Search Results (6,155)

Deoxynivalenol (DON), a trichothecene mycotoxin commonly found in cereal grains and their derived products, poses significant risks to human and animal health. In previous work, a fusion enzyme composed of the dehydrogenase DADH and the aldo-keto reductase AKR13B3 was engineered to convert DON into the non-toxic 3-epi-DON in a single step. However, the poor thermal stability of this fusion enzyme limited its industrial application. In this study, EVcouplings and the GRAPE-WEB platform were utilized to identify key amino acid residues governing the thermal stability of the fusion enzyme AKR13B3–DADH. Through single-point mutation screening and the combination of beneficial mutation sites, a triple mutant M361L/T508Y/Y603F (M1) was obtained. The half-life of M1 at 50 °C reached about 500 min, representing a 16.4-fold increase compared with the wild type, while its catalytic activity increased by 2.7-fold. The apparent melting temperature increased by approximately 4 °C. Molecular dynamics simulations verified that the improved thermostability results from reduced conformational flexibility in key regions, enhanced structural packing, and a strengthened hydrogen bond network. These results demonstrate the successful development of a thermostable DON-detoxifying fusion enzyme and provide a practical basis for its industrial application. Full article

Privacy-by-design (PbD) considers privacy in the entire lifecycle of information systems and personal data. Although a wide variety of techniques for PbD exist, a framework that considers privacy in the full context of both systems design and organizational development remains absent. This research aims to design, validate, implement, and evaluate a PbD Focus Area Maturity Model as a guiding artifact for the application of PbD (PbD-MM). The PbD-MM was created using a design science approach. A set of previously coded PbD activities were used to formulate capabilities for the maturity matrix. The PbD-MM describes 14 focus areas and 60 capabilities, with their dependencies creating 10 maturity levels. The PbD-MM was validated through a focus group with PbD practitioners and implemented in a web application offering self-assessment and reporting. A total of 46 completed assessments were collected through online distribution. We find a broad basis of capabilities in PbD practice, with further developed governance and compliance, and a positive correlation between the highest-scoring focus area and higher overall maturity. The PbD-MM offers a structuring of PbD activities and an assessment instrument to support the development of organizational PbD capabilities. Full article

Background/Objectives: Artificial intelligence (AI) is rapidly transitioning from a theoretical concept to an active clinical tool in dentistry and medicine. These are fields in which the current market-driven shift towards automation is observed. The aim of this study was to describe the possible applications of artificial intelligence particularly in oral surgery and maxillofacial surgery, fields that are deeply rooted in manual work with patients. Methods: A review of current literature was conducted using PubMed/MEDLINE, Scopus, and Web of Science databases. Keywords like: “oral surgery”, “maxillofacial surgery”, “implantology”, “dentistry”, “artificial intelligence”, and “orthognathic surgery” and their combinations were applied. Non-English articles were excluded. Results: Based on the literature, current applications of artificial intelligence in oral surgery and maxillofacial surgery are presented. Results indicate high efficacy of convolutional neural networks (CNNs) in radiographic triage and large language models (LLMs) in postoperative patient communication. Conclusions: Artificial intelligence is a useful tool that can significantly improve the work of physicians. However, it should not be considered a “replacement” for physicians, especially in fields requiring manual work. It can provide important guidance for patients and be a form of support for physicians. Full article

Background/Objectives: Fuzzy logic has been increasingly investigated for managing uncertainty in dental diagnosis, risk assessment, image analysis, and clinical decision support. However, the available literature remains fragmented across computational methodologies and clinical applications. This systematic review synthesized the available evidence regarding the diagnostic, predictive, and clinical decision-support performance of fuzzy logic and fuzzy-based artificial intelligence systems in dentistry. Methods: A systematic literature search was conducted in PubMed/MEDLINE, Scopus, Web of Science, Embase, the Cochrane Library, and IEEE Xplore in accordance with PRISMA 2020 guidelines. The review protocol was prospectively registered in the Open Science Framework (Registration No. zs3w6). Study selection was performed independently by two reviewers, while data extraction and methodological quality assessments were performed by one reviewer and subsequently verified by a second reviewer. Eligible studies evaluated fuzzy logic or fuzzy-based AI methodologies across dental applications. Results: Fifteen studies published between 2005 and 2025 met the inclusion criteria. Applications primarily involved dental disease diagnosis, periodontal risk assessment, oral cancer risk prediction, and radiographic image segmentation. Reported diagnostic accuracy ranged from 82.1% to 100%. However, the evidence base consisted predominantly of proof-of-concept, retrospective, or simulation-based investigations using relatively small datasets, with external validation and prospective clinical evaluation rarely reported. Conclusions: Fuzzy logic and fuzzy-based AI systems demonstrate potential for uncertainty-sensitive dental applications involving diagnostic decision support, risk assessment, and radiographic image analysis. However, the current evidence remains exploratory and methodologically heterogeneous, with limited prospective validation and minimal real-world clinical implementation. Larger, externally validated studies are needed before routine clinical adoption can be recommended. Full article

Background/Objectives: Probiotic preparations have attracted increasing attention because of their potential to modulate the gut microbiota and improve health outcomes in a wide range of gastrointestinal and extraintestinal diseases. However, their efficacy and safety are strain-specific and remain inconsistent across many clinical indications. The aim of this review was to evaluate current evidence regarding the efficacy, safety, mechanisms of action, and clinical applications of probiotic preparations. Methods: A narrative literature review was conducted using the PubMed, Scopus, and Web of Science databases to identify publications addressing the efficacy, safety, and clinical applications of probiotic preparations. The literature search was last updated in July 2026. No publication-year restrictions were applied; the studies included in this review were published between 2000 and 2026. Priority was given to systematic reviews, meta-analyses, randomized controlled trials, international clinical practice guidelines, and mechanistic studies. Results: The reviewed evidence indicates that probiotics may contribute to intestinal homeostasis through modulation of the gut microbiota, enhancement of intestinal barrier function, and regulation of immune responses. The strongest evidence supports the use of selected probiotic strains for the prevention of antibiotic-associated diarrhea, whereas evidence for the prevention of necrotizing enterocolitis is generally favorable but depends on the specific probiotic preparation, product quality, and clinical setting. Evidence for most other gastrointestinal and extraintestinal disorders remains heterogeneous and strain-specific. Although probiotics generally exhibit a favorable safety profile, rare infectious complications have been reported in high-risk patients. Conclusions: Current evidence indicates that the efficacy and safety of probiotics depend on the specific strain, clinical indication, and patient characteristics. Further high-quality, strain-specific clinical studies are needed to optimize their use in medical practice. Full article

Microalgae facade systems have emerged as an innovative approach in sustainable architecture, offering benefits in energy efficiency, carbon reduction, and biomass production. However, research remains fragmented across biotechnology, environmental engineering, and architectural design, with limited synthesis of its intellectual structure and thematic evolution. This study presents a bibliometric and thematic analysis of microalgae facade research in buildings from 2016 to 2025 using a combined Web of Science and Scopus dataset of 59 publications. The analysis examines publication trends, influential authors, journals, collaboration networks, and key research themes, while science mapping identifies conceptual clusters and their evolution. A sustainability-oriented perspective further links these themes to the environmental, economic, and social dimensions of sustainability and the United Nations Sustainable Development Goals (SDGs). The findings show that research has primarily focused on photobioreactor technologies, energy performance, and biomass production, whereas architectural integration and real-world building applications remain underexplored. Although interdisciplinary collaboration has increased in recent years, the field is still at an early stage of development. This study provides a systematic overview of the current intellectual landscape, identifies research gaps, and offers directions for advancing microalgae facade technologies toward sustainable and net-zero built environments. Full article

Background/Objectives: Bone metastasis is one of the most common manifestations of advanced malignancy and a major cause of morbidity, particularly when involving the spine. Magnetic resonance imaging (MRI) plays a central role in its detection and characterisation due to its high sensitivity for bone marrow infiltration. However, bone metastases may be missed on MRI, whilst interpretation can be time-consuming and challenging. The purpose of this study is to review and summarise the present evidence for artificial intelligence (AI) applications in the detection and classification of bone metastasis on MRI. Methods: A systematic, detailed search of the main electronic medical databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov, last accessed on 1 January 2026) was undertaken in concordance with the PRISMA guidelines. Results: A total of 34 studies were included. AI applications were identified across several domains, including lesion detection, segmentation, disease classification, and predictive modelling. Deep learning approaches demonstrated strong performance for automated detection and segmentation, while radiomics-based models were frequently used for lesion differentiation and prediction tasks. Reported performance metrics were generally high, with area under the curve values commonly ranging from approximately 0.72–0.94, with most studies reporting AUCs exceeding 0.80 in internal validation, although substantial heterogeneity in study design, datasets, and validation strategies was observed. External validation and prospective evaluation were limited across most studies. Conclusions: Within the domain of bone metastasis, AI-based approaches have demonstrated encouraging performance and hold substantial potential to support clinical decision-making, including prognostication and prediction of treatment response. Nevertheless, further research is required to validate their clinical utility and to facilitate successful integration into routine clinical practice. Full article

STEAM education integrates science, technology, engineering, arts, and mathematics to foster interdisciplinary learning and support the development of 21st-century skills. However, implementing this approach requires the development of educational resources that meaningfully connect these disciplines. Augmented Reality (AR) can contribute to this effort by enabling interactive and immersive learning experiences. This paper presents AR-GenSTEAM, a generator for creating STEAM-based serious games with AR for web and mobile platforms. The generator follows a three-stage development process comprising analysis, configuration, and generation. Through this process, users select STEAM areas, target skills, game templates, AR resources, and an export platform to generate a serious game. A case study of Enkrypto, a serious game focused on encryption and decryption, is presented to illustrate the use of AR-GenSTEAM. A hybrid evaluation was conducted to evaluate the performance of AR-GenSTEAM and students’ experiences with the AR component of the Enkrypto game. The generator successfully generated web applications in 90% of attempts and mobile applications in 80%, while students rated the AR component positively in terms of pragmatic quality, hedonic quality, and overall user experience. Full article

Soil salinization affects more than 954 Mha of arable land globally, with approximately 10–20 Mha abandoned annually. Conventional engineering and chemical remediation suffer from high water demand, salt re-accumulation, and secondary pollution risks. While this review draws primarily on the extensive body of research from China—where saline–alkali land covers approximately 99.13 Mha—it also incorporates key international case studies for comparative analysis. The review synthesizes biological and physical technologies for saline–alkali land reclamation, identifies critical challenges, and proposes an integrated remediation framework. A systematic search of Web of Science, Scopus, and CNKI databases (2000–2026) yielded 2847 records, of which 41 studies formed the systematic evidence base for the five technology clusters and 41 were retained as background references following the PRISMA framework. Because the search included the Chinese CNKI database and China contains one of the world’s largest saline–alkali land areas, particular emphasis is placed on Chinese case studies, complemented by representative international examples. Data were extracted on technology type, salt removal efficiency, crop yield, and application stage, and synthesized through quantitative cross-technology comparison. Five dominant technical clusters were summarized: (i) gene-based breeding (CRISPR/Cas, MAS) achieving 20–28% yield gains on sodic soils; (ii) halophyte phytoremediation removing 83–91% of soil salts over three growing seasons; (iii) microbial inoculants improving crop salt tolerance by 15–35% under controlled experimental conditions; (iv) agronomic rotations and straw amendment reducing topsoil salinity by 30–50%; and (v) solar-driven interfacial evaporation achieving 91.4% salt removal in a single proof-of-concept field trial at a material cost of approximately USD 0.004 per straw unit. Integrated bio-physical deployment, however, remains at the experimental scale. Combining rapid physical desalination with long-term biological remediation represents a promising research direction that requires systematic field validation before practical deployment. Key knowledge gaps include the long-term edaphic consequences of solar desalination, field-scale reliability of microbial consortia, and absence of regionally validated integrated protocols. We propose a structured roadmap with explicit timelines and policy recommendations to accelerate translation from research to practice. Full article

Cybercrimes that exploit virtual assets—including laundering, concealment, and illicit financing through the dark web—are increasing rapidly, while existing tracking tools remain limited when offenders leverage multi-layer blockchain architectures and off-chain mechanisms to obscure fund flows. This paper proposes a practical full-node-based blockchain forensic framework for the automated detection and tracking of illicit virtual asset transactions across Layer-1 and Layer-2 environments. The framework operates a full-node network to construct a continuously updated database of all on-chain transactions, from which exchange-controlled internal addresses are identified using six formalized heuristics (H1–H6) expressed as a weighted-sum scoring model. A unified multi-layer transaction graph incorporates Layer-2 events—payment channel closures, rollup batch submissions, and bridge deposits and withdrawals—as contextual edge attributes correlated with Layer-1 settlement. Protocol-specific cross-layer correlation procedures, covering Arbitrum retryable tickets, Optimism cross-domain messages, zkSync Era batch commitments, and third-party bridge relays, were validated on live main-net transactions. Applying the framework to 7511 suspect wallet addresses, 821 (10.93%) were attributed to four Korean exchanges, and real laundering cases involving mixing and swapping—together with integrated real-time alerting and transaction-freeze request functions—demonstrate its direct applicability to law enforcement investigations. In addition, attribution reliability is quantified through the 98.80% labeling consistency observed across repeated independent collections of the same addresses, the standard forensic metrics are formally defined together with publicly released evaluation tooling, and the end-to-end detection latency is bounded analytically by the confirmation properties of the underlying protocols, substantiating the real-time capability of the framework. Full article

Background/Objectives: Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are frequently co-occurring neurodevelopmental conditions with partially overlapping neurophysiological profiles. Electroencephalography (EEG) provides non-invasive access to candidate biomarkers, yet the literature remains largely organized around single-diagnosis frameworks, limiting comparison across conditions and constraining translation into intervention selection. This review compares EEG signatures across ASD and ADHD from a transdiagnostic perspective and examines how such signatures might inform the selection of non-pharmacological interventions. Methods: A structured search of PubMed, Scopus, IEEE Xplore and Web of Science identified peer-reviewed studies published between 2010 and 2026 reporting EEG findings in ASD and/or ADHD, spanning resting-state, task-based, connectivity, event-related potential, machine learning and intervention studies. Sixty-eight sources were synthesized thematically. Given substantial heterogeneity in acquisition parameters and analytic pipelines, evidence was integrated interpretively rather than pooled quantitatively, and no formal risk-of-bias assessment was undertaken. Results: Shared features across both conditions frequently included low-frequency theta excess, reduced alpha modulation under cognitive load, and flattened aperiodic (1/f) slopes—a pattern compatible with, though not a direct measurement of, altered excitation/inhibition balance. While substantial heterogeneity exists, disorder-specific signatures often comprised the ASD “U-shaped” spectral profile alongside elevated epileptiform activity, and frontally pronounced theta/beta ratio elevation in subsets of individuals with ADHD. Machine-learning studies increasingly emphasize interpretable, multidomain feature sets over binary classification. Mindfulness-based and neurofeedback interventions converge on theta reduction and alpha enhancement, although reported effects are frequently conditional on responder status, task context, or outcome-rater blinding. Conclusions: Convergent EEG features support a transdiagnostic account of neurodevelopmental dysregulation. A biomarker-informed framework for intervention selection is proposed, which requires prospective validation before clinical application. Full article

Objectives: To synthesize current evidence regarding advances in periodontal diagnosis and therapy, with emphasis on molecular biomarkers, omics technologies, microbiome profiling, digital imaging, and artificial intelligence-based analytical models that support the transition toward precision periodontology. Methods: This narrative review examines contemporary evidence on emerging molecular, microbiological, and digital technologies applied to periodontal diagnosis, prognostic assessment, and therapeutic planning. The review includes studies addressing salivary and gingival crevicular fluid biomarkers, microbiome characterization, omics approaches, cone-beam computed tomography, three-dimensional imaging, machine-learning algorithms, and personalized periodontal therapies. Relevant literature was identified through searches in major biomedical databases, including PubMed/MEDLINE, Scopus, and Web of Science, focusing on studies published on periodontal diagnostics, biomarkers, digital technologies, artificial intelligence, and precision medicine approaches in periodontology. Results: Peer-reviewed articles addressing innovative diagnostic and therapeutic approaches in periodontology were considered. Priority was given to studies evaluating clinical applicability, diagnostic performance, prognostic utility, and personalized treatment strategies integrating molecular and digital technologies. Conclusions: Emerging molecular and digital technologies are reshaping periodontal diagnosis and therapy by improving disease detection, risk prediction, and individualized treatment planning. Biomarkers, omics technologies, microbiome profiling, and artificial intelligence-assisted imaging may enhance diagnostic precision and clinical decision-making. These developments support the implementation of precision periodontology; however, challenges related to biomarker validation, algorithm standardization, cost, and accessibility remain barriers to routine clinical adoption. Further research is necessary to validate these approaches and facilitate their integration into periodontal practice. The integration of biomarkers, omics technologies, advanced imaging, and artificial intelligence may improve early periodontal diagnosis, prognostic assessment, and personalized treatment planning. These innovations support the transition toward precision periodontology and have the potential to enhance clinical decision-making, treatment outcomes, and long-term periodontal health in routine dental practice. Full article

Quality control data for construction materials is frequently exchanged as heterogeneous documents, limiting traceability and making element-level retrieval slow and error-prone. This study develops and assesses a digital methodology that integrates laboratory test results with BIM-referenced assets and delivers the integrated information via interactive 3D-enabled dashboards. The methodology comprises three stages, starting with the acquisition of data from laboratory deliverables and 3D models, followed by data standardisation and relational structuring in the software Power BI Desktop (version 2.157.879.0, Microsoft Corporation, Redmond, WA, USA), and finally the publishing of generated dashboards embedded in a web application environment. The methodology is assessed through a real case study of a railway infrastructure asset, showing how laboratory records can be accessed and interpreted within a 3D model context, while preserving stakeholder-specific visibility through access control. The proposed approach supports element-level navigation of quality control and provides a practical pathway for laboratories to centralise, filter, and communicate test results without embedding full datasets into the BIM environment. Full article

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a heterogeneous inflammatory disease in which type 2 inflammation predominates in most patients in Western populations. Galectin-10 (Gal-10) and Charcot-Leyden crystals (CLCs), linked to eosinophil activation, cytolysis and eosinophil extracellular trap cell death (EETosis), have emerged as potential biomarkers and mediators of eosinophilic inflammation. This narrative review summarises current evidence on their biological role and clinical relevance in CRSwNP. The literature was identified through searches of PubMed/MEDLINE, Web of Science and Google Scholar, with emphasis on mechanistic studies, clinical investigations and recent evidence concerning type 2 inflammatory airway diseases. Available evidence suggests that Gal-10 and CLCs are associated with eosinophil activation and may contribute to persistent type 2 inflammation through epithelial injury, increased mucus viscosity, impaired mucociliary clearance, tissue remodelling and inflammatory amplification. Gal-10/CLCs may have value as investigational biomarkers of disease activity, postoperative recurrence and response to biologic therapy. Current biologics may indirectly modulate the Gal-10/CLC pathway by reducing eosinophil survival, recruitment or activation, although direct evidence remains limited. Gal-10 and CLCs are promising investigational biomarkers and mechanistically plausible therapeutic targets in CRSwNP. However, clinical application is limited by the lack of standardised analytical methods, validated cut-off values and prospective studies demonstrating incremental value beyond established biomarkers. Further research is required to clarify their role in stratification, prognosis and monitoring of biologic therapy. Full article

Mobile health (mHealth) apps increasingly capture the physiological, biomechanical, and psychological variables involved in sport training, but the evidence remains fragmented across single-domain reviews, leaving practitioners without a consolidated basis for selecting and deploying these tools across the training process. This scoping review aimed to identify and characterize research on mHealth apps in sport training, focusing on their performance testing, training load and recovery monitoring, technical and skill development, injury screening and prevention, and athlete self-management. It also synthesized evidence regarding their applications, intended purposes, technical characteristics, and the evidence supporting their effectiveness. Five databases (PubMed, Scopus, Web of Science, SPORTDiscus, Embase) were searched from inception to July 2026 for journal articles reporting original empirical data on app research on the sport training process in athletes. Studies involving only the promotion of physical activity or lacking human-subject testing, including commercially available apps without supporting research on their effectiveness, were excluded. Findings were synthesized narratively, and methodological quality was appraised with the Mixed Methods Appraisal Tool. Of 9476 records identified, 111 studies met the inclusion criteria and were inductively classified into ten application categories: sport skill training (n = 26), performance measurement (n = 20), vertical jump measurement (n = 18), self-reported monitoring (n = 12), physiological measurement (n = 12), musculoskeletal screening (n = 10), psychological intervention (n = 5), nutrition (n = 3), anthropometric and maturation screening (n = 3), and tactical and match analysis (n = 2). Most apps relied on built-in smartphone sensors or no sensing at all and used manual or deterministic computation; processing location went unreported in 74.8% of studies, which reflects a reporting gap rather than an architectural profile of the field, and reported that AI or machine learning labels did not track with actual method disclosure. Validation and reliability designs dominated the evidence base (52%), while randomized or controlled effectiveness trials were rare (10%). Apps generally showed good relative validity but limited absolute accuracy against criterion instruments, and wherever apps were deployed longitudinally, adherence rather than accuracy determined their real-world value. mHealth apps now support nearly every stage of sport training and can substitute for laboratory instruments in select, validated use cases, including video-based sprint and jump timing and chest-strap-paired heart-rate variability monitoring. However, the field remains organized around demonstrating measurement accuracy rather than showing that app-guided decisions improve athlete outcomes. A successful pathway for mHealth app development should progress from technical validity, through measurement reliability and responsiveness, to decision rules, and then to practitioner adoption by coaches and athletes, ultimately yielding better athlete outcomes. Full article