Search Results (760)

Metabolic reprogramming is a core hallmark of malignancy, enabling tumor cells to sustain rapid proliferation, evade immune elimination, and develop resistance to therapy. Although a wide range of plant-derived phytochemicals exhibit anticancer activity with comparatively low toxicity, their capacity to disrupt specific metabolic dependencies exploited by tumors has not been comprehensively synthesized. This review brings together current mechanistic evidence showing how major phytochemical classes, including polyphenols, terpenes and terpenoids, glucosinolates, and alkaloids, interfere with pathways central to tumor metabolic fitness, such as aerobic glycolysis, pentose phosphate pathway flux, mitochondrial substrate oxidation, glutamine dependence, and redox homeostasis. It further introduces a pathway-focused framework that links phytochemical mechanisms to quantifiable metabolic outcomes and highlights their potential to remodel the tumor microenvironment by altering nutrient competition, oxidative stress responses, and hypoxia-driven signaling. Key barriers such as poor systemic bioavailability, rapid metabolic degradation, and limited tissue penetration are assessed alongside emerging formulation and delivery strategies designed to enhance therapeutic exposure while preserving low-toxicity profiles. Mapping these mechanistic insights onto clinical development needs allows prioritization of specific phytochemical-metabolic pathway pairs with the strongest potential for translation. This positions plant-derived metabolic disruptors as promising candidates for next-generation, low-toxicity anticancer therapies that strategically exploit defined metabolic vulnerabilities. Full article

Industrial development is commonly described as a sequence of technological stages, from automation to artificial intelligence. This study examines whether successive industrial paradigms—from Industry 3.0 to the emerging Industry 6.0—can be more adequately understood as transformations in technological rationality rather than merely technological upgrades. The analysis adopts a conceptual–philosophical methodology informed by targeted review of peer-reviewed literature indexed in Scopus and Web of Science, integrating Kuhn’s notion of paradigms with Peircean inferential logic. Through systematic comparison of technological configurations, problem-framing practices, and epistemic assumptions, the study maps each paradigm onto a dominant mode of inference. The findings indicate that Industry 3.0 privileges deductive rule-based control, Industry 4.0 relies on inductive data-driven optimization, Industry 5.0 foregrounds hermeneutic interpretation and normative judgment, and prospective Industry 6.0 can be coherently interpreted as oriented toward abductive hypothesis generation within human–AI systems. Industrial change thus emerges as a reconfiguration of epistemic limits rather than a linear trajectory of technical improvement. The analysis concludes that expanding machine intelligence does not eliminate human authority but intensifies epistemic responsibility, understood as the obligation to determine relevance, value, and legitimacy in socio-technical systems. Full article

Background/Objectives: Multidrug-resistant (MDR) Enterobacter spp. are critical pathogens within the ESKAPE group, frequently exhibiting resistance to carbapenems. Antimicrobial photodynamic therapy (aPDT) represents a promising non-antibiotic strategy to circumvent these resistance mechanisms. This scoping review aims to map the current evidence regarding the efficacy of aPDT in inactivating Enterobacter spp., identifying the most effective photosensitizers (PS), light parameters, and existing research gaps. Methods: A systematic search was performed across PubMed, Scopus, and Google Scholar (2013–2025) following PRISMA-ScR guidelines and registered on OSF. Studies were included if they evaluated aPDT against Enterobacter spp. (in vitro or in vivo) and provided quantitative data on microbial reduction. Data was extracted using a standardized charting form covering bacterial strains, PS type, light source, and viability reduction. The results from the eligible sources of evidence were synthesized narratively to address the review objectives. Results: Despite the clinical priority of Enterobacter, only seven studies met the eligibility criteria. Methylene Blue remains the most frequently studied PS, achieving reductions of 3–8 log₁₀. Emerging evidence highlights the synergistic efficacy of monocationic chlorins and graphene-based nanomaterials in enhancing the bactericidal effect of light-based treatments. Notably, aPDT demonstrated the ability to inactivate carbapenemases, the bacterial enzymes responsible for carbapenem resistance. However, only two studies evaluated in vivo applications, primarily within dental settings. Conclusions: aPDT is a promising method against MDR Enterobacter spp. and bypasses traditional resistance mechanisms. However, the limited number of studies indicates a significant knowledge gap. Future research should focus on standardized in vivo protocols and the synergy between aPDT and conventional antibiotics to support clinical translation. Full article

Gluten allergy is linked to high risk of anaphylaxis. The relative allergenicity of glutens (alcohol-soluble gliadin and acid-soluble glutenin) from the three commercially grown wheat species (diploid Triticum monococcum, tetraploid Triticum durum, hexaploid Triticum aestivum) is unknown. A comparative gluten allergenicity map (CGAM) from these species will enable the identification of potentially hyper-/hypo-/iso-allergenic species/varieties of wheat as well as the determination of substantial equivalence of genetically engineered (GE) or other novel wheat lines. Here, using a recently described novel mouse model, we tested the hypothesis that the three different wheat species will exhibit natural variation in their gluten allergenicity. Groups of Balb/c mice were transdermally sensitized to alcohol-soluble or acid-soluble gluten extracts followed by elicitation of systemic anaphylaxis. Initial studies were performed to validate the model for glutens from the three wheat species. Both glutens from all three wheat species elicited robust specific IgE responses, as well as systemic anaphylaxis. However, comparative mapping analysis revealed differences in capacity to elicit specific IgE among the three wheat species with T. aestivum being the most potent in both gluten extracts. Hypothermic shock response analysis revealed that the three species elicited similar kinetics and intensity of anaphylaxis. Nevertheless, when analyzing mucosal mast cell response, it was revealed that the glutens from T. aestivum emerged as the most potent elicitor. Collectively, these results yield the first CGAM that may be utilized for preclinical testing of the allergenic potential of glutens from novel (e.g., GE) wheats and processed wheat products against existing wheat glutens. Full article

The study presents an exploratory design-space mapping approach for analysing knitted fabrics through the combined consideration of structural, comfort-related, and optical parameters. The methodology addresses the multi-parameter nature of knitted macrostructures, where functional behaviour emerges from the interaction of yarn composition, stitch architecture, and structural configuration rather than from isolated descriptors. Twelve knitted samples differing in stitch type and yarn linear density, and incorporating photoluminescent and reflective yarns, were analysed. Fabric thickness and air permeability were selected as representative structural and comfort-related parameters, while optical response was characterised using a dimensionless reflectance ratio under multiple illumination conditions. All parameters were normalised to enable comparative representation within a unified design space. The resulting maps reveal visual clusters, structurally isolated cases, and illumination-dependent optical equivalence between structurally different configurations. The findings demonstrate that similar optical performance can be achieved through alternative structural solutions, depending on the illumination context. The proposed approach provides a qualitative, design-oriented framework that supports engineering decision-making without implying optimisation or ranking, while revealing alternative design pathways and context-dependent equivalence. Full article

Although odor classification and odor quantification by e-nose have been studied for a long time, the next stage is to express a detected scent using language. The methods used to map molecular structure parameters, mass spectra, and sensor responses onto language expression are reviewed first. NLP (Natural Language Processing) is useful for that purpose. Conversely, the linguistic expression of the scent can be transformed into sensing data. The odor mixture can be generated so that the measured response pattern can be identical to that of the scent to be created. Two methods, optimization-based and generative AI-based ones, to search for the recipe of the created scent, are explained. Finally, the intended odor is generated using an olfactory display. We provide the latest information on the emerging technology of scent creation. Full article

Background/Objectives: Hypertensive disorders of pregnancy (HDPs) affect approximately one in seven hospital deliveries in the United States and increase the risk of pregnancy-associated mortality. Home blood pressure monitoring (HBPM) for patients with HDPs has emerged as a model of care poised to improve ascertainment of blood pressure and triage of care during pregnancy and postpartum periods. However, the strength of evidence supporting HBPM approaches has been variable. This rapid review aimed to understand how HBPM approaches for pregnant and postpartum populations with HDPs have been evaluated in order to strengthen future research. Methods: Search criteria included peer-reviewed literature in English and French published during 2018–2024 that assessed HBPM approaches for pregnant and postpartum populations in high-income countries. A total of 370 records were screened and reviewed to identify 52 eligible articles. Key study characteristics, methodologies, and outcome measures were extracted. Identified outcome measures were mapped by outcome type (implementation, health service, and client) to assess gaps in evaluation of HBPM approaches. Results: A range of study designs were employed to evaluate HBPM approaches: experimental (17%), observational (52%), qualitative (10%), mixed method (10%), and economic (11%) designs. Over a third employed a comparison group, most of which compared HBPM approaches to usual antepartum or postpartum care. Only 11 studies reported on impact outcomes (long-term blood pressure control, adverse maternal and perinatal outcomes). Significant gaps were identified among the implementation outcomes examined. While patient engagement measures were common, assessment of provider adherence and engagement was limited. Hospital admissions and emergency department visits were often employed as proxies to measure HBPM effectiveness, efficiency, and safety. However, no studies adequately reported effectiveness measures for remote patient triage. Conclusions: Our results call for improved HBPM metrics to ensure patients are receiving high-quality care responsive to their clinical condition. Future studies on HBPM approaches should prioritize more transparent reporting on health actor engagement. A composite measure including both patient and provider adherence to monitoring and triage processes will provide stronger evidence on the effectiveness of HBPM for pregnant and postpartum patients and share impactful learning for health systems interested in adopting HBPM approaches. Full article

Ports are increasingly recognized as actors that influence the sustainability of urban environments due to their spatial footprint, operational intensity, and close interaction with surrounding cities. As digital technologies become more embedded in infrastructure management, Digital Twins (DTs) are emerging in port systems as tools that can support more integrated and sustainable port–city development. This paper investigates how DT technologies applied in ports can contribute to broader urban sustainability objectives within port–city systems. The analysis is based on a synthesis of documented DT practices from selected European ports. Geographic Information System (GIS) visualization is used to illustrate the spatial relationship between port infrastructure and the surrounding urban environment, as well as to map the connections between DT application fields and relevant Sustainable Development Goals (SDGs). A comparative interpretation of the extent to which DT applications align with urban sustainability goals across the examined ports is achieved through the development of an SDG contribution scale. Insights derived from the European cases are subsequently contextualized for the Port of Piraeus, exploring how similar DT approaches could support both operational efficiency and the long-term climate resilience of the port–city environment. Overall, the findings provide practical insights for port authorities, urban planners, and policymakers seeking to align digital transformation strategies with sustainable and climate-responsive infrastructure development in port–city systems. Full article

Contemporary emergency systems operate at the intersection of climate volatility, digital interdependence, and cascading institutional disruptions. Despite growing research on resilience, adaptive governance, and digital transformation, these fields remain largely disconnected, leaving a theoretical gap in explaining how emergency systems perform under compound uncertainty. This integrative review synthesizes 32 peer-reviewed articles (post-2020) using structured narrative methodology and VOSviewer bibliometric analysis to map the field’s intellectual architecture and identify its structural gaps. The analysis reveals six thematic clusters organized around resilience as the central construct, yet characterized by three recurring disconnections: the weak integration between digital transformation and governance theory, the operational underdevelopment of polycentric governance frameworks, and the temporal separation between emergency response and climate adaptation. Drawing on this structural diagnosis, the study advances the Complex Adaptive Governance (CAG) model—a three-layer framework encompassing systemic architecture, adaptive mechanisms, and operational resilience—in which digital interoperability functions as a cross-cutting accelerator. The CAG model reconceptualizes resilience as a relational property of governance ecosystems, enhanced by digital interoperability, and offers design principles for climate-resilient emergency systems aligned with SDG 9, SDG 11, SDG 13, and SDG 16. Full article

Generally, forest trees with medicinal value present diverse chemotypes considered key determinants of efficacy, safety, and commercial valuation. Such heterogeneity varies among tissues, genotypes, and seasons, and stress exposure. This review summarizes how regulatory controls and genome architecture affect the stability and synthesis of secondary metabolites in woody medicinally important taxa. Detailed haplotypic and chromosomal analyses have recently identified diverse and repeatable architectural drivers. Among these, LTR/transposon-mediated revamping, neofunctionalization, biosynthetic gene clusters, and tandem duplication play a special role in reshaping pathway capacity. The enzymatic regulation of these drivers translates this “capacity” into harvest-pertinent chemistry by employing conserved TF modules, hormone crosstalk, and emergent chromatin/epigenetic layers. Nevertheless, major parameters pertaining to the tissue-specific storage, transport, and compartmentalization of these chemotypes are contextualized with certain limitations. In this review, the integration of GWAS/eQTL/TWAS with multi-tissue is explained in addition to the replacement of a single reference with pangenome/haplotype frameworks, and explicit modeling of G × E further strengthen genotype-to-chemotype mapping. Therefore, in this review we summarize practical workflows for chemotype discovery utilizing staged validation models of heterologous reconstitution, isotope/spatial evidence, and chemistry. These findings were supported by data on saponins, alkaloids, iridoids, and defense response. Such an integration links mechanistic understanding to authentication, standardization, and sustainable utilization strategies in woody medicinal trees. Full article

Background: Neurodegenerative diseases are a major health problem, and the neuropsychiatric symptoms seen in these diseases adversely impact the lives of patients, families, and caregivers. Inappropriate aggressive behavior is a highly disruptive symptom and a leading cause of institutionalization. There are no approved drugs specifically for the treatment of problematic aggression, and the off-label use of antipsychotics has limited benefit with significant side effects and safety risks. This review discusses dysregulated arginine vasopressin (AVP) signaling in fear–threat circuitry as a key driver of inappropriate aggression. Because the AVP 1a receptor (V1aR) is the dominant subtype in the CNS, the selective antagonism of this receptor represents a well-rationalized target for the treatment of aggression across neurodegenerative, psychiatric, and neurodevelopmental disorders. Objectives: Our goal was to summarize the basis for using V1aR antagonists as a treatment for irritability and aggressive behavior. We describe its discovery, biosynthesis, receptor pharmacology, and CNS distribution, emphasizing V1aR localization in central fear–threat circuits. Translational evidence from animal studies, pharmacological neuroimaging, and lesion network mapping is presented. These data support the suggestion that heightened vasopressinergic tone biases socioemotional information processing toward negative valence, increasing threat sensitivity and the likelihood of inappropriate aggressive responses. Emerging clinical data support this framework. Highly selective, CNS-penetrant V1aR antagonists reduced aggressive behavior and had an excellent safety profile in phase 2 studies in Huntington’s disease and intermittent explosive disorder, with efficacy signals across caregiver-reported, clinician-rated, and incident-based measures. Furthermore, pharmacological neuroimaging showed that V1aR antagonism normalizes AVP-induced alterations in activity within fear–threat circuitry. Conclusions and Future Directions: Preclinical, translational, and clinical findings to date support V1aR antagonism as a promising strategy for treating pathological aggression across disorders. Additional experimental medicine studies and clinical trials are needed to conclusively establish efficacy in various disease populations, and we note the need for improved trial designs and analytical methods as part of the development process. Full article

Periodontal disease represents a major global health burden, beginning with gingivitis and progressing to periodontitis, which causes connective tissue breakdown, alveolar bone resorption, and eventual tooth loss. Beyond local pathology, periodontitis is a chronic inflammatory condition with systemic associations, including cardiovascular disease, diabetes, and metabolic disorders. Mesenchymal stem cells (MSCs) and their extracellular vesicles (EVs) have emerged as promising candidates for periodontal regeneration. This review aimed to map the current evidence on MSC-derived EVs (MSC-EVs) in periodontal regeneration, focusing on their mechanisms of action, therapeutic potential, and translational challenges. A comprehensive literature search was conducted across a major biomedical database (PubMed) to identify preclinical and clinical studies investigating MSC-EVs in the context of periodontitis. Data were charted on EV cargo composition, biological functions, regenerative outcomes, and reported limitations. Evidence indicates that MSC-EVs encapsulate bioactive molecules—including antimicrobial peptides, proteins, lipids, and microRNAs—that modulate immune responses, suppress pro-inflammatory signaling, and promote angiogenesis and tissue repair. In periodontal models, MSC-EVs attenuate osteoclast activity, enhance fibroblast proliferation, and stimulate extracellular matrix remodeling, supporting regeneration of periodontal ligament and alveolar bone. Exosome-based approaches demonstrate advantages such as reduced immunogenicity, improved safety, and feasibility for storage and standardization. However, most findings remain preclinical, with limited human data available. To bridge the translational gap, well-designed clinical trials are needed to confirm efficacy and safety while addressing regulatory challenges, GMP standards, and outcome measures. Harnessing their regenerative capacity while mitigating side effects may guide precision-targeted therapies, and continued mechanistic studies with standardized production will be key to advancing MSC-EVs into clinical practice. Full article

The proliferation of marine data presents both an opportunity for ocean governance and a challenge, contributing to fragmentation across disciplines, institutions, and sectors. Marine Spatial Data Infrastructure (MSDI) stands out as a major framework for integrating marine information. However, an integrated synthesis that combines quantitative mapping of publication patterns with qualitative analysis of thematic evolution remains absent. This study employs a two-step approach combining systematic review and bibliometric analysis of Scopus-indexed literature (2000–2024). Based on a focused corpus of 20 publications rigorously screened for explicit MSDI relevance, we examine publication trends, collaboration patterns, thematic structures, and evolutionary trajectories. Results indicate accelerating scholarly interest in MSDI, with European institutions contributing 75% of the analysed publications. Policy frameworks such as the INSPIRE Directive (Infrastructure for Spatial Information in the European Community) and the Marine Strategy Framework Directive (MSFD) emerge as key drivers of research activity. Temporal analysis of this corpus suggests a tentative five-phase evolution in MSDI research: (1) foundational technical standardisation, (2) governance model implementation, (3) semantic interoperability enhancement, (4) policy integration, and (5) advanced applications incorporating FAIR (Findable, Accessible, Interoperable, Reusable) and CARE (Collective Benefit, Authority to Control, Responsibility, Ethics) principles and Artificial Intelligence (AI). These phases, derived from systematic coding of thematic focus across publications, represent observed patterns within the analysed literature rather than definitive stages. This paper concludes that MSDI is moving toward a more socio-technical approach that requires the consideration of a technical-focused tool in present-day ocean governance. Future work should combine semantic AI, decentralised architectures, polycentric governance models, and impact assessment frameworks to align MSDI development with the objectives of equity, inclusion, and sustainability. Full article

Drones have proven effective for acquiring aerial imagery, and when equipped with onboard analysis tools, they can automatically identify objects of interest. Neural-network methods for image analysis typically require large training datasets and substantial computational resources. By contrast, algorithmic techniques can detect objects using simple features, such as pixel colors, thereby reducing the need for extensive training and computational resources. Once trained, both types of system can analyze images in a short time. In our experiments, each approach has distinct strengths. The YOLO-based detector is more accurate for complex-shaped objects, such as trees, whereas the pixel-color approach performs better on sparser objects. This paper proposes YOLO-C3, a hybrid system designed for onboard drone image processing. By leveraging the strengths of both YOLO-based and pixel-based approaches, YOLO-C3 balances detection accuracy with estimation confidence. Trained on Mediterranean imagery dataset, the system is optimized for identifying natural objects, including citrus groves and trees. To assess the robustness of the image classifier, a K-fold cross-validation is performed. Compared to existing models, YOLO-C3 detects a wider range of natural objects with high accuracy and minimal latency, achieving a processing speed of 0.01 s per image. By performing object detection locally, drones can adapt their trajectories to support emergency response, helping to map safe corridors and locate buildings where people may be awaiting rescue after a natural disaster. Full article

Intelligent road distress detection technology has emerged as an important research topic in the field of highway maintenance. However, the accuracy and practicality of pavement distress detection are constrained by multiple factors, primarily including the irregular shapes of distress, the tendency for fine cracks to be overlooked, and the high parameter count of detection models that makes deployment difficult. Therefore, this study proposes a lightweight road distress detection model based on an improved RT-DETR architecture—LRD-DETR. First, this work integrates the C2f-LFEM module with the ADown adaptive down-sampling strategy into the backbone network, significantly reducing the number of model parameters and computational load while effectively enhancing the representation capacity of multi-scale pavement distress features. Second, a frequency-domain spatial attention is embedded in the S4 feature layer, where synergistic integration of frequency-domain filtering and spatial attention enables detail enhancement of distress edges and contours, automatically focuses on the distress regions, and suppresses background interference. The polarity-aware linear attention is incorporated into the S5 feature layer, by explicitly modeling polarity interactions, it effectively captures textural discrepancies between damaged regions and the intact road surface, and a learnable power function dynamically rescales attention weights to strengthen distress-specific feature responses. Finally, a cross-scale spatial feature fusion module (CSF²M) is developed to reconstruct and fuse multi-level spatial featurez, thereby improving detection robustness for pavement distresses with diverse morphologies under complex background conditions. Quantitative experiments indicate that, in contrast with the baseline RT-DETR, the presented framework improves the F1-score by 7.1% and mAP@50 by 9.0%, while reducing computational complexity and parameter quantity by 43.8% and 38.0%, respectively. These advantages enable LRD-DETR to be suitably deployed on resource-limited embedded platforms for real-time road distress detection. Full article