Notably, semi-rigid and rigid homopolymeric and copolymeric linear architectures induce a dimensional crossover, yielding quasi-2D (β ≈ 0.14) and quasi-1D (β ≈ 0.0) morphologies. These findings establish a direct link between tunable fluidic interactions, chain dynamics, and the emergence of optimal bicontinuous percolation networks. Full article

Background: Tumour volume percentage (TVP) is considered an important pathological parameter, particularly in prostate cancer, representing the ratio of tumour volume to the total gland, and it can be used to measure the quantity of malignancy. Previous reports have already demonstrated that an elevated tumour volume percentage is associated with unfavourable factors, including extraprostatic extension, positive surgical margins, and lymph node metastasis. The independent value of TVP, especially in high-risk prostate cancer treated by radical prostatectomy, remains an area of active research, despite established prognostic factors such as PSA, ISUP grade, and TNM stage. Materials and Methods: We retrospectively analyzed the records of 159 high-risk prostate cancer patients who underwent open or laparoscopic radical prostatectomy between January 2016 and January 2025 at the Clinic of Urology of Targu Mures. High-risk patients were defined as those with ISUP grade 4–5 or PSA >20 ng/mL or clinical stage ≥T2c or stage cT3–4 and/or lymph node metastasis. Tumour volume percentage was calculated from the final pathology result and was determined as the proportion of prostate cancer volume relative to the total prostate volume. Clinical and pathological features, including PSA, ISUP grade, TNM stage, surgical margin, and lymph node involvement, were reported. To assess TVP as an indicator of tumour aggressiveness, univariate and multivariate regression analyses were performed. A p-value <0.05 was considered statistically significant. Results: A total of 159 high-risk prostate cancer patients (100%), with a median age of 66 years, who underwent open or laparoscopic radical prostatectomy were included. The median tumour volume percentage was 7.6%, and the median prostate volume was 43.8 cc. On univariate analysis, patients with extraprostatic extension (p < 0.001), positive surgical margins (p = 0.005), a higher ISUP grade (p < 0.001), and lymph node metastasis (p = 0.006) exhibited higher TVP compared to their counterparts. A significant correlation was also observed between TVP and the number of positive biopsy cores (p < 0.001), a higher PSA value (p = 0.005), and a younger age (p = 0.041). Conversely, no correlation was identified between TVP and perioperative factors such as hospital stay, surgery duration, ICU days, type of approach, or positive urine culture. Two regression models on multivariate analyses were performed with TVP as the dependent variable. In the continuous variable model (Adjusted R² = 0.43, p < 0.001), independent predictors of higher TVP were the number of positive biopsy cores (B = 0.54, p < 0.001), the number of positive lymph nodes (B = 2.59, p < 0.001), and surgical margin dimension (B = 1.19, p < 0.001). Age, PSA, and perioperative variables showed no significant correlation with TVP on multivariate analysis. In the categorical regression model (Adjusted R² = 0.438), statistical significance was confirmed (F-test, p < 0.001). Independent predictors of increased tumour volume percentage included ISUP grade 5 in the effect-coded model (B = +6.60, 95% CI: 0.96–12.25, p = 0.022), and pathological TNM stage pT4 (B = +24.70, 95% CI: 17.69–31.70, p < 0.001). ROC analysis showed limited-to-moderate discrimination for positive surgical margins (AUC = 0.655; 95% CI 0.565–0.744; p = 0.001) and stronger discrimination for pN1 (AUC = 0.793; 95% CI 0.650–0.936; p = 0.002). The Youden-derived cut-offs were 4.90% for positive surgical margins and 5.77% for lymph-node metastasis. Conclusions: Tumour volume percentage is significantly associated with several adverse pathological features in high-risk prostate cancer. Rather than a standalone biomarker, its association with adverse pathological features underscores its potential role in risk stratification models, and the incorporation into pathology reports and prognostic nomograms may improve clinical decision-making. Full article

Adolescence is a key period of significant physiological and social development, during which social anxiety symptoms often emerge and can impact academic and social functioning. Social anxiety disorder (SAD) involves heightened sensitivity to social cues and impaired social information processing, potentially contributing to persistent anxiety symptoms. However, research exploring the neural mechanisms of social information processing in adolescents with social anxiety remains limited. The investigation employed a facial dot-probe paradigm combined with EEG measurements to assess differences in attentional processing and neurophysiological activity between two adolescent groups: a high-social-anxiety (HSA) group (N = 27) and a low-social-anxiety (LSA) group (N = 18). Results showed (1) there was a significant reduction in P2 amplitudes in the HSA group compared to the LSA group. (2) A significant negative correlation between the disengagement index (DI) and P2 amplitude was found. (3) Weaker functional connectivity in the theta band was found in the HSA group. (4) In the graph theory analysis, the HSA group exhibited significantly higher node efficiency across various frequency bands compared to the LSA group. The findings suggest that socially anxious adolescents have impaired attentional control toward social cues. This difficulty may reinforce their anxiety symptoms over time. Full article

Noise contamination is a common challenge in the analysis of time series data, where stochastic perturbations can obscure deterministic dynamics and complicate the interpretation of signals from chaotic and physiological systems. Reliable identification of noise regimes and their intensity is therefore essential for robust analysis of dynamical and biomedical signals, where incorrect attribution of stochastic perturbations can lead to misleading interpretations of system behavior. For this reason, the present study examines the role of complexity-based descriptors for identifying stochastic perturbations in time series and analyzes how these metrics respond to different noise regimes across heterogeneous dynamical systems. A supervised learning approach based on complexity descriptors was developed to analyze controlled perturbations in multiple signal types. Gaussian, pink, and low-frequency noise disturbances were injected at predefined intensity levels into the Rössler and Lorenz chaotic systems, the Hénon map, and synthetic electrocardiogram signals, while AR(1) processes were used for validation on inherently stochastic signals. From these systems, eighteen entropy-based, fractal, statistical, and singular value decomposition-based complexity metrics were extracted from either raw signals or reconstructed phase spaces. These features were used to perform three classification tasks that capture different aspects of noise characterization, including detecting the presence of noise, identifying the perturbation type, and discriminating between different noise intensities. In addition to predictive modeling, the study evaluates the complexity profiles and feature relevance of the metrics under varying perturbation regimes. The results show that no single complexity metric consistently discriminates noise regimes across all systems. Instead, system-specific relevance patterns emerge. Under given experimental constraints (data partitioning, machine learning algorithm, etc.), Approximate Entropy provides the strongest discrimination for the Lorenz system and the Hénon map, the Coefficient of Variation, Sample and Permutation Entropy dominate classification for ECG signals, and the Condition Number and Variance of first derivative together with Fisher Information are most informative for the Rössler system. Across all datasets, the proposed framework achieves an average accuracy of 99% for noise presence detection, 98.4% for noise type classification, and 98.5% for noise intensity classification. These findings demonstrate that complexity metrics capture structural and statistical signatures of stochastic perturbations across a diverse set of dynamic systems. Full article

Monkeypox virus, a zoonotic DNA virus belonging to the Orthopoxvirus genus, has emerged as a global health issue because of its fast spread to 104 nations over six continents. In the current study, an immunoinformatics pipeline was used to design a multiepitope-based prophylactic vaccine targeting the A35R glycoprotein and E8L membrane proteins of the monkeypox virus. Selected target proteins were surface-exposed, non-homologous to the human proteome, and essential for viral pathogenesis. B-cell and T-cell (MHC-I and MHC-II) epitopes with high antigenicity (>0.5), non-allergenicity, non-toxicity, and highly soluble in water with strong affinity towards innate and adaptive receptors, were prioritized. Shortlisted epitopes were combined to design the final vaccine utilizing an adjuvant (50S ribosomal L7/L12) and appropriate linkers for improved immunogenicity. Population coverage analysis showed wide HLA representation with 83.57% (MHC-I) and 88.8% (MHC-II) global coverage, including 89.6% for West Africa and 87.3% for Central Africa. Docking analysis of the vaccine construct with the TLR-4 receptor revealed stable interactions (−695.6 kcal/mol). Molecular dynamics simulations and binding free energies further confirmed structural stability. Immune simulations predicted strong activation of both humoral and cellular immune responses. These results indicate that the designed multiepitope vaccine construct is a viable option for additional experimental validation against the monkeypox virus. Full article

Extraordinary emergencies trigger disruptive demand surges that frequently exceed the operational limits of existing supply chains. While traditional studies focus on optimizing stock resource efficiency, the mobilization of emergency production to generate incremental resources is critical under extreme shocks. However, a standardized methodology for assessing the “stress tolerance limit” of emergency material supply chains (EMSCs) remains lacking. This paper establishes a theoretical framework for EMSC stress testing, integrating conceptual definitions, operational mechanisms, and a standardized implementation procedure. To demonstrate its practical applicability, a multi-objective mathematical model is developed and applied to a case study of ibuprofen production during a sudden crisis. By identifying structural bottlenecks such as production latency and supply lead-time gaps, the results validate that the proposed framework provides a reproducible quantitative approach for evaluating EMSC supply capacity. This study offers guidance for prepositioned inventory and dynamic capacity reserve, fundamentally enhancing societal risk mitigation capabilities under extreme stress. Full article

To address the high hardware costs associated with hyperspectral imaging in precision agriculture, spectral reconstruction (SR) is emerging as a feasible solution for obtaining hyperspectral images. However, existing methods, mainly including CNN and Transformer, face a notable dilemma: convolutional neural networks (CNNs) are limited by their local receptive fields, while Transformers encounter the problem of quadratic computational complexity. Effectively balancing computational efficiency with the capture of long-range spatial dependencies remains a significant challenge. To this end, this study proposes FGA-Mamba (Frequency-Gradient Attention Mamba), a novel reconstruction network based on the Mamba architecture. This network introduces a Frequency-Visual State Space (F-VSS) module, which combines the linear long-range modeling capability of state space models (SSMs) with a frequency-domain self-calibration mechanism to enhance global structural consistency by explicitly modulating frequency features. In addition, we designed an Enhanced Gradient Attention Module (EGAM). This module optimizes local feature representation through a gradient-aware mechanism, effectively compensating for the loss of spatial details. Experimental results on 3 datasets shows that FGA-Mamba have significant improvement in both quantitative and qualitative metrics. Moreover, the high consistency observed in vegetation index (VI) calculations confirms its potential for practical agricultural application. Full article

Background/Objectives: Cardiac rehabilitation (CR) is a cornerstone of secondary prevention, traditionally delivered through supervised center-based models. However, significant logistical barriers and high healthcare costs necessitate a paradigm shift. This review aims to assess the impact of emerging digital frontiers, specifically telerehabilitation (CTR) and artificial intelligence (AI), on overcoming these challenges and improving clinical outcomes. Methods: This study is a narrative, clinically oriented review informed by a structured search of PubMed/MEDLINE and EMBASE for literature published between January 2015 and January 2026. Results: Evidence indicates that CTR is non-inferior to center-based programs in terms of exercise capacity and quality of life (QoL). Digital tools, such as wearable devices and mobile health (mHealth) applications, have significantly increased program participation and improved adherence to lifestyle modifications. Furthermore, the integration of AI facilitates early detection of cardiac events and personalized exercise prescription, while prehabilitation models have been shown to reduce postoperative hospital stays. Conclusions: Digitalization of CR may represent a cost-effective alternative that bridges the gap in global access. While technology serves as an essential diagnostic partner, a robust regulatory and privacy framework is required to protect data sovereignty. Ultimately, multidisciplinary synergy between human expertise and digital innovation is important for providing an equitable and personalized pathway to recovery. Full article

Xanthoceras sorbifolium (Yellowhorn) is an underutilized, multipurpose, climate-resilient oilseed with emerging food and industrial potential. This review consolidates current knowledge on its botany, agronomy, kernel composition, extraction technologies, protein and bioactive functionality, food uses, regulatory considerations, and sustainability challenges. Yellowhorn offers high-quality oil with ≈94% unsaturated fatty acids (notably 3.5–4% nervonic acid), while defatted kernel meal contains 31–37% protein (w/w). The matrix also carries bioactives such as tocopherols in the oil (70–530 mg/kg), phytosterols (1420–2970 mg/kg), and saponins (up to 11.62%), alongside flavonoid extracts that show promising antioxidant activity (DPPH EC₅₀ ≈ 10.7 µg/mL). Extraction methods, including cold pressing, solvent systems, and supercritical CO₂, present trade-offs in yield (≈87.8%, ≈60.4–98.04%, and ≈56.5–89.63% respectively), bioactive retention, and scalability, while co-product valorization can improve economic and environmental performance. Regulatory acceptance in the U.S. will likely depend on a refined-oil, specification-driven Generally Recognized as Safe (GRAS) pathway supported by compositional and toxicological evidence. Sustainability priorities include breeding improvements and supply-chain development on marginal lands, valorization of co-products, and integration of life cycle assessment (LCA), both of which are currently under-reported for Yellowhorn. Future directions emphasize process optimization for simultaneous oil-protein recovery, selective purification of functional lipids, encapsulation for stability, and human studies to substantiate claims. Collectively, Yellowhorn represents a promising climate-ready ingredient system requiring targeted research to enable safe, scalable, and sustainable adoption. Full article

Iron Age settlements in the Salento peninsula (Southern Italy, 8th–6th century BC) underwent fundamental transformations in social organization, marked by the emergence of local elites through trade development and intense contacts with the Greek world. This study examines organic residue assemblages from 99 ceramic sherds from one key Iron Age site to clarify the role of locally produced ceramics—both coarse ware containers and Japigian matt-painted vessels—in commensal and beverage production practices. Chromatographic analyses identified a wide variety of animal and plant by-products, including fats, oils, waxes, and resin compounds. Integrated phytolith and starch analysis revealed evidence consistent with fermentation processes, particularly through the identification of fungal remains and damaged starch granules suggesting brewing activities in a subset of vessels. Matt-painted pottery forms—characterized by conical rims, funnel-shaped necks, bowls, and jugs—show distinctive use-alteration patterns and residue profiles associated with fermented beverage consumption and preparation in approximately 26% of the analyzed assemblage. Integrating organic residue analysis, experimental archaeology, and microfossil investigation suggests the central role of locally produced pottery in Iron Age commensal activities and status display, though alternative interpretations for some biomarker profiles cannot be excluded. This multiproxy approach demonstrates functional differentiation and consumption practices, refining interpretations of vessel use and providing new insights into food economies and social life during the Iron Age in southern Italy. Full article

Sepsis is a clinical syndrome defined as life-threatening organ dysfunction caused by a dysregulation in immune response to infection. Dysregulated neutrophil activity plays a critical role in sepsis-induced organ failure through interactions with the vascular endothelial cells during forward and reverse migration, resulting in vascular barrier disruption and increased neutrophil trafficking into vital organs. Therapeutic approaches for treating sepsis are mainly supportive. Due to limited clinical translation from rodent models, complexity of the pathophysiology, and most importantly, the heterogenous nature of sepsis, no significant therapeutics have been successfully developed to address the underlying immune dysregulation. In this review, we will discuss the important gap in knowledge on the fundamental mechanisms of neutrophil–endothelial interaction, the role that neutrophil forward and reverse migration plays in organ damage in sepsis, and how neutrophil and endothelial cell heterogeneity impact cell–cell communication. We will explore emerging methodologies, including novel omic and microphysiological systems, to study the underlying mechanism of neutrophil–endothelial interaction and neutrophil forward migration/reverse migration. Finally, we will review potential therapeutic targets modulating neutrophil–endothelial interaction and the challenges of translating them from bench to bedside. Full article

The transition from a linear to a circular, resource-efficient economy is crucial in order to address the growing scarcity of resources, environmental degradation and the rapid increase in electronic waste and end-of-life products. Artificial Intelligence (AI) has emerged as a key enabling technology, capable of enhancing decision making, automation and optimization across Circular Economy (CE) pathways, including reuse, remanufacturing and recycling. This perspective paper presents a comprehensive and critical overview of AI’s role in supporting the transition to a circular, resource-efficient economy, introducing the Digital CE Architecture (DCEA-4) as a novel framework for integrating AI across the circular value chain. Recent advances in machine learning, deep learning and data-driven optimization are analyzed in the context of electronic waste and used battery management. This highlights how AI-based solutions can improve material recovery rates, reduce environmental impact and enhance system-level efficiency. Additionally, we examine major challenges concerning data availability, model generalization, industrial deployment, and explainability, together with relevant industrial case studies. Although AI offers substantial potential for optimizing circular resource systems, its environmental benefits must be balanced against the computational energy demands of large-scale AI models. This perspective discusses the potential rebound effects associated with AI deployment and emphasizes the importance of energy-efficient algorithms and sustainable digital infrastructures. By bringing together current developments and highlighting future opportunities, this paper aims to help researchers, practitioners and policymakers leverage AI to speed up the transition to sustainable, circular and resource-efficient systems. Full article

Parkinson’s disease (PD) is characterized by the progressive degeneration of dopaminergic neurons and the accumulation of α-synuclein-rich inclusions, largely resulting from impaired protein clearance mechanisms. Copper is an essential redox-active metal in the central nervous system (CNS), but alterations in its homeostasis can promote oxidative stress, mitochondrial dysfunction, and proteostatic failure. In vitro studies indicate that copper can promote α-synuclein misfolding, enhance oxidative stress, and interfere with both the ubiquitin–proteasome system (UPS) and the autophagy–lysosome pathway (ALP). In this review, we critically evaluate mechanistic evidence from cellular models, integrating available animal and clinical data to assess the biological significance of copper-mediated impairment of α-synuclein clearance. We highlight the current research, identify methodological limitations, and discuss whether copper imbalance acts as a primary pathogenic trigger or as a disease-modifying amplifier of proteostatic failure. Furthermore, we consider the translational implications of selectively modulating intracellular copper pools as a therapeutic strategy in PD. Finally, we will highlight unresolved issues, methodological limitations, and emerging targeted therapeutic prospects. Full article

Over the past decade, metasurfaces have offered significant promise for miniaturized and flat photonics by enabling precise manipulation of light’s phase, amplitude, and polarization through geometric design. The pursuit of multifunctional wavefront modulation at the micro-nano scale using these surfaces has emerged as a prominent research area. In this paper, we first explore the phase modulation principles underlying meta-atoms, then investigate how multiple degrees of freedom can be harnessed to achieve multifunctional optical behavior, thereby aligning with current research trends, and offer a consolidated overview of multifunctional meta-devices. Full article