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Background and Objectives: Gastric cancer remains a leading cause of cancer-related mortality worldwide and develops through complex interactions between environmental factors, microbial dysbiosis, and host molecular pathways. Although Helicobacter pylori infection is a well-established risk factor, emerging evidence suggests that broader alterations in the gastric microbiome may also contribute to carcinogenesis. However, the associations between gastric cancer-associated microbial taxa and host gene expression profiles remain insufficiently characterized. This study aimed to identify host gene signatures associated with gastric cancer-related microbial taxa through a descriptive analysis integrating microbiome-derived taxa with transcriptome data. Materials and Methods: Microbial taxa associated with gastric cancer were systematically retrieved from the Disbiome database. Taxon set enrichment analysis (TSEA) was performed using the MicrobiomeAnalyst platform to identify host genes associated with gastric cancer-associated taxa. Importantly, TSEA relies on healthy reference data from the Human Microbiome Project and does not establish gastric cancer-specific interactions or causal relationships. Gene expression levels were subsequently evaluated using The Cancer Genome Atlas (TCGA) PanCancer stomach adenocarcinoma (STAD) dataset by comparing tumor and matched normal gastric tissues. Gene interaction network and transcription factor (TF) enrichment analyses were conducted to explore predicted regulatory relationships. Results: Among 64 microbial taxa associated with gastric cancer, 43 were reported as elevated. After removing overlapping taxa across studies, 37 elevated and 21 reduced taxa were retained for analysis. TSEA identified 11 host genes associated with gastric cancer-related microbial taxa. Transcriptomic analysis demonstrated significant downregulation of DPP6 and DLG2, while KDM4D, USP34, and VDR were significantly upregulated in gastric cancer tissues compared with normal controls. Network and TF enrichment analyses revealed predicted co-expression and co-localization patterns among these genes, suggesting their potential involvement in immune-related processes, epigenetic regulation, and cellular organization. Conclusions: This descriptive study identifies distinct host gene expression signatures associated with gastric cancer-associated microbial dysbiosis. This study is purely associative and hypothesis-generating; no causal or mechanistic inferences are made. TSEA used healthy reference data and therefore does not reflect gastric cancer-specific host–microbe interactions. The findings provide a basis for future hypothesis-driven research but require validation in independent cohorts. Full article

This study aims to ascertain whether academic co-creation and attitudes towards research serve as predictors of environmental action among a sample of Peruvian environmental engineering students. The study sample comprised 460 environmental engineering students enrolled in Peruvian universities. For the analysis, the Environmental Action Scale, the Attitudes Towards Research Scale, and the Academic Co-Creation Scale were utilized. Additionally, the data collected were analyzed using correlation coefficients and multiple linear regression models. The findings reveal positive and significant correlations between academic co-creation, attitudes towards research, and environmental action within the studied sample. Linear regression analyses indicated a satisfactory model fit (test F = 70.1, p < 0.001), with attitudes towards research (β = 0.431, p < 0.001) and academic co-creation (β = 0.108, p < 0.05) emerging as significant predictors of environmental action in the participating students (adjusted R² = 0.231). Consequently, the results suggest that, in this group of Peruvian environmental engineering students, attitudes towards research and academic co-creation are relevant predictors of environmental action. These results may inform directors and educators working with similar student populations in the promotion of pedagogical strategies and institutional policies that integrate academic co-creation as a transversal axis in the training process, while also fostering positive attitudes towards research, without implying generalization to other academic disciplines or broader populations. Full article

Background/Objectives: The clinical management of bladder cancer is severely impeded by high recurrence rates and the rapid emergence of chemoresistance, necessitating the discovery of novel therapeutic agents with distinct mechanisms of action. Dehydrodiisoeugenol (DHE), a bioactive neolignan, exhibits potent anti-tumor efficacy, yet its direct molecular targets and mode of action remain elusive. Methods: To deconvolute the mechanism of DHE, we integrated a phenotypic screening approach using 2D cell lines and 3D patient-derived organoids with a chemoproteomics-based activity-based protein profiling (ABPP) strategy. We synthesized a functionalized photoaffinity probe to capture the specific interactome of DHE under physiological conditions and validated targets via cellular thermal shift assays (CETSA), quantitative mass spectrometry, and 100 ns molecular dynamics (MD) simulations. Results: DHE exhibited potent dose-dependent cytotoxicity in bladder cancer cells, with IC50 values of 39.23μM in T24 and 34.58μM in 5637 cells. In 3D patient-derived organoids, DHE significantly reduced viability (p < 0.0001). Using a dual-filtering ABPP strategy, we identified 65 high-confidence candidate targets, prioritizing PTPN1 (PTP1B) as the primary functional interactor. Comparative molecular docking and 100 ns MD analyses showed that multiple stereoisomers of DHE could adopt plausible PTPN1-binding modes. Mechanistically, organoid proteomics indicated that DHE engagement with PTPN1 disrupts ER membrane homeostasis, thereby modulating the PI3K-Akt signaling axes. Conclusions: These findings establish PTPN1 as a critical druggable vulnerability in bladder cancer and define the molecular basis for the therapeutic potential of DHE. This study highlights the power of combining chemoproteomics with physiological 3D models to accelerate the translation of natural products into precision cancer therapies. Full article

The first influenza A(H5N1) human case associated with the A(H5N1) dairy cattle outbreak in the United States was identified in April 2024. The U.S. CDC response to this outbreak was activated days later and remained active until July 2025. During this time, 70 human cases of influenza A(H5N1) were detected with a range of epidemiological links to sources of exposure. Next-generation sequencing (NGS) of human samples was an effectual mechanism for tracking and analyzing the outbreak evolution throughout the response. Due to the specimens’ importance and their variable physical quality, an assortment of laboratory methods was utilized including influenza segment-specific amplification, enrichment capture, short-read, and long-read sequencing. Combining these methods allowed for high-quality genomic data production with rapid turnaround times—typically 2 days from sample receipt to public database submission. By leveraging replicate sequencing, enrichment capture, and sequencing of diagnostic amplicons, valuable genomic data could be produced directly from human clinical specimens that would have normally been considered too weak for routine virologic surveillance sequencing. The resulting assemblies were characterized and analyzed by CDC and shared with local and state public health authorities to facilitate case investigations and risk assessment. These data were further used for phylogenetic analyses of viruses from human cases to investigate likely animal-to-human transmission events and identify clusters within the outbreak that might indicate trends in the types of exposures. Through the adaptable laboratory workflow and the rapid release of viral genomic data, the public health risk mitigation strategies could be evaluated and adjusted in real time. Full article

This paper examines how scam compounds, money mules and crypto-assets operate as interdependent elements of contemporary money-laundering chains. It assesses whether existing anti-money laundering (AML) and crypto-asset regulatory frameworks are capable of disrupting these chains holistically, rather than addressing individual components in isolation, with particular reference to scam-compound activity in Southeast Asia. The study adopts a qualitative comparative case-study methodology grounded in legal and regulatory analysis. Four empirically grounded cases are examined: two Southeast Asian scam-compound enforcement cases (Cambodia and Myanmar) and two European crypto-asset seizure cases (Ireland and Italy). Judicial decisions, enforcement actions and regulatory instruments are analysed through a chain-based analytical framework aligned with Financial Action Task Force (FATF) standards, the EU Markets in Crypto-Assets Regulation (MiCA) and the Anti-Money Laundering Authority (AMLA) framework. The analysis reveals a structural divergence in enforcement strategies: Southeast Asian responses increasingly prioritise network- and infrastructure-level disruption of scam compounds, whereas European approaches remain largely centred on post-offence crypto-asset seizure through traditional proceeds-of-crime mechanisms. Across all jurisdictions, money mules emerge as a critical yet systematically under-regulated intermediary layer enabling the resilience of crypto-laundering operations. The paper advances existing AML typologies by conceptualising scam compounds, money mules and crypto-assets as interconnected components of a single crypto-laundering chain. This chain-based perspective offers a novel analytical and regulatory lens for understanding organised crypto-enabled fraud. The study is based on a qualitative, case-based design and does not aim for statistical generalisation. However, the analytical framework developed is transferable to other jurisdictions experiencing similar scam-compound and crypto-laundering dynamics. The findings suggest that effective AML enforcement requires coordinated intervention across multiple nodes of the laundering chain, including scam compound infrastructure and money mule networks, alongside traditional asset-seizure mechanisms and CASP supervision. By highlighting the structural links between scam compounds, coercive labour and crypto-laundering mechanisms, the paper underscores the broader social harms of crypto-enabled fraud and the need for integrated regulatory responses that address both financial crime and human exploitation. Full article

Media coverage of disasters frequently frames self-interested behavior in contrast to collective responsibility and coordinated response. This study aims to explore how such behavior is emotionally constructed in disaster-related media, using a carefully selected corpus of 12 text-centered news articles focusing on selfish behavior. The analysis combines transformer-based sentence-level emotion classification using the tweetnlp RoBERTa model, which predicts 11 emotion categories, with Latent Dirichlet Allocation topic modeling across single-sentence and three-sentence windows in a small purposively selected corpus. Emotion–topic relationships are quantified by weighting emotion probabilities by topic distributions and visualized using bar charts, network graphs, and heatmaps. The findings suggest that fear and disgust dominate portrayals of self-interested behavior, while anticipation appears in projections of harm and anger is linked to inequality and institutional accountability. Two discursive configurations emerge: Responsibility Across Individuals and Institutions, emphasizing public accountability and authority, and Collective Fear and Self-Protective Practices, reflecting affect-driven responses under uncertainty. Although negative emotions predominate, optimism appears conditionally, signaling coordination and recovery. Overall, disaster reporting constructs selfishness through integrated emotional–semantic patterns that position individual actions within broader social risk and collective responsibility. 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

Pompe disease (PD) is a neuromuscular autosomal recessive disorder caused by mutation in the GAA gene, which encodes acid α-glucosidase (GAA), an enzyme responsible for hydrolyzing glycogen to glucose. Deficiency of this enzyme leads to pathological accumulation of glycogen in almost all tissues of the body, with the most pronounced effects in cardiac and skeletal muscle, as well as in the central nervous system. Two major clinical forms of PD are recognized: infantile-onset PD, characterized by almost complete absence of GAA activity and severe cardiomyopathy and neurological abnormalities, and late-onset PD, which primarily presents with impairment of respiratory and motor function. Since 2006, enzyme replacement therapy with recombinant GAA has been used to treat PD, improving survival and quality of life. However, this approach has several limitations: the need for lifelong infusions, the risk of immune responses, and the inability of the enzyme to cross the blood–brain barrier, which is particularly critical for infantile-onset PD. Consequently, alternative strategies are being developed, including gene therapy using adeno-associated virus vectors for GAA delivery to target tissues; these approaches are currently in phase I/II clinical trials. Transplantation of genetically modified hematopoietic stem cells also represents a promising therapeutic strategy, offering a single-intervention treatment with long-lasting effects. This review discusses the molecular mechanisms of PD, current and emerging disease models, and therapeutic approaches, which together open prospects for the development of potentially one-time curative treatments, despite persistent challenges such as immunogenicity and the need for long-term efficacy monitoring. Full article

Cognitive rehabilitation represents a cornerstone of disease management in multiple sclerosis (MS), targeting the progressive cognitive decline that affects a significant proportion of patients. Despite growing evidence supporting its clinical utility, rehabilitation outcomes remain variable, and identifying modifiable factors that limit its efficacy has become a research priority. Sleep disorders are common in MS and have been increasingly linked to cognitive impairment, yet evidence based on objective polysomnographic assessment remains limited, and the specific parameters that influence cognitive function are poorly understood. This review synthesizes evidence from polysomnographic studies examining how sleep disturbances influence cognitive performance in MS patients. Following a systematic search of PubMed, EMBASE, and the Cochrane Library, 488 patients were included. Sleep fragmentation, reduced sleep efficiency, and oxygen desaturation indices were associated with impairments in attention, information processing speed, and verbal memory, with nocturnal hypoxia emerging as a potentially important mechanism of cognitive impairment. These findings suggest that identifying and treating sleep disorders may be essential for optimizing cognitive rehabilitation outcomes in MS. Further longitudinal studies are needed to determine whether addressing sleep pathology can enhance rehabilitation efficacy and preserve cognitive function over time. 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

As the population increases, the demand for power continues to rise. As fossil fuel resources reduce, wind energy emerges as a sustainable alternative and helps address adverse effects of global warming and environmental pollution caused by fossil fuels. Thus, this study focuses on increasing the efficiency of wind turbines by improving their energy conversion. In this study, the NREL Phase VI wind turbine blade was modified by adding a Gurney flap at trailing edge along the entire span. Computational fluid dynamics simulations using ANSYS CFX 19.2 were performed on the modified blades to evaluate their aerodynamic performance. Three different flap lengths were investigated with six wind speeds varying from 5 m/s to 20 m/s. The results obtained were compared with those from NREL Phase VI original shape and a blade equipped with a winglet. Computational domain was divided into a rotating cylindrical region and a stationary rectangular part. The aerodynamic parameters calculated include torque, thrust, and normal and tangential forces coefficients. At low velocities, the addition of a Gurney flap had an insignificant impact on torque and thrust, whereas at medium to high wind speeds, significant increases were observed on torque, indicating more power production. Full article

Wire Arc Additive Manufacturing (WAAM) has emerged as a promising additive manufacturing technique due to its high deposition rate and low material cost. WAAM is increasingly adopted in various industries for the production of large-scale metal components, yet optimizing productivity without sacrificing mechanical integrity remains a critical challenge, particularly for low-carbon steels. This study systematically investigates the influence of key WAAM parameters—welding current (100–350 A) and travel speed (5–30 mm/s) on the deposition stability, microstructure, and mechanical properties of thin walls made of low-carbon Fe–0.09 C–1.10 Cr–1.47 Mn–0.59 Si–0.56 Mo–0.11 Ni–0.23 V steel. A stable processing window for defect-free wall fabrication was established for currents of 100–250 A, while higher currents of 300–350 A resulted in melt pool instability and geometrical distortions due to excessive heat input. Microstructural characterization revealed a dual-phase structure consisting of allotriomorphic ferrite (ALF) and acicular ferrite (AF) in all samples. The microstructural evolution was critically governed by variations in the cooling time in the critical temperature range of 800 °C to 500 °C (t_8/5) within the thermal cycles, a direct consequence of the heat input quantified through volumetric energy density. Low heat input at 100 A, 5 mm/s promoted a microstructure with minimal ALF fraction of ∼10%, whereas high heat input at 350 A, 30 mm/s induced significant ferrite recrystallization and coarsening, increasing ALF fraction to ∼55%. These microstructural changes directly affected mechanical properties: YS/UTS decreased from 512 MPa/668 MPa to 401 MPa/602 MPa, respectively. Concurrently, the deposition rate increased substantially from ∼1.6 kg/h to ∼6.3 kg/h. The results demonstrate a critical trade-off between productivity and mechanical performance, providing a practical framework for parameter selection in WAAM-fabricated low-carbon steel components. Full article

Wheat processing generates large volumes of co-products, particularly wheat bran (WB) and wheat germ (WG), which remain underutilized despite their high content of dietary fiber, phenolic compounds, bioactive peptides, and lipophilic antioxidants. Although their composition and processing have been widely investigated, an integrated and application-oriented evaluation of these fractions remains limited. This review provides a structured and critical analysis of WB, raw and defatted WG, and wheat germ oil (WGO), linking composition, processing strategies, and functional performance within a unified framework. Conventional and emerging technologies, including enzymatic hydrolysis, fermentation, thermomechanical treatments, and supercritical CO₂ extraction, are discussed in terms of selectivity, impact on techno-functional properties, and scalability. An evidence-grading approach is introduced to distinguish bioactivities supported by chemical assays, cell-based models, animal studies, or human data, enabling a more rigorous interpretation of health-related effects. Across applications, these co-products have been incorporated into food systems and related sectors, primarily showing improvements in nutritional composition, oxidative stability, and product performance under experimental conditions. However, translation to an industrial scale remains constrained by techno-economic limitations, regulatory requirements, and stability challenges. This work highlights the need for integrated processing strategies aligned with industrial feasibility to support the development of sustainable cereal biorefineries. Full article

Emergency vehicle authentication in vehicular ad hoc networks must satisfy strict latency, privacy, and trust constraints. Existing Public Key Infrastructure- and Conditional Privacy-Preserving Authentication-based schemes incur substantial overhead from certificate management and expensive per-hop verification, making them unsuitable for real-time emergency scenarios. We propose a lightweight zero-knowledge- and blockchain-assisted authentication scheme that eliminates certificates, pseudonym pools, and the requirement for online interaction with a trusted authority during the authentication phase. The Certificate Authority (CA) is involved only during offline initialization stages (vehicle enrollment and Merkle tree construction); once provisioning is complete, the runtime authentication process operates without any online CA interaction. Each emergency vehicle registers one-time hash commitments on-chain after proving membership in a category-specific Merkle tree, and authenticates messages by broadcasting a hash along with a zero-knowledge proof of preimage knowledge. Roadside units verify the proof and consult the on-chain state to enforce single-use semantics, creating a tamper-resistant audit trail. Evaluation using the Veins framework (OMNeT++/SUMO) demonstrated a constant 288-byte authenticated payload, millisecond-level end-to-end delay independent of hop count, and stable blockchain processing under sustained load. Full article

Water electrolysis has emerged as a strategically appealing route for the sustainable production of green hydrogen (H₂) via the hydrogen evolution reaction (HER), though the sluggish kinetics of the oxygen evolution reaction (OER) remains a bottleneck hindering large-scale practical applications. In this regard, an attractive solution is offered by the integration of the ethanol oxidation reaction (EOR) into hybrid water-splitting systems, favorably reducing anodic overpotentials. Nonetheless, an open challenge is related to the fabrication of eco-friendly and economically viable catalysts free from noble metals, combining efficiency and stability. Herein, we explore nickel-oxide-based nanostructures grown onto porous Ni foam scaffolds by a scalable hydrothermal (HT) approach as EOR electrocatalysts. Material properties arising from modulation of the sole HT growth time are investigated by complementary structural, microscopic, and spectroscopic techniques. Electrochemical tests demonstrate good durability and very attractive EOR performances, mainly influenced by the morphology and the NiOOH surface content of the target systems. Overall, the present work advances an attractive route to transition-metal-based electrocatalysts for efficient alcohol-oxidation-assisted water electrolysis. Full article